To be fully operational for facilitating decisions made at any spatial level, models and indicators of soil ecosystem functions require the use of precise spatially referenced soil information as ...inputs. This study aimed at exploring the capacity for Sentinel-2A (S2A) multispectral satellite images to predict several topsoil properties in two contrasted pedoclimatic environments: a temperate region marked by intensive annual crop cultivation patterns and soils derived from loess or colluvium and/or marine limestone or chalk (Versailles Plain, 221 km2); and a Mediterranean region marked by vineyard cultivation and soils derived from lacustrine limestone, calcareous sandstones, colluvium, or alluvial deposits (Peyne catchment, 48 km2). Prediction models of soil properties based on partial least squares regressions (PLSR) were built from S2A spectra of 72 and 143 sampling locations across the Versailles Plain and Peyne catchment, respectively. Eight soil surface properties were investigated in both regions: pH, cation exchange capacity (CEC), texture fractions (Clay, Silt, Sand), Iron, Calcium Carbonate (CaCO3) and Soil Organic Carbon (SOC) content. Predictive abilities were studied according to the root mean square error of cross-validation (RMSECV) tests, cross-validated coefficient of determination (R2cv) and ratio of performance to deviation (RPD). Intermediate prediction performance outcomes (R2cv and RPD greater than or equal to 0.5 and 1.4, respectively) were obtained for 4 topsoil properties found across the Versailles Plain (SOC, pH, CaCO3 and CEC), and near-intermediate performance outcomes (0.5 > R2cv > 0.39, 1.4 > RPD > 1.3) were yielded for 3 topsoil properties (Clay, Iron, and CEC) found across the Peyne catchment and for 1 property (Clay) found across the Versailles Plain. The study results show what can be expected from Sentinel-2 images in terms of predictive capacities at the regional scale. The spatial structure of the estimated soil properties for bare soils pixels is highlighted, promising further improvements made to spatial prediction models for these properties based on the use of Digital Soil Mapping (DSM) techniques.
•The aim was to assess the capacity for S2A to predict topsoil properties regionally.•SOC, pH, CaCO3, CEC prediction models yielded R2cv ≥ 0.5 RPD ≥ 1.4 for Temperate area.•Iron, CEC outcomes were 0.5 > R2cv > 0.39, 1.4 > RPD > 1.3 for the Mediterranean area.•Clay prediction models yielded 0.5 > R2cv > 0.39, 1.4 > RPD > 1.3 for both areas.•Best predictions did well approximate the spatial patterns of soil properties.
Visible, near-infrared and short wave infrared (VNIR/SWIR) hyperspectral imagery has proven to be a useful technique for mapping the soil surface properties over bare soils pixels. Multivariate ...regression models are usually built linking a set of soil surface properties (response Y-variables) to a set of imaging reflectance spectra over bare soil pixels (predictor X-variables), and then, they are applied to all bare soil pixels to map the soil surface properties. The applicability of VNIR/SWIR hyperspectral imagery for soil properties mapping decreases when surfaces are partially covered by vegetation. The objective of this research was to develop a “Double-Extraction” approach for clay content estimation over semi-vegetated surfaces and to evaluate its performance using VNIR/SWIR HyMap airborne data acquired in a Mediterranean region over an area of 24km2. The “Double-Extraction” approach consists of 1) an extraction of a soil reflectance spectrum, s^soil, using a semi-blind source separation (SBSS) technique applied to couples of semi-vegetated spectra and 2) an extraction of clay content from the soil reflectance spectrum s^soil using a multivariate regression method. The source separation approach is semi-blind due to the use of available knowledge about expected soil and vegetation spectra. The multiplicative algorithm of Lee & Seung, belonging to the family of non-negative matrix factorization (NMF) methods, is used to solve the blind source separation (BSS) problem. The multivariate regression method used in this study is the partial least squares regression (PLSR) method. The “Double-Extraction” approach was compared to a “Direct” approach consisting of the application of the multivariate regression model built from bare soil spectra over the semi-vegetated area.
