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Nicolas P.a. Saby - One of the best experts on this subject based on the ideXlab platform.
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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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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 P. Martin, Nicolas P.a. SabyAbstract: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
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Refining a reconnaissance Soil Map by calibrating regression models with data from the same Map (Normandy, France)
Geoderma Regional, 2014Co-Authors: Fanny Collard, Bas Kempen, Gerard B. M. Heuvelink, Nicolas P.a. Saby, Anne Richer De Forges, Sébastien Lehmann, Pierre Nehlig, Dominique ArrouaysAbstract:Reconnaissance Soil Maps at 1:250,000 scale are the most detailed source of Soil information for large parts of France. For many environmental applications, however, the level of detail and accuracy of these Maps is insufficient. Funds are lacking to refine and update these Maps by traditional Soil survey. In this study we investigated the merit of digital Soil Mapping to refine and improve the 1:250,000 reconnaissance Soil Map of a 1580 km2 area in Haute-Normandie, France. The Soil Map was produced in 1988 and distinguishes nine Soil class units. The approach taken was to predict Soil class from a large number of environmental covariates using regression techniques. The covariates used include DEM derivatives, geology and land cover Maps. Because very few Soil point observations were available within the area, we calibrated the regression model by sampling the Soil Map on a grid. We calibrated three models: classification tree (CT), multinomial logistic regression (MLR) and random forests (RF), and used these models to predict the nine Soil classes across the study area. The new and original Maps were validated with field data from 123 locations selected with a stratified simple random sampling design. For MLR, the estimate of the overall purity was 65.9%, while that of the reconnaissance Map was 55.5%. The difference between the purity estimates of these Maps was statistically significant (p = 0.014). The significant improvement over the existing Soil Map is remarkable because the regression model was calibrated with the existing Soil Map and uses no additional Soil observations.
Dominique Arrouays - One of the best experts on this subject based on the ideXlab platform.
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Impressions of digital Soil Maps: The good, the not so good, and making them ever better
Geoderma Regional, 2020Co-Authors: Dominique Arrouays, Alex B. Mcbratney, Zamir Libohova, Johan Bouma, Anne C. Richer-de-forges, Cristine L.s. Morgan, Pierre Roudier, Laura Poggio, V.l. MulderAbstract:Abstract Since the turn of the millennium, digital Soil Mapping (DSM) has revolutionized the production of fine resolution gridded Soil data with associated uncertainty. However, the link to conventional Soil Maps has not been sufficiently explained nor are the approaches complementary and synergistic. Further training on the digital Soil Mapping approaches, and associated strengths and weaknesses is required. The user community requires training in, and experience with, the new digital Soil Map products, especially about the use of uncertainties for risk modelling and policy development. Standards are required for public and private sector digital Soil Map products to prevent the production of poor-quality information which will become misleading and counter-productive. Machine-learning methods are to be used with caution with respect to their interpretability and parsimony. The use of DSM products for improved pedological understanding and Soil survey interpretations requires urgent investigation.
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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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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 P. Martin, Nicolas P.a. SabyAbstract: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
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Assessment of Uncertainty on a Digital Soil Map: a sensitivity analysis on the uncertainty indicators
2017Co-Authors: Philippe Lagacherie, Dominique Arrouays, Hocine Bourennane, Cécile Gomez, Manuel Martin, Nicolas SabyAbstract:Digital Soil Map uncertainty is usually evaluated from a set of independent Soil observations – i.e. that are not used for calibrating the DSM model -. As any statistical parameters derived from a set of individuals, the uncertainty indicators – e.g., R2, ME, PICP,...- could be sensitive to the number and the locations of these Soil observations. To our knowledge, this issue has not be considered yet in the literature since it would require performing a sensitivity study from a base spatial sampling that had to be dense and extended enough for picturing the real underlying Soil pattern and allowing the test of multiple sampling schemes, which is not feasible in practice. In this paper, such sensitivity analysis is performed from the virtual pattern of topSoil clay content of bare Soil surfaces at 5 meter resolution over 300 km2 in the Cap Bon region (Tunisia). This pattern, derived from a hyperspectral image, was accurate enough (R2val
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Refining a reconnaissance Soil Map by calibrating regression models with data from the same Map (Normandy, France)
Geoderma Regional, 2014Co-Authors: Fanny Collard, Bas Kempen, Gerard B. M. Heuvelink, Nicolas P.a. Saby, Anne Richer De Forges, Sébastien Lehmann, Pierre Nehlig, Dominique ArrouaysAbstract:Reconnaissance Soil Maps at 1:250,000 scale are the most detailed source of Soil information for large parts of France. For many environmental applications, however, the level of detail and accuracy of these Maps is insufficient. Funds are lacking to refine and update these Maps by traditional Soil survey. In this study we investigated the merit of digital Soil Mapping to refine and improve the 1:250,000 reconnaissance Soil Map of a 1580 km2 area in Haute-Normandie, France. The Soil Map was produced in 1988 and distinguishes nine Soil class units. The approach taken was to predict Soil class from a large number of environmental covariates using regression techniques. The covariates used include DEM derivatives, geology and land cover Maps. Because very few Soil point observations were available within the area, we calibrated the regression model by sampling the Soil Map on a grid. We calibrated three models: classification tree (CT), multinomial logistic regression (MLR) and random forests (RF), and used these models to predict the nine Soil classes across the study area. The new and original Maps were validated with field data from 123 locations selected with a stratified simple random sampling design. For MLR, the estimate of the overall purity was 65.9%, while that of the reconnaissance Map was 55.5%. The difference between the purity estimates of these Maps was statistically significant (p = 0.014). The significant improvement over the existing Soil Map is remarkable because the regression model was calibrated with the existing Soil Map and uses no additional Soil observations.
