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 $\left({\text{R}}_{\mathrm{val}}^2\right)$ 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., ${\text{R}}_{\mathrm{val}}^2$ = 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.

可见，近红外和短波红外（VNIR / SWIR，400-2500 nm）遥感图像是用于表土特性映射的有用工具，但仅限于裸露的土壤像素。随着免费提供的VNIR / SWIR卫星图像（例如Landsat TM，ETM +，OLI和Sentinel-2A / B）数量的增加，可以利用大量的时间序列数据来增加裸土衍生信息的空间覆盖率。这项研究的目的是评估与单一日期图像相比，使用根据时间序列中裸土像素的平均光谱反射率创建的裸土图像的好处。通过（i）测绘土壤比例和（ii）预测粘土含量的准确性来分析收益。这项研究是在Cap-Bon地区（突尼斯北部）进行的，该地区是受过教育对比和耕种的地区。为此，使用了262个表土样品和三个在夏季采集的Landsat-TM图像。进行了基于多日期和单日期Landsat衍生光谱数据集的多元线性回归（MLR）模型来量化粘土含量。我们的结果表明，（1）仅由普通裸土像素沿时间序列的平均光谱反射率创建的裸土图像提供了最佳的粘土含量预测准确度（即验证的确定系数）$\left({\text{[R}}_{\mathrm{值}}^2\right)$为0.75，预测的均方根误差（RMSEP）为88 g / kg），土壤覆盖度中等（即研究面积的23％）；（2）由混合的普通裸土像素沿时间序列的平均光谱反射率与单日影像的裸土像素的光谱反射率混合而成的裸土图像提供了可接受的粘土含量预测精度（即，${\text{[R}}_{\mathrm{值}}^2$= 0.64，RMSEP = 109 g / kg），裸土覆盖率较高（即研究面积的44％）；（3）所有裸土图像都提供了类似的粘土含量预测的空间结构。有了整个地球的VNIR / SWIR卫星图像的实际可用性，这项研究提供了一种简单而准确的方法，可以向地球科学界提供大范围的准确土壤特性图。