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Remote Sensing
Volume 11
Issue 18
10.3390/rs11182143
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Article

The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands

by
Emmanuelle Vaudour
1,*,
Cécile Gomez
2,3,
Thomas Loiseau
4,
Nicolas Baghdadi
5,
Benjamin Loubet
1,
Dominique Arrouays
4,
Leïla Ali
1,2 and
Philippe Lagacherie
2
1
UMR ECOSYS, AgroParisTech, INRA, Université Paris-Saclay, 78850 Thiverval-Grignon, France
2
LISAH, University of Montpellier, INRA-IRD-SupAgro, F-34060 Montpellier, France
3
Indo-French Cell for Water Sciences, IRD, Indian Institute of Science, Bangalore 560012, India
4
INRA, InfoSol Unit, US 1106, 45075 Orléans, France
5
Irstea, University of Montpellier, UMR TETIS, 500 rue François Breton, 34093 Montpellier CEDEX 5, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(18), 2143; https://doi.org/10.3390/rs11182143
Submission received: 5 August 2019 / Revised: 7 September 2019 / Accepted: 11 September 2019 / Published: 14 September 2019
(This article belongs to the Special Issue High Spectral Resolution Remote Sensing of Soil Organic Carbon Dynamics)
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Abstract

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 optical data to predict SOC content over temperate agroecosystems characterized by annual crops, using a single acquisition date. Considering a Sentinel-2 time series, this work intends to analyze the impact of acquisition date, and related weather and soil surface conditions on the prediction performance of topsoil SOC content (plough layer). A Sentinel-2 time-series was gathered, comprised of the dates corresponding to both the maximum of bare soil coverage and minimum of cloud coverage. Cross-validated partial least squares regression (PLSR) models were constructed between soil reflectance image spectra, and SOC content analyzed from 329 top soil samples collected over the study area. Cross-validation R2 ranged from 0.005 to 0.58, root mean square error from 5.86 to 3.02 g·kg−1 and residual prediction deviation values from 1.0 to 1.5 (without unit), according to date. The main factors influencing these differences were soil roughness, in conjunction with soil moisture, and the cloud and cloud shadow cover of the entire tile. The best performing dates were spring dates characterized by both lowest soil surface roughness and moisture content. Normalized difference vegetation index (NDVI) values below 0.35 did not influence prediction performance. This consolidates the previous results obtained during single date acquisitions and offers wider perspectives for the further use of Sentinel-2 into multidate mosaics for digital soil mapping.
Keywords: soil organic carbon content; Sentinel-2; surface roughness; soil moisture; Versailles; France soil organic carbon content; Sentinel-2; surface roughness; soil moisture; Versailles; France

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MDPI and ACS Style

Vaudour, E.; Gomez, C.; Loiseau, T.; Baghdadi, N.; Loubet, B.; Arrouays, D.; Ali, L.; Lagacherie, P. The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands. Remote Sens. 2019, 11, 2143. https://doi.org/10.3390/rs11182143

AMA Style

Vaudour E, Gomez C, Loiseau T, Baghdadi N, Loubet B, Arrouays D, Ali L, Lagacherie P. The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands. Remote Sensing. 2019; 11(18):2143. https://doi.org/10.3390/rs11182143

Chicago/Turabian Style

Vaudour, Emmanuelle, Cécile Gomez, Thomas Loiseau, Nicolas Baghdadi, Benjamin Loubet, Dominique Arrouays, Leïla Ali, and Philippe Lagacherie. 2019. "The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands" Remote Sensing 11, no. 18: 2143. https://doi.org/10.3390/rs11182143

APA Style

Vaudour, E., Gomez, C., Loiseau, T., Baghdadi, N., Loubet, B., Arrouays, D., Ali, L., & Lagacherie, P. (2019). The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands. Remote Sensing, 11(18), 2143. https://doi.org/10.3390/rs11182143

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