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Open AccessArticle

Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects

1
ILVO—Flanders Research Institute for Agriculture, Fisheries and Food, Technology and Food Science-Agricultural Engineering. Burg. Van Gansberghelaan 115 bus 1, 9820 Merelbeke, Belgium
2
Helmholtz-Zentrum Potsdam—Deutsches GeoForschungsZentrum GFZ, 14473 Potsdam, Germany
3
Thünen Institute of Climate-Smart Agriculture. Bundesallee 65, 38116 Braunschweig, Germany
4
Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Universite Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(18), 2121; https://doi.org/10.3390/rs11182121
Received: 1 August 2019 / Revised: 10 September 2019 / Accepted: 10 September 2019 / Published: 12 September 2019
Soil organic carbon (SOC) loss is one of the main causes of soil degradation in croplands. Thus, spatial and temporal monitoring of SOC is extremely important, both from the environmental and economic perspective. In this regard, the high temporal, spatial, and spectral resolution of the Sentinel-2 data can be exploited for monitoring SOC contents in the topsoil of croplands. In this study, we aim to test the effect of the threshold for a spectral index linked to soil moisture and crop residues on the performance of SOC prediction models using the Multi-Spectral Instrument (MSI) Sentinel-2 and the European Land Use/cover Area frame Statistical survey (LUCAS) topsoil database. The LUCAS spectral data resampled according to MSI/Sentinel-2 bands, which were used to build SOC prediction models combining pairs of the bands. The SOC models were applied to a Sentinel-2 image acquired in North-Eastern Germany after removing the pixels characterized by clouds and green vegetation. Then, we tested different thresholds of the Normalized Burn Ratio 2 (NBR2) index in order to mask moist soil pixels and those with dry vegetation and crop residues. The model accuracy was tested on an independent validation database and the best ratio of performance to deviation (RPD) was obtained using the average between bands B6 and B5 (Red-Edge Carbon Index: RE-CI) (RPD: 4.4) and between B4 and B5 (Red-Red-Edge Carbon Index: RRE-CI) (RPD: 2.9) for a very low NBR2 threshold (0.05). Employing a higher NBR2 tolerance (higher NBR2 values), the mapped area increases to the detriment of the validation accuracy. The proposed approach allowed us to accurately map SOC over a large area exploiting the LUCAS spectral library and, thus, avoid a new ad hoc field campaign. Moreover, the threshold for selecting the bare soil pixels can be tuned, according to the goal of the survey. The quality of the SOC map for each tolerance level can be judged based on the figures of merit of the model. View Full-Text
Keywords: Sentinel-2; SOC; multispectral; Normalized Burn Ratio 2; LUCAS spectral library Sentinel-2; SOC; multispectral; Normalized Burn Ratio 2; LUCAS spectral library
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MDPI and ACS Style

Castaldi, F.; Chabrillat, S.; Don, A.; van Wesemael, B. Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects. Remote Sens. 2019, 11, 2121.

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