ORCID Profile
0000-0003-0801-3546
Current Organisation
University of Guelph
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Publisher: Copernicus GmbH
Date: 22-02-2023
DOI: 10.5194/EGUSPHERE-EGU23-3756
Abstract: & & Arid areas in the world are pressed to grow more crop per drop of water to meet food security. Soil salinity, an unavoidable challenges in arid areas is exacerbating the situations. Management practices such as use of salt tolerant crops including cotton and sugarbeet, plastic mulched drip irrigation, intercropping and application of biochar are often recommended and adopted in many arid areas including Xinjiang, one of the northwestern provinces in China with arid climate. Studying the effectiveness of these management practices, however, are often difficult, costly and time-consuming. For ex le. difficulty in obtaining information on the dynamics of soil water and salt restricts comprehensive understanding on the effectiveness of soil amendments such as biochar& #8217 s impact in increasing yield and optimizing irrigation schedules, while numerical simulations show promise. The objectives of this study were to simulate the dynamics of soil water, salt and root water update (RWU) of cotton and sugarbeet in monoculture and intercropping systems in an arid climatic condition to minimize soil water loss through optimal irrigation under plastic mulched drip irrigation systems. A 3-year field experimental results from a cotton and sugarbeet monoculture and intercropping systems from Xinjiang, China was used to calibrate and validate HYDRUS-2D model. Soil water and salt dynamics were measured at fields with biochar applied at 0 t ha-1 (CK), 10 t ha& sup& -1& /sup& (B10) and 25 t ha& sup& -1& /sup& (B25) and the soil hydraulic and solute transport parameters were optimized in HYDRUS-2D, that was calibrated and validated to satisfy the requirements of minimum simulation accuracy (R& sup& & /sup& & .75, RRMSE& .2% and NSE& .73). Simulation showed that the application of biochar increased storage of soil water and salt. The RWU ranked as B10& B25& CK, which was consistent with soil water storage (SWS) and yield. Soil water balance components indicated that the application of biochar at 10 t ha& sup& -1& /sup& increased RWU, reduced Ea and water drainage. to the results were helpful to understand the mechanisms of biochar and intercropping in increasing crop yield. The adjusted irrigation schedule can save up to 50 mm of irrigation water and 50 Yuan costs per hectare for farmers. The research provides a reference for agricultural production in arid and semi-arid areas.& &
Publisher: Elsevier BV
Date: 06-2020
Publisher: Wiley
Date: 29-09-2020
Publisher: Elsevier BV
Date: 06-2020
Publisher: Copernicus GmbH
Date: 28-02-2020
Publisher: Copernicus GmbH
Date: 15-08-2016
Abstract: Abstract. Spatiotemporal behavior of soil water is essential to understand the science of hydrodynamics. Data intensive measurement of surface soil water using remote sensing has established that the spatial variability of soil water can be described using the principle of self-similarity (scaling properties) or fractal theory. This information can be used in determining land management practices provided the surface scaling properties are kept at deep layers. The current study examined the scaling properties of sub-surface soil water and their relationship to surface soil water, thereby serving as supporting information for plant root and vadose zone models. Soil water storage (SWS) down to 1.4 m depth at seven equal intervals was measured along a transect of 576 m for 5 years in Saskatchewan. The surface SWS showed multifractal nature only during the wet period (from snowmelt until mid- to late June) indicating the need for multiple scaling indices in transferring soil water variability information over multiple scales. However, with increasing depth, the SWS became monofractal in nature indicating the need for a single scaling index to upscale/downscale soil water variability information. In contrast, all soil layers during the dry period (from late June to the end of the growing season in early November) were monofractal in nature, probably resulting from the high evapotranspirative demand of the growing vegetation that surpassed other effects. This strong similarity between the scaling properties at the surface layer and deep layers provides the possibility of inferring about the whole profile soil water dynamics using the scaling properties of the easy-to-measure surface SWS data.
Publisher: Copernicus GmbH
Date: 28-02-2020
Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 11-2021
Publisher: Wiley
Date: 16-10-2018
DOI: 10.1002/HYP.13287
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 09-2019
DOI: 10.1016/J.SCITOTENV.2019.05.037
Abstract: Due to frequent salt migration and large spatial variability within soil profiles, salinity characterisation by traditional drilling s ling methods is time-consuming and labour-intensive. Thus, it is necessary to develop monitoring technology and three-dimensional (3D) characterisation methods for rapid, non-invasive, and accurate soil salinity measurement. This study presents a new framework combining sensor technology and an inversion algorithm to characterise 3D soil salinity. Four typical land-use types (natural desert, natural vegetation, apple orchard, and winter wheat farmland) in the Aksu region of southern Xinjiang were surveyed and apparent conductivity (ECa) data were recorded at depths of 0.75 m and 1.50 m. ECa data were converted to electrical conductivity and salinity characterisation was conducted following U.S. Salinity Laboratory recommendations. Ordinary Kriging interpolation was used to map the spatial distribution and an iterative inversion model was used to map the vertical distribution of soil salinity. Model parameters were adjusted several times and the accuracy of different inversion algorithms was compared to obtain the best inversion effect. As a result, the Multilevel Orthogonal Inversion model was developed to characterise 3D soil salinity for different land-use types. Due to crop activities including irrigation, managed land use types (apple orchard and winter wheat plots) typically exhibited weaker salinity than natural systems (desert and vegetation plots) but greater spatial variability overall. The proposed framework combining EM (electromagnetic) sensing and the 3D inversion algorithm can effectively characterise and visualise soil salinity for the entire soil profile, which is important for land evaluation and improvement.
Publisher: MDPI AG
Date: 23-09-2022
DOI: 10.3390/LAND11101638
Abstract: The purpose of this study was to present a new strategy based on fusion at the decision level for modeling the crop residue. To this end, a set of satellite imagery and field data, including the Residue Cover Fraction (RCF) of corn, wheat and soybean was used. Firstly, the efficiency of Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Partial-Least-Squares Regression (PLSR) in RCF modeling was evaluated. Furthermore, to increase the accuracy of RCF modeling, different algorithms results were combined based on their modeling error, which is called the decision-based fusion strategy. The R2 (RMSE) between the actual and modeled RCF based on ANN, RFR, SVR and PLSR algorithms for corn were 0.83 (3.89), 0.86 (3.25), 0.76 (4.56) and 0.75 (4.81%), respectively. These values were 0.81 (4.86), 0.85 (4.22), 0.78 (5.45) and 0.74 (6.20%) for wheat and 0.81 (3.96), 0.83 (3.38), 0.76 (5.01) and 0.72 (5.65%) for soybean, respectively. The error of corn, wheat and soybean RCF estimating decision-based fusion strategy was reduced by 0.90, 0.96 and 0.99%, respectively. The results showed that by implementing the decision-based fusion strategy, the accuracy of the RCF modeling was significantly improved.
Publisher: MDPI AG
Date: 04-09-2021
DOI: 10.3390/RS13173516
Abstract: Land degradation and development (LDD) has become an urgent global issue. Quick and accurate monitoring of LDD dynamics is key to the sustainability of land resources. By integrating normalized difference vegetation index (NDVI) and net primary productivity (NPP) based on the Euclidean distance method, a LDD index (LDDI) was introduced to detect LDD processes, and to explore its quantitative relationship with climate change and human activity in China from 1985 to 2015. Overall, China has experienced significant land development, about 45% of China’s mainland, during the study period. Climate change (temperature and precipitation) played limited roles in the affected LDD, while human activity was the dominant driving force. Specifically, LDD caused by human activity accounted for about 58% of the total, while LDD caused by climate change only accounted for 0.34% of the total area. Results from the present study can provide insight into LDD processes and their driving factors and promote land sustainability in China and around the world.
Publisher: Elsevier BV
Date: 04-2020
Publisher: Copernicus GmbH
Date: 27-04-2016
Publisher: Wiley
Date: 17-07-2013
DOI: 10.1111/EJSS.12063
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 02-2019
Publisher: MDPI AG
Date: 14-11-2022
DOI: 10.3390/S22228803
Abstract: Soil tests for plant-available phosphorus (P) are suggested to provide offsite P analysis required to monitor P fertilizer application and reduce P losses to downstream water. However, procedural and cost limitations of current soil phosphate tests have restricted their widespread use and have made them accessible only in laboratories. This study proposes a novel paper-based reagentless electrochemical soil phosphate sensor to extract and detect soil phosphate using an inexpensive and simple approach. In this test, concentrated Mehlich-3 and molybdate ions were impregnated in filter paper, which served as the phosphate extraction and reaction zone, and was followed by electrochemical detection using cyclic voltammetry signals. Soil s les from 22 s ling sites were used to validate this method against inductively coupled plasma optical emission spectroscopy (ICP) soil phosphate tests. Regression and correlation analyses showed a significant relationship between phosphate determinations by ICP and the proposed method, delivering a correlation coefficient, r, of 0.98 and a correlation slope of 1.02. The proposed approach provided a fast, portable, low-cost, accessible, reliable, and single-step test to extract and detect phosphate simultaneously with minimum waste (0.5 mL per s le), which made phosphate characterization possible in the field.
Publisher: MDPI AG
Date: 26-02-2021
Abstract: Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties, and therefore similar crop fertility requirements. Consequently, such MZs can often be used for site-specific management of crop production inputs. This study evaluated the effectiveness of four classification methods for delineating MZs in an 8-ha commercial potato field located in Prince Edward Island, Canada. The apparent electrical conductivity (ECa) at two depths from a commercial Veris sensor were used to delineate MZs using three classification methods without spatial constraints (i.e., fuzzy k-means, ISODATA and hierarchical) and one with spatial constraints (i.e., spatial segmentation method). Soil s les (0.0–0.15 m depth) from 104 s ling points was used to measure soil physical and chemical properties and their spatial variation in the field were used as reference data to evaluate four delineation methods. Significant Pearson correlations between ECa and soil properties were obtained (0.22 r 0.85). The variance reduction indicated that two to three MZs were optimal for representing the field’s spatial variability of soil properties. For two MZs, most soil physical and chemical properties differed significantly between MZs for all four delineation methods. For three MZs, there was greater discrimination among MZs for several soil properties for the spatial segmentation-based method compared with other delineation methods. Moreover, consideration of the spatial coordinates of the data improved the delineation of MZs and thereby increased the number of significant differences among MZs for in idual soil properties. Therefore, the spatial segmentation method had the greatest efficiency in delineation of MZs from statistical and agronomic perspectives.
Publisher: Springer Netherlands
Date: 2011
Publisher: MDPI AG
Date: 07-02-2021
DOI: 10.3390/SU13041794
Abstract: The supply and demand of ecosystem services and their mutual feedback are important for the formulation of basin ecological environmental policies. Simulation of the spatial flow of ecosystem services can clarify the ision of areas and can support policy development. This paper takes the Jinghe Basin in the Loess Plateau of China as the case-study area to simulate the spatial flow of soil conservation service on different scales. The results showed that (1) soil erosion situations in Jinghe Basin improved overall, with a trend of first aggravating and then recovering between 2005 and 2015 (2) the amount of annual soil conservation in the basin accounted for more than 75% of the potential soil erosion and showed a trend of first increasing and then decreasing and (3) using digital elevation model (DEM) data and ArcGIS software, the experiment ided the basin into sub-basins (58 in total) and hydrological response units (HRUs) (e.g., 2181 HRUs in sub-basin #1), which were used to quantify the spatial flow direction and the corresponding amount of soil conservation service on the “HRU—river-sub-basin” scale. The ided supply and demand helped quantify the spatial flow pattern of soil conservation services from HRU to the sub-basin.
