ORCID Profile
0000-0001-5410-5762
Current Organisation
Woodwell Climate Research Center
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 2020
Publisher: MDPI AG
Date: 27-09-2018
DOI: 10.3390/RS10101555
Abstract: Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil s les were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI (b) Relief-Derived Covariates (RDC) and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83 RMSE = 65.0 g kg−1) and sand (R2 = 0.86 RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.
Publisher: Springer Science and Business Media LLC
Date: 10-03-2020
DOI: 10.1038/S41598-020-61408-1
Abstract: The Earth’s surface dynamics provide essential information for guiding environmental and agricultural policies. Uncovered and unprotected surfaces experience several undesirable effects, which can affect soil ecosystem functions. We developed a technique to identify global bare surface areas and their dynamics based on multitemporal remote sensing images to aid the spatiotemporal evaluation of anthropic and natural phenomena. The bare Earth’s surface and its changes were recognized by Landsat image processing over a time range of 30 years using the Google Earth Engine platform. Two additional products were obtained with a similar technique: a) Earth’s bare surface frequency, which represents where and how many times a single pixel was detected as bare surface, based on Landsat series, and b) Earth’s bare soil tendency, which represents the tendency of bare surface to increase or decrease. This technique enabled the retrieval of bare surfaces on 32% of Earth’s total land area and on 95% of land when considering only agricultural areas. From a multitemporal perspective, the technique found a 2.8% increase in bare surfaces during the period on a global scale. However, the rate of soil exposure decreased by ~4.8% in the same period. The increase in bare surfaces shows that agricultural areas are increasing worldwide. The decreasing rate of soil exposure indicates that, unlike popular opinion, more soils have been covered due to the adoption of conservation agriculture practices, which may reduce soil degradation.
Publisher: Elsevier BV
Date: 02-2094
Publisher: Wiley
Date: 14-03-2023
DOI: 10.1002/SAJ2.20513
Abstract: Large and publicly available soil spectral libraries, such as the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC‐KSSL) mid‐infrared (MIR) spectral library, are enormously valuable resources enabling laboratories around the world to make rapid low‐cost estimates of a number of soil properties. A limitation to widespread sharing of soil spectral data is the need to ensure that spectra collected on a secondary spectrometer are compatible with the spectra in the primary or reference library. Various spectral preprocessing and calibration transfer techniques have been proposed to overcome this limitation. We tested the transferability of models developed using the USDA NSSC‐KSSL MIR library to a secondary instrument. For the soil properties, total carbon (TC), pH, and clay content, we found that good performance (ratio of performance to deviation [RPD] = 4.9, 2.0, and 3.6, respectively) could be achieved on an independent test set with Savitzky‐Golay smoothing and first derivative preprocessing of the secondary spectra using a memory‐based learning chemometric approach. We tested three calibration transfer techniques (direct standardization [DS], piecewise direct standardization [PDS], and spectral space transformation [SST]) using different size transfer sets selected to be representative of the entire NSSC‐KSSL library. Among the transfer methods, SST consistently outperformed DS and PDS with 50 transfer s les being an optimal number for transfer model development. For TC and pH, performance was improved using the SST transfer (RPD = 7.7 and 2.2, respectively) primarily through the elimination of bias. Calibration transfer could not improve predictions for clay. These findings suggest that calibration transfer may not always be necessary, but users should test to confirm this assumption using a small set of representative s les scanned at both laboratories.
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 05-2022
Publisher: Revista Brasileira de Ciencia do Solo
Date: 2021
Publisher: MDPI AG
Date: 10-2018
DOI: 10.3390/RS10101571
Abstract: The mapping of soil attributes provides support to agricultural planning and land use monitoring, which consequently aids the improvement of soil quality and food production. Landsat 5 Thematic Mapper (TM) images are often used to estimate a given soil attribute (i.e., clay), but have the potential to model many other attributes, providing input for soil mapping applications. In this paper, we aim to evaluate a Bare Soil Composite Image (BSCI) from the state of São Paulo, Brazil, calculated from a multi-temporal dataset, and study its relationship with topsoil properties, such as soil class and geology. The method presented detects bare soil in satellite images in a time series of 16 years, based on Landsat 5 TM observations. The compilation derived a BSCI for the agricultural sites (242,000 hectare area) characterized by very complex geology. Soil properties were analyzed to calibrate prediction models using 740 soil s les (0–20 cm) collected of the area. Partial least squares regression (PLSR) based on the BSCI spectral dataset was performed to quantify soil attributes. The method identified that a single image represents 7 to 20% of bare soil while the compilation of the multi-temporal dataset increases to 53%. Clay content had the best soil attribute prediction estimates (R2 = 0.75, root mean square error (RMSE) = 89.84 g kg−1, and accuracy = 74%). Soil organic matter, cation exchange capacity and sandy soils also achieved moderate predictions. The BSCI demonstrates a strong relationship with legacy geological maps detecting variations in soils. From a single composite image, it was possible to use spectroscopy to evaluate several environmental parameters. This technique could greatly improve soil mapping and consequently aid several applications, such as land use planning, environmental monitoring, and prevention of land degradation, updating legacy surveys and digital soil mapping.
Publisher: Elsevier BV
Date: 06-2019
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 08-2021
Publisher: Informa UK Limited
Date: 25-05-2022
Location: Germany
Location: Brazil
No related grants have been discovered for Jose Lucas Safanelli.