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Journal Article

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

Authors

Vaudour,  Emmanuelle
External Organizations;

Gholizadeh,  Asa
External Organizations;

Castaldi,  Fabio
External Organizations;

/persons/resource/saberioo

Saberioon,  Mohammadmehdi
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Borůvka,  Luboš
External Organizations;

Urbina-Salazar,  Diego
External Organizations;

Fouad,  Youssef
External Organizations;

Arrouays,  Dominique
External Organizations;

Richer-de-Forges,  Anne C.
External Organizations;

Biney,  James
External Organizations;

Wetterlind,  Johanna
External Organizations;

Van Wesemael,  Bas
External Organizations;

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5012983.pdf
(Publisher version), 4MB

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Citation

Vaudour, E., Gholizadeh, A., Castaldi, F., Saberioon, M., Borůvka, L., Urbina-Salazar, D., Fouad, Y., Arrouays, D., Richer-de-Forges, A. C., Biney, J., Wetterlind, J., Van Wesemael, B. (2022): Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview. - Remote Sensing, 14, 12, 2917.
https://doi.org/10.3390/rs14122917


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5012983
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.