The Experts below are selected from a list of 79167 Experts worldwide ranked by ideXlab platform
Cécile Gomez - One of the best experts on this subject based on the ideXlab platform.
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Mean spectral reflectance from bare soil pixels along a Landsat-TM time series to increase both the prediction accuracy of soil Clay Content and mapping coverage
Geoderma, 2021Co-Authors: Anis Gasmi, Philippe Lagacherie, Cécile Gomez, Hedi Zouari, Ahmed Laamrani, Abdelghani ChehbouniAbstract:Visible, near-infrared and short wave infrared (VNIR/SWIR, 400–2500 nm) remote sensing imagery is a useful tool for topsoil property mapping, but limited to bare soils pixels. With the increasing amount of freely available VNIR/SWIR satellite imagery (e.g. Landsat TM, ETM+, OLI and Sentinel-2A/B), extensive time series data can be exploited to increase the spatial coverage of bare soil derived information. The objective of this study was to evaluate the benefits of using a bare soil image created from the mean spectral reflectance from bare soil pixels along a time series, compared to a single-date image. The benefits were analyzed in term of (i) proportion of soil mapping and (ii) accuracy of Clay Content prediction. The study was conducted over the Cap-Bon region (Northern Tunisia) which is a pedologically contrasted and cultivated area. To this end, 262 topsoil samples and three Landsat-TM images acquired during the summer season were used. Multiple linear regression (MLR) models based on the multi-date and single-date Landsat-derived spectral dataset were performed to quantify Clay soil Content. Our results have shown that (1) a bare soil image created from only mean spectral reflectance from common bare soil pixels along a time series provided the best accuracy of Clay Content prediction (i.e., coefficient of determination of validation of 0.75, a root mean square error of prediction (RMSEP) of 88 g/kg) with a moderate bare soil coverage (i.e., 23% of the study area); (2) a bare soil image created from a mix of mean spectral reflectance from common bare soil pixels along a time series and of spectral reflectance from bare soil pixels of single-date images provided acceptable accuracy of Clay Content prediction (i.e., = 0.64, RMSEP = 109 g/kg) with a relatively high bare soil coverage (i.e., 44% of the study area); and (3) all the bare soil images provided similar spatial structures of the Clay Content predictions. With the actual availability of the VNIR/SWIR satellite imagery for the entire globe, this study offer a simple and accurate method for delivering accurate soil property maps over large areas, to the geoscience community.
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how far can the uncertainty on a digital soil map be known a numerical experiment using pseudo values of Clay Content obtained from vis swir hyperspectral imagery
Geoderma, 2019Co-Authors: Philippe Lagacherie, Dominique Arrouays, Hocine Bourennane, Cécile Gomez, Manuel Martin, Nicolas P.a. SabyAbstract:Abstract Digital Soil Map uncertainty is usually evaluated from a set of independent soil observations that are used to determine various uncertainty indicators. However, the number and locations of the sites that constitute these evaluations may impact the value of these indicators. In this paper, a numerical experiment on uncertainty indicators was performed using the pseudo values of topsoil Clay Content obtained from an airborne hyperspectral image in the Cap Bon region (Tunisia). These pseudo values form a soil pattern with a large extent (46% of 300 km2), high resolution (5 m) and good accuracy (R2val = 0.75) while being free of visible artefacts and pedologically plausible. Therefore, the dataset was considered a fair representation of reality while providing a quasi-unlimited choice of sites. The numerical experiment considered three Quantile Regression Forests as examples of DSM models by using inputs from relief soil covariates and geographical locations that were calibrated from 200, 2000 and 100,000 individuals respectively (low, medium and high quality models). Their uncertainty indicators were first evaluated by calculating four uncertainty indicators (ME, MSE, SSMSE and PICP) from a large independent validation set of 100,000 sites. These uncertainty indicators were then computed from independent evaluation sets of different sizes (from 50 to 500 sites) and from different locations (500 evaluation sets of each size). The independent evaluation sets were selected following a stratified random sampling using compact geographical strata. The numerical experiment showed that the values of the uncertainty indicators were highly variable across numbers and locations of sites. The largest variations were observed for evaluation sets with fewer than 100 sites, but non-negligible variations remained for larger evaluation datasets. This result suggested that evaluations from independent sets convey a non-negligible error on the uncertainty indicators, which increases as the number of sites decrease. Evaluations of DSM models from independent evaluation sets should be interpreted with care and uncertainty on validation results should be systematically estimated. For that, numerical experiments for benchmarking DSM models on known soil patterns across the world would be a valuable complement to the analytical expressions for the uncertainty indicators and the many DSM applications for which these analytical expressions are not valid. This would serve also to improve the sampling techniques for the calibration and evaluation datasets to reduce the error when estimating the uncertainty of a DSM product.
