Aboveground biomass (AGB) data are important for profitable and sustainable pasture management. In this study, we hypothesized that vegetation indexes (VIs) obtained through analysis of moderate spatial resolution satellite data (Landsat-8 and Sentinel-2) and meteorological data can accurately predict the AGB of Brachiaria (syn. Urochloa) pastures in Brazil. We used AGB field data obtained from pastures between 2015 and 2019 in four distinct regions of Brazil to evaluate (i) the relationship between three different VIs—normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2) and optimized soil adjusted vegetation index (OSAVI)—and meteorological data with pasture aboveground fresh biomass (AFB), aboveground dry biomass (ADB) and dry-matter content (DMC); and (ii) the performance of simple linear regression (SLR), multiple linear regression (MLR) and random forest (RF) algorithms for the prediction of pasture AGB based on VIs obtained through satellite imagery combined with meteorological data. The results highlight a strong correlation (r) between VIs and AGB, particularly NDVI (r = 0.52 to 0.84). The MLR and RF algorithms demonstrated high potential to predict AFB (R² = 0.76 to 0.85) and DMC (R² = 0.78 to 0.85). We conclude that both MLR and RF algorithms improved the biomass prediction accuracy using satellite imagery combined with meteorological data to determine AFB and DMC, and can be used for Brachiaria (syn. Urochloa) AGB prediction. Additional research on tropical grasses is needed to evaluate different VIs to improve the accuracy of ADB prediction, thereby supporting pasture management in Brazil.

中文翻译：

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结合气象数据和卫星图像预测臂形草场地上生物量和干物质含量

地上生物量 (AGB) 数据对于盈利和可持续牧场管理很重要。在本研究中，我们假设通过分析中等空间分辨率卫星数据（Landsat-8 和 Sentinel-2）和气象数据获得的植被指数（VIs）可以准确预测臂形草（同义词Urochloa) 巴西的牧场。我们使用从 2015 年至 2019 年巴西四个不同地区牧场获得的 AGB 实地数据来评估 (i) 三种不同 VI 之间的关系——归一化差异植被指数 (NDVI)、增强型植被指数 2 (EVI2) 和优化的土壤调整植被指数 (OSAVI)——以及牧场地上新鲜生物量 (AFB)、地上干生物量 (ADB) 和干物质含量 (DMC) 的气象数据；(ii) 简单线性回归 (SLR)、多元线性回归 (MLR) 和随机森林 (RF) 算法基于通过卫星图像结合气象数据获得的 VI 预测牧场 AGB 的性能。结果强调了 VI 和 AGB 之间的强相关性 (r)，尤其是 NDVI ( r = 0.52 到 0.84）。MLR 和 RF 算法显示出预测 AFB（R ²  = 0.76 至 0.85）和 DMC（R ²  = 0.78 至 0.85）的巨大潜力。我们得出结论，MLR 和 RF 算法都使用卫星图像结合气象数据来确定 AFB 和 DMC，提高了生物量预测精度，并可用于Brachiaria (syn. Urochloa ) AGB 预测。需要对热带草进行更多研究以评估不同的 VI，以提高亚行预测的准确性，从而支持巴西的牧场管理。