Publisher: MDPI AG
Date: 05-2019
DOI: 10.3390/RS11091036
Abstract: Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected by these sensors may provide essential information for precision or site-specific management in a production field. Data clustering techniques are crucial for data mining, and high-density data analysis is important for field management. A new clustering technique was introduced and compared with existing clustering tools to determine the relatively homogeneous parts of agricultural fields. A DUALEM-21S sensor, along with high-accuracy topography data, was used to characterize soil variability in three agricultural fields situated in Ontario, Canada. Sentinel-2 data assisted in quantifying bare soil and vegetation indices (VIs). The custom Neighborhood Search Analyst (NSA) data clustering tool was implemented using Python scripts. In this algorithm, part of the variance of each data layer is accounted for by sub iding the field into smaller, relatively homogeneous, areas. The algorithm’s attributes were illustrated using field elevation, shallow and deep apparent electrical conductivity (ECa), and several VIs. The unique feature of this proposed protocol was the successful development of user-friendly and open source options for defining the spatial continuity of each group and for use in the zone delineation process.
Publisher: MDPI AG
Date: 12-2022
DOI: 10.3390/LAND11122180
Abstract: The knowledge about the spatial distribution of soil organic carbon stock (SOCS) helps in sustainable land-use management and ecosystem functioning. No such study has been attempted in the complex topography and land use of Himalayas, which is associated with great spatial heterogeneity and uncertainties. Therefore, in this study digital soil mapping (DSM) was used to predict and evaluate the spatial distribution of SOCS using advanced geostatistical methods and a machine learning algorithm in the Himalayan region of Jammu and Kashmir, India. Eighty-three soil s les were collected across different land uses. Auxiliary variables (spectral indices and topographic parameters) derived from satellite data were used as predictors. Geostatistical methods—ordinary kriging (OK) and regression kriging (RK)—and a machine learning method—random forest (RF)—were used for assessing the spatial distribution and variability of SOCS with inter-comparison of models for their prediction performance. The best fit model validation criteria used were coefficient of determination (R2) and root mean square error (RMSE) with resulting maps validated by cross-validation. The SOCS concentration varied from 1.12 Mg/ha to 70.60 Mg/ha. The semivariogram analysis of OK and RK indicated moderate spatial dependence. RF (RMSE = 8.21) performed better than OK (RMSE = 15.60) and RK (RMSE = 17.73) while OK performed better than RK. Therefore, it may be concluded that RF provides better estimation and spatial variability of SOCS however, further selection and choice of auxiliary variables and higher soil s ling density could improve the accuracy of RK prediction.
Publisher: Elsevier BV
Date: 12-2016
Publisher: Elsevier BV
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 26-07-2010
Publisher: Informa UK Limited
Date: 24-11-2020
Publisher: Copernicus GmbH
Date: 13-05-2008
Abstract: Abstract. Understanding of the nitrogen (N) cycle and its spatial variability is important for managing ecosystems. Soil δ15N, as an important indicator of different soil nitrogen cycling processes, may provide critical information about the spatial variability in soil N cycling. The objective of this study was to examine the dominant landscape scale variability of δ15N, the location of the variability and its spatial relationship with elevation. Soil δ15N and elevation were measured along two transects (Davidson and Elstow, Saskatchewan, Canada). Each transect had 128 points with 3 m s ling intervals. Higher δ15N values typically occurred in topographic depressions as compared to knolls. The coefficient of determination revealed a significant linear relationship between δ15N and elevation (r2=0.27) at Davidson whereas no relationship (r2=0.00) was detected for the Elstow transect. However, wavelet spectra, cross wavelet, and squared wavelet coherency analysis revealed spatial relationships between δ15N and elevation at both sites. A strong coherency between δ15N and elevation at large scales (96 m or more) was detected for both transects. The Davidson transect showed an out of phase coherency at a topographically elevated area at the beginning and the end of the transect. The Elstow transect had a strong out of phase correlation (negative relationship) at the middle of the transect (corresponding to a depressions) indicating a location dependent relationship between δ15N and elevation. The relationship between δ15N and elevation reflects the effects of hydrology and soil water content over the landscape on N cycling processes.
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 06-2014
Publisher: Elsevier BV
Date: 08-2019
Publisher: MDPI AG
Date: 11-10-2021
DOI: 10.3390/RS13204063
Abstract: Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.
Publisher: Copernicus GmbH
Date: 09-04-2020
Publisher: Copernicus GmbH
Date: 27-03-2012
Abstract: Abstract. Knowledge about the scaling properties of soil water storage is crucial in transferring locally measured fluctuations to larger scales and vice-versa. Studies based on remotely sensed data have shown that the variability in surface soil water has clear scaling properties (i.e., statistically self similar) over a wider range of spatial scales. However, the scaling property of soil water storage to a certain depth at a field scale is not well understood. The major challenges in scaling analysis for soil water are the presence of localized trends and nonstationarities in the spatial series. The objective of this study was to characterize scaling properties of soil water storage variability through multifractal detrended fluctuation analysis (MFDFA). A field experiment was conducted in a sub-humid climate at Alvena, Saskatchewan, Canada. A north-south transect of 624-m long was established on a rolling landscape. Soil water storage was monitored weekly between 2002 and 2005 at 104 locations along the transect. The spatial scaling property of the surface 0 to 40 cm depth was characterized using the MFDFA technique for six of the soil water content series (all gravimetrically determined) representing soil water storage after snowmelt, rainfall, and evapotranspiration. For the studied transect, scaling properties of soil water storage are different between drier periods and wet periods. It also appears that local controls such as site topography and texture (that dominantly control the pattern during wet states) results in multiscaling property. The nonlocal controls such as evapotranspiration results in the reduction of the degree of multiscaling and improvement in the simple scaling. Therefore, the scaling property of soil water storage is a function of both soil moisture status and the spatial extent considered.
Publisher: MDPI AG
Date: 29-05-2023
DOI: 10.3390/SU15118740
Abstract: The ability to obtain accurate precipitation data from various geographic locations is crucial for many applications. Various global products have been released in recent decades for estimating precipitation spatially and temporally. Nevertheless, it is extremely important to provide reliable and accurate products for estimating precipitation in a variety of environments. This is due to the complexity of topographic, climatic, and other factors. This study proposes a multi-product information combination for improving precipitation data accuracy based on a generalized regression neural network model using global and local optimization strategies. Firstly, the accuracy of ten global precipitation products from four different categories (satellite-based, gauge-corrected satellites, gauge-based, and reanalysis) was assessed using monthly precipitation data collected from 1896 gauge stations in Iran during 2003–2021. Secondly, to enhance the accuracy of the modeled precipitation products, the importance score of effective and auxiliary variables—such as elevation, the Enhanced Vegetation Index (EVI), the Land Surface Temperature (LST), the Soil Water Index (SWI), and interpolated precipitation maps—was assessed. Finally, a generalized regression neural network (GRNN) model with global and local optimization strategies was used to combine precipitation information from several products and auxiliary characteristics to produce precipitation data with high accuracy. Global precipitation products scored higher than interpolated precipitation products and surface characteristics. Furthermore, the importance score of the interpolated precipitation products was considerably higher than that of the surface characteristics. SWI, elevation, EVI, and LST scored 53%, 20%, 15%, and 12%, respectively, in terms of importance. The lowest RMSE values were associated with IMERGFinal, TRMM3B43, PERSIANN-CDR, ERA5, and GSMaP-Gauge. For precipitation estimation, these products had Kling–Gupta efficiency (KGE) values of 0.89, 0.86, 0.77, 0.78, and 0.60, respectively. The proposed GRNN-based precipitation product with a global (local) strategy showed RMSE and KGE values of 9.6 (8.5 mm/mo) and 0.92 (0.94), respectively, indicating higher accuracy. Generally, the accuracy of global precipitation products varies depending on climatic conditions. It was found that the proposed GRNN-derived precipitation product is more efficient under different climatic conditions than global precipitation products. Moreover, the local optimization strategy based on climatic classes outperformed the global optimization strategy.
Publisher: Wiley
Date: 31-08-2017
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 10-2020
Publisher: Elsevier BV
Date: 10-2022
Publisher: MDPI AG
Date: 12-05-2020
DOI: 10.3390/S20102757
Abstract: Land use and cover change (LUCC) is an important issue affecting the global environment, climate change, and sustainable development. Detecting and predicting LUCC, a dynamic process, and its driving factors will help in formulating effective land use and planning policy suitable for local conditions, thus supporting local socioeconomic development and global environmental protection. In this study, taking Gansu Province as a case study ex le, we explored the LUCC pattern and its driving mechanism from 1980 to 2018, and predicted land use and cover in 2030 using the integrated LCM (Logistic-Cellular Automata-Markov chain) model and data from satellite remote sensing. The results suggest that the LUCC pattern was more reasonable in the second stage (2005 to 2018) compared with that in the first stage (1980 to 2005). This was because a large area of green lands was protected by ecological engineering in the second stage. From 1980 to 2018, in general, natural factors were the main force influencing changes in land use and cover in Gansu, while the effects of socioeconomic factors were not significant because of the slow development of economy. Landscape indices analysis indicated that predicted land use and cover in 2030 under the ecological protection scenario would be more favorable than under the historical trend scenario. Besides, results from the present study suggested that LUCC in arid and semiarid area could be well detected by the LCM model. This study would hopefully provide theoretical instructions for future land use planning and management, as well as a new methodology reference for LUCC analysis in arid and semiarid regions.
