Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification 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 “tanks”) 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 proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration 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 procedure 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.

由于人为因素对景观的影响越来越大，对土壤特性进行测绘变得越来越具有挑战性，这就要求采用新的方法来识别这些变化。人为改变土壤特性的一个突出例子是在印度南部普遍采用的做法，将来自干河大坝（或“坦克”）的大量淤泥应用于农田。尽管有几项研究表明了储罐淤泥对土壤肥力的兴趣，但尚没有评估这种古老的传统做法的实际程度。在南印度的教育学背景下，这种做法的特点是将黑色的淤泥应用于红色土壤，例如Ferralsols。这项工作的目的是评估Sentinel-2图像在绘制储罐淤泥应用中的实用性，假设观察到的土壤表面颜色的变化可以替代储罐淤泥。我们使用了在印度南部的一个耕作流域收集的数据，包括以孟塞尔颜色为特征的217个土壤表面样本。我们使用了两个在2017年2月和2017年4月采集的Sentinel-2图像。每个Sentinel-2图像上的表面土壤颜色分为两种土壤类型（“黑色”和“红色”土壤）。土壤颜色从2017年2月的“红色”变为2017年4月的“黑色”，这归因于储罐淤泥的施用。分析了土壤颜色变化，解释了可能的表层土壤水分变化。拟议的方法是基于综合的少数民族过采样技术（SMOTE）方法，从最初的不平衡校准数据集中创建的平衡良好的校准数据，结合成本敏感的分类和回归树（Cost-Sensitive CART）算法。估计不确定性i）在每个日期进行两类分类，并且ii）将土壤颜色从“红色”更改为“黑色”，使用了自举程序，为每个Sentinel-2图像提供了五十种两类分类。结果表明：1）CART方法允许从Sentinel-2图像中以正确的整体准确度对“红色”和“黑色”土壤进行分类； 2）在202个田地上识别出了储罐淤泥的应用； 3）土壤颜色的变化与两个日期之间的地表土壤水分变化无关。有了Sentinel-2的实际可用性以及LANDSAT卫星图像的过去可用性，这项研究可能会开辟一条通往简单，准确的方法来传递储罐淤泥应用图的途径，从而研究并可能追溯量化这种农民的做法。