Search

International audience; This work presents the first high-resolution map of soil organic carbon (SOC) in mainland France, including soils below 30 cm. The research was performed within the framework of GlobalSoilMap (GSM). SOC predictions for different depth layers (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and > 100 cm) were made at 90 and 500 m resolution for mainland France, along with their upper and lower confidence intervals. The maps were developed using data mining and an elaborate cross-validation scheme. The 90 m maps were compared to 500 m resolution GlobalSoilMap maps and the SoilGrids1km (SG1km) product. The latter is a global model for predicting soil properties for the same depth layers, at 1 km resolution. At 90 and 500 m resolution, the predicted SOC content was unbiased and showed good agreement with the measured SOC, despite the poor model diagnostics and decrease of performance with depth. It was found that the subsoil (> 30 cm) carbon pool for France contributes 49% to the total soil carbon stock. The use of coarser resolution prediction grids resulted in smoother spatial patterns and wider confidence intervals; however, it did not bias the estimated carbon stocks. Applying GlobalSoilMap specifications to France, using a large soil dataset and all the exhaustive spatially available data outperformed SG1km predictions. The overall spatial patterns of the SG1km SOC content were found to be very similar to the GlobalSoilMap maps. However, the SG1km overestimated the SOC content and carbon stocks (> 75% for the total carbon stock, and 100% for the stocks below 30 cm) and showed a similar spatial distribution over the different soil depth layers. The main reason for the overestimation was that the local data used in SG1km was rather small (56 samples) and not representative in terms of SOC content or represented soil types; the profiles had far higher SOC content and this may have propagated in the modelled vertical profile and the kriging part of the residuals. Improvements for SG1km may entail the use of a representative national subsample from large national soil databases. Furthermore, a bottom-up approach such as GlobalSoilMap may be more favourable when considering prediction accuracies, data privacy policies and local acceptance of generated products.

This article reviews the use of optical and microwave remote sensing data for soil and terrain mapping with emphasis on applications at regional and coarser scales. Remote sensing is expected to offer possibilities for improving incomplete spatial and thematic coverage of current regional and global soil databases. Traditionally, remotely sensed imagery have been used to support segmentation of the landscape into rather homogeneous soil–landscape units for which soil composition can be established by sampling. Soil properties have also been inferred from optical and microwave data using physically-based and empirical methods. Used as a secondary data source, remotely sensed imagery may support spatial interpolation of sparsely sampled soil property data. Soil properties that have been measured using remote or proximal sensing approaches include mineralogy, texture, soil iron, soil moisture, soil organic carbon, soil salinity and carbonate content. In sparsely vegetated areas, successful use of space borne, airborne, and in situ measurements using optical, passive and active microwave instruments has been reported. On the other hand, in densely vegetated areas, soil data acquisition typically relied on indirect retrievals using soil indicators, such as plant functional groups, productivity changes, and Ellenberg indicator values. Several forms of kriging, classification and regression tree analyses have been used jointly with remotely sensed data to predict soil properties at unvisited locations aiming at obtaining continuous area coverage. We expect that remotely sensed data from existing platforms and planned missions can provide an important data source supporting digital soil mapping. Yet, most studies so far have been performed on a local scale and only few on regional or smaller map scale. Although progress has been made, current methods and techniques still bear potential to further explore the full range of spectral, spatial and temporal properties of existing data sources. For example, space borne spectroscopy has been of limited use in retrieving soil data when compared to laboratory or field spectroscopy. To date, there is no coherent methodology established, where approaches of spatial segmentation, measurements of soil properties and interpolation using remotely sensed data are integrated in a holistic fashion to achieve complete area coverage. Such approaches will enhance the perspectives of using remotely sensed data for digital soil mapping.