Publisher: Elsevier BV
Date: 09-2011
Publisher: MDPI AG
Date: 29-11-2022
DOI: 10.3390/SOILSYSTEMS6040089
Abstract: Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (ECa), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil attributes and evaluate the potential of site-specific management (SSM) of nutrients. Elevation and ECa at multiple depths were collected from two experimental fields (referred as FieldUnd, FieldFlat) in Normandin, Quebec, Canada. Soil s les were collected at two depths (0–0.05 m and 0.05–0.15 m) and analyzed for a range of soil properties. Statistical analyses of fruit yield, soil, and sensor data were used to characterize within-field variability. Fruit yield showed large variability in both fields (CVUnd = 54.4%, CVFlat = 56.5%), but no spatial dependence. However, several soil attributes showed considerable variability and moderate to strong spatial dependence. Elevation and the shallowest depths of both the Veris (0.3 m) and DUALEM (0.54 m) ECa sensors showed moderate to strong spatial dependence and correlated significantly to most soil properties in both study sites, indicating the feasibility of SSM. In place of management zone delineation, a quadrant analysis of the shallowest ECa depth vs. elevation provided four sensor combinations (scenarios) for theoretical field conditions. ANOVA and Tukey–Kramer’s post hoc test showed that the greatest differentiation of soil properties in both fields occurred between the combinations of high ECa/low elevation versus low ECa/high elevation. Vegetation indices (VIs) obtained from satellite data showed promise as a biomass indicator, and bare spots classified with satellite imagery in FieldUnd revealed significantly distinct soil properties. Combining proximal and multispectral data predicted within-field variations of yield-determining soil properties and offered three theoretical scenarios (high ECa/low elevation low ECa/high elevation bare spots) on which to base SSM. Future studies should investigate crop response to fertilization between the identified scenarios.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Springer Science and Business Media LLC
Date: 21-01-2011
Publisher: Elsevier BV
Date: 10-2018
Publisher: Wiley
Date: 07-09-2017
DOI: 10.1002/HYP.11289
Publisher: Informa UK Limited
Date: 12-09-2021
Publisher: Springer Science and Business Media LLC
Date: 09-02-2018
DOI: 10.1038/S41598-018-20873-5
Abstract: The spatio-temporal distribution soil pH is critical for understanding the productivity and long-term sustainability of our agri-ecosystem. This study quantified the spatio-temporal distribution of paddy soil pH in Jiangxi province, China, and the potential driver of the change between 1980 and 2010. Data from the Soil Survey Information of Jiangxi province (1980s) and Jiangxi Soil Testing and Fertilizer Recommendation study (2010s) were collected and categorized into six pH ranges from strongly-acidic to strongly-alkaline with unit pH differences. Changes were calculated from the maps developed using the Pedological Knowledge base for 1980s data (without geolocation) and geostatistical methods for the 2010s data (geolocated). An overall 0.6-unit decrease and a major shift of soil pH from weakly-acidic (54% → 18%) to acidic (35% → 74%) was observed over the province in a scattered fashion with concentration in the central part and the Poyang Lake area. About half of the area under paddy cultivation went through acidification by at least one pH unit and 7% by at least 2 pH units, while 40% of the area remained unchanged. Excessive fertilizer application and acid-rain intensity contributed to the acidification. Thus, a more knowledge-based and comprehensive fertilizer management should be adopted to make paddy production sustainable in the province.
Publisher: Sciencedomain International
Date: 10-01-2015
Publisher: Elsevier BV
Date: 10-2011
Publisher: Elsevier BV
Date: 04-2020
Publisher: MDPI AG
Date: 07-12-2018
DOI: 10.3390/RS10121974
Abstract: Long-term precipitation estimates with both finer spatial resolution and better quality are vital and highly needed in various related fields. Numerous downscaling algorithms have been investigated based on the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), to obtain precipitation data with finer resolution (~1 km). However, this research was restricted by the time span of the TMPA dataset, as the starting time of TMPA was 1998. In this study, a new methodological framework incorporating wavelet coherence and Cubist was proposed to retrospectively obtain downscaled precipitation estimates (DS) over the Tibetan Plateau (TP), based on TMPA and ground observations, in 1990s. The correlations and similarities of precipitation patterns between the target years, from 1990 to 1999, and reference years, from 2000 to 2013, were firstly determined using wavelet coherence based on ground observations. Following this, the TMPA data in the reference years were regarded as the reference in the corresponding target years, which were adopted to be downscaled using Cubist models and land surface variables, to obtain the DS in the target years. We found that the DS showed continuous trends, which corresponded well with the ground observations. Additionally, the performances of the DS were better than those of the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over the TP. Therefore, this methodological framework has great potential for obtaining precipitation estimates for the period of the 1990s for which TMPA data is inaccessible.
Publisher: Elsevier BV
Date: 11-2013
Publisher: MDPI AG
Date: 23-08-2023
DOI: 10.3390/SU151712754
Abstract: Due to the limited number and sparse distribution of meteorological and hydrometric stations in most watersheds, the runoff estimation based on these stations may not be accurate. However, the accurate determination of the Antecedent Soil Moisture (ASM) in watersheds can improve the accuracy of runoff forecasting. The objective of this study is to utilize the ASM derived from satellite imagery to enhance the accuracy of runoff estimation in a mountainous watershed. In this study, a range of Remote Sensing (RS) data, including surface biophysical and topographic features, climate data, hydrometric station flow data, and a ground-based measured SM database for the Balikhli-Chay watershed in Iran, were utilized. The Soil Conservation Service Curve Number (SCS-CN) method was employed to estimate runoff. Two approaches were used for estimating the ASM: (1) using the precipitation data recorded in ground stations, and (2) using the SM data obtained from satellite images. The accuracy of runoff estimation was then calculated for these two scenarios and compared. The mean Nash–Sutcliffe statistic was found to be 0.63 in the first scenario and 0.74 in the second scenario. The inclusion of ASM derived from the satellite imagery in the precipitation–runoff model resulted in a 51% increase in the accuracy of runoff estimation compared to using precipitation data recorded in ground stations. These findings have significant implications for improving the accuracy of ASM and runoff modeling in various applications.
Publisher: Springer Science and Business Media LLC
Date: 29-08-2012
DOI: 10.1007/S00128-012-0799-0
Abstract: In the present study, Arsenic (As) concentrations in underground water, soil, and plants (rice) and their inter-relationships in central and sub-mountainous Punjab, India were studied. Approximately, 32% of the tubewell water s les had As concentrations greater than the maximum permissible limit (10 μg As L(-1)) set by the World Health Organization (WHO) whereas in hand pump waters, As concentrations were within the safe range (i.e. <10 μg As L(-1)). As concentrations in tubewell waters were significantly correlated with As concentrations in surface soil (r = 0.57 P < 0.05) and plant s les (r = 0.27-0.82 P < 0.05) in central and sub-mountainous Punjab. The estimated daily intake of As through human consumption in rural and urban population was 0.016 and 0.012 μg day(-1) kg(-1) body weight respectively.
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 05-2021
Publisher: Elsevier BV
Date: 11-2020
Publisher: Elsevier BV
Date: 02-2021
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 02-2018
Publisher: MDPI AG
Date: 19-01-2021
Abstract: Topography affects soil hydrological, pedological, and biochemical processes and may influence nitrous oxide (N2O) emissions into the atmosphere. While N2O emissions from agricultural fields are mainly measured at plot scale and on flat topography, intrafield topographical and crop growth variability alter soil processes and might impact N2O emissions. The objective of this study was to examine the impact of topographical variations on crop growth period dependent soil N2O emissions at the field scale. A field experiment was conducted at two agricultural farms (Baggs farm BF and Research North RN) with undulating topography. Dominant slope positions (upper, middle, lower and toeslope) were identified based on elevation difference. Soil and gas s les were collected from four replicated locations within each slope position over the whole corn growing season (May–October 2019) to measure soil physio-chemical properties and N2O emissions. The N2O emissions at BF ranged from −0.27 ± 0.42 to 255 ± 105 g ha−1 d−1. Higher cumulative emissions were observed from the upper slope (1040 ± 487 g ha−1) during early growing season and from the toeslope (371 ± 157 g ha−1) during the late growing season with limited variations during the mid growing season. Similarly, at RN farm, (emissions ranged from −0.50 ± 0.83 to 70 ± 15 g ha−1 d−1), the upper slope had higher cumulative emissions during early (576 ± 132 g ha−1) and mid (271 ± 51 g ha−1) growing season, whereas no impact of slope positions was observed during late growing season. Topography controlled soil and environmental properties differently at different crop growth periods thus, intrafield variability must be considered in estimating N2O emissions and emission factor calculation from agricultural fields. However, due to large spatial variations in N2O emissions, further explorations into site-specific analysis of in idual soil properties and their impact on N2O emissions using multiyear data might help to understand and identify hotspots of N2O emissions.
Publisher: PeerJ
Date: 15-11-2018
DOI: 10.7717/PEERJ.5659
Abstract: Salt marshes are highly effective carbon (C) sinks and bury more C per square meter annually than any other ecosystem. Reclamation and anthropogenic impacts, however, have resulted in extensive losses of salt marshes. Carbon credits can be generated and sold by restoring marshes, but only if C sequestration and net reductions in greenhouse gases (GHG) are reliably quantified. Restored marshes, however, may exhibit different patterns of GHG emissions than natural marshes and it is possible that they could temporarily become sources of N 2 O even in the usually N-limited estuarine environment. Research on short-term GHG flux following salt marsh restoration is limited to studies of two restored marshes which examined GHG flux more than six months after the return of tidal flooding. Here we report on a laboratory experiment in which soil cores collected from a drained agricultural marsh on the St. Lawrence Estuary were flooded with estuary water. Gas flux measurements immediately after flooding revealed small increases in N 2 O and CH 4 , but a large decline in CO 2 yielding, from a climatic perspective, a net cooling effect over the observation period. In addition to restoring the land’s capacity to sequester C once a marsh develops, returning tidal flooding thus appears to have the added benefit of stemming large ongoing C losses. With more than 400 km 2 of undeveloped dykeland, Eastern Canada is well positioned to restore large sections of marsh and contribute to reducing atmospheric CO 2 concentrations.
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.JENVMAN.2019.01.016
Abstract: Groundwater is an important source of water supply and ecosystem resilience. However, limited information on spatio-temporal dynamics makes a complete assessment of available groundwater resources difficult, impairing sustainable water management. The Gravity Recovery and Climate Experiment mission (GRACE) has recently made this possible. In this study, we used the Yellow River Basin (YRB) as a model system to explore the use of spatio-temporal dynamics information about groundwater change derived from the GRACE datasets for regional groundwater management. While there was an overall decreasing trend (R
Publisher: Canadian Science Publishing
Date: 12-2018
Abstract: Stagnating potato (Solanum tuberosum L.) yields in eastern Canada have resulted in loss of competitive advantage in global potato markets. Therefore, there is a need to investigate the potential to increase yield by adopting precision agriculture technology. This study evaluated the efficiency of an apparent soil electrical conductivity (EC a ) sensor to delineate management zones (MZs) in two commercial potato fields in New Brunswick, Canada, using an unsupervised fuzzy k-means clustering algorithm. Georeferenced soil s les from 0 to 15 cm depth were analyzed for physicochemical properties. Tuber yields were recorded using a yield monitor. The two MZs delineated using soil EC a differed significantly in soil physicochemical properties for both fields however, tuber yield differed significantly between MZs only in Field 1. The yield difference (7.1 Mg ha −1 ) in Field 1 was attributed to a difference in soil moisture (23.5% vs 28.5%) resulting from a difference in clay content (141 vs 189 g kg −1 ). The lack of a yield difference between MZs in Field 2 may reflect relatively low within-field spatial variability. The soil EC a sensor showed promise for use in commercial potato production in New Brunswick, especially in fields with high spatial variability.
