Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands

Autor: Vaudour, Emmanuelle, Gomez, Cécile, Lagacherie, Philippe, Loiseau, Thomas, Baghdadi, Nicolas, Urbina-Salazar, Diego, Loubet, Benjamin, Arrouays, Dominique
Přispěvatelé: Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), InfoSol (InfoSol), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National D'etudes Spatiales, TOSCA 'Cartographie Numerique des Sols (CNS)' program of the CNES3261-3264 CES Theia CartoSols, POLYPHEME project through the TOSCA program of the CNES 200769/id5917, TELEMOS project through the Programme National de Teledetection Spatiale (PNTS) PNTS2020-17, LE STUDIUM Loire Valley Institute for Advanced Studies through its LE STUDIUM Research Consortium Programme, Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: International Journal of Applied Earth Observation and Geoinformation
International Journal of Applied Earth Observation and Geoinformation, 2021, 96, pp.102277. ⟨10.1016/j.jag.2020.102277⟩
International Journal of Applied Earth Observation and Geoinformation, Elsevier, 2021, 96, pp.102277. ⟨10.1016/j.jag.2020.102277⟩
ISSN: 1569-8432
1872-826X
0303-2434
Popis: The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 to predict SOC content over temperate agroecosystems characterized with annual crops. However, because spectral models are only applicable on bare soils, the mapping of SOC is often obtained on limited areas. A possible improvement for increasing the number of pixels on which SOC can be retrieved by inverting bare soil reflectance spectra, consists of using optical images acquired at several dates. This study compares different approaches of Sentinel-2 images temporal mosaicking to produce a composite multi-date bare soil image for predicting SOC content over agricultural topsoils. A first approach for temporal mosaicking was based on a per-pixel selection and was driven by soil surface characteristics: bare soil or dry bare soil with/without removing dry vegetation. A second approach for creating composite images was based on a per-date selection and driven either by the models performance from single date, or by average soil surface indicators of bare soil or dry bare soil. To characterize soil surface, Sentinel-1 (S1)-derived soil moisture and/or spectral indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio 2 (NBR2), bare soil index (BSI) and a soil surface moisture index (S2WI) were used either separately or in combination. This study highlighted the following results: i) none of the temporal mosaic images improved model performance for SOC prediction compared to the best single-date image; ii) of the per-pixel approaches, temporal mosaics driven by the S1-derived moisture content, and to a lesser extent, by NBR2 index, outperformed the mosaic driven by the BSI index but they did not increase the bare soil area predicted; iii) of the per-date approaches, the best trade-off between predicted area and model performance was achieved from the temporal mosaic driven by the S1-derived moisture content (R-2 similar to 0.5, RPD similar to 1.4, RMSE similar to 3.7 g.kg(-1)) which enabled to more than double (*2.44) the predicted area. This study suggests that a number of bare soil mosaics based on several indicators (moisture, bare soil, roughness...), preferably in combination, might maintain acceptable accuracies for SOC prediction whilst extending over larger areas than single-date images.
Databáze: OpenAIRE
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