Abstract A satellite-derived hysteresis model is presented for estimate heat storage in urban areas. Storage heat flux, one of the dominant terms in the urban surface energy budget (USEB), is largely unknown despite its critical relationship to various urban environmental processes. This study introduces a novel technique for quantifying heat storage by relating multispectral satellite radiances and geophysical properties to ground-truth residual heat storage computed with flux instruments. Gradient-boosted regression trees serve as the method of maximizing the relationship between satellite data and flux measurements. Several flux networks are used to train and validate the model over varying land cover types, which strengthens the robustness of the model. The model performs well under variable weather conditions such as cloudy rainy days. In comparison with other studies, the RMSE and MAE values were found to be lower than some ground-to-ground studies, and is one of few satellite-derived methods that computes direct comparison over a range of different land cover types.

中文翻译：

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使用多光谱卫星数据和机器学习估算城市地区的储热

摘要 提出了一种卫星衍生的滞后模型，用于估计城市地区的蓄热。储存热通量是城市地表能量收支 (USEB) 中的主要术语之一，尽管它与各种城市环境过程具有重要关系，但在很大程度上是未知的。本研究介绍了一种通过将多光谱卫星辐射和地球物理特性与使用通量仪器计算的地面实况剩余热量存储相关联来量化热量存储的新技术。梯度提升回归树作为最大化卫星数据和通量测量之间关系的方法。几个通量网络用于在不同的土地覆盖类型上训练和验证模型，这增强了模型的鲁棒性。该模型在多云阴雨天等多变天气条件下表现良好。