Publisher: MDPI AG
Date: 12-10-2020
DOI: 10.3390/AGRICULTURE10100468
Abstract: Non-point source (NPS) pollution is an important problem that has been threatening freshwater resources throughout the world. Best Management Practices (BMPs) can reduce NPS pollution delivery to receiving waters. For economic reasons, BMPs should be placed at critical source areas (CSAs), which are the areas contributing most of the NPS pollution. The CSAs are the areas in a watershed where source coincides with transport factors, such as runoff, erosion, subsurface flow, and channel processes. Methods ranging from simple index-based to detailed hydrologic and water quality (HWQ) models are being used to identify CSAs. However, application of these methods for Canadian watersheds remains challenging due to the ersified hydrological conditions, which are not fully incorporated into most existing methods. The aim of this work is to review potential methods and challenges in identifying CSAs under Canadian conditions. As such, this study: (a) reviews different methods for identifying CSAs (b) discusses challenges and the current state of CSA identification and (c) highlights future research directions to address limitations of currently available methods. It appears that applications of both simple index-based methods and detailed HWQ models to determine CSAs are limited in Canadian conditions. As no single method/model is perfect, it is recommended to develop a ‘Toolbox’ that can host a variety of methods to identify CSAs so as to allow flexibility to the end users on the choice of the methods.
Publisher: MDPI AG
Date: 11-10-2021
DOI: 10.3390/S21206745
Abstract: The absorbance spectra for air-dried and ground soil s les from Ontario, Canada were collected in the visible and near-infrared (VIS-NIR) region from 343 to 2200 nm. The study examined thirteen combination of six preprocessing (1st derivative, 2nd derivative, Savitzky-Golay, Gap, SNV and Detrend) method included in ‘prospectr’ R package along with four modeling approaches: partial least square regression (PLSR), cubist, random forest (RF), and extreme learning machine (ELM) for prediction of the soil organic matter (SOM). The 1st derivative + gap, 2nd derivative + gap and standard normal variance (SNV) were the best preprocessing algorithms. Thus, only these three preprocessing algorithms along with four modeling approaches were used for prediction of soil pH, electrical conductively (EC), %sand, %silt, %clay, %very coarse sand (VCS), %coarse sand (CS), %medium sand (ms) and %fine sand (fs). The results showed that OM, pH, %sand, %silt and %CS were all predicted with confidence (R2 0.60) and the combination of 1st derivative + gap and RF gained the best performance. A detailed comparison of the preprocessing and modeling algorithms for various soil properties in this study demonstrate that for better prediction of soil properties using VIS-NIR spectroscopy requires different preprocessing and modeling algorithms. However, in general RF and 1st derivative + gap can be labeled at the best combination of preprocessing and modelling algorithms.
Publisher: MDPI AG
Date: 14-04-2021
DOI: 10.3390/AGRICULTURE11040348
Abstract: While the capability of integrated nutrient management (INM) in rice systems has been adequately studied, little is known about the related short-term carbon sequestration and changes in soil carbon fractions. Our study examined the responses of organic carbon pools, carbon sequestration and rice yields after application of different organic manures combined with chemical fertilizers in a rice–rice (Oryza sativa L.) cropping system in the red and laterite agro-climatic zones of West Bengal, India. The treatments included non-fertilized control rice straw (RS) + nitrogen, phosphorus and potassium fertilizer (NPK) Gliricidia (GL) + NPK farmyard manure (FYM) + NPK vermicompost (VC) + NPK and NPK only. Rice straw + NPK treatment resulted in the highest total organic carbon and passive pool of carbon. Vermicompost + NPK treatment resulted in the highest oxidizable organic carbon (0.69%), dissolved organic carbon (0.007%) and microbial biomass carbon (0.01%), followed by FYM + NPK, GL + NPK and RS + NPK as compared to control. Rice straw + NPK sequestered the highest amount of carbon dioxide (CO2) as the total organic carbon (91.10 t ha−1) and passive pool of carbon (85.64 t ha−1), whereas VC + NPK resulted in the highest amount of CO2 (10.24 t ha−1) being sequestered as the active pool of carbon, followed by FYM + NPK (8.33 t ha−1) and GL + NPK (7.22 t ha−1). The application of both NPK only and VC + NPK treatments resulted in the highest grain yields over the three cropping seasons. In spite of high carbon sequestration being observed in more recalcitrant carbon pools, RS + NPK resulted in little increase (3.52 t ha−1) in rice yield over the short term. The results of this study suggest that the short-term changes of soil carbon fractions and carbon sequestration primarily depend on the type of organic manure used. Vermicompost, FYM and GL provide more labile carbon, which can improve rice yield over the short term. However, it is suggested to explore the dynamics of different carbon fractions, carbon sequestration in different pools and rice yields over longer periods of time.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-13010
Abstract: Nitrous Oxide (N2O) emissions in Soil and Water Assessment Tool (SWAT) is heavily dependent on soil temperature and moisture. However, SWAT has been known to highly under-estimate soil temperature which limits movement of water and nutrients throughout the soil profile and does not replicate freeze-thaw cycles which is of paramount importance in the N2O emissions. Thus, we integrated modules developed by in idual researchers pertaining to energy balanced snow melt, rain-on-snow, energy balanced soil temperature and N2O emission into a single SWAT model and termed in SWAT Cold Climate N2O (SWAT-CCN2O). SWAT-CCN2O was then tested for flows, sediments, soil temperature and N2O emission simulation in a representative watershed in Ontario, Canada, the Speed River basin. Compared with the unaltered SWAT model, SWAT-CCN2O was able to significantly capture the pre-spring snowmelt induced flows. A more realistic simulation of soil temperature (soil temperatures did not go below -4oC) and a satisfactory simulation of sediments and N2O emissions were observed in the basin, which highlights the potential to use SWAT-CCN2O for streamflow and N2O simulation in cold climatic catchments. This version of SWAT is made publicly available for further improvements and applications in similar watersheds.& &
Publisher: Elsevier BV
Date: 2015
Publisher: MDPI AG
Date: 22-11-2022
DOI: 10.3390/GEOSCIENCES12120429
Abstract: Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast capabilities. In this context, this paper sought to review the modeling procedure of GESM using ML models, including the required datasets and model development and validation. The results showed that elevation, slope, plan curvature, rainfall and land use/cover were the most important factors for GESM. It is also concluded that although ML models predict the locations of zones prone to gullying reasonably well, performance ranking of such methods is difficult because they yield different results based on the quality of the training dataset, the structure of the models, and the performance indicators. Among the ML techniques, random forest (RF) and support vector machine (SVM) are the most widely used models for GESM, which show promising results. Overall, to improve the prediction performance of ML models, the use of data-mining techniques to improve the quality of the dataset and of an ensemble estimation approach is recommended. Furthermore, evaluation of ML models for the prediction of other types of gully erosion, such as rill–interill and ephemeral gully should be the subject of more studies in the future. The employment of a combination of topographic indices and ML models is recommended for the accurate extraction of gully trajectories that are the main input of some process-based models.
Publisher: MDPI AG
Date: 25-10-2022
DOI: 10.3390/LAND11111887
Abstract: Climate change imprints on soil are projected primarily through the changes in soil moisture and surge in soil temperature and CO2 levels in response to climate change and is anticipated to have varying impacts on soil characteristics and processes that are instrumental in the restoration of soil fertility as well as productivity. Climate change encompasses a major concern of sharing its impact on the stability and functionality of soil microbiome and is characterized by one or more chief stability metrics encircling resistance, resilience, and functional redundancy. Nevertheless, the explorations over the past years have unveiled the potential of microbial interventions in the regeneration of soils or assurance of perked-up resilience to crops. The strategies involved therein encompass harnessing the native capability of soil microbes for carbon sequestration, phyto-stimulation, bio fertilization, rhizo-mediation, biocontrol of plant pathogens, enzyme-mediated breakdown, antibiosis, prompting of anti-oxidative defense mechanism, exudation of volatile organic compounds (VOCs) and induced systemic resistance (ISR) response in the host plant. However, the short storage and shelf-life of microbe-based formulations stay a significant constraint and rigorous efforts are necessary to appraise their additive impact on crop growth under changing climate scenarios.
Publisher: Canadian Science Publishing
Date: 08-2009
DOI: 10.4141/CJSS08052
Abstract: The relationship between soil properties may vary with their spatial separation. Understanding this relationship is important in predicting hydraulic parameters from other soil physical properties. The objective of this study was to identify spatially dependent relationships between hydraulic parameters and soil physical properties. Regularly spaced (3-m) undisturbed soil s les were collected along a 384 m transect from a farm field at Smeaton, Saskatchewan. Saturated hydraulic conductivity, the soil water retention curve, and soil physical properties were measured. The scaling parameter, van Genuchten scaling parameter α (VGα), and curve shape parameter, van Genuchten curve shape parameter n (VGn), were obtained by fitting the van Genuchten model to measured soil moisture retention data. Results showed that the semivariograms of soil properties exhibited two different spatial structures at spatial separations of 20 and 120 m, respectively. A strong spatial structure was observed in organic carbon, saturated hydraulic conductivity (Ks), sand, and silt whereas a weak structure was found for VGα and VGn. Correlation circle analysis showed strong spatially dependent relationships of Ks and VGα with soil physical properties, but weak relationships of θs and VGn with soil physical properties. The spatially dependent relationships between soil physical and soil hydraulic parameters should be taken into consideration when developing pedotransfer functions. Key words: Spatial relationship, geostatistics, linear coregionalization model, principal component analysis, pedotransfer function
Publisher: CSIRO Publishing
Date: 2016
DOI: 10.1071/SR15102
Abstract: Despite the large number of studies on biochar and soil properties, few studies have investigated the effects of biochar in contrasting soils. A study was conducted including four rice-husk biochar rates (0%, 0.1%, 0.5% and 1%) to understand the effects on selected soil properties of two Alfisols (sand and sandy loam) in Sri Lanka. Significant changes in soil properties including increases in pH, cation exchange capacity (CEC), organic carbon, water retention at field capacity and saturated hydraulic conductivity, and reduction in bulk density, were observed at higher rates of biochar (0.5% and 1%). Mean-weight-diameter increased only at 1% biochar application rate in sandy soil, whereas it significantly increased across all the rates in sandy loam soil over the control. Electrical conductivity showed no significant increase in either soil, indicating no threat of salinity. Biochar showed a potential for ameliorating acidity, especially in slightly acidic sandy soil. Soil aggregation and water flow improved markedly in sandy loam soil over sandy soil. Further, CEC and water retention of sandy soil had pronounced effects compared with sandy loam soil. Our study highlights the importance of soil type in determining the value of rice-husk biochar as a soil amendment to improve soil aggregation, water retention and flow and CEC.
