The objective of this work was to characterize the spectral responses of soil and plant indicators utilizing the Soil-Adjusted Vegetation Index (SAVI) and Leaf Area Index (LAI) in areas under the cultivation of ‘Orelha de Elefante Mexicana’ forage cactus (Opuntia stricta (Haw.) Haw.) and ‘Miúda’ (Nopalea cochenillifera (L.) Salm-Dyck) in Brazilian semi-arid region and to establish a model for characterizing vegetation cover in crop production areas. The research was conducted in Brazilian semi-arid region, which had two areas under the cultivation of forage cactus in the period from 2017 to 2020. Soil physical attributes and plant morphometric variables were recorded, concomitantly the determination of SAVI and LAI, from images from the Sentinel-2 satellite. The recorded data were submitted to principal component analysis. The cumulative total variance of the principal components showed satisfactory results for both areas. There was a correlation between plant height, the number of cladodes, and vegetation indices, selected to compose the predictive variables of the multiple regression model. It is recommended to use images from the Sentinel-2 satellite in small areas, given its lower spatial resolution in capturing different spectral responses.

这项工作的目的是利用“ Orelha de Elefante Mexicana”饲用仙人掌（仙人掌）栽培的地区，通过土壤调整植被指数（SAVI）和叶面积指数（LAI）来表征土壤和植物指示剂的光谱响应。（山楂）和“Miúda”（Nopalea cochenillifera）（L.）Salm-Dyck）在巴西半干旱地区建立了一个模型来表征农作物生产区的植被覆盖度。这项研究是在巴西半干旱地区进行的，该地区在2017年至2020年期间有两个区域进行了饲用仙人掌栽培。记录了土壤物理属性和植物形态变量，并根据来自以下地区的图像确定了SAVI和LAI： Sentinel-2卫星。记录的数据已提交给主成分分析。两个区域主成分的累积总方差均显示令人满意的结果。选择株高，枝条数和植被指数之间存在相关性，以构成多元回归模型的预测变量。