# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 1 Introduction
Commercial operations are also entering the market, providing competitive products with high spatiotemporal (e.g., Planet, [[https://www.planet.com/products/planet-imagery/](https://www.planet.com/products/planet-imagery/)]([https://www.planet.com/products/planet-imagery/](https://www.planet.com/products/planet-imagery/)) accessed on 29 May 2023 or EarthDaily [[https://earthdaily.com/earthdaily/](https://earthdaily.com/earthdaily/)]([https://earthdaily.com/earthdaily/](https://earthdaily.com/earthdaily/)) accessed on 29 May 2023) and even spectral resolution (e.g., Kuva Space, [[https://kuvaspace.com/](https://kuvaspace.com/)]([https://kuvaspace.com/](https://kuvaspace.com/)) accessed on 29 May 2023). In this review, we mainly focus on satellite and airborne imagery as we aim to review DSM studies covering rather large areas. Nevertheless, we do not totally exclude examples on UAV when they are relevant for field- or farm-scale soil mapping or monitoring. French research has a long history in the field of soil RS. As early as the early 1960s, they developed innovative approaches for the photo-interpretation of aerial photographs for soil mapping [26; 27; 28], visual interpretation, and/or digital processing of RS images. It was stimulated by the launch of the first Earth observation satellite, Landsat, in 1972 and, in 1986, the French _Satellite Proba t d'Observation de la Terre_ (SPOT). Some pioneering French works include general books on RS applications to the characterization of the biosphere orterrestrial environment (e.g., [29; 30; 31; 32; 33; 34]). The development of soil RS was notably based on French research focused on the spectral characterization of minerals [35; 36; 37] and on the soil surface status [38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. In the latter case, the aim was to define soil spectral indices such as the widely used brightness index [49; 50; 51], the Transformed Soil-Adjusted Vegetation Index (TSAVI) [52], and the theorization of the soil line concept [53]. This led to the physical modeling of soil bidirectional reflectance [54; 55] thanks to the SOILSPECT model of [PERSON] [56], which was followed by the MARMIT model of [PERSON] [57; 58]. The same thing has happened in the microwave and thermal infrared domains [59; 60]. Observation and hence, understanding the spectral behavior of soil as a function of its composition and structure has been the cornerstone for establishing relationships between the spatial distribution of soil properties at different scales, soil types, or soilscapes, and RS imagery products in the solar (e.g., [61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73]), as well as in the microwave (e.g., [65; 70; 71; 73]) and the thermal infrared (e.g., [59; 78; 79]) domains. Several studies have also addressed the mapping of soil color (e.g., [46; 51; 80]) or soil moisture (e.g., [81; 78; 79; 82; 83; 84; 85; 86]). Others aimed to consider the bare soil surface reflectance, roughness, rockiness, or soil cover by vegetation or crop residues for several applications: modeling/mapping of soil crusting, surface water runoff, erodibility or erosion risk (e.g., [40; 47; 87; 88; 89]), and salinity [41; 48; 99; 100; 101]. Vegetation is a problem for direct RS of some primary soil properties that are easier to capture by images acquired on bare soils [102]. Therefore, several works have attempted to develop vegetation indexes that are nearly insensitive to the soil spectral response, unlike the NDVI (e.g., [103; 104]). However, (i) vegetation is one of the main controlling factors of soil evolution (e.g., inputs in soil organic carbon and influence on pH); (ii) vegetation information may also be related to some soil properties that are not easily detected using only soil surface response (e.g., soil depth and available water capacity (AWC)) and thus need to be captured for DSM.