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

• Published in: Geoderma 2021-04, Vol.388, p.114864, Article 114864
• Main Authors: Gasmi, Anis, Gomez, Cécile, Lagacherie, Philippe, Zouari, Hédi, Laamrani, Ahmed, Chehbouni, Abdelghani
• Format: Article
• Language: English
• Subjects: Agricultural sciences; Bare soil coverage; Landsat-TM; Life Sciences; MLR; Multi-Date imagery; Prediction accuracy; Soil clay mapping; Soil study
• ISSN: 0016-7061; 1872-6259
• DOI: 10.1016/j.geoderma.2020.114864

•Soil clay content was mapped by Landsat-TM data time series.•Work dedicated to a Tunisian cultivated area with contrasted pedological structure.•Use of mean spectral reflectance along the time series, compared to single date.•Aims to increase both soil properties prediction accuracy and mapping coverage.•Mean spectral reflectance from Landsat-TM data time series provided best predictions. 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 delive