Lin Yang - One of the best experts on this subject based on the ideXlab platform.
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selection of training samples for updating conventional Soil Map based on spatial neighborhood analysis of environmental covariates
Geoderma, 2020Co-Authors: Lin Yang, Hong Gao, Xinyue Zhang, Liangjie Wang, Feixue ShenAbstract:Abstract Selection of training samples plays an important role in updating conventional Soil Maps with data mining models. In this paper, we developed a method to determine spatial locations of training samples based on spatial neighborhood analysis of environmental covariates for each Soil polygon. Training samples were selected based on a single environmental variable or integrated variables generated using multiple variables. Sensitivity analysis was also conducted to test the effect of different spatial neighborhood sizes and selected sample numbers on Soil Mapping accuracy. Random selection of training samples from Soil polygons and Soil types respectively were applied to compare with the proposed method in a study area in Raffelson watershed in La Crosse, Wisconsin of USA. Random forest was adopted as the Soil prediction model. Results showed that training samples selected using single variables such as Topographic Wetness Index (TWI), slope, plan curvature, profile curvature or slope length factor with the proposed method improved the overall Mapping accuracies compared with the conventional Soil Map, of which using TWI achieved the highest improvement of 27%. The proposed method using TWI, slope or slope length factor performed better than random selection strategies. Random selection from Soil polygons generated higher overall Mapping accuracies than from Soil types. It was concluded that using composite environmental variables which could represent the Soil forming environment of a study area well is recommended when applying the proposed method. The proposed method is not sensitive to the selected sample number, but an appropriate neighborhood size is needed for using the proposed method. In our study area with small spatial coverage, neighborhood size 5 × 5 or 3 × 3 is recommended.
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updating conventional Soil Maps through digital Soil Mapping
Soil Science Society of America Journal, 2011Co-Authors: Lin Yang, A-xing Zhu, You Jiao, Sherif H Fahmy, Sheldon Hann, James E. BurtAbstract:Conventional Soil Maps, as the major data source for information on the spatial variation of Soil, are limited in terms of both the level of spatial detail and the accuracy of Soil attributes. These Soil Maps, however, contain valuable knowledge on Soil-environment relationships. Such knowledge can be extracted for updating conventional Soil Maps through the use of available high-quality data on environmental variables and data analysis techniques. We developed a method to update conventional Soil Maps using digital Soil Mapping techniques without additional field work, which can be used in situations where the study area contains no or few Soil profile descriptions at points. The basis of the method is that Soil polygons on a conventional Soil Map correspond to landscape units, which can be considered as combinations of environmental factors. Such environmental combinations were approximated through fuzzy clustering on the environmental factors. We extracted the knowledge on Soil-environment relationships by relating the environmental combinations to the Mapped Soil types. The extracted knowledge was then used for Soil Mapping using the Soil Land Inference Model (SoLIM) framework. This method was demonstrated through a case study for updating a conventional 1:20,000 Soil Map of Wakefield, NB, Canada. The case study showed that the updated digital Soil Map contained much greater spatial detail than the conventional Soil Map. Field validation indicated that the accuracy of the updated Soil Map was much higher than the conventional Soil Map at the level of Soil associations with drainage classes, indicating that the proposed method is an effective approach to updating conventional Soil Maps.
Philippe Lagacherie - One of the best experts on this subject based on the ideXlab platform.
