Grassland aboveground biomass (AGB) is a key variable to measure grassland productivity, and accurate assessment of AGB is important for optimizing grassland resource management and understanding carbon, water, and energy fluxes. Current approaches on large scales such as the Mongolian Steppe Ecosystem often combine field measurements with optical and/or synthetic aperture radar (SAR) data. Meanwhile, especially the representativeness of the field measurements for large‐scale analysis have seldom been accounted for. Therefore, we provide the first remotely sensed AGB product for central and Eastern Mongolia which (1) uses random forest (RF), (2) is fully validated against over 600 field samples, and (3) applies a novel method, dissimilarity index (DI), to derive the area of applicability of the model with respect to the training data. Therefore, different remote sensing data sources such as multi‐scale and multi‐temporal optical images—Worldview 2 (WV2), Sentinel 2 (S2), and Landsat 8 (L8) in combination with SAR data are tested for their suitability to provide an area‐wide estimation on large scale. The results showed that the AGB prediction by combining Sentinel 1 (S1) and S2 using RF had the highest accuracy. Furthermore, the model was applicable to at least 72.61% of the steppe area. Areas where the model was not applicable are mostly distributed along the edges of grassland. This study demonstrates the potential of combining Sentinel‐derived indices and machine learning to provide a reliable AGB prediction for grassland for extremely large ecosystems with strong climatic gradients.

中文翻译：

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多源数据地上生物量反演：蒙古东部地区预测及适用性分析

草地地上生物量（AGB）是衡量草地生产力的关键变量，准确评估草地地上生物量对于优化草地资源管理和了解碳、水和能量通量具有重要意义。当前大规模的方法（例如蒙古草原生态系统）通常将现场测量与光学和/或合成孔径雷达（SAR）数据结合起来。与此同时，特别是大规模分析的现场测量的代表性很少被考虑。因此，我们为蒙古中部和东部提供了第一个遥感AGB产品，该产品（1）使用随机森林（RF），（2）针对600多个现场样本进行了充分验证，以及（3）应用了一种新颖的方法，即相异指数（ DI)，得出模型相对于训练数据的适用范围。因此，不同的遥感数据源，如多尺度、多时相光学影像——Worldview 2 (WV2)、Sentinel 2 (S2)和Landsat 8 (L8)与SAR数据相结合，测试其是否适合提供大规模区域估计。结果表明，使用RF结合Sentinel 1（S1）和S2进行的AGB预测具有最高的准确度。此外，该模型适用于至少72.61%的草原面积。模型不适用的区域大多分布在草地边缘。这项研究证明了将 Sentinel 衍生指数和机器学习相结合的潜力，可以为具有强气候梯度的超大型生态系统的草地提供可靠的 AGB 预测。