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sensitivity of Clay Content prediction to spectral configuration of vnir swir imaging data from multispectral to hyperspectral scenarios
Remote Sensing of Environment, 2018Co-Authors: Cécile Gomez, Philippe Lagacherie, S. Bacha, Karine Adeline, B Driessen, Nathalie Gorretta, Jeanmichel Roger, X. BriottetAbstract:Abstract The use of digital soil mapping, with the help of spectroscopic data, provides a non-destructive and cost-efficient alternative to soil property laboratory measurements. Visible, near-infrared and short wave infrared (VNIR/SWIR, 400–2500 nm) hyperspectral imaging is one of the most promising tools for topsoil property mapping. The aim of this study was to test the sensitivity of soil property prediction results to coarsening image spectral resolution. This may offer an analysis of the potential of forthcoming hyperspectral satellite sensors, e.g., HYPerspectral X IMagery (HYPXIM) or Environmental Mapping and Analysis Program (EnMAP), and existing multispectral sensors, e.g., SENTINEL-2 Multispectral Sensor Instrument (MSI) or LANDSAT-8 Operational Land Imager (OLI), for soil properties mapping. This study used VNIR/SWIR hyperspectral airborne data acquired by the AISA-DUAL sensor (initial spectral and spatial resolutions of approximately 5 nm and 5 m, respectively) over a 300 km2 Mediterranean rural region. Ten spectral configurations were built and divided in the following two groups: i) six spectral configurations corresponding to simulated sensors with regular spectral resolution from 5 nm to 200 nm (i.e., the Full Width at Half Maximum (FWHM) remains constant throughout the considered spectral domain; this includes the simulation of the forthcoming HYPXIM and EnMAP hyperspectral satellites) and ii) four spectral configurations corresponding to existing multispectral sensors with irregular spectral resolution (i.e., the FWHM differs from spectral sampling interval; Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SENTINEL-2 MSI, LANDSAT-7 Enhanced Thematic Mapper (ETM +) and LANDSAT-8 OLI). The soil property studied in this paper is the Clay Content, defined as the percentage of granulometric fraction finer than 2 μm by weight of the soil, which will be estimated using the partial least squares regression method. Our results showed that i) spectral configurations with regular spectral resolutions from 5 to 100 nm provided similar and good Clay Content prediction performances (R2val > 0.7 and RPIQ > 3) and allowed Clay mapping with correct short-scale variations, ii) the spectral configuration with a regular spectral resolution of 200 nm provided unsatisfactory Clay Content prediction performance (R2val ≃ 0.01 and RPIQ ≃ 1.65) and iii) the ASTER sensor was the only existing multispectral sensor that provided both correct performance of Clay Content estimation (R2val ≃ 0.8 and RPIQ ≃ 3.72) and correct Clay mapping. Therefore, Clay mapping by the ASTER multispectral data should be pursued while awaiting the launch of forthcoming hyperspectral satellite sensors (e.g., HYPXIM and EnMAP), which will be good candidates for future large Clay mapping campaigns over bare soils.