Publisher: Copernicus GmbH
Date: 26-05-2016
Publisher: MDPI AG
Date: 30-12-2021
DOI: 10.3390/LAND11010054
Abstract: Desertification is a serious threat to human survival and to ecosystems, especially to inland desert oases. An assessment of desertification severity is essential to ensure national sustainable development for agricultural and land expansion processes in this region. In this study, Index of Land Susceptibility to Wind Erosion (ILSWE) was integrated with a Modified Mediterranean Desertification and Land Use (MEDALUS) method and factor analysis (FA) to develop a GIS-based model for mapping desertification severity. The model was then applied to 987.77 km2 in the El-Farafra Oasis, located in the Western Desert of Egypt, as a case study. Climate and field survey data together with remote sensing images were used to generate five quality indices (soil, climate, vegetation, land management and wind erosion). Based on the FA, a weighted value was assigned to each index. Five thematic layers representing the indices were created within the GIS environment and overlaid using the weighted sum model. The developed model showed that 59% of the total area was identified as high-critical and 38% as medium-critical. The results of an environmentally sensitive area index suggested by the original MEDALUS model indicated similar results: 18.37% of the total area was classified as high-critical and 78.73% as medium-critical. However, the sensitivity analysis indicated that weights derived from FA resulted in better performance of the developed spatial model than that derived from the original MEDALUS method. The proposed model would be a suitable tool for monitoring vulnerable zones, and could be a starting point for sustainable agricultural development in inland oases.
Publisher: Copernicus GmbH
Date: 26-05-2016
Publisher: Copernicus GmbH
Date: 26-05-2016
Publisher: Elsevier BV
Date: 12-2018
DOI: 10.1016/J.SCITOTENV.2018.06.210
Abstract: Environmental factors have shown localized and scale-dependent controls over soil organic matter (SOM) distribution in the landscape. Previous studies have explored the relationships between SOM and in idual controlling factors however, few studies have indicated the combined control from multiple environmental factors. In this study, we compared the localized and scale-dependent univariate and multivariate controls of SOM along two long transects (northeast, NE transect and north, N transect) from China. Bivariate wavelet coherence (BWC) between SOM and in idual factors and multiple wavelet coherence (MWC) between SOM and factor combinations were calculated. Average wavelet coherence (AWC) and percent area of significant coherence (PASC) were used to assess the relative dominance of in idual and a combination of factors to explain SOM variations at different scales and locations. The results showed that (in BWC analysis) mean annual temperature (MAT) with the largest AWC (0.39) and PASC (16.23%) was the dominant factor in explaining SOM variations along the NE transect. The topographic wetness index (TWI) was the dominant factor (AWC = 0.39 and PASC = 20.80%) along the N transect. MWC identified the combination of Slope, net primary production (NPP) and mean annual precipitation (MAP) as the most important combination in explaining SOM variations along the NE transect with a significant increase in AWC and PASC at different scales and locations (e.g. AWC = 0.91 and PASC = 58.03% at all scales). The combination of TWI, NPP and normalized difference vegetation index (NDVI) was the most influential along the N transect (AWC = 0.83 and PASC = 32.68% at all scales). The results indicated that the combined controls of environmental factors on SOM variations at different scales and locations in a large area can be identified by MWC. This is promising for a better understanding of the multivariate controls in SOM variations at larger spatial scales and may improve the capability of digital soil mapping.
Publisher: MDPI AG
Date: 29-04-2020
Abstract: Crops can uptake only a fraction of nitrogen from nitrogenous fertilizer, while losing the remainder through volatilization, leaching, immobilization and emissions from soils. The emissions of nitrogen in the form of nitrous oxide (N2O) have a strong potency for global warming and depletion of stratospheric ozone. N2O gets released due to nitrification and denitrification processes, which are aided by different environmental, management and soil variables. In recent years, researchers have focused on understanding and simulating the N2O emission processes from agricultural farms and/or watersheds by using process-based models like Daily CENTURY (DAYCENT), Denitrification-Decomposition (DNDC) and Soil and Water Assessment Tool (SWAT). While the former two have been predominantly used in understanding the science of N2O emission and its execution within the model structure, as visible from a multitude of research articles summarizing their strengths and limitations, the later one is relatively unexplored. The SWAT is a promising candidate for modeling N2O emission, as it includes variables and processes that are widely reported in the literature as controlling N2O fluxes from soil, including nitrification and denitrification. SWAT also includes three-dimensional lateral movement of water within the soil, like in real-world conditions, unlike the two-dimensional biogeochemical models mentioned above. This article aims to summarize the N2O emission processes, variables affecting N2O emission and recent advances in N2O emission modeling techniques in SWAT, while discussing their applications, strengths, limitations and further recommendations.
Publisher: Elsevier BV
Date: 03-2017
Publisher: MDPI AG
Date: 16-12-2020
DOI: 10.3390/RS12244118
Abstract: Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in erse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil s les in a field c aign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were ided into vegetation-covered and bare soil s les and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.
Publisher: Elsevier BV
Date: 10-2019
Publisher: Copernicus GmbH
Date: 26-05-2016
Publisher: Elsevier BV
Date: 10-2023
Publisher: Copernicus GmbH
Date: 04-02-2016
DOI: 10.5194/NPG-2015-81
Abstract: Abstract. Spatio-temporal behavior of soil water is essential to understand the science of hydrodynamics. Data intensive measurement of surface soil water using remote sensing has established that the spatial variability of soil water can be described using the principle of self-similarity (scaling properties) or fractal theory. This information can be used in determining land management practices provided the surface scaling properties hold at deep layer. Current study examined the scaling properties of sub-surface soil water and its relationship to surface soil water, thereby serving as the supporting information for the plant root and vadose zone models. Soil water storage (SWS) down to 1.4 m depth at seven equal intervals was measured along a transect of 576 m for 5 years. The surface SWS showed multifractal nature only during the wet period (from snowmelt until mid to late June with large SWS) indicating the need of multiple scaling indices in transferring soil water variability information over multiple scales. However, with increasing depth, the SWS became monofractal in nature indicating the need of single scaling index to upscale/downscale soil water variability information. The dynamic nature made the surface layer soil water in the wet period highly variable compared to the deep layers. In contrast, all soil layers during the dry period (from late June to the end of the growing season with low SWS) were monofractal in nature, probably resulting from the high evapotranspirative demand of the growing vegetation that surpassed other effects. This strong similarity between the scaling properties at the surface layer and deep layers provides the possibility of inferring about the whole profile soil water dynamics using the scaling properties of the easy-to-measure surface SWS data.
Publisher: Springer Science and Business Media LLC
Date: 06-2020
Publisher: Elsevier BV
Date: 06-2016
Publisher: Elsevier BV
Date: 10-2019
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 07-2020
Publisher: MDPI AG
Date: 06-10-2020
Abstract: Leaf stoichiometry can enhance our understanding of leaf elements’ (C, N and P) concentrations and their corresponding ratios in an ecosystem with seasonal environment changes. This study quantified the seasonal dynamics of leaf stoichiometry of P. australis from Yangguan wetland, Dunhuang, China as a case study ex le. The leaf C concentration (LC) of P. australis changed between seasons and was 392.26 (g·kg−1), 417.35 (g·kg−1) and 392.58 (g·kg−1) in spring, summer and autumn, respectively. Leaf N and P concentrations (LN and LP) were 23.49 (g·kg−1), and 17.54 (g·kg−1) and 5.86 (g·kg−1), and 1.00 (g·kg−1), 0.75 (g·kg−1) and 0.16 (g·kg−1), respectively, in the three seasons. The maximum (77.68) and the minimum values (17.00) of LC:LN were observed in the autumn and spring, respectively. Seasonal variations in LC:LP also showed a similar trend, with the greatest value of 3015.91 in autumn and the lowest value of 429.39 in spring. However, the highest (45.67) and the lowest values (24.18) of LN:LP were observed in autumn and summer, respectively, indicating that the growth of P. australis was mainly affected by P. Based on these results, it can be concluded that P. australis adopted a competition strategy during the early growth stage but took on a defense life strategy at the late growth stage to cope with various environments.
Publisher: Elsevier BV
Date: 05-2021
Publisher: MDPI AG
Date: 10-01-2021
DOI: 10.3390/S21020447
Abstract: The soil water retention curve (SWRC) shows the relationship between soil water (θ) and water potential (ψ) and provides fundamental information for quantifying and modeling soil water entry, storage, flow, and groundwater recharge processes. While traditionally it is measured in a laboratory through cumbersome and time-intensive methods, soil sensors measuring in-situ θ and ψ show strong potential to estimate in-situ SWRC. The objective of this study was to estimate in-situ SWRC at different depths under two different soil types by integrating measured θ and ψ using two commercial sensors: time-domain reflectometer (TDR) and dielectric field water potential (e.g., MPS-6) principles. Parametric models were used to quantify θ—ψ relationships at various depths and were compared to laboratory-measured SWRC. The results of the study show that combining TDR and MPS-6 sensors can be used to estimate plant-available water and SWRC, with a mean difference of −0.03 to 0.23 m3m−3 between the modeled data and laboratory data, which could be caused by the sensors’ lack of site-specific calibration or possible air entrapment of field soil. However, consistent trends (with magnitude differences) indicated the potential to use these sensors in estimating in-situ and dynamic SWRC at depths and provided a way forward in overcoming resource-intensive laboratory measurements.
Publisher: Elsevier BV
Date: 05-2014
Publisher: Elsevier BV
Date: 02-2014
Publisher: Elsevier BV
Date: 05-2014
Publisher: Springer Science and Business Media LLC
Date: 22-05-2023
Publisher: Elsevier BV
Date: 05-2019
Publisher: Springer Science and Business Media LLC
Date: 14-06-2022
Publisher: Springer Science and Business Media LLC
Date: 20-01-2022
DOI: 10.1007/S10661-021-09716-5
Abstract: Invasive plants can alter the function and structure of ecosystems resulting in social, economic, and ecological damage. Effective methods to reduce the dominance of invasive plants are needed. The present study was aimed at modeling the invasive species Leucanthemum vulgare Lam. in the rangelands of the Namin region in northwest Iran, as well as predicting the habitat connectivity of this species to detect areas with high habitat connectivity. Modeling of potential habitats was performed using logistic regression (LR) and maximum entropy (MaxEnt) the ensemble map which resulted from these was used to predict habitat connectivity using the electrical circuit method. Topography (elevation, slope, and aspect), climate (precipitation and temperature), and soil (acidity, electrical conductivity, soil texture, calcium, magnesium, sodium, phosphorus, potassium, organic carbon, organic matter, saturation percentage, and total neutralizing value) were used in this study. The presence and absence points of the L. vulgare were recorded using a stratified-random s ling method by means of a global positioning system. Soil s les were collected at a depth of 0 to 30 cm where L. vulgare was present and also where it was absent. According to the results, in LR, the variables of temperature, phosphorus, organic matter, and sand and in the MaxEnt, the variables of sand, total neutralizing value (TNV), and silt were the most influential factors on the distribution of L. vulgare. The appraisal of the MaxEnt performance and the precision of the model prediction were 0.97. The Kappa indices for the predicted map obtained from the LR and MaxEnt models were 0.80 and 0.73, respectively. The models' evaluation indicated that both models were able to predict the distribution of L. vulgare habitats with a high level of accuracy however, LR was more reliable. According to the LR prediction, 9.91% (10,556.25 ha) of the Namin region was attacked by L. vulgare. Connectivity assessment showed that the current density spread of L. vulgare continued from the northeast of the Namin region toward the southeast. On the other hand, the higher current density spread was demonstrated in the eastern region (rangelands adjacent to Fandoghlu forests), and other rangelands which are more threatened by the invasion of L. vulgare. Identifying sites exposed to invasive species can help implement programs to prevent invasive species from invading areas where management and prevention should be implanted to prevent and/or reduce the spread.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Elsevier BV
Date: 05-2020
DOI: 10.1016/J.SCITOTENV.2019.135387
Abstract: Precise and spatially explicit regional estimates of soil salinity are necessary to efficiently management and utilise limited land and water resources. Despite advances achieved in remote sensing over the past century, knowledge about the distribution and severity of soil salinization in economically important areas, such as oasis agroecosystems and desert-oasis ecotones (OADoE), is currently limited. An ex le of an area is southern Xinjiang, where the OADoE has a high anthropogenic influence. This study was conducted with the aim of mapping soil salinity in typical OADoE using remote sensing and machine learning techniques (Cubist and Random Forest, RF). A range of covariates was obtained from the multi-temporal Landsat-8 operational land imager (OLI) satellite for the period from 2013 to 2018. The values of coefficients of determination (R
Publisher: Elsevier BV
Date: 09-2022
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 10-2020
Publisher: MDPI AG
Date: 28-06-2022
Abstract: Drought has great negative impacts on crop growth and production. In order to select appropriate drought indices to quantify drought influences on crops to minimize the risk of drought-related crops as much as possible, climate and spring wheat yield-related data from eight sites in the Qinghai Province of China were collected for selecting better drought index between standardized precipitation evapotranspiration index (SPEI, denoting meteorological drought) and soil moisture deficit index (SMDI, denoting agricultural drought) as well as the key parameters (timescale and month) in denoting drought impacts on spring wheat yields. The spring wheat yields during 1961–2018 were simulated by the DSSAT–CERES–Wheat model. Pearson correlations were used to investigate the relationship between SPEI and SMDI and between spring wheat yields and drought indices at different timescales. The results showed that: (1) SMDI reflected more consistent dry/wet conditions than SPEI when the timescales changed and (2) There were one- and two-month lags in SMDI compared to SPEI (with the higher correlation coefficients values of 0.35–0.68) during May to August and (3) May (the jointing period of spring wheat) and the two-month timescale of SMDI0–10 (with the higher correlation coefficients values of 0.21–0.37) were key parameters denoting drought influences on spring wheat yield and (4) The correlations between the linear slopes of spring wheat yield reduction rate and linear slopes of SMDI0–10 in May at the studied eight sites were considerable between 1961–2018 (r = 0.85). This study provides helpful references for mitigating the drought risk of spring wheat.
