%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Gasmi, A.
%A Gomez, Cécile
%A Lagacherie, P.
%A Zouari, H.
%A Laamrani, A.
%A Chehbouni, Abdelghani
%T 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
%D 2021
%L fdi:010081038
%G ENG
%J Geoderma
%@ 0016-7061
%K Multi-Date imagery ; Landsat-TM ; Bare soil coverage ; Soil day mapping ; MLR ; Prediction accuracy
%K TUNISIE ; CAP BON
%M ISI:000621894500004
%P 114864 [12 ]
%R 10.1016/j.geoderma.2020.114864
%U https://www.documentation.ird.fr/hor/fdi:010081038
%> https://www.documentation.ird.fr/intranet/publi/2021/03/010081038.pdf
%V 388
%W Horizon (IRD)
%X 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 (R-val(2)) 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., R-val(2) = 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.
%$ 068 ; 126