# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 6 Limitations and Challenges
### Most RS Products Only Capture Topsoil Information  
Almost all DSM attempts show that the prediction performance decreases with depth (e.g., [21; 259]). This is easily understandable because most RS products only capture information about the soil surface and/or terrain attributes at the time of acquisition. For example, it is physically impossible to detect deep soil attributes with visible-near infrared RS data. Some products, however, have the capacity to integrate a response over a less limited depth (radar and gamma-ray). Therefore, one challenge is to better incorporate different sensors that are capable of obtaining information at different depths into the DSM framework. Other information than bare soil spectra can be useful to map soil properties at depth. Gamma-ray spectrometry data, however, comes mainly from low elevation airborne sensors, which are not yet available in many parts of the world. Magnetic surveys are most often used for detecting soil pollution (e.g., [327; 328; 329]). They may also, however, reveal lithology, weathering, and water regime of soils (e.g., [330; 331; 332]). Yet, to our knowledge, there are no studies using airborne magnetic data as covariates in DSM.  
Another challenge is to integrate some soil knowledge into the DSM. For example, it is well known that in many cases, soils are organized in topo-sequences or \"catenas\". This is the case of erosion catenas from a plateau to a gentle slope. At the top of the eroded portion of the catena, one can obtain information about deeper horizons present on soils developed on the plateau, as these underlying horizons may have been exposed at the surface by erosion. The same inferential information can be incorporated into DSM in the case of steep slopes, where outcrops can provide information about the parent soil material. In this case, a sampling design based on topo-sequences and available high-resolution RS imagery can be very efficient (e.g., [333]).  
This type of integration of soil knowledge into the DSM is not trivial because it requires a soil scientist to know the soil spatial organization and the scales involved in order to incorporate such information into the DSM. In some cases, these rules are described in conventional soil maps, and a challenge is to take them into account to disaggregate complex soilscape map units into more precise classes (e.g., [146; 147; 334; 335; 336]).  
Finally, a solution often chosen to address the limitations of bare soil RS and to obtain information on some deep soil properties is to incorporate other RS data, such as vegetation indices or land-cover changes, as covariates in a DSM model (see Section 4.2, and further, Section 7.2.2). At the global scale, [PERSON] et al. [337] recently used a semi-mechanistic model combined with an MODIS time series of changes in land cover to model changes in the global SOC stocks from 2001 to 2020. Though this approach is still not perfect, because it does not take into account climate change and some inherent soil properties, it provided an estimate of the global losses of SOC stocks, mainly due to the conversion of forests to cultivated lands.