Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.

The effects of environmental factors on topsoil nutrient distribution have been extensively discussed, but it remains unclear how they affect spatial characteristics of soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry at different depths. We collected 184 soil samples in the typical black soil region of northeast China. Ordinary kriging was performed to describe the spatial distribution of soil C, N, and P eco-stoichiometry. Redundancy analysis was used to explore relationships between C:N:P ratios and physicochemical characteristics. The soil classification was studied by hierarchical cluster analysis. The mean C, N, and P contents ranged from 15.67 to 20.08 g·kg⁻¹, 1.15 to 1.51 g·kg⁻¹, and 0.80 to 0.90 g·kg⁻¹ within measured depths. C, N, and P concentrations and stoichiometry increased from southwest to northeast, and the Songhua River was identified as an important transition zone. At 0–20 cm, soil water content explained most of the C, N, and P content levels and ratios in cluster 1, while latitude had the highest explanatory ability in cluster 2. For 20–40 cm, soil bulk density was the main influencing factor in both clusters. Our findings contribute to an improved knowledge of the balance and ecological interactions of C, N, and P in northeast China for its sustainability.

The spatial distribution of soil organic carbon (SOC) is assessed using digital soil mapping (DSM) methods, where remote sensing data serve as a key factor. Although soil maps provide valuable information, they are not frequently employed as predictors in soil modelling. This study explored and compared the utilisation of a multi-temporal mosaic of Sentinel-2 (S2) data, in combination with a conventional soil map, for SOC mapping in Chernozem soils of the forest-steppe zone. We implemented two algorithms to estimate SOC content in topsoil (0-30 cm) under several scenarios. As initial predictive covariates, we used a temporal mosaic of S2 bare soil spectra and a large-scale soil type map. We first implemented either partial least square regression (PLSR) or random forest (RF) to predict SOC from S2-derived bare soil spectral responses. Using solely the bare soil temporal mosaic spectra, PLSR proved more effective than RF to predict SOC. The PLSR-derived map of SOC predictions was then combined with the soil type map using RF. This latter combination led to the best SOC prediction performance and least uncertainty. Our findings indicate that integrating a soil map into a remote-sensing-based DSM prediction of SOC yields benefits in SOC mapping compared to using solely remote sensing or soil map data.

With the needs of efficient acquisition of soil information, Digital Soil Mapping (DSM) has been greatly developed and widely applied for over the past two decades. The spatial estimates of soil properties produced with diverse methods over various study areas, have been often seen as the main output of DSM, as they play an important role in environmental modelling and policy. However, compared with the soil property maps, their prediction uncertainty is still less emphasized, which may potentially lead to mis-uses of results and inappropriate decisions if the uncertainty is not assessed, reported, and taken into account by end-users.In this communication, we present a preliminary review of the sources of prediction uncertainties in DSM coming from learning soil data (data source, sampling in space and time, measurements), covariates, and models. We also summarize the methods used to estimate the uncertainty, and to assess the reliability of the uncertainty estimates. We also consider the propagation of uncertainties when several soil attributes are combined to derive information and/or used as inputs for modelling. Furthermore, we discuss some strategies for mitigating the uncertainty, challenges, and future perspectives. This review aims to consolidate the understanding of DSM uncertainties and to contribute to reliable DSM practices, facilitating more informed decision-making in soil-related research and management.