Yield forecasting and within-field yield variation is essential information that helps farmers develop sustainable agriculture. However, such information still needs to be included for most of them, and remote sensing is an alternative to provide it. Our objective was to assess Random Forest regression models composed of unique GLCM texture measures as an alternative to usual empirical models that use spectral response and auxiliary data, which is complex and reaches varied results. Eleven GLCM texture models based on eight texture measures of a single spectral layer were assessed to represent soybean field yield variation in two sites and seasons. Several models achieved satisfactory results, reaching R² from 0.90 to 0.95 and RMSE from 0.06 to 0.26 t/ha. Models above 15-window size are recommended for the soybean yield prediction as window size is an essential attribute to GLCM performance. Models derived from the bands individually (red, red-edge, near-infrared, and short wavelength infrared) were more sensitive to the window size than those derived from vegetation indices (EVI, GNDVI, GRNDVI, NDMI, NDRE, NDVI, SFDVI). The data aggregated by texture measures improve the individual spectral responses, providing alternatives to predict soybean within-field yield variation using random forest models.

产量预测和田间产量变化是帮助农民发展可持续农业的重要信息。然而，大多数此类信息仍然需要包含在内，而遥感是提供此类信息的替代方案。我们的目标是评估由独特的 GLCM 纹理测量组成的随机森林回归模型，作为使用光谱响应和辅助数据的常用经验模型的替代方案，该模型很复杂并且得出不同的结果。评估了基于单个光谱层八个纹理测量的 11 个 GLCM 纹理模型，以代表两个地点和季节的大豆田产量变化。多个模型取得了令人满意的结果，R ²达到了 0.90 至 0.95，RMSE 达到了 0.06 至 0.26 t/ha。建议使用超过 15 个窗口大小的模型进行大豆产量预测，因为窗口大小是 GLCM 性能的重要属性。与从植被指数（EVI、GNDVI、GRNDVI、NDMI、NDRE、NDVI、SFDVI）导出的模型相比，从各个波段（红色、红边、近红外和短波长红外）导出的模型对窗口大小更敏感。通过纹理测量汇总的数据改善了个体光谱响应，为使用随机森林模型预测大豆田内产量变化提供了替代方案。