# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 4 Incorporating RS as Covariates in DSM
### Remote Sensing Data as Proxies of Soil Properties Controlling Factors in the SCORPAN DSM Model
#### 4.2.2 Land-Cover and Vegetation Characteristics  
Land-cover classification based on RS products is a relevant covariate for predicting many soil attributes, in particular SOC. In mainland France, studies have used land-cover maps as covariates to map SOC concentrations or stocks (e.g., [138; 250; 257; 258; 259]). Land-cover classification from RS products can be a relevant covariate for predicting many soil attributes. Other studies have also shown that SOC sequestration potential was also related to land cover (e.g., [260; 261; 261]). Land cover is also clearly related to soil pH as well as parent material [259]. RS products are one of the main tools used for assessing human  
Figure 6: Global DSM map of SOC carbon stocks. Adapted from an image downloaded from [[https://soilgrids.org/](https://soilgrids.org/)]([https://soilgrids.org/](https://soilgrids.org/)) accessed on 29 May 2023. © ISRIC-World Soil Information, from the original work by [PERSON] et al. [249].  
pressure on soil contamination through the distance to urban or industrial areas and major roads (e.g., [262; 263; 264]).  
Changes in land cover (and the transition duration from one land cover to another) are also direct controlling factors of erosion risks. In a review paper, [PERSON] et al. [222] summarized soil erosion monitoring and modeling from RS. The first interesting piece of information accessible via RS is vegetation cover, as bare soils are generally the areas that contribute most to runoff (e.g., [265; 266; 267]). RS has also been used as an indicator of slaking [268]. [PERSON] et al. [269] monitored land degradation, soil erosion, and desertification in a French Mediterranean region and demonstrated excellent accuracy in identifying soil degradation levels using airborne imaging spectroscopy (AVIRIS) and Landsat TM data. For erosion as well, the distributions of land use across the landscape and connectivity between fields are studied using RS and provide useful information on runoff concentration risks [270; 271]. RS information on soil roughness, for example using radar data, can also significantly improve erosion prediction and modeling [93; 222].  
Finally, land cover may sometimes be correlated to some specific local soil properties. For example, in large, cultivated plains such as the Beauce region in France, small, forested plots are often related to very shallow soils [272].  
The identification of bare soils uses vegetation indexes such as NDVI (Normalized Difference Vegetation Index). These were designed to assess the amount of green vegetation [273]. As such, they can also be used as proxies of organic carbon inputs to the soil when integrated over rather long periods of time (e.g., monthly or annual averages, [259; 260]). In this case, the amount of green vegetation is supposed to be related to the carbon originating from roots and/or crop residues.  
Vegetation indexes, such as land surface temperature (LST), as evapotranspiration factors, can also be an indicator of vegetation stress and the soil's available water capacity (AWC) for plants during dry periods [274; 275; 276]. In general, the AWC can be calculated using podtransfer functions (PTFs), established between water retention characteristics and some other spatialized information (e.g., texture, depth or rooting depth, bulk density, and coarse elements, [277; 278]). Another way to obtain indirect information about the AWC is to assimilate RS data into vegetation functioning models (e.g., [279; 280; 281]). Fusion approaches have also been developed to combine local soil property measurements with RS data [282; 283; 284]. A recent review by [PERSON] et al. [285] provides an insightful overview of the ways to estimate the AWC using RS data as covariates for DSM AWC prediction from field to regional scales.