Studies on soil degradation are essential for environmental preservation. Since almost 30% of the global soils are degraded, it is important to study and map them for improving their management and use. We aimed to obtain a Soil Degradation Index (SDI) based on multi-temporal satellite images associated with climate variables, land use, terrain and soil attributes. The study was conducted in a 2598 km² area in São Paulo State, Brazil, where 1562 soil samples (0–20 cm) were collected and analyzed by conventional methods. Spatial predictions of soil attributes such as clay, cation exchange capacity (CEC) and soil organic matter (OM) were performed using machine learning algorithms. A collection of 35-year Landsat images was used to obtain a multi-temporal bare soil image, whose spectral bands were used as soil attributes predictors. The maps of clay, CEC, climate variables, terrain attributes and land use were overlaid and the K-means clustering algorithm was applied to obtain five groups, which represented levels of soil degradation (classes from 1 to 5 representing very low to very high soil degradation). The SDI was validated using the predicted map of OM. The highest degradation level obtained in 15% of the area had the lowest OM content. Levels 1 and 4 of SDI were the most representative covering 24% and 23% of the area, respectively. Therefore, satellite images combined with environmental information significantly contributed to the SDI development, which supports decision-making on land use planning and management.

对土壤退化的研究对于环境保护至关重要。由于全球将近30％的土壤已经退化，因此研究和绘制土壤图对于改善其管理和使用非常重要。我们旨在基于与气候变量，土地利用，地形和土壤属性相关的多时相卫星图像来获得土壤退化指数（SDI）。该研究在2598 km ²巴西圣保罗州的该地区，收集了1562份土壤样品（0-20厘米），并通过常规方法进行了分析。使用机器学习算法对土壤属性（如粘土，阳离子交换容量（CEC）和土壤有机质（OM））进行空间预测。使用35年的Landsat图像集合来获得多时相裸土图像，其光谱带用作土壤属性的预测指标。覆盖了粘土，CEC，气候变量，地形属性和土地利用的地图，并应用K-means聚类算法获得了代表土壤退化程度的五组（1-5类代表从非常低到非常高的土壤）降解）。使用OM的预测图验证了SDI。在15％的面积中获得的最高降解水平具有最低的OM含量。SDI的第1级和第4级是最具代表性的，分别占该区域的24％和23％。因此，将卫星图像与环境信息相结合，极大地促进了SDI的发展，这为土地使用规划和管理的决策提供了支持。