Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

Abstract

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.

European agricultural soil management: Towards climate‐smart and sustainability, knowledge needs and research approaches

“…(iv) Improve mechanistic understanding of the impact of organic amendments (spatio-temporal dynamics, interaction with soil microbes, distribution of SOC over soil fractions), and taking account of starting material for bioproducts (e.g., biochar, digestates, compost) production and processing in SOC restoration SOC sequestration (Table 1). High throughput methods based on proximal and remote sensing must be developed in that perspective (Barbetti, 2021;Vaudour et al, 2022) as well as robust models and the use of accurate soil information (Smith et al, 2020).…”

Remote Sensing Data for Digital Soil Mapping in French Research—A Review

“…This is not surprising considering that soils are the largest terrestrial organic C stocks and the question of their role in climate mitigation and adaptation is a hot topic [176][177][178][179]. Reviews on SOC mapping using RS are recent [180,181], especially using satellites [181]. Most studies rely on observation in the solar domain, at scales ranging from the field to regional, and finally, to national or even transnational scales.…”

“…It also links to the COP21 Paris Agreement and the four per Mille initiative [177,214,215]. In addition to advances in temporal frequency, high spatial and spectral resolution RS imagery offers an unprecedented opportunity to study and monitor the spatiotemporal dynamics of SOC changes [111,181].…”

Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes

“…Soil maps can serve as a basis for estimating and mapping soil carbon stocks as part of a national carbon balance inventory, as well as provide baseline data for monitoring climate change impacts and informing land management decisions [128][129][130][131].…”