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Evaluating the sensitivity of Clay Content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery
Remote Sensing of Environment, 2015Co-Authors: Cécile Gomez, Philippe Lagacherie, R. Oltra-carrió, S. Bacha, X. BriottetAbstract:Visible, near-infrared and short wave infrared (VNIR/SWIR, 0.4-2.5 µm) hyperspectral satellite imaging is one of the most promising tools for topsoil property mapping for the following reasons: i) it is derived from a laboratory technique that has proven to be a good alternative to costly physical and chemical laboratory soil analysis for estimating a large range of soil properties ; ii) it can benefit from the increasing number of methodologies developed for VNIR/SWIR hyperspectral airborne imaging ; and iii) it provides a synoptic view of the area under study. Despite the significant potential of VNIR/SWIR hyperspectral airborne data for topsoil property mapping, the transposition to satellite data must be evaluated. The objective of this study was to test the sensitivity of Clay Content prediction to atmospheric effects and to degradation of spatial resolution. This study may offer an initial analysis of the potential of future hyperspectral satellite sensors (HYPerspectral X Imagery – HYPXIM, Spaceborne Hyperspectral Applicative Land and Ocean Mission - SHALOM, PRecursore IperSpettrale della Missione Applicativa - PRISMA, Environmental Mapping and Analysis Program - EnMAP and Hyperspectral Infrared Imager - HyspIRI) for soil applications. This study employed VNIR/SWIR AISA-DUAL airborne data acquired in a Mediterranean region over a large area (300 km²), with an initial spatial resolution of 5 m. These hyperspectral airborne data were simulated at the top of the atmosphere and aggregated at 6 spatial resolutions (10, 15, 20, 30, 60 and 90 m) to fit with the future hyperspectral satellite sensors. The predicted Clay Content maps were obtained using the partial least squares regression (PLSR) method. The large area of the studied region allows analysis of different pedological patterns in terms of soil composition and spatial structures. Our results showed the following: (i) when a correct compensation of atmosphere effects was performed, only slight differences were detected between Clay maps retrieved from the airborne imagery and those from spaceborne imagery (both at 5 m of spatial resolution), (ii) the PLSR models built from data with 5 to 30 m spatial resolution had robust performances and allowed Clay mapping, although variation in Clay Content related to short scale succession of parent material was imperfectly captured beyond 15 m of spatial resolution; (iii) the PLSR models built from data with 60 and 90 m spatial resolution were inaccurate and did not enable Clay mapping; and (iv) the two latter results could be explained by the combination of a small short-scale Clay Content variability and small field sizes observed in the study area. Therefore, in the Mediterranean context with short-scale Clay Content variability and under the spectral specifications of the airborne sensors, most of the future hyperspectral satellite sensors (four among the five sensors which are studied here) would be useful for Clay Content mapping.
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applying blind source separation on hyperspectral data for Clay Content estimation over partially vegetated surfaces
Geoderma, 2011Co-Authors: W Ouerghemmi, Cécile Gomez, S Naceur, Philippe LagacherieAbstract:Abstract Hyperspectral imagery has proven to be a useful technique for mapping soil surface properties. However vegetation cover has a significant influence on spectral reflectance and the applicability of hyperspectral images for soil property estimations decreases when surfaces are partially covered by vegetation. To maximize information extraction from hyperspectral data, we apply a “double-extraction” technique: 1) extraction of a soil reflectance spectrum s , using blind source separation (BSS) techniques from mixed hyperspectral spectra without any information about the proportion of the components in the mixture nor the original spectra that composed the mixed spectra and 2) extraction of soil property Contents from the soil reflectance spectrum s by classical chemometric methods. The Infomax algorithm is used as the BSS algorithm for this approach, and the chemometric method is the partial least squares regression (PLSR). The estimated soil property after soil signals extraction is the Clay Content, and the hyperspectral datasets are from Hymap airborne data. First, experiments were performed using simulated linear spectral mixtures of one soil spectrum and one vegetation spectrum (vineyards). Second, the “double-extraction” method was applied to grids of 3 × 3 Hymap mixed spectra, which were centered on surfaces partially covered by vineyards. Our simulated experiments and applications to Hymap data show that the BSS concept provides accurate soil reflectance spectra for Clay Content estimation. The Clay Content estimations are accurate compared to physico-chemical values (the mean error of estimation is always inferior to 50 g/kg in simulated experiments and predominantly inferior to 90 g/kg in Hymap mixed pixels treatments). We conclude that the “double-extraction” method, which requires no a priori information is a promising method for soil property prediction using hyperspectral imagery over partially vegetated surfaces.
Philippe Lagacherie - One of the best experts on this subject based on the ideXlab platform.
