Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modifica-tion of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or & ldquo;tanks & rdquo;) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over South-Indian pedological context, this practice is characterized by an application of black-colored tank silt to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application. We used data collected in a cultivated watershed in South India including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February and April 2017. The surface soil color over each Sentinel-2 image was classified into two soil types ("Black" and "Red" soils). A change of soil color from "Red" in February 2017 to "Black" in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The pro-posed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibra-tion dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from "Red" to "Black" a bootstrap pro-cedure was used providing fifty two-class classifications for each Sentinel-2 image. The results showed that 1) the CART method allowed to classify the "Red" and "Black" soil with correct overall accuracy from both Sentinel-2 images, 2) the tank silt application was identified over 202 fields and 3) the soil color changes were not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.
