Skip to main content
Review article - Peer-reviewed, 2022

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

Vaudour, Emmanuelle; Gholizadeh, Asa; Castaldi, Fabio; Saberioon, Mohammadmehdi; Boruvka, Lubos; Urbina-Salazar, Diego; Fouad, Youssef; Arrouays, Dominique; Richer-de-Forges, Anne C.; Biney, James; Wetterlind, Johanna; Van Wesemael, Bas

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 km(2): 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 similar to 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.

Keywords

soil organic carbon; spectral models; satellite imagery

Published in

Remote Sensing
2022, volume: 14, number: 12, article number: 2917
Publisher: MDPI

Authors' information

Vaudour, Emmanuelle
Universite Paris Saclay
Gholizadeh, Asa
Czech University of Life Sciences Prague
Castaldi, Fabio
Consiglio Nazionale delle Ricerche (CNR)
Saberioon, Mohammadmehdi
Institute For Agricultural and Fisheries Research
Boruvka, Lubos
Czech University of Life Sciences Prague
Urbina-Salazar, Diego
Universite Paris Saclay
Urbina-Salazar, Diego
INRAE
Fouad, Youssef
INRAE
Arrouays, Dominique
INRAE
Richer-de-Forges, Anne C.
INRAE
Biney, James
Czech University of Life Sciences Prague
Biney, James
Silva Tarouca Research Institute for Landscape and Ornamental Gardening
Swedish University of Agricultural Sciences, Department of Soil and Environment
Van Wesemael, Bas
Universite Catholique Louvain

UKÄ Subject classification

Environmental Sciences
Remote Sensing
Geology

Publication Identifiers

DOI: https://doi.org/10.3390/rs14122917

URI (permanent link to this page)

https://res.slu.se/id/publ/118293