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Mean spectral reflectance from bare soil pixels along a Landsat-TM time series to increase both the prediction accuracy of soil Clay Content and mapping coverage
Geoderma, 2021Co-Authors: Anis Gasmi, Philippe Lagacherie, Cécile Gomez, Hedi Zouari, Ahmed Laamrani, Abdelghani ChehbouniAbstract:Visible, near-infrared and short wave infrared (VNIR/SWIR, 400–2500 nm) remote sensing imagery is a useful tool for topsoil property mapping, but limited to bare soils pixels. With the increasing amount of freely available VNIR/SWIR satellite imagery (e.g. Landsat TM, ETM+, OLI and Sentinel-2A/B), extensive time series data can be exploited to increase the spatial coverage of bare soil derived information. The objective of this study was to evaluate the benefits of using a bare soil image created from the mean spectral reflectance from bare soil pixels along a time series, compared to a single-date image. The benefits were analyzed in term of (i) proportion of soil mapping and (ii) accuracy of Clay Content prediction. The study was conducted over the Cap-Bon region (Northern Tunisia) which is a pedologically contrasted and cultivated area. To this end, 262 topsoil samples and three Landsat-TM images acquired during the summer season were used. Multiple linear regression (MLR) models based on the multi-date and single-date Landsat-derived spectral dataset were performed to quantify Clay soil Content. Our results have shown that (1) a bare soil image created from only mean spectral reflectance from common bare soil pixels along a time series provided the best accuracy of Clay Content prediction (i.e., coefficient of determination of validation of 0.75, a root mean square error of prediction (RMSEP) of 88 g/kg) with a moderate bare soil coverage (i.e., 23% of the study area); (2) a bare soil image created from a mix of mean spectral reflectance from common bare soil pixels along a time series and of spectral reflectance from bare soil pixels of single-date images provided acceptable accuracy of Clay Content prediction (i.e., = 0.64, RMSEP = 109 g/kg) with a relatively high bare soil coverage (i.e., 44% of the study area); and (3) all the bare soil images provided similar spatial structures of the Clay Content predictions. With the actual availability of the VNIR/SWIR satellite imagery for the entire globe, this study offer a simple and accurate method for delivering accurate soil property maps over large areas, to the geoscience community.
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how far can the uncertainty on a digital soil map be known a numerical experiment using pseudo values of Clay Content obtained from vis swir hyperspectral imagery
Geoderma, 2019Co-Authors: Philippe Lagacherie, Dominique Arrouays, Hocine Bourennane, Cécile Gomez, Manuel Martin, Nicolas P.a. SabyAbstract:Abstract Digital Soil Map uncertainty is usually evaluated from a set of independent soil observations that are used to determine various uncertainty indicators. However, the number and locations of the sites that constitute these evaluations may impact the value of these indicators. In this paper, a numerical experiment on uncertainty indicators was performed using the pseudo values of topsoil Clay Content obtained from an airborne hyperspectral image in the Cap Bon region (Tunisia). These pseudo values form a soil pattern with a large extent (46% of 300 km2), high resolution (5 m) and good accuracy (R2val = 0.75) while being free of visible artefacts and pedologically plausible. Therefore, the dataset was considered a fair representation of reality while providing a quasi-unlimited choice of sites. The numerical experiment considered three Quantile Regression Forests as examples of DSM models by using inputs from relief soil covariates and geographical locations that were calibrated from 200, 2000 and 100,000 individuals respectively (low, medium and high quality models). Their uncertainty indicators were first evaluated by calculating four uncertainty indicators (ME, MSE, SSMSE and PICP) from a large independent validation set of 100,000 sites. These uncertainty indicators were then computed from independent evaluation sets of different sizes (from 50 to 500 sites) and from different locations (500 evaluation sets of each size). The independent evaluation sets were selected following a stratified random sampling using compact geographical strata. The numerical experiment showed that the values of the uncertainty indicators were highly variable across numbers and locations of sites. The largest variations were observed for evaluation sets with fewer than 100 sites, but non-negligible variations remained for larger evaluation datasets. This result suggested that evaluations from independent sets convey a non-negligible error on the uncertainty indicators, which increases as the number of sites decrease. Evaluations of DSM models from independent evaluation sets should be interpreted with care and uncertainty on validation results should be systematically estimated. For that, numerical experiments for benchmarking DSM models on known soil patterns across the world would be a valuable complement to the analytical expressions for the uncertainty indicators and the many DSM applications for which these analytical expressions are not valid. This would serve also to improve the sampling techniques for the calibration and evaluation datasets to reduce the error when estimating the uncertainty of a DSM product.