Publisher: MDPI AG
Date: 06-04-2018
DOI: 10.3390/S18041116
Publisher: Wiley
Date: 05-2011
Publisher: MDPI AG
Date: 06-03-2021
DOI: 10.3390/AGRICULTURE11030218
Abstract: Plant roots are an integral part of soil ecosystems and contribute to various services, including carbon and nutrient cycling, weathering, and soil formation. They also modify soil physical properties (e.g., soil water content, pore size distribution, and bulk density) and impact subsequent crops’ growth. Cover crops have been reported to improve soil and environmental quality by reducing nutrient losses, improving soil water content, and increasing soil organic matter. Understanding the complex interactions between cover crop roots and soil (RS) is of utmost importance. However, cover crop RS interactions have not been critically reviewed. In this article, we investigated the nature of cover crop physical RS interactions and explored the emerging technologies for their study. We also assessed technologies that may be readily applied to the study of physical RS interactions in cover crop systems and discussed ways to improve related research in the future.
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 02-2021
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 02-2016
Publisher: Canadian Science Publishing
Date: 05-2012
DOI: 10.4141/CJSS2011-045
Abstract: Biswas, A., Chau, H. W., Bedard-Haughn, A. K. and Si, B. C. 2012. Factors controlling soil water storage in the hummocky landscape of the Prairie Pothole Region of North America. Can. J. Soil Sci. 92: 649–663. The Prairie Pothole Region (PPR) in North America is unique hummocky landscape containing hydrologically closed topographic depressions with no permanent inlet or outlet. Knowledge about the controls of soil water distribution in the landscape is important for understanding the hydrology in the PPR. In this study, we investigated the correlation between soil water storage and different controlling factors over time. Time domain reflectometry and neutron probe were used to measure soil water storage up to 1.4 m depth over 4 yr along a 576-m long transect at St. Denis National Wildlife Area, Saskatchewan, Canada, which represent a typical landscape of the PPR. Soil and vegetation properties were measured along the transect, and various terrain indices were calculated from the digital elevation map of the study area. Soil texture (e.g., correlation coefficient, r=−0.57 to −0.73 for sand) provided one of the best explanations for the variations in soil water storage by controlling the entry and transmission of water within soil in the semi-arid climate of study area. Bulk density (r=−0.22 to −0.56), depth of A horizon, (r=0.18 to 0.49), C horizon (r=0.29 to 0.69), and CaCO 3 layer (r=0.31 to 0.79) influenced the water transmission through soil and were correlated to soil water storage. Beside soil properties, topographic wetness index (r=0.47 to 0.67), slope (r=−0.41 to −0.56), convergence index (r=−0.29 to −0.60), and flow connectivity (r=0.27 to 0.60) were also correlated to soil water storage. However, multiple linear regressions showed a consistent high contribution from soil properties such as sand, organic carbon, depth of CaCO 3 layer, and bulk density in explaining the variability in soil water storage. A substantial contribution from topographic variables such as wetness index, gradient, and solar radiation was also observed. Therefore, unlike other geographic regions, the soil-water storage variations in the PPR are controlled by a combination of soil and terrain properties with dominant control from soil characteristics at the field scale.
Publisher: Elsevier BV
Date: 10-2019
Publisher: MDPI AG
Date: 08-08-2022
Abstract: Soil quality assessment serves as an index for appraising soil sustainability under varied soil management approaches. Our current investigation was oriented to establish a minimum data set (MDS) of soil quality indicators through the selection of apt scoring functions for each indicator, thus evaluating soil quality in the Himalayan foothills. The experiment was conducted during two consecutive years, viz. 2016 and 2017, and comprised of 13 treatments encompassing different combinations of chemical fertilizers, organic manure, and biofertilizers, viz. (i) the control, (ii) 20 kg P + PSB (Phosphorus solubilizing bacteria), (iii) 20 kg P + PSB + Rhizobium, (iv) 20 kg P + PSB + Rhizobium+ FYM, (v) 20 kg P + 0.5 kg Mo + PSB, (vi) 20 kg P + 0.5 kg Mo + PSB + Rhizobium, (vii) 20 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM, (viii) 40 kg@ P + PSB, (ix) 40 kg P + PSB + Rhizobium, (x) 40 kg P + PSB + Rhizobium+ FYM, (xi) 40 kg P + 0.5 kg Mo + PSB, (xii) 40 kg P + 0.5 kg Mo + PSB + Rhizobium, and (xiii) 40 kg P + 0.5 kg Mo + PSB + Rhizobium + FYM. Evaluating the physical, chemical, and biological indicators, the integrated module of organic and inorganic fertilization reflected a significant improvement in soil characteristics such as the water holding capacity, available nitrogen, phosphorus, potassium, and molybdenum, different carbon fractions and soil biological characteristics encircling microbial biomass carbon (MBC), and total bacterial and fungal count. A principal component analysis (PCA) was executed for the reduction of multidimensional data ensued by scoring through the transformation of selected indicators. The soil quality index (SQI) established for different treatments exhibited a variation of 0.105 to 0.398, while the magnitude of share pertaining to key soil quality indicators for influencing soil quality index encircled the water holding capacity (WHC), the dehydrogenase activity (DHA), the total bacteria count, and the available P. The treatments that received an integrated nutrient package exhibited a higher SQI (T10—0.398 T13—0.372 T7—0.307) in comparison to the control treatment (T1—0.105). An enhanced soil quality index put forth for all organic treatments reflected an edge of any conjunctive package of reduced synthetic fertilizers with prime involvement of organic fertilizers over the sole application of inorganic fertilizers.
Publisher: Wiley
Date: 29-05-2014
DOI: 10.1111/EJSS.12145
Publisher: MDPI AG
Date: 13-01-2019
DOI: 10.3390/W11010135
Abstract: While traditional soil water sensors measure soil water content (SWC) at point scale, the actively heated fiber-optics (AHFO) sensor measures the SWC at field scale. This study compared the performance of a distributed (e.g., AHFO) and a point-based sensor on closing the field water balance and estimating the evapotranspiration (ET). Both sensors failed to close the water balance and produced larger errors in estimated ET (ETε), particularly for longer time periods with mm change in soil water storage (ΔSWS), and this was attributed to a lack of SWC measurements from deeper layers ( .24 m). Performance of the two sensors was different when only the periods of ˂60 mm ΔSWS were considered significantly lower residual of the water balance (Re) and ETε of the distributed sensor showed that it could capture the small-scale spatial variability of SWC that the point-based sensor missed during wet (70–104 mm SWS) periods of ˂60 mm ΔSWS. Overall, this study showed the potential of the distributed sensor to provide a more accurate value of SWS at field scale and to reduce the errors in water balance for shorter wet periods. It is suggested to include SWC measurements from deeper layers to better evaluate the performance of the distributed sensor, especially for longer time periods of mm ΔSWS, in future studies.
Publisher: Wiley
Date: 24-01-2012
DOI: 10.1002/HYP.8459
Publisher: Elsevier BV
Date: 09-2020
Publisher: MDPI AG
Date: 21-03-2022
DOI: 10.3390/W14060988
Abstract: Potential evapotranspiration (ETp) and reference crop evapotranspiration (ETo) are two key parameters in hydrology, meteorology, and agronomy. ETp and ETo are related to each other but have different meanings and applications. In this study, the ETp and ETo were distinguished and calculated with the Penman and FAO56 PM equations using the weather data of 551 stations in China from 1961 to 2018. The differences in their spatiotemporal variations were examined with an MMK test, an R/S test, and wavelet analysis. The monthly ETp and ETo were close but the ETp was always larger than the ETo, with values ranging from 1 to 356 mm and 2 to 323 mm, respectively. Their differences varied in different months and sub-regions. The maximum monthly difference transferred from south to north and then back to the south in a yearly cycle, showing spatiotemporal heterogeneity. The annual values of the ETp and ETo were also close, but the ETp was significantly higher than the ETo. The increasing future trends of ETp but decreasing trends of ETo were tested at most sites in China. Although the primary periods were almost the same, their spatial distribution was slightly different. In conclusion, ETp is different from ETo and they should be applied carefully. This study performs a thorough comparison and reveals the underlying basis of and discrepancy between ETp and ETo.
