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

Vaudour, Emmanuelle and Gholizadeh, Asa and Castaldi, Fabio and Saberioon, Mohammadmehdi and Boruvka, Lubos and Urbina-Salazar, Diego and Fouad, Youssef and Arrouays, Dominique and Richer-de-Forges, Anne C. and Biney, James and Wetterlind, Johanna and Van Wesemael, Bas (2022). Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview. Remote Sensing. 14 :12 , 2917
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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.

Authors/Creators:Vaudour, Emmanuelle and Gholizadeh, Asa and Castaldi, Fabio and Saberioon, Mohammadmehdi and Boruvka, Lubos and Urbina-Salazar, Diego and Fouad, Youssef and Arrouays, Dominique and Richer-de-Forges, Anne C. and Biney, James and Wetterlind, Johanna and Van Wesemael, Bas
Title:Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview
Series Name/Journal:Remote Sensing
Year of publishing :2022
Volume:14
Number:12
Article number:2917
Number of Pages:22
Publisher:MDPI
Language:English
Publication Type:Article Review/Survey
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Environmental Sciences (social aspects to be 507)
(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Geology
Keywords:soil organic carbon, spectral models, satellite imagery
URN:NBN:urn:nbn:se:slu:epsilon-p-118293
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-118293
Additional ID:
Type of IDID
DOI10.3390/rs14122917
Web of Science (WoS)000818248300001
ID Code:28706
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Soil and Environment
(S) > Dept. of Soil and Environment
Deposited By: SLUpub Connector
Deposited On:01 Sep 2022 09:32
Metadata Last Modified:01 Sep 2022 09:41

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