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sensitivity of Clay Content prediction to spectral configuration of vnir swir imaging data from multispectral to hyperspectral scenarios
Remote Sensing of Environment, 2018Co-Authors: Cécile Gomez, Philippe Lagacherie, S. Bacha, Karine Adeline, B Driessen, Nathalie Gorretta, Jeanmichel Roger, X. BriottetAbstract:Abstract The use of digital soil mapping, with the help of spectroscopic data, provides a non-destructive and cost-efficient alternative to soil property laboratory measurements. Visible, near-infrared and short wave infrared (VNIR/SWIR, 400–2500 nm) hyperspectral imaging is one of the most promising tools for topsoil property mapping. The aim of this study was to test the sensitivity of soil property prediction results to coarsening image spectral resolution. This may offer an analysis of the potential of forthcoming hyperspectral satellite sensors, e.g., HYPerspectral X IMagery (HYPXIM) or Environmental Mapping and Analysis Program (EnMAP), and existing multispectral sensors, e.g., SENTINEL-2 Multispectral Sensor Instrument (MSI) or LANDSAT-8 Operational Land Imager (OLI), for soil properties mapping. This study used VNIR/SWIR hyperspectral airborne data acquired by the AISA-DUAL sensor (initial spectral and spatial resolutions of approximately 5 nm and 5 m, respectively) over a 300 km2 Mediterranean rural region. Ten spectral configurations were built and divided in the following two groups: i) six spectral configurations corresponding to simulated sensors with regular spectral resolution from 5 nm to 200 nm (i.e., the Full Width at Half Maximum (FWHM) remains constant throughout the considered spectral domain; this includes the simulation of the forthcoming HYPXIM and EnMAP hyperspectral satellites) and ii) four spectral configurations corresponding to existing multispectral sensors with irregular spectral resolution (i.e., the FWHM differs from spectral sampling interval; Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SENTINEL-2 MSI, LANDSAT-7 Enhanced Thematic Mapper (ETM +) and LANDSAT-8 OLI). The soil property studied in this paper is the Clay Content, defined as the percentage of granulometric fraction finer than 2 μm by weight of the soil, which will be estimated using the partial least squares regression method. Our results showed that i) spectral configurations with regular spectral resolutions from 5 to 100 nm provided similar and good Clay Content prediction performances (R2val > 0.7 and RPIQ > 3) and allowed Clay mapping with correct short-scale variations, ii) the spectral configuration with a regular spectral resolution of 200 nm provided unsatisfactory Clay Content prediction performance (R2val ≃ 0.01 and RPIQ ≃ 1.65) and iii) the ASTER sensor was the only existing multispectral sensor that provided both correct performance of Clay Content estimation (R2val ≃ 0.8 and RPIQ ≃ 3.72) and correct Clay mapping. Therefore, Clay mapping by the ASTER multispectral data should be pursued while awaiting the launch of forthcoming hyperspectral satellite sensors (e.g., HYPXIM and EnMAP), which will be good candidates for future large Clay mapping campaigns over bare soils.
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Evaluating the sensitivity of Clay Content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery
Remote Sensing of Environment, 2015Co-Authors: Cécile Gomez, Philippe Lagacherie, R. Oltra-carrió, S. Bacha, X. BriottetAbstract:Visible, near-infrared and short wave infrared (VNIR/SWIR, 0.4-2.5 µm) hyperspectral satellite imaging is one of the most promising tools for topsoil property mapping for the following reasons: i) it is derived from a laboratory technique that has proven to be a good alternative to costly physical and chemical laboratory soil analysis for estimating a large range of soil properties ; ii) it can benefit from the increasing number of methodologies developed for VNIR/SWIR hyperspectral airborne imaging ; and iii) it provides a synoptic view of the area under study. Despite the significant potential of VNIR/SWIR hyperspectral airborne data for topsoil property mapping, the transposition to satellite data must be evaluated. The objective of this study was to test the sensitivity of Clay Content prediction to atmospheric effects and to degradation of spatial resolution. This study may offer an initial analysis of the potential of future hyperspectral satellite sensors (HYPerspectral X Imagery – HYPXIM, Spaceborne Hyperspectral Applicative Land and Ocean Mission - SHALOM, PRecursore IperSpettrale della Missione Applicativa - PRISMA, Environmental Mapping and Analysis Program - EnMAP and Hyperspectral Infrared Imager - HyspIRI) for soil applications. This study employed VNIR/SWIR AISA-DUAL airborne data acquired in a Mediterranean region over a large area (300 km²), with an initial spatial resolution of 5 m. These hyperspectral airborne data were simulated at the top of the atmosphere and aggregated at 6 spatial resolutions (10, 15, 20, 30, 60 and 90 m) to fit with the future hyperspectral satellite sensors. The predicted Clay Content maps were obtained using the partial least squares regression (PLSR) method. The large area of the studied region allows analysis of different pedological patterns in terms of soil composition and spatial structures. Our results showed the following: (i) when a correct compensation of atmosphere effects was performed, only slight differences were detected between Clay maps retrieved from the airborne imagery and those from spaceborne imagery (both at 5 m of spatial resolution), (ii) the PLSR models built from data with 5 to 30 m spatial resolution had robust performances and allowed Clay mapping, although variation in Clay Content related to short scale succession of parent material was imperfectly captured beyond 15 m of spatial resolution; (iii) the PLSR models built from data with 60 and 90 m spatial resolution were inaccurate and did not enable Clay mapping; and (iv) the two latter results could be explained by the combination of a small short-scale Clay Content variability and small field sizes observed in the study area. Therefore, in the Mediterranean context with short-scale Clay Content variability and under the spectral specifications of the airborne sensors, most of the future hyperspectral satellite sensors (four among the five sensors which are studied here) would be useful for Clay Content mapping.
