Mapping the spatial variability of crops is critical for precision agriculture. In this sense, remote sensing is a key technology generally dependent on the result of vegetation indices (VIs). Therefore, investigating the sensitivity of VIs and their contribution toward explaining crop variability and assisting in predicting yield is one of the pathways scientific research needs to explore. In this study, we evaluated 12 VIs with different acquisition principles in four soybean-producing fields. Using these VIs proved to be interesting to increase the performance of yield prediction models using the Randon Forest algorithm. However, simply adding VIs to the model is not enough; these VIs must aggregate information on crop variability. Some VIs are calculated based on the variation of the scene under study, and these can be an interesting option to complement the information provided by more traditional VIs, such as NDVI, assisting in predictive models, even if their direct correlation with crop yield is low in some situations. We found that using VIs groups with the same acquisition principle in isolation did not allow reaching performance of models that contained more than one principle simultaneously. In this study, the CI and TC2 indices stood out. Thus, associating VIs with different acquisition principles and, consequently, capturing different responses to variability in vegetation vigor and canopy structure is more important than the number of predictor variables itself.

绘制作物的空间变异图对于精准农业至关重要。从这个意义上说，遥感是一项通常依赖于植被指数（VI）结果的关键技术。因此，研究VI的敏感性及其对解释作物变异性和协助预测产量的贡献是科学研究需要探索的途径之一。在本研究中，我们评估了四个大豆生产田中具有不同采集原理的 12 个 VI。事实证明，使用这些 VI 可以提高使用 Randon Forest 算法的产量预测模型的性能。然而，仅仅将 VI 添加到模型中还不够。这些 VI 必须汇总有关作物变异性的信息。一些 VI 是根据所研究场景的变化来计算的，这些可能是一个有趣的选择，可以补充更传统的 VI（例如 NDVI）提供的信息，有助于预测模型，即使它们与作物产量的直接相关性很低在某些情况下。我们发现，单独使用具有相同采集原理的 VI 组无法达到同时包含多个原理的模型的性能。在这项研究中，CI 和 TC2 指数脱颖而出。因此，将 VI 与不同的采集原理相关联，从而捕获对植被活力和冠层结构变化的不同响应，比预测变量数量本身更重要。