Mean spectral reflectance from bare soil pixels along a Landsat-TM time series to increase both the prediction accuracy of soil clay content and mapping coverage

Gasmi, A and Gomez, C and Lagacherie, P and Zouari, H and Laamrani, A and Chehbouni, A (2021) Mean spectral reflectance from bare soil pixels along a Landsat-TM time series to increase both the prediction accuracy of soil clay content and mapping coverage. In: Geoderma, 388 .

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Abstract

Visible, near-infrared and short wave infrared (VNIR/SWIR, 400–2500 nm) remote sensing imagery is a useful tool for topsoil property mapping, but limited to bare soils pixels. With the increasing amount of freely available VNIR/SWIR satellite imagery (e.g. Landsat TM, ETM+, OLI and Sentinel-2A/B), extensive time series data can be exploited to increase the spatial coverage of bare soil derived information. The objective of this study was to evaluate the benefits of using a bare soil image created from the mean spectral reflectance from bare soil pixels along a time series, compared to a single-date image. The benefits were analyzed in term of (i) proportion of soil mapping and (ii) accuracy of clay content prediction. The study was conducted over the Cap-Bon region (Northern Tunisia) which is a pedologically contrasted and cultivated area. To this end, 262 topsoil samples and three Landsat-TM images acquired during the summer season were used. Multiple linear regression (MLR) models based on the multi-date and single-date Landsat-derived spectral dataset were performed to quantify clay soil content. Our results have shown that (1) a bare soil image created from only mean spectral reflectance from common bare soil pixels along a time series provided the best accuracy of clay content prediction (i.e., coefficient of determination of validation Rval2 of 0.75, a root mean square error of prediction (RMSEP) of 88 g/kg) with a moderate bare soil coverage (i.e., 23% of the study area); (2) a bare soil image created from a mix of mean spectral reflectance from common bare soil pixels along a time series and of spectral reflectance from bare soil pixels of single-date images provided acceptable accuracy of clay content prediction (i.e., Rval2 = 0.64, RMSEP = 109 g/kg) with a relatively high bare soil coverage (i.e., 44% of the study area); and (3) all the bare soil images provided similar spatial structures of the clay content predictions. With the actual availability of the VNIR/SWIR satellite imagery for the entire globe, this study offer a simple and accurate method for delivering accurate soil property maps over large areas, to the geoscience community. © 2020 Elsevier B.V.

Item Type:	Journal Article
Publication:	Geoderma
Publisher:	Elsevier B.V.
Additional Information:	The copyright for this article belongs to Elsevier B.V.
Keywords:	Clay; Forecasting; Infrared devices; Infrared radiation; Linear regression; Mean square error; Pixels; Reflection; Remote sensing; Satellite imagery; Soil surveys; Time series, Coefficient of determination; Multiple linear regression models; Prediction accuracy; Remote sensing imagery; Root-mean-square error of predictions; Short wave infrared; Soil clay content; Spectral reflectances, Photomapping, clay soil; Landsat; pixel; prediction; satellite imagery; spatial analysis; spectral analysis; time series analysis
Department/Centre:	Division of Mechanical Sciences > Centre for Atmospheric & Oceanic Sciences
Date Deposited:	24 Feb 2023 04:45
Last Modified:	24 Feb 2023 04:45
URI:	https://eprints.iisc.ac.in/id/eprint/80412

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