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applying blind source separation on hyperspectral data for Clay Content estimation over partially vegetated surfaces
Geoderma, 2011Co-Authors: W Ouerghemmi, Cécile Gomez, S Naceur, Philippe LagacherieAbstract:Abstract Hyperspectral imagery has proven to be a useful technique for mapping soil surface properties. However vegetation cover has a significant influence on spectral reflectance and the applicability of hyperspectral images for soil property estimations decreases when surfaces are partially covered by vegetation. To maximize information extraction from hyperspectral data, we apply a “double-extraction” technique: 1) extraction of a soil reflectance spectrum s , using blind source separation (BSS) techniques from mixed hyperspectral spectra without any information about the proportion of the components in the mixture nor the original spectra that composed the mixed spectra and 2) extraction of soil property Contents from the soil reflectance spectrum s by classical chemometric methods. The Infomax algorithm is used as the BSS algorithm for this approach, and the chemometric method is the partial least squares regression (PLSR). The estimated soil property after soil signals extraction is the Clay Content, and the hyperspectral datasets are from Hymap airborne data. First, experiments were performed using simulated linear spectral mixtures of one soil spectrum and one vegetation spectrum (vineyards). Second, the “double-extraction” method was applied to grids of 3 × 3 Hymap mixed spectra, which were centered on surfaces partially covered by vineyards. Our simulated experiments and applications to Hymap data show that the BSS concept provides accurate soil reflectance spectra for Clay Content estimation. The Clay Content estimations are accurate compared to physico-chemical values (the mean error of estimation is always inferior to 50 g/kg in simulated experiments and predominantly inferior to 90 g/kg in Hymap mixed pixels treatments). We conclude that the “double-extraction” method, which requires no a priori information is a promising method for soil property prediction using hyperspectral imagery over partially vegetated surfaces.
Zoltan Nagy - One of the best experts on this subject based on the ideXlab platform.
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dependence of soil respiration on soil moisture Clay Content soil organic matter and co2 uptake in dry grasslands
Soil Biology & Biochemistry, 2011Co-Authors: Janos Balogh, Krisztina Pinter, Sz Foti, D Cserhalmi, Marianna Papp, Zoltan NagyAbstract:The effects of abiotic and biotic drivers on soil respiration (Rs) were studied in four grassland and one forest sites in Hungary in field measurement campaigns (duration of studies by sites 2e7 years) between 2000 and 2008. The sites are within a 100 km distance of each other, with nearly the same climate, but with different soils and vegetation. Soil respiration model with soil temperature (Ts) and soil water Content (SWC) as independent variables explained larger part of variance (range 0.47e0.81) than the Lloyd and Taylor model (explained variance: 0.31e0.76). Direct effect of SWC on Rs at much smaller temporal and spatial scale (1.5 h, and a few meters, respectively) was verified. Soil water Content optimal for Rs (SWCopt) was shown to significantly (positively) depend on soil Clay Content, while parameter related to activation energy (E0) was significantly (negatively) correlated to the total organic carbon Content (TOC) in the upper 10 cm soil layer. Dependence of model parameters on soil properties could easily be utilized in models of soil respiration. The effect of current (a few hours earlier) assimilation rates on soil respiration after removing the effect of abiotic covariates (i.e. temperature and water supply) is shown. The correlation maximum between the Rs residuals (Rs_res, from the Rs (SWC, Ts) model) and net ecosystem exchange (NEE) was found at 13.5 h time lag at the sandy grassland. Incorporating the time-lagged effect of NEE on Rs into the model of soil respiration improved the agreement between the simulated vs. measured Rs data. Use of SWCopt and E0 parameters and consideration of current assimilation in soil respiration models are proposed.