Publisher: Springer Science and Business Media LLC
Date: 09-09-2020
DOI: 10.1038/S41598-020-71448-2
Abstract: Application of phosphorus (P), a major plant nutrient, as fertilizer is critical to maintain P level for crop production and yield in most cultivated soils. While, it may impact the dynamics, limited studies have examined the long-term effects of fertilization on P fractions in a soil profile in Mollisol. A long-term field experiment was conducted at the State Key Experimental Station of Agroecology of the Chinese Academy of Sciences in Hailun county, Heilongjiang Province, China. A sequential fractionation procedure was used to determine the effect of fertilizer (types) treatments including no fertilizer (CK), chemical fertilizer (NPK), chemical fertilizer plus straw (NPK + S) and pig manure (OM) on fractions of P and their distribution within 0–100 cm soil profiles. Unlike CK treatment, the long-term application of fertilizers increased the concentration and accumulation of total and available P in 0–20 and 0–40 cm soil depths than deeper soils, respectively. The phosphorus activity coefficient (PAC) ranged from 1.5 to 13.8% within 0–100 cm soil depth. The largest PAC value was observed under OM treatment at 0–40 cm soil depth and under NPK + S treatment at 40–100 cm soil depth. The Ca 2 -P and Ca 8 -P concentrations increased significantly by 0.5–7.5 times and 0.5–10.4 times, respectively in OM treatment with the largest value in 0–40 cm soil depth over CK treatment. The Al-P concentration under NPK + S and OM treatments increased throughout the soil profile. The OM treatment increased all Po concentrations in the 0–40 cm soil depth, while NPK and NPK + S treatments increased labile organic P, moderately labile organic P, and highly stable organic P in the 0–20 cm soil depth. Thus, the application of fertilizer and straw, or organic manure may enhance inorganic and organic P pool in a Mollisol in Northeast China. Thus, organic manure application in the subsoil as a potential P source and their impact should be considered in developing management practices and policies regarding nutrient management.
Publisher: The Electrochemical Society
Date: 30-11-2020
Abstract: Phosphorous (P) is critical for food production and is vital to both plant and animal life. Measurement and management of soil P are crucial for soil fertility maintenance and optimum plant growth while reducing P losses from agricultural fields and improving downstream water quality. Several costly and time-consuming analytical methods provide offsite P analysis, while onsite sensor-based P analysis method shows the potential to provide fast and cost-effective P measurements. This study presented a portable electrochemical adaptation of the Environmental Protection Agency (EPA) recommended colorimetric method for measuring inorganic P. In this research, cyclic voltammetry was used to quantify inorganic P in the range between 0.25 and 3.08 mg · l −1 (typical soil P range). The limit of detection achieved was 0.18 mg · l −1 . Other common ions did not interfere with P detection and confirmed P selectivity. Unlike the EPA recommended method, this method only required molybdate ions as the complexing agent. Processed soil s les in the laboratory were used to validate this method against inductively coupled plasma optical emission spectroscopy. This method showed an average recovery of 98.27% of P, highlighting its suitability for field P measurements. The proposed electrochemical approach is promising for low-cost, simple, and portable infield soil P tests.
Publisher: Elsevier BV
Date: 2018
Publisher: Springer Singapore
Date: 2017
Publisher: Springer Singapore
Date: 2017
Publisher: MDPI AG
Date: 10-12-2020
DOI: 10.3390/RS12244043
Abstract: Information on spatial, temporal, and depth variability of soil salinity at field and landscape scales is important for a variety of agronomic and environment concerns including irrigation in arid and semi-arid areas. However, challenges remain in characterizing and monitoring soil secondary salinity as it can largely be impacted by managements including irrigation and mulching in addition to natural factors. The objective of this study is to evaluate apparent electrical conductivity (ECa)-directed soil s ling as a basis for monitoring management-induced spatio-temporal change in soil salinity in three dimensions. A field experiment was conducted on an 18-ha saline-sodic field from Alar’s Agricultural Science and Technology Park, China between March, and November 2018. Soil ECa was measured using an electromagnetic induction (EMI) sensor for four times over the growing season and soil core s les were collected from 18 locations (each time) selected using EMI survey data as a-priori information. A multi-variate regression-based predictive relationship between ECa and laboratory-measured electrical conductivity (ECe) was used to predict EC with confidence (R2 between 0.82 and 0.99). A three-dimensional inverse distance weighing (3D-IDW) interpolation clearly showed a strong variability in space and time and with depths within the study field which were mainly attributed to the human management factors including irrigation, mulching, and uncovering of soils and natural factors including air temperature, evaporation, and groundwater level. This study lays a foundation of characterizing secondary salinity at a field scale for precision and sustainable management of agricultural lands in arid and semi-arid areas.
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.JENVMAN.2022.116558
Abstract: Tile-back type slopes comprise ephemeral gullies (EGs) and hillslopes they are a unique and widely distributed micro-landform in the Loess Plateau region of China. Gully erosion from these landforms is a serious issue, but the micro-landform makes the erosion process and its estimation complex. Quantifying soil erosion processes and their distribution characteristics at different positions on tile-back type slopes will provide a clearer picture for ecological restoration to control further soil degradation. This study investigated the erosion process of tile-back type slope with non-uniform slopes using a 3D photo-reconstruction method during eight successive simulated rainfall events. The results showed that EG erosion began with a chain of intermittent headcuts. When the accumulated rainfall reached 76 mm, serious collapses dramatically increased the amount of sediment by 216% after the first rainfall (cumulative rainfall was about 15 mm). We quantified the sediment contribution of EG erosion (46.20%), rill erosion (35.62%), and inter-rill erosion (18.18%) to total soil loss. The erosion area of the steep slope section and extremely steep slope section accounted for 33.26% and 66.74% of the total erosion area, respectively. Moreover, sediment amounts significantly correlated with morphological parameters, particularly the amount of EG erosion and maximum gully depth, with a correlation coefficient of 0.98. Cumulative gully length and erosion area had the greatest effect on rill erosion, with a correlation coefficient of 0.97. These results provide insight into the qualitative and quantitative understanding of EG erosion process on Loess Plateau of China and an important reference for the rational arrangement of EG control measures.
Publisher: MDPI AG
Date: 12-11-2022
DOI: 10.3390/LAND11112025
Abstract: In remote sensing (RS), shadows play an important role, commonly affecting the quality of data recorded by remote sensors. It is, therefore, of the utmost importance to detect and model the shadow effect in RS data as well as the information that is obtained from them, particularly when the data are to be used in further environmental studies. Shadows can generally be categorized into four types based on their sources: cloud shadows, topographic shadows, urban shadows, and a combination of these. The main objective of this study was to review the recent literature on the shadow effect in remote sensing. A systematic literature review was employed to evaluate studies published since 1975. Various studies demonstrated that shadows influence significantly the estimation of various properties by remote sensing. These properties include vegetation, impervious surfaces, water, snow, albedo, soil moisture, evapotranspiration, and land surface temperature. It should be noted that shadows also affect the outputs of remote sensing processes such as spectral indices, urban heat islands, and land use/cover maps. The effect of shadows on the extracted information is a function of the sensor–target–solar geometry, overpass time, and the spatial resolution of the satellite sensor imagery. Meanwhile, modeling the effect of shadow and applying appropriate strategies to reduce its impacts on various environmental and surface biophysical variables is associated with many challenges. However, some studies have made use of shadows and extracted valuable information from them. An overview of the proposed methods for identifying and removing the shadow effect is presented.
Publisher: Elsevier BV
Date: 05-2012
Publisher: MDPI AG
Date: 30-09-2022
DOI: 10.3390/S22197428
Abstract: The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features’ capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.
Publisher: Elsevier BV
Date: 08-2019
Publisher: MDPI AG
Date: 19-02-2018
DOI: 10.3390/W10020222
Publisher: Elsevier BV
Date: 2021
Publisher: The Electrochemical Society
Date: 09-01-2020
Abstract: Inorganic phosphorus (orthophosphate) determination is crucial within environmental applications. Conventional accredited measurement methods of orthophosphate provide accurate measurements for a limited number of s les due to cost, time, and labor involved with laboratory analysis and are insufficient to characterize phosphate variability within environmental applications. Precise electrochemical sensing has the potential to provide accurate phosphate measurements and has the advantage of being inexpensive to produce and portable. Cobalt is a robust metal that has shown a unique selectivity towards phosphate in potentiometric sensors. In this manuscript, we reviewed the cobalt phosphate ion-selective electrodes with cobalt matrices in the form of pure metal, microelectrode, thin-film, and heterogeneous metal membrane in building integrated probes for determining phosphate concentrations in aqueous solutions. We reviewed different proposals of the cobalt-phosphate chemical reactions on the electrode surface, the factors affecting the stability of the phosphate measurement, and the success stories in the form of the limit of detection, linear range, and sensitivity. With strong progress in recent decades, we restricted ourselves at the time between 1995 and 2018. We discussed future opportunities of cobalt sensors towards more reliable phosphate sensing using novel approaches like cobalt alloys, three in one cobalt phosphate sensors, and external interference elimination methods.
Publisher: Wiley
Date: 07-2011
Publisher: Elsevier BV
Date: 05-2016
Publisher: Elsevier BV
Date: 05-2023
Publisher: MDPI AG
Date: 28-09-2021
DOI: 10.3390/F12101325
Abstract: Leaf stoichiometry of plants can respond to variation in environments such as elevation ranging from low to high and success in establishing itself in a given montane ecosystem. An evaluation of the leaf stoichiometry of Qinghai Spruce (Picea crassifolia Kom.) growing at different elevations (2400 m, 2600 m, 2800 m, 3000 m, and 3200 m) in eastern China’s Qilian Mountains, showed that leaf carbon (LC) and leaf phosphorus (LP) were similar among elevations, with ranges of 502.76–518.02 g·kg−1, and 1.00–1.43 g·kg−1, respectively. Leaf nitrogen (LN) varied with changes of elevation, with a maxima of 12.82 g·kg−1 at 2600 m and a minima of 10.74 g·kg−1 at 2800 m. The LC:LN under 2400 m and 2600 m was lower than that under other elevations, while LC:LP and LN:LP were not different among these elevations. Except for LN and LC:LN, P. crassifolia’s other leaf stoichiometries remained relatively stable across elevations, partly supporting the homeostasis hypothesis. Variations in leaf stoichiometry across elevations were mainly linked to mean annual precipitation, mean annual temperature, soil pH, and the soil organic C to soil total N ratio. P. crassifolia growth within the study area was more susceptible to P limitation.
Publisher: MDPI AG
Date: 11-07-2022
Abstract: Agricultural practices affect arbuscular mycorrhizal fungal (AMF) hyphae growth and glomalin production, which is a recalcitrant carbon (C) source in soil. Since the spatial distribution of glomalin is an interesting issue for agronomists in terms of carbon sequestration, digital maps are a cost-free and useful approach. For this study, a set of 120 soil s les was collected from an experimental area of 310 km2 from the Sarab region of Iran. Soil total glomalin (TG) and easily extractable glomalin (EEG) were determined via ELISA using the monoclonal antibody 32B11. Soil organic carbon (OC) was also measured. The ratios of TG/OC and EEG/OC as the glomalin–C quotes of OC were calculated. A total of 17 terrain-related attributes were also derived from the digital elevation model (DEM) and used as static environmental covariates in digital soil mapping (DSM) using three predictive models, including multiple linear regression (MLR), random forests (RF), and Cubist (CU). The major findings were as follows: (a) DSM facilitated the interpretation of recalcitrant C source variation (b) RF outperformed MLR and CU as models in predicting and mapping the spatial distribution of glomalin using available covariates (c) the best accuracy in predictions was for EEG, followed by EEG/OC, TG, and TG/OC.
