# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 3 Developments Related to the Extraction of Soil Properties from Remote Sensing Data
### Use of RS Data as a Substitute for Soil Properties' Measurement
Several studies show a faster saturation of the radar signal with the soil roughness when the wavelength and/or the incidence angle is small [93; 175]. The third most important soil property is primarily related to soil organic carbon (SOC). This is not surprising considering that soils are the largest terrestrial organic C stocks and the question of their role in climate mitigation and adaptation is a hot topic [176; 177; 178]. Reviews on SOC mapping using RS are recent [180; 181], especially using satellites [181]. Most studies rely on observation in the solar domain, at scales ranging from the field to regional, and finally, to national or even transnational scales. Most approaches have been conducted on bare, cultivated soils: the higher the scale, the lowers the accuracy. The prediction performance varies according to the type of soil [123], the date of acquisition [182], and the presence of crop residues on the surface [22; 183]. As far as French research is concerned, RS studies have been conducted at local or regional scales and none of them have yet specifically addressed the use of RS at the national scale of metropolitan France. Studies at smaller scales have mainly relied on UAVs [173] and airborne imagery [184] at a very high spatial resolution (\(\leq\)2.5 m), while at larger scales, most studies have relied on high-resolution satellite images: Hyperion [185], SPOT [186; 187], Sentinel-2 [123; 182], and/or Sentinel-2 with the joint use of Sentinel-1 [128; 129]. The accuracies found for mainland France range from 2 g kg\({}^{-1}\) to 6 g kg\({}^{-1}\), leaving open the feasibility of monitoring SOC according to agricultural C-stocking practices. This feasibility, especially the influence of the composition of the soil surface, is the subject of studies at the French and European levels. Soil color is an important soil property that is directly related to the reflectance of soil in the visible [46; 51; 80], which depends mainly on the so-called \"chromophores\" [108]: soil organic carbon content, Fe sesquuoxydes, calcareous content, moisture, and to a lesser extent, soil texture and mineralogy. It is widely used in conventional mapping and soil taxonomy. The Munsell color coordinate system was related to visible bands by [PERSON] [46; 80], resulting in the cluster diagram of soil lines versus Munsell color. In our literature database, it was not always easy to distinguish soil color from soil brightness and soil albedo. While the term \"soil color\" is often used in soil science, the RS community that focuses on vegetation monitoring has investigated color-related soil surface properties such as soil brightness derived from the soil line concept or soil albedo, mainly derived from inversion of kernel-driven reflectance models on selected directional reflectance data [188]. Topsoil texture defined as the relative proportion of clay, silt, and sand is one of the most important characteristics of soils. It determines many of their physical and chemical properties and behaviors (e.g., water retention, mechanical behavior, infiltration capacity,nutrient and pollutant concentrations, friability, and trafficability). Attempts to map soil texture have shown that the best results were achieved in the visible, near infrared, and shortwave infrared from hyperspectral airborne images (e.g., [124; 126; 189; 190]) or from multispectral satellite images [130; 191; 192; 193]. These works have mainly focused on clay texture and/or clay content estimation due to the specific absorption of clay minerals around 2200 nm [194]. As observed for SOC estimation, the texture prediction performance of topsoil varies with the date of acquisition and the presence of crop residues on the surface [193]. Finally, in the early stages of the use of RS for soil mapping, RS was often used to delineate/identify soil types or soil classes (e.g., [61; 63; 64; 65; 66; 67; 68; 69; 70; 71; 75; 76; 77; 75; 76; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 8