# 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
#### 4.2.6 Soil Management Practices  
In the \"o\" factor or the SCORPAN model, all organisms are included, although in most cases, \"o\" is represented only by vegetation indexes or biomass-related vegetation characteristics such as NPP. Among the organisms that have a major effect on soil properties and changes, one of them is obviously humans, through their impact on soil management practices. We refer here to a recent review by [PERSON] et al. [309], which emphasizes that the potential of RS to characterize cropping practices is not yet optimal, and that the new generation of high-resolution sensors (space, wavelength, and time) should allow for the improved characterization of cropping practices. In another review, [PERSON] et al. [310] stated that \"less than 10% of the publications on remote sensing and agriculture actually focused on cropping practices\" in the previous decade. In their 2018 review, they distinguished between three broad groups of cropping practices ([309], Figure 1):  
1. Crop succession (i.e., the sequence of crops or fallows in consecutive years at the field scale);
2. Cropping patterns (e.g., temporal sequence and spatial arrangement of crop, fallows, and landscape features in a particular land area);
3. Cropping techniques (e.g., irrigation, organic amendment, crop residue management, tillage, harvesting, duration of bare soil, grazing of moving cattle feed, and implemented on a piece of land). It is beyond the scope of this study to detail all the sensors, indices, and data they listed in their review to detect all these soil management practices. Figure 8 shows the main cropping practices reviewed by [PERSON] et al. [309]. One of their main conclusions is \"the majority of the studies are exploratory investigations, tested at a local scale with a high dependence on ground data, involving one remote sensing sensor at a time, and are constrained by local knowledge and conditions\". However, they pointed out the high potential related to the increasing availability of RS data suitable for fine resolution and time-scale monitoring (e.g., Sentinel 1 and 2, hyperspectral sensors, Lidar) and the combination of different satellite sensors [311; 312]. Research should therefore be conducted to better map these soil management practices. One of the major difficulties was obviously related to the low spectral, spatial, and time resolution. Another outstanding question is what type of information derived from RS should be considered as a covariate for DSM? There is probably not a universal answer to this question; it may depend on the case, area, or soil property. Nevertheless, we believe that this essential information will be incorporated into future DSM and will provide major advances in soil knowledge. First, it will better filter out some information that is often considered \"noise\" for some DSM predictions (e.g., soil roughness or dead vegetation residues for SOC). Second, it will provide critical information on the controlling factors of soil changes on the local and landscape scales. Such quantified information may include, but is not limited to, crop rotation [179], grassland management [313], spatial arrangement of crops and land use, linear features such as hedges or drips [314], soil water regimes and their consequences on SOC inputs and salinization, etc. Indeed, a large field of research is still open in order to find the right RS soil management and cropping practices to incorporate in the DSM covariates. A major difficulty could be the diversity of situations in which we will approach large-scale studies. A preliminary large-scale delineation of \"agarian\" regions could be a solution, as suggested by [PERSON] et al. [315]. Furthermore, this typically \"multi-scale\" RS challenge may benefit from data fusion of different RS data at different wavelengths, resolutions, and revisit periods.