Our results showed poor prediction performances for both approaches when applied to all pixels; however, a slight improvement was observed when correcting the bias prediction that occurs when using the PLSR model. Conversely, satisfactory prediction performances were obtained by restricting the prediction to the weakly vegetated area (NDVI <0.55) that covered 63% of the study area. The resulting clay map over this restricted vegetated area exhibited patterns of variations that matched the previous expertise acquired on the spatial structures of soils in this area.
•Semi Blind Source Separation is used to extract a soil spectrum from mixed spectra.•PLSR is used to estimate clay content from the extracted soil spectrum.•The approach offers poor clay prediction performances when considering all pixels.•The performances are slightly improved by correcting the bias prediction.•Encouraging clay predictions are obtained when processing moderate vegetated area.
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, 400–2500nm) 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 5nm and 5m, respectively) over a 300km2 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 5nm to 200nm (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 2μm 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 100nm 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 200nm 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.
•Ten spectral configurations were simulated from AISA-DUAL VNIR/SWIR airborne data.•Spectral configurations of four existing multispectral sensors were studied.•Six spectral configurations with constant FWHM were studied.•Sensors simulated with constant FWHM up to 100nm provide correct clay content maps.•ASTER is the only multispectral sensor to provide correct clay content maps.
Pedometricians have spent a lot of effort on mapping soil types and basic soil properties. However, end‐users typically need a more elaborate soil quality index for land management. Soil quality ...indices are typically derived from multiple individual soil properties by evaluating whether specific criteria are met. If this is based on individually mapped soil properties, then an important consequence is that cross‐correlations between soil properties are ignored. This makes it impossible to quantify the uncertainties associated with the mapped indices. The objective of this study was to map a soil potential multifunctionality index for agriculture (Agri‐SPMI) over a 12,125 km2 study region located along the French Mediterranean coast to help urban planners preserve soils of the highest quality. The index considered the ability of soils to fulfil four functions under five land use scenarios. A binary map represented each soil function fulfilment for a given scenario. The final soil quality index map was the sum of the 20 binary maps. A regression cokriging model was developed to map the basic soil properties first individually from legacy soil data and spatial soil covariates using a random forest algorithm, and next, interpolate the residuals using cokriging and the linear model of coregionalisation. The mapping uncertainties of soil properties were propagated by calculating the soil quality index over 300 stochastic simulations of soil properties derived from the linear models of coregionalisation. Results showed a poor prediction accuracy of the quality index, mainly because some soil properties were poorly predicted (notably available water capacity and coarse fragments) and used in combination with extreme thresholds that defined land suitability. Overall, the uncertainty was correctly quantified because the stochastic simulations reproduced the width of the observed distribution well, but the shapes of the distributions differed considerably from those of the observations. We envisage some ways for improvement, such as creating probability maps instead of the mean from simulations, and changing the prediction support from point to area.
Highlights
The study mapped a soil quality index (Agri‐SPMI) to help preserve soils of highest quality along the French Mediterranean coast.
The Agri‐SPMI considered the ability of soils to fulfill four functions under five land use scenarios, derived from multiple individual soil properties.
A regression cokriging model was developed to map the basic soil properties, followed by interpolating the residuals using cokriging and the linear model of coregionalisation.
The study accurately quantified mapping uncertainties using stochastic simulations, but the soil quality index prediction accuracy was poor, with suggestions for improvement.
