ORCID Profile
0000-0002-0225-144X
Current Organisation
University of Naples Federico II
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Publisher: MDPI AG
Date: 20-04-2018
DOI: 10.3390/RS10040641
Publisher: MDPI AG
Date: 02-07-2021
DOI: 10.3390/RS13132606
Abstract: Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because s ling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 s les with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the s ling procedure, and separating the s les according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we res led the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field s les, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.
Publisher: MDPI AG
Date: 14-03-2018
DOI: 10.20944/PREPRINTS201803.0097.V1
Abstract: Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems, enhancing the understanding hydrological processes, optimizing the allocation and distribution of water resources, and assessing, forecasting and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors or satellite observations. These data are utilized in describing both small and large scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal ides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically evolve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing not just high spatial detail over relatively large areas in a cost-effective way, but as importantly providing an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and applications specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, post-processing techniques, retrieval algorithms and evaluations techniques need to be harmonized. The aim of this paper is to provide a comprehensive general overview of the existing research on studies and applications of UAS in environmental monitoring in order to suggest users and researchers on future research directions, applications, developments and challenges.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Elsevier BV
Date: 10-2007
Publisher: MDPI AG
Date: 20-03-2020
DOI: 10.3390/RS12061001
Abstract: With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.
Publisher: Informa UK Limited
Date: 18-11-2016
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-19560
Abstract: & & Quantification of the spatial and temporal behavior of soil moisture is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Unmanned Aerial Systems (UAS) offer a great potential in monitoring this parameter at sub-meter level and at relatively low cost. The standardization of operational procedures for soil moisture monitoring with UAS can be beneficial to understanding and quantify the quality of retrieved soil moisture (e.g., from different platforms and sensors).& & & & In this study, soil moisture retrieved from UAS using different retrieval algorithms was compared to collocated ground measurements. The thermal inertia model builds upon the dependence of the thermal diffusion on soil moisture. The soil thermal inertia is quantified by processing visible and near-infrared (VIS-NIR) and thermal infrared (TIR) images, acquired at two different times of a day. The temperature& #8211 vegetation trapezoidal model is also used to map soil moisture over vegetated pixels. This trapezoidal model depicts the soil moisture dependence of the surface energy balance. The comparison of the two algorithms helps define a preliminary standard procedure for retrieving soil moisture with UAS.& & & & As a case study, a typical cropland area with olive orchard, cherry and walnut trees in the region of Monteforte Cilento (Italy, Salerno) is used, where optical and thermal images and in situ data were simultaneously acquired. In the Alento observatory, long-term studies on vadose zone hydrology have been conducting across a range of spatial scales. Our findings provide an important contribution towards improving our knowledge on evaluating the ability of UAS to map soil moisture, in support of sustainable natural resources management and climate change studies.& & & & This research is a part of EU COST-Action & #8220 HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring& #8221 .& & & & & strong& Keywords:& /strong& soil moisture, Unmanned Aerial Systems, thermal inertia, HARMONIOUS& &
Publisher: Wiley
Date: 26-05-2010
DOI: 10.1002/ECO.131
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-2092
Abstract: & & Soil is an essential component in the environment and is vital for food security. It provides ecosystem services, filters water, supplies nutrients to plants, provides us with food, stores carbon, regulates greenhouse gases emissions and it affects our climate. Traditional soil survey methodologies are complicated, expensive, and time-consuming. Visible and infrared spectroscopy can effectively characterize soil properties. Spectral measurements are rapid, precise and inexpensive. The spectra contain information about soil properties, which comprises minerals, organic compounds, and water. Today, several Soil Spectral Libraries (SSLs) are being created worldwide because these datasets have a notable potential to be used as training datasets for machine learning methods that will benefit precision agriculture activity for better management of food production. Nonetheless, as SSL's are created under laboratory conditions it is not clear if it can be used to infer field conditions in situ and/or from the sky. Thus, study the relationship between RS, field spectroscopy and the laboratory measurements of soil is very important. Accordingly, this study postulates that traditional SSLs don't simulate the real spectral signatures in the field that both, satellite and airborne sensors measure as well, because they are affected by factors that are not an integral part of the soil, such as: moisture, litter, human and animal activity, plow, grass, dung, waste, etc& #8230 However, under laboratory conditions, these factors are usually removed for the preparation of SSLs. Thus, given the several SSLs available, it is necessary to evaluate the protocols that were used in these SSLs. The objective of this study is to evaluate the gap between field and laboratory spectral measurements through the analysis of the performance of spectral based models. This procedure combined two soil spectral libraries that contain 114 s les that were measured in the laboratory as well as in the field. The nature of the dataset is varied, because these s les were collected from six different fields in three countries of the Mediterranean basin: Israel, Greece and Italy. Moreover, 63 s les are mainly sandy and 51 are mainly clayey. In order to obtain optimal spectral measurements in the field, we used a new optical apparatus that simulates the sun's radiation. Next, we generated PLSR models to estimate one of the most important hydrological parameters namely & #8220 infiltration rate& #8221 that control the runoff stage, soil erosion and water storage in the soil profile. This property is strongly affected by the surface characteristics. Finally, the field based spectral model was adapted to an UAV hyperspectral sensor in order to estimate the infiltration rate from the sky. The results were successfully validated in field, and we concluded that for the estimation of the infiltration rate, SSLs must be created using surface reflectance in field because laboratory protocols can be detrimental for the performance of the dataset in question.& & & & & & &
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2017
Publisher: MDPI AG
Date: 23-05-2020
DOI: 10.3390/W12051495
Abstract: The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.
No related grants have been discovered for Salvatore Manfreda.