Publisher: Copernicus GmbH
Date: 18-10-2019
DOI: 10.5194/ISPRS-ARCHIVES-XLII-4-W18-395-2019
Abstract: Abstract. Modeling of Near-Surface Temperature Lapse Rate (NSTLR) is very important in various environmental applications. The Land Surface Temperature (LST) is influenced by many properties and conditions including surface biophysical and topographic characteristics. Some researches have considered the LST - Digital Elevation Model (DEM) feature space to model NSTLR. However, the influence of detailed surface characteristics is rare. This study investigated the impact of surface characteristics on the LST-DEM feature space for NSTLR modeling. A set of remote sensing data including Landsat 8 images, MODIS products, and surface features including DEM and land use of the Balikhli-Chay on 01/07/2018, 18/08/2018 and 03/09/2018 were collected and used in this study. First, Split Window (SW) algorithm was used to estimate LST, and spectral indices were employed to model surface biophysical characteristics. Owing to the impact of surface biophysical and topographic characteristics on the LST-DEM feature space, the NSTLR was calculated for different classes of surface biophysical characteristics, land use, and solar local incident angle. The modeled NSTLR values based on the LST-DEM feature space on 01/07/2018, 18/08/2018 and 03/09/2018 were 8.5, 1.5 and 2.4 °C Km−1 respectively. The NSTLR in different classes of surface biophysical characteristics, land use type and topographical parameters were variable between 0.5 to 14 °C Km−1. This clearly showed the dependence of NSTLR on topographic and biophysical conditions. This provides a new way of calculating surface characteristic specific NSTLR.
Publisher: Elsevier BV
Date: 11-2018
Publisher: Springer Science and Business Media LLC
Date: 09-12-2019
DOI: 10.1007/S11356-019-06800-9
Abstract: Identifying critical source areas (CSAs) of a watershed by phosphorus (P) loss assessment tools is essential for optimal placement of beneficial management practices (BMPs) to address diffuse P pollution. However, lack of significant progress in tackling diffuse P pollution could be, in part, associated with inefficacy of P loss assessment tools for accurately identifying CSAs. Phosphorus loss assessment tools have been developed to simulate P loss from the landscape where runoff is mainly driven by rainfall events. Therefore, they may underperform in cold climates where the land is often frozen during winter and runoff is dominated by snowmelt. This paper (i) reviews the strengths and weaknesses of current P loss assessment tools and their underlying assumptions in simulating soil P dynamics and P transfer to runoff, and (ii) highlights a number of challenges associated with modeling P transfer from agricultural land to surface waters in cold climates. Current P loss assessment tools do not appear to fully represent hydrological and biogeochemical processes responsible for P loss from CSAs, particularly in cold climates. Effort should be made to develop P loss assessment tools that are capable of considering P dynamics through the landscape as a result of abiotic perturbations that are common in cold climates, predicting runoff and P movement over frozen artially frozen soils, and considering material-P connectivity between landscape and surface waters. Evaluating P loss assessment tools with water quality data is necessary to ensure such modifications result in improved identification of CSAs.
Publisher: Elsevier BV
Date: 08-2015
Publisher: Elsevier BV
Date: 08-2020
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 06-2019
DOI: 10.1016/J.SCITOTENV.2019.02.294
Abstract: Groundwater is a most accessible freshwater resource for human beings, and it is increasingly important as an alternative to surface water under the threat of climate change. However, its complex spatio-temporal dynamic remains unattended from management perspective. Past studies on groundwater management were stalled by a relative dearth of high-quality data and a lack of synthetic analysis on both spatial and temporal information. Thanks to NASA's launch of Gravity Recovery and Climate Experiment (GRACE) satellite mission, our study has solved these two problems by innovatively applying time stability analysis to GRACE-based groundwater data. Taking the Yellow River Basin (YRB) as an ex le, we employed GRACE satellite data to obtain monthly changes of groundwater tables from Jan. 2003 to Dec. 2016 in 1.0-degree grid of spatial resolution. Then we identified hotspots (which indicated severe groundwater declines and fluctuations over time) and representative monitoring areas (which stably represented the spatial average over time) using time stability analysis. Time stability employs multiple coefficients to identify the spatial relations between local variables and global variables overtime, thus showing the overall effect of spatial-wise and temporal-wise factors but never used in groundwater studies before. Based on this innovative method, we further identified management categories across the YRB using multivariate cluster analysis. As a result, the YRB has been ided into five zones for different management strategies. We identified the hotspots in west-most and east-most areas of the YRB, where we suggest a strengthened groundwater protections and risk response system. The northern part of the middle reach in the YRB was also identified as the representative monitoring areas. With these knowledge, decision-makers can have a clearer regional plan for groundwater protection, monitoring, and risk response system. This new method enables a quick decision on the prioritized areas for different groundwater management strategies while not losing the scope of spatio-temporal heterogeneity.
Publisher: Wiley
Date: 14-10-2021
DOI: 10.1002/LDR.4110
Abstract: Efforts to enhance food, fibre, and forage yields and achieve global food security have included the conversion of natural grasslands to cultivated grasslands. The quantitative effects of a global shift in grassland management on soil properties and microbial communities critical to ecosystem function have remained largely unexplored, particularly on China's Qinghai–Tibetan Plateau. Accordingly, the distinct and contrasting effects of annual Avena grasslands (AAG) versus perennial Elymus nutans Griseb. cultivated grasslands (PEG) on the region's soil properties and microbiome were investigated in an effort to examine their contribution to maintaining soil carbon and nutrients. Across three sites per grassland type, soil moisture content (45.55%), soil organic carbon (48.97 g kg −1 ), soil total nitrogen (5.13 g kg −1 ), and soil ammonium nitrogen (298.32 mg kg −1 ) were 19%–32% greater at PEG sites than AAG sites, whereas soil pH (7.81), soil total phosphorus (0.44 g kg −1 ), and soil available phosphorus (0.81 mg kg −1 ) were 2%–31% lower. The AAG and PEG site soils had different bacterial and fungal β‐ ersities but similar α‐ ersities. The relative combined abundance of Gemmatimonadetes and Chytridiomycota , and that of Rozellomycota in idually, were, respectively, higher and lower in AAG versus PEG site soils. This suggests that, on the Qinghai–Tibetan Plateau, contrasting grassland cultivation practices affect soil properties and microbes differently. Given the strong interaction between a soil and its microbiome, changes in a soil's microbial community structure can be expected to substantially alter soil function. This will have important ecological service implications, particularly in terms of carbon storage and water conservation in this ecologically fragile region.
Publisher: Elsevier BV
Date: 03-2021
Publisher: CRC Press
Date: 24-07-2012
DOI: 10.1201/B12728-29
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 07-2017
Publisher: Elsevier BV
Date: 03-2021
Publisher: American Society of Agricultural and Biological Engineers
Date: 26-07-2015
Publisher: Wiley
Date: 11-10-2013
Publisher: American Society of Civil Engineers (ASCE)
Date: 10-2016
Publisher: MDPI AG
Date: 02-2021
DOI: 10.3390/S21030962
Abstract: The actively heated fiber optics (AHFO) technique has the potential to measure soil water at high spatial and temporal resolutions, and thus it can bridge the measurement gap from point to large scales. However, the availability of power might restrict its use, since high power is required to heat long fiber optic cables under field conditions this can be a challenge for long-term soil water monitoring under field conditions. This study investigated the performance of different heating strategies (power intensity and heating duration) on soil water measurement by the AHFO technique on three different textured soils. Different heating strategies: high power–short pulses (20 Wm−1–3 min), low power–short pulses (10 Wm−1–3 min, 5 Wm−1–3 min, 2.5 Wm−1–3 min) and low power–long pulses (10 Wm−1–5 min, 5 Wm−1–10 min, 2.5 Wm−1–15 min) were tested using laboratory soil columns. The study compared the sensitivity of the thermal response, NTcum to volumetric water content (VWC) and the predictive error of different heating strategies and soils. Results of this study showed that the sensitivity of NTcum increased and the predictive error decreased with increasing power intensity, irrespective of the soil type. Low power–short heat pulses such as 5 Wm−1–3 min and 2.5 Wm−1–3 min produced high predictive errors, RMSE of 5–6% and 6–7%, respectively. However, extending the heating duration was effective in reducing the error for both 10 and 5 Wm−1 power intensities, but not for the 2.5 Wm−1. The improvement was particularly noticeable in 5 Wm−1 –10 min it reduced the RMSE by 1.5% (sand and clay loam) and 2.73% (sandy loam). Overall, the results of this study suggested that extending the heating duration of 10 and 5 Wm−1 power intensities can improve the sensitivity of the thermal response and predictive accuracy of the estimated soil water content (SWC). The results are particularly important for field applications of the AHFO technique, which can be limited by the availability of high power, which restricts the use of 20 Wm−1. For ex le, 5 Wm−1–10 min improved the predictive accuracy to 3–4%, which has the potential to be used for validating soil water estimations at satellite footprint scales. However, the effects of diurnal temperature variations should also be considered, particularly when using low power intensity such as 5 Wm−1 in surface soils under field conditions.
Publisher: Copernicus GmbH
Date: 04-02-2016
Publisher: Elsevier BV
Date: 03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 07-2019
Publisher: MDPI AG
Date: 16-10-2020
DOI: 10.3390/RS12203407
Abstract: The regional and national scales variation and propagation characteristics of different types of droughts are critical for improving drought resilience, while information is limited in China. The objective of this research was to investigate the evolution and propagation characteristics of three types of droughts using standardized indices at multi-timescales in different sub-regions of China. The indices included Standardized Precipitation/Soil Moisture/Runoff Index (SPI/SSI/SRI) using the optimal probability density function, representing meteorological, agricultural, and hydrological droughts based on precipitation, soil water storage, and baseflow-groundwater runoff, respectively. Wavelet analysis was used to reveal their periodical characteristics. Modified Mann-Kendall trend test was used to compare the trend among drought indices. Correlation coefficients between SPI and SSI/SRI were calculated to identify the time-lags of SPI with SSI and SRI. In general, droughts indicated by SPI agreed well with the historical drought events at different sub-regions. The main periods of SSI were closer to SPI than SRI, indicating stronger connections of agricultural drought with meteorological drought. A weaker connection between meteorological and agricultural/hydrological droughts at shorter timescales was observed in northwestern arid and semi-arid regions. The propagation from meteorological to agricultural or hydrological droughts were well denoted by the lagged time (months) from SPI to SSI or SRI at a timescale ranged from 0 (mostly located in south China) to 5 months (mostly located in northeastern China) for 1-, 3-, 6-, 12-, or 24-month timescale this was a new finding for China. The methods of wavelet combining trend test and Pearson coefficient showed meaningful power for revealing the drought propagation characteristics and the obtained results can be a good reference for other regions of the world since this study compared different climate zones from arid to humid conditions. The study provides crucial information and guidance to develop drought management strategies at regional to national scale and their critical time of action.
No related grants have been discovered for Asim Biswas.