X. Briottet - One of the best experts on this subject based on the ideXlab platform.
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sensitivity of Clay Content prediction to spectral configuration of vnir swir imaging data from multispectral to hyperspectral scenarios
Remote Sensing of Environment, 2018Co-Authors: Cécile Gomez, Philippe Lagacherie, S. Bacha, Karine Adeline, B Driessen, Nathalie Gorretta, Jeanmichel Roger, X. BriottetAbstract:Abstract The use of digital soil mapping, with the help of spectroscopic data, provides a non-destructive and cost-efficient alternative to soil property laboratory measurements. Visible, near-infrared and short wave infrared (VNIR/SWIR, 400–2500 nm) hyperspectral imaging is one of the most promising tools for topsoil property mapping. The aim of this study was to test the sensitivity of soil property prediction results to coarsening image spectral resolution. This may offer an analysis of the potential of forthcoming hyperspectral satellite sensors, e.g., HYPerspectral X IMagery (HYPXIM) or Environmental Mapping and Analysis Program (EnMAP), and existing multispectral sensors, e.g., SENTINEL-2 Multispectral Sensor Instrument (MSI) or LANDSAT-8 Operational Land Imager (OLI), for soil properties mapping. This study used VNIR/SWIR hyperspectral airborne data acquired by the AISA-DUAL sensor (initial spectral and spatial resolutions of approximately 5 nm and 5 m, respectively) over a 300 km2 Mediterranean rural region. Ten spectral configurations were built and divided in the following two groups: i) six spectral configurations corresponding to simulated sensors with regular spectral resolution from 5 nm to 200 nm (i.e., the Full Width at Half Maximum (FWHM) remains constant throughout the considered spectral domain; this includes the simulation of the forthcoming HYPXIM and EnMAP hyperspectral satellites) and ii) four spectral configurations corresponding to existing multispectral sensors with irregular spectral resolution (i.e., the FWHM differs from spectral sampling interval; Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SENTINEL-2 MSI, LANDSAT-7 Enhanced Thematic Mapper (ETM +) and LANDSAT-8 OLI). The soil property studied in this paper is the Clay Content, defined as the percentage of granulometric fraction finer than 2 μm by weight of the soil, which will be estimated using the partial least squares regression method. Our results showed that i) spectral configurations with regular spectral resolutions from 5 to 100 nm provided similar and good Clay Content prediction performances (R2val > 0.7 and RPIQ > 3) and allowed Clay mapping with correct short-scale variations, ii) the spectral configuration with a regular spectral resolution of 200 nm provided unsatisfactory Clay Content prediction performance (R2val ≃ 0.01 and RPIQ ≃ 1.65) and iii) the ASTER sensor was the only existing multispectral sensor that provided both correct performance of Clay Content estimation (R2val ≃ 0.8 and RPIQ ≃ 3.72) and correct Clay mapping. Therefore, Clay mapping by the ASTER multispectral data should be pursued while awaiting the launch of forthcoming hyperspectral satellite sensors (e.g., HYPXIM and EnMAP), which will be good candidates for future large Clay mapping campaigns over bare soils.
