The Caatinga is an important ecosystem in the semi-arid region of northeast Brazil and a natural laboratory for the study of plant adaptation to seasonal water stress or prolonged droughts. The soil water availability for plants depends on plant root depth and soil properties. Here, we combined for the first time the remote sensing classification of Caatinga physiognomies with soil information derived from geostatistical analysis to relate vegetation distribution with physico-chemical attributes of soils. We evaluated the potential of multi-temporal data acquired by the MultiSpectral Instrument (MSI)/Sentinel-2 for Random Forest (RF) classification of seven physiognomies. In addition, we analyzed the contribution of airborne LiDAR metrics to improve classification accuracy compared to six vegetation indices (VIs) and 10 reflectance bands from the MSI instrument. Using a detailed soil survey, the spatial distribution of the vegetation physiognomies mapped by RF was associated with the variability of 20 physico-chemical attributes of 75 soil profiles submitted to principal components analysis (PCA) and ordinary kriging. The results showed gains in overall classification accuracy with use of the multi-temporal data over the mono-temporal observations. Gains in classification of arboreous Caatinga were also observed after the insertion of LiDAR metrics in the analysis, especially the percentage of vegetation cover with height greater than 5 m, the terrain elevation and the standard deviation of vegetation height. Overall, the most important metrics for classification were the VIs, especially the Enhanced Vegetation Index (EVI), Normalized Difference Infrared Index (NDII-1), Optimized Soil-Adjusted Vegetation Index (OSAVI) and the Normalized Difference Vegetation Index (NDVI). The most important MSI/Sentinel-2 bands were positioned in the red-edge spectral interval. From PCA, soil attributes responsible for most of the data variance were related to soil fertility, soil depth and rock fragments in the surface horizon. The amounts of gravels and pebbles were factors of physiognomic variability with shrub and sub-shrub Caatinga occurring preferentially over shallow and stony soils. By contrast, arboreous Caatinga occurred over soils with total profile depth greater than 1 m. Finally, areas of sub-shrub Caatinga had greater values of cation exchange capacity (CEC) and water retention at field capacity than areas of arboreous Caatinga. The differences were statistically significant at 95% confidence level, as indicated by Mann-Whitney U tests.

该卡丁加群落是在巴西东北部的半干旱地区和植物适应的季节性缺水或长期干旱研究的天然实验室的重要生态系统。植物的土壤水分可用性取决于植物的根深和土壤特性。在这里，我们第一次结合了Caatinga的遥感分类根据地统计学分析得出的具有土壤信息的地貌，将植被分布与土壤的物理化学属性相关联。我们评估了通过多光谱仪器（MSI）/ Sentinel-2获得的多时相数据对7种地貌的随机森林（RF）分类的潜力。此外，与MSI仪器的六个植被指数（VI）和10个反射带相比，我们分析了机载LiDAR指标对提高分类精度的贡献。使用详细的土壤调查，RF绘制的植被生理特征的空间分布与提交给主成分分析（PCA）和普通克里金法的75种土壤剖面的20种理化属性的变异性有关。结果表明，与单时态观测相比，使用多时态数据可以提高整体分类的准确性。归类为树状卡廷加在分析中插入LiDAR指标后，还观察到了观测值，特别是高度大于5 m的植被覆盖率，地形标高和植被高度的标准差。总体而言，最重要的分类指标是VI，尤其是增强植被指数（EVI），归一化差异红外指数（NDII-1），优化土壤调整植被指数（OSAVI）和归一化植被指数（NDVI）。最重要的MSI / Sentinel-2频段位于红边频谱间隔中。在PCA中，造成大多数数据差异的土壤属性与土壤肥力，土壤深度和地表岩石碎块有关。砾石和卵石的数量是灌木和亚灌木的生理学变异性的因素Caatinga优先出现在浅而石质的土壤上。相比之下，在整个剖面深度大于1 m的土壤上都发生了树状Caatinga。最后，亚灌木Caatinga地区的阳离子交换能力（CEC）和保水量在田间持水量均大于乔木Caatinga地区。如Mann-Whitney U检验所示，在95％的置信水平下，差异具有统计学意义。