Visible, near-infrared and short wave infrared (VNIR/SWIR, 0.4-2.5 mu m) 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 been demonstrated 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 study area. 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, such as the HYPerspectral X Imagery (HYPXIM), the Spaceborne Hyperspectral Applicative Land and Ocean Mission (SHALOM), the PRecursore IperSpettrale della Missione Applicativa (PRISMA), the Environmental Mapping and Analysis Program (EnMAP) and the 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 (300km2) with an initial spatial resolution of 5m. These hyperspectral airborne data were simulated at the top of the atmosphere and aggregated at six spatial resolutions (10, 15, 20, 30, 60 and 90m) to correlate 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 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 5m of spatial resolution); (ii) the PLSR models, built from data with 5 to 30m spatial resolutions, performed well, and allowed clay mapping, although variations in clay content related to short scale succession of parent material was imperfectly captured beyond 15m of spatial resolution; (iii) the PLSR models built from data with 60 and 90m spatial resolutions 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 and under the spectral specifications of the AISA-DUAL airborne sensor, most of the future hyperspectral satellite sensors (four of the five sensors examined in this study) will be potentially useful for clay content mapping.
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.
► Blind source separation studied to extract the soil spectrum from a spectral mixture. ► PLSR is used to predict clay content from the soil spectra extracted by BSS. ► From simulated spectral mixtures, accurate soil spectra are extracted by BSS. ► From mixed Hymap spectra, BSS extracts accurate soil spectra up to NDVI
<
0.5.
Airborne hyperspectral imagery has been recently proved to be a successful technique for predicting soil properties of the bare soil surfaces that are usually scattered in the landscape. This new ...soil covariate could much improve the digital soil mapping (DSM) of soil properties over larger areas. To illustrate this, we experimented with digital soil mapping in a 24.6‐km2 area located in the vineyard plain of Languedoc. As input data, we used 200 points with clay content measurements and 192 bare soil fields representing 3.5% of the total area in which the clay contents of the soil surface were successfully mapped at 5‐m resolution by hyperspectral remote sensing. The clay contents were estimated from CR2206, a spectrometric indicator that quantifies specific absorption features of clay at 2206 nm. We demonstrated by cross‐validation that the co‐kriging procedure based on our co‐regionalization model provided accurate error estimates at the clay measurement sites. Then, we applied a block co‐kriging model to map the mean clay content at increasing resolutions (50 , 100, 250 and 500 m). The results showed the following: (i) using hyperspectral data significantly increased the accuracy of the mean clay content estimations; (ii) a block co‐kriging procedure with reliable estimates of error variance can be used to estimate mean clay contents over larger areas and at coarser resolutions with acceptable and predictable errors and (iii) various maps can be produced that represent different compromises between prediction accuracy and spatial resolution.
Over the past decades, numerous practical applications of Digital Soil Mapping have emerged to respond to the need of land managers. One important contribution to this effort is the release of ...regional‐scale soil maps from the GlobalSoilMap (GSM) project. While the GSM project aims at producing soil property predictions on a fine 90 × 90 m grid at the global scale, land managers often require aggregated information over larger areas of interest (e.g. farms, watersheds, municipalities). This study evaluated a geostatistical procedure aiming at aggregating GSM grids to a land management scale, thereby providing land suitability maps with associated uncertainty for the French region ‘Languedoc‐Roussillon’ (27 236 km2). Specifically, maps were derived from three GSM prediction grids (pH, organic carbon and clay content) by calculating the proportion of ‘suitable’ agricultural land within a municipality, where suitability was defined as having soil property values below or above a predefined threshold (pH < 5.5, OC < 10 g/kg, clay > 375 g/kg). Calculation of these nonlinear spatial aggregates and the associated uncertainty involved a three‐step approach: (i) sampling from the conditional probability distributions of the soil properties at all grid cells by means of sequential Gaussian simulation applied to a regression kriging model, (ii) transformation of soil properties to suitability indicators for all grid cell samples generated in the first step and (iii) spatial aggregation of the suitability indicators from grid cells to municipalities. The maps produced show large differences between municipality areas for all three land suitability indicators. The uncertainties associated with the aggregated suitability indicators were moderate. This approach demonstrated that fine‐scale GSM products may also fulfil user demands at coarser land management scales, without jeopardizing uncertainty quantification requirements.
