# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 1 Introduction  
Soils are a key component of the Critical Zone and a limited resource. They are essential to addressing the climate crisis we are facing. Soils are at the crossroads of existential issues such as food and water security and safety, climate change mitigation, and adaptation, human health, sustainable energy, and biodiversity protection [1; 2]. Yet, around the world, they are under threat [3; 4], even though they are essential for achieving most of the sustainable development goals (e.g., [5; 6; 7; 8]). These threats continue to grow under human pressure [3; 4]. To address this problem, policy initiatives are emerging in Europe, such as the Soil Thematic Strategy [9] or the E.U. Mission \"A Soil Deal for Europe\" [10]. There is an urgent need to map and monitor soils at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high/complex spatial variability inherent in soils. Providing such information by mapping and monitoring soil properties in a reproducible way is now possible through Digital Soil Mapping (DSM, [11]). The concept of DSM has rapidly developed and become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatial covariates [12; 13], including the considerable increase in remote sensing data. DSM emerged in the early 2000s [11; 14] as a new concept for mapping soil properties and/or soil classes using soil information and spatially comprehensive covariates, which builds on the work of [PERSON] [15]. In their seminal article, [PERSON] et al. [11] proposed a framework called the scorpan-SSPFe. This framework included a soil spatial prediction function for soil mapping with spatially auto-correlated errors, often referred to as SCORPAN. As defined by [PERSON] et al. [11], SCORPAN is based on seven predictive factors of soil types or soil attributes, namely: (1) \(s\): soil, other or previously measured attributes of the soil at a point; (2) \(c\): climate, climatic properties of the environment at a point; (3) \(o\): organisms, including land cover and natural vegetation; (4) \(r\): topography, including terrain attributes and classes; (5) \(p\): parent material, including lithology; (6) \(a\): age, the time factor; (7) \(n\): space, spatial or geographic position. The prediction functions can use a wide variety of statistical tools, including regression, classification, geostatistics, machine learning, etc. It is not the purpose of this review to detail these methods or their advantages and limitations. We refer to [PERSON] et al. [11], [PERSON] [16], [PERSON] and [PERSON] [12; 17], [PERSON] et al. [18], [PERSON] et al. [19], [PERSON] et al. [20], [PERSON] et al. [19] and [PERSON] et al. [21]. A recent trend is that the availability of RS products, and in particular those derived from satellite [22], airborne, or unmanned aerial vehicle (UAV) imagery [23], has increased significantly. Thus, almost all DSM approaches rely on RS data to obtain spatially exhaustive environmental covariates [21], as well as legacy soil, geology, geomorphology, and landforms maps. [PERSON] and [PERSON] [24] identified and described 48 instruments and 13 multi-instrument platforms that collected data in the 2000s, were recently launched, or are expected to be launched in this decade. Indeed, some of these are now being considered as a direct measurement of some targeted soil properties [25].