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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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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 P. Martin, Nicolas P.a. SabyAbstract: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
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Assessment of Uncertainty on a Digital Soil Map: a sensitivity analysis on the uncertainty indicators
2017Co-Authors: Philippe Lagacherie, Dominique Arrouays, Hocine Bourennane, Cécile Gomez, Manuel Martin, Nicolas SabyAbstract:Digital Soil Map uncertainty is usually evaluated from a set of independent Soil observations – i.e. that are not used for calibrating the DSM model -. As any statistical parameters derived from a set of individuals, the uncertainty indicators – e.g., R2, ME, PICP,...- could be sensitive to the number and the locations of these Soil observations. To our knowledge, this issue has not be considered yet in the literature since it would require performing a sensitivity study from a base spatial sampling that had to be dense and extended enough for picturing the real underlying Soil pattern and allowing the test of multiple sampling schemes, which is not feasible in practice. In this paper, such sensitivity analysis is performed from the virtual pattern of topSoil clay content of bare Soil surfaces at 5 meter resolution over 300 km2 in the Cap Bon region (Tunisia). This pattern, derived from a hyperspectral image, was accurate enough (R2val
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globalSoilMap net a new digital Soil Map of the world
Digital soil mapping : bridging research environmental application and operation, 2010Co-Authors: Alfred E. Hartemink, Alex B. Mcbratney, Budiman Minasny, Jon Hempel, Philippe Lagacherie, R. A. Macmillan, Luca Montanarella, Maria Lourdes De Mendonça Santos, N J Mckenzie, Pedro A. SanchezAbstract:Knowledge of the world Soil resources is fragmented and dated. There is a need for accurate, up-to-date and spatially referenced Soil information as frequently expressed by the modelling community, farmers and land users, and policy and decision makers. This need coincides with an enormous leap in technologies that allow for accurately collecting and predicting Soil properties. We work on a new digital Soil Map of the world using state-of-the-art and emerging technologies for Soil Mapping and predicting Soil properties. The global land surface will be Mapped in 5 years and the Map consists of the primary functional Soil properties at a grid resolution of 90 by 90 m. It will be freely available, web-accessible and widely distributed and used. The Maps will be produced by a global consortium with centres in each of the continents: NRCS for North America, Embrapa for Latin America, JRC for Europe, TSBF-CIAT for Africa, ISSAS for parts of Asia and CSIRO for Oceania. This new global Soil Map will be supplemented by interpretation and functionality options that aim to assist better decisions in a range of global issues like food production and hunger eradication, climate change, and environmental degradation. In November 2008, a grant has of US$ 18 million has been obtained from the Bill & Melinda Gates foundation to Map most parts in Sub-Sahara Africa, and make all Sub-Saharan Africa data available. From this grant there are funds for coordinating efforts in the global consortium.
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GlobalSoilMap.net – A New Digital Soil Map of the World
Digital Soil Mapping, 2010Co-Authors: Alfred E. Hartemink, Alex B. Mcbratney, Budiman Minasny, Jon Hempel, Philippe Lagacherie, Neil Mckenzie, R. A. Macmillan, Luca Montanarella, Maria Lourdes De Mendonça Santos, Pedro A. SanchezAbstract:Knowledge of the world Soil resources is fragmented and dated. There is a need for accurate, up-to-date and spatially referenced Soil information as frequently expressed by the modelling community, farmers and land users, and policy and decision makers. This need coincides with an enormous leap in technologies that allow for accurately collecting and predicting Soil properties. We work on a new digital Soil Map of the world using state-of-the-art and emerging technologies for Soil Mapping and predicting Soil properties. The global land surface will be Mapped in 5 years and the Map consists of the primary functional Soil properties at a grid resolution of 90 by 90 m. It will be freely available, web-accessible and widely distributed and used. The Maps will be produced by a global consortium with centres in each of the continents: NRCS for North America, Embrapa for Latin America, JRC for Europe, TSBF-CIAT for Africa, ISSAS for parts of Asia and CSIRO for Oceania. This new global Soil Map will be supplemented by interpretation and functionality options that aim to assist better decisions in a range of global issues like food production and hunger eradication, climate change, and environmental degradation. In November 2008, a grant has of US$ 18 million has been obtained from the Bill & Melinda Gates foundation to Map most parts in Sub-Sahara Africa, and make all Sub-Saharan Africa data available. From this grant there are funds for coordinating efforts in the global consortium.
Hocine Bourennane - One of the best experts on this subject based on the ideXlab platform.
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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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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 P. Martin, Nicolas P.a. SabyAbstract: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
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Assessment of Uncertainty on a Digital Soil Map: a sensitivity analysis on the uncertainty indicators
2017Co-Authors: Philippe Lagacherie, Dominique Arrouays, Hocine Bourennane, Cécile Gomez, Manuel Martin, Nicolas SabyAbstract:Digital Soil Map uncertainty is usually evaluated from a set of independent Soil observations – i.e. that are not used for calibrating the DSM model -. As any statistical parameters derived from a set of individuals, the uncertainty indicators – e.g., R2, ME, PICP,...- could be sensitive to the number and the locations of these Soil observations. To our knowledge, this issue has not be considered yet in the literature since it would require performing a sensitivity study from a base spatial sampling that had to be dense and extended enough for picturing the real underlying Soil pattern and allowing the test of multiple sampling schemes, which is not feasible in practice. In this paper, such sensitivity analysis is performed from the virtual pattern of topSoil clay content of bare Soil surfaces at 5 meter resolution over 300 km2 in the Cap Bon region (Tunisia). This pattern, derived from a hyperspectral image, was accurate enough (R2val