Bedrock depth provides important information for many applications, such as groundwater modelling, the estimation of soil water availability and crop production potential. Direct estimates of bedrock ...depth from destructive soil observations are discontinuous and too expensive to be used in large areas. Geophysical methods are often cited as possible alternatives; however, their ability to provide reliable estimations of bedrock depth relies greatly on local site characteristics. Therefore, this study examines the ability of Electrical Resistivity Tomographie (ERT) in dry conditions, diachronic ERT and the Spectral Analysis of Surface Waves (SASW) method to determine bedrock depth (BD) in different geopedological contexts that are representatives of the Mediterranean landscape (Southern France). SASW was performed using the data that were measured in the field with classical seismic equipment (impulse source and geophones) along a transect in each study site. In the same place, transects of ERT (Wenner–Schlumberger array, 1-m electrode spaced) were measured under wet and dry conditions. To calibrate the geophysical measurements, 131 boreholes (from two to 5m deep) were interpreted to determine the bedrock depth. Dry ERT, diachronic ERT and SASW were found to have highly variable performances for the estimation of bedrock depth in all geopedological contexts. SASW correctly estimated the bedrock depth (RMSE=0.3–0.7m) in all situations except in the case of shallow soils. Conversely, dry ERT only estimated bedrock depth (RMSE=0.2m) in the case of a high contrast of the resistivity between the soil and bedrock. By analysing the pattern of the water uptake by grapevine, diachronic ERT determined the bedrock depth in the case of very low contrasts in resistivity (RMSE=0.5). According to a prior pedological knowledge such soil maps, the combination with the use of a sensor based on different physical parameters may enhance the bedrock detection.
► Bedrock depth (BD) is an important issue in soil science. ► BD estimations were obtained with a new seismic methodology (SASW). ► BD estimations from SASW were compared with classical ERT. ► Each tested methodologies are highly dependent on the geopedological situation. ► SASW is effective to estimate BD in the most geopedological situations.
Bedrock depth provides important information for many environmental and agricultural applications, such as shallow groundwater monitoring, the determination of soil water availability, and the ...estimation of crop production potential. Direct estimates of bedrock depth from destructive soil observations are discontinuous and too expensive to be used in large areas. Geophysical methods are often cited as possible alternatives. However, their ability to provide reliable estimations of bedrock depth is known to depend greatly on local site characteristics. Therefore, combining geophysical methods based on different physical parameters may help to provide better predictions. This study examines the ability of the Spectral Analysis of Surface Waves (SASW) method combined with the classical high resolution Electrical Resistivity Tomography (ERT) method to predict soil depths in a 500m ranged Mediterranean hillslope (Southern France) with increasing soil depths along the slope. SASW was performed using the data measured in the field with classical seismic equipment (impulse source and geophones distributed along a line). In the same place, eight transects of ERT (Wenner–Schlumberger array, 1m electrode spaced) were measured under wet and dry conditions. To calibrate the geophysical measurements, 81 boreholes (from two to 5m deep) were interpreted to determine the bedrock depth, which was defined as the occurrence in the depth of heterogeneous marine Miocene loose sandstone with centimetric laminations. ERT and SASW were found to have highly variable performances for predicting separately the bedrock depth along the hillslope. SASW correctly predicted the bedrock depth in the lower part of the hillslope, whereas the data from ERT were disrupted by shallow permanent groundwater. Conversely, ERT correctly predicted bedrock depth within the upper part of the hillslope, whereas a high variability of SASW data near the topsoil caused difficulties for bedrock depth prediction. From these results, it was possible to define an estimator of bedrock depth according to the presence of shallow groundwater, which varies along the slope, such that more importance is given to ERT estimates in the upper part of the hillslope and more importance is given to SASW in the lower part. This study shows the usefulness of such a sensor combination to estimate soil properties when the uncertainties of making predictions vary according to the geophysical methods.
► Bedrock depth (BD) is an important issue in soil science. ► A new seismic methodology (SASW) and diachronic ERT were tested to predict BD. ► Diachronic ERT predicted BD in shallow soils. ► SASW predicted BD in deeper soils despite the presence of groundwater. ► A combination of these methodologies enhanced the prediction of BD.
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