1. Assessing the capability of Sentinel-2 time-series to estimate soil organic carbon and clay content at local scale in croplands
- Author
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Castaldi, Fabio, Halil Koparan, Muhammed, Wetterlind, Johanna, Žydelis, Renaldas, Vinci, Ialina, Özge Savaş, Ayşe, Kıvrak, Cantekin, Tunçay, Tülay, Volungevičius, Jonas, Obber, Silvia, Ragazzi, Francesca, Malo, Douglas, Vaudour, Emmanuelle, Institute for BioEconomy [Sesto Fiorentino] (IBE | CNR), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Soil, Fertilizer and Water Resources Central Research Institute, Swedish University of Agricultural Sciences (SLU), Lithuanian Research Centre for Agriculture and Forestry, Regional Agency for Environmental Prevention and Protection of the Veneto (ARPAV), Atatürk Soil Water and Agricultural Meteorology Research Institute, South Dakota State University (SDSTATE), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), elsevier, and European Project: 862695,H2020,H2020-SFS-2019-1,EJP SOIL(2020)
- Subjects
Soil organic carbon ,Uncertainty ,[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy ,[SHS.GEO]Humanities and Social Sciences/Geography ,[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study ,Atomic and Molecular Physics, and Optics ,Computer Science Applications ,Sentinel2 ,[SDE]Environmental Sciences ,Local scale ,Clay ,Time-series ,Temporal mosaics ,Computers in Earth Sciences ,Engineering (miscellaneous) - Abstract
International audience; The use of remote sensing data methods is affordable for the mapping of soil properties of the plowed layer over croplands. Carried out in the framework of the ongoing STEROPES project of the European Joint H2020 Program SOIL, this work is focused on the feasibility of Sentinel-2 based approaches for the high resolution mapping of topsoil clay and organic carbon (SOC) contents at the within-farm or within-field scales, for cropland sites of contrasted climates and soil types across the Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series and several spectral indices (NDVI, NBR2, BSI, S2WI), were developed and compared for i) pure bare soil condition (maxBSI), ii) driest soil condition (minS2WI), iii) average bare soil condition (Median) and iv) dry soil conditions excluding extreme reflectance values (R90). Three spectral modeling approaches, using the Sentinel-2 bands of the output temporal mosaics as covariates, were tested and compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude and latitude as covariates (QRFxy); (iii) a hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation of the soil properties. We tested pairs of mosaic and spectral approaches on ten sites in Türkiye, Italy, Lithuania, and USA where soil samples were collected and SOC and clay content were measured in the lab. The average RPIQ of the best performances among the test sites was 2.50 both for SOC (RMSE = 0.15%) and clay (RMSE = 3.3%). Both accuracy level and uncertainty were mainly influenced by site characteristics of cloud frequency, soil types and management. Generally, the models including a spatial component (QRFxy and LMEM) were the best performing, while the best spatial mosaicking approaches mostly were Median and R90. The most frequent optimal combination of mosaicking and model type was Median or R90 and QRFxy for SOC, and R90 and LMEM for clay estimation.
- Published
- 2023
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