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Topsoil clay content mapping in croplands from Sentinel-2 data: Influence of atmospheric correction methods across a season time series

Gomez, C and Vaudour, E and Feret, JB and de Boissieu, F and Dharumarajan, S (2022) Topsoil clay content mapping in croplands from Sentinel-2 data: Influence of atmospheric correction methods across a season time series. In: Geoderma, 423 .

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Official URL: https://doi.org/10.1016/j.geoderma.2022.115959


Recent studies demonstrated the capability of Sentinel-2 (S2) data to estimate topsoil properties and highlighted the sensitivity of these estimations to soil surface conditions depending on the S2 acquisition date. These estimations are based on Bottom of Atmosphere (BOA) reflectance images, obtained from Top of Atmosphere (TOA) reflectance values using Atmospheric Correction (AC) methods. AC of optical satellite imagery is an important pre-processing stage before estimating biophysical variables, and several AC methods are currently operational to perform such conversion. This study aims at evaluating the sensitivity of topsoil clay content estimation to atmospheric corrections along an S2 time series. Three AC methods were tested (MAJA, Sen2Cor, and LaSRC) on a time series of eleven Sentinel-2 images acquired over a cultivated region in India (Karnataka State) from February 2017 to June 2017. Multiple Linear Regression models were built using clay content analyzed from topsoil samples collected over bare soil pixels and corresponding BOA reflectance data. The influence of AC methods was also analysed depending on bare soil pixels selections based on two spectral indices and several thresholds: the normalized difference vegetation index (NDVI below 0.25, 0.3 and 0.35) and the combination of NDVI (below 0.3) and Normalized Burned Ratio 2 index (NBR2 below 0.09, 0.12 and 0.15) for masking green vegetation, crop residues and soil moisture. First, this work highlighted that regression models were more sensitive to acquisition date than to AC method, suggesting that soil surface conditions were more influent on clay content estimation models than variability among atmospheric corrections. Secondly, no AC method outperformed other methods for clay content estimation, and the performances of regression models varied mostly depending on the bare soil pixels selection used to calibrate the regression models. Finally, differences in BOA reflectance among AC methods for the same acquisition date led to differences in NDVI and NBR2, and hence in bare soil coverage identification and subsequent topsoil clay content mapping coverage. Thus, selecting S2 images with respect to the acquisition date appears to be a more critical step than selecting an AC method, to ensure optimal retrieval accuracy when mapping topsoil properties assumed to be relatively stable over time. © 2022 Elsevier B.V.

Item Type: Journal Article
Publication: Geoderma
Publisher: Elsevier B.V.
Additional Information: The copyright for this article belongs to the Elsevier B.V.
Keywords: Agricultural wastes; Crops; Mapping; Pixels; Reflection; Satellite imagery; Soil moisture; Time series; Vegetation, Atmospheric corrections; Bare soils; Clay content; Correction method; India; Properties mappings; Sentinel-2; Soil property; Soil property mapping; Topsoil, Multiple linear regression, atmospheric correction; clay soil; mapping; multiple regression; Sentinel; soil property; topsoil, India; Karnataka
Department/Centre: Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research
Date Deposited: 21 Jun 2022 08:49
Last Modified: 21 Jun 2022 08:49
URI: https://eprints.iisc.ac.in/id/eprint/73911

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