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Evaluating the sensitivity of Clay Content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery
Remote Sensing of Environment, 2015Co-Authors: Cécile Gomez, Philippe Lagacherie, R. Oltra-carrió, S. Bacha, X. BriottetAbstract:Visible, near-infrared and short wave infrared (VNIR/SWIR, 0.4-2.5 µm) hyperspectral satellite imaging is one of the most promising tools for topsoil property mapping for the following reasons: i) it is derived from a laboratory technique that has proven to be a good alternative to costly physical and chemical laboratory soil analysis for estimating a large range of soil properties ; ii) it can benefit from the increasing number of methodologies developed for VNIR/SWIR hyperspectral airborne imaging ; and iii) it provides a synoptic view of the area under study. Despite the significant potential of VNIR/SWIR hyperspectral airborne data for topsoil property mapping, the transposition to satellite data must be evaluated. The objective of this study was to test the sensitivity of Clay Content prediction to atmospheric effects and to degradation of spatial resolution. This study may offer an initial analysis of the potential of future hyperspectral satellite sensors (HYPerspectral X Imagery – HYPXIM, Spaceborne Hyperspectral Applicative Land and Ocean Mission - SHALOM, PRecursore IperSpettrale della Missione Applicativa - PRISMA, Environmental Mapping and Analysis Program - EnMAP and Hyperspectral Infrared Imager - HyspIRI) for soil applications. This study employed VNIR/SWIR AISA-DUAL airborne data acquired in a Mediterranean region over a large area (300 km²), with an initial spatial resolution of 5 m. These hyperspectral airborne data were simulated at the top of the atmosphere and aggregated at 6 spatial resolutions (10, 15, 20, 30, 60 and 90 m) to fit with the future hyperspectral satellite sensors. The predicted Clay Content maps were obtained using the partial least squares regression (PLSR) method. The large area of the studied region allows analysis of different pedological patterns in terms of soil composition and spatial structures. Our results showed the following: (i) when a correct compensation of atmosphere effects was performed, only slight differences were detected between Clay maps retrieved from the airborne imagery and those from spaceborne imagery (both at 5 m of spatial resolution), (ii) the PLSR models built from data with 5 to 30 m spatial resolution had robust performances and allowed Clay mapping, although variation in Clay Content related to short scale succession of parent material was imperfectly captured beyond 15 m of spatial resolution; (iii) the PLSR models built from data with 60 and 90 m spatial resolution were inaccurate and did not enable Clay mapping; and (iv) the two latter results could be explained by the combination of a small short-scale Clay Content variability and small field sizes observed in the study area. Therefore, in the Mediterranean context with short-scale Clay Content variability and under the spectral specifications of the airborne sensors, most of the future hyperspectral satellite sensors (four among the five sensors which are studied here) would be useful for Clay Content mapping.
Abdelghani Chehbouni - One of the best experts on this subject based on the ideXlab platform.
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Mean spectral reflectance from bare soil pixels along a Landsat-TM time series to increase both the prediction accuracy of soil Clay Content and mapping coverage
Geoderma, 2021Co-Authors: Anis Gasmi, Philippe Lagacherie, Cécile Gomez, Hedi Zouari, Ahmed Laamrani, Abdelghani ChehbouniAbstract:Visible, near-infrared and short wave infrared (VNIR/SWIR, 400–2500 nm) remote sensing imagery is a useful tool for topsoil property mapping, but limited to bare soils pixels. With the increasing amount of freely available VNIR/SWIR satellite imagery (e.g. Landsat TM, ETM+, OLI and Sentinel-2A/B), extensive time series data can be exploited to increase the spatial coverage of bare soil derived information. The objective of this study was to evaluate the benefits of using a bare soil image created from the mean spectral reflectance from bare soil pixels along a time series, compared to a single-date image. The benefits were analyzed in term of (i) proportion of soil mapping and (ii) accuracy of Clay Content prediction. The study was conducted over the Cap-Bon region (Northern Tunisia) which is a pedologically contrasted and cultivated area. To this end, 262 topsoil samples and three Landsat-TM images acquired during the summer season were used. Multiple linear regression (MLR) models based on the multi-date and single-date Landsat-derived spectral dataset were performed to quantify Clay soil Content. Our results have shown that (1) a bare soil image created from only mean spectral reflectance from common bare soil pixels along a time series provided the best accuracy of Clay Content prediction (i.e., coefficient of determination of validation of 0.75, a root mean square error of prediction (RMSEP) of 88 g/kg) with a moderate bare soil coverage (i.e., 23% of the study area); (2) a bare soil image created from a mix of mean spectral reflectance from common bare soil pixels along a time series and of spectral reflectance from bare soil pixels of single-date images provided acceptable accuracy of Clay Content prediction (i.e., = 0.64, RMSEP = 109 g/kg) with a relatively high bare soil coverage (i.e., 44% of the study area); and (3) all the bare soil images provided similar spatial structures of the Clay Content predictions. With the actual availability of the VNIR/SWIR satellite imagery for the entire globe, this study offer a simple and accurate method for delivering accurate soil property maps over large areas, to the geoscience community.