Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel
Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel
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DOI：
10.1175/jhm-d-19-0169.1
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发表时间：
2020-03-01
影响因子：
3.8
通讯作者：
Shen, Chaopeng
中科院分区：
文献类型：
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作者：
Fang, Kuai;Shen, Chaopeng
基于土壤水分主动和被动（SMAP）使命的土壤水分临近预报或近实时（NRT）预报可为包括灾害监测和农业规划在内的一系列应用提供重要价值。为了提供这种高保真的NRT预测，我们增强了时间序列深度学习架构，即长短期记忆（LSTM），并使用新的数据集成（DI）内核来吸收最新的SMAP观测结果。核是自适应的，因为它可以适应不规则的观测时间表。在CONUS上进行测试，该NRT预测产品在与随后的SMAP检索进行评估时，展示了前所未有的准确性。它表现出较小的误差比NRT预测在文献中报道，特别是在较长的预测潜伏期。相对优势是由于LSTM的结构改进，以及它利用更多输入变量和更多训练数据的能力。DI-LSTM与原始的LSTM模型进行了比较，原始的LSTM模型在没有数据集成的情况下运行，在这里称为投影模型。我们发现，DI过程中删除的自相关效应的强迫误差和错误，由于过程中不代表的输入，例如，灌溉和洪泛区/湖泊淹没，以及由于看不见的强迫条件的不匹配。这种纯粹的数据驱动的DI内核的影响首次在地球科学中进行了讨论。此外，这项工作提出了一个上限估计的随机分量的SMAP检索错误。
Nowcasts, or near-real-time (NRT) forecasts, of soil moisture based on the Soil Moisture Active and Passive (SMAP) mission could provide substantial value for a range of applications including hazards monitoring and agricultural planning. To provide such a NRT forecast with high fidelity, we enhanced a time series deep learning architecture, long short-term memory (LSTM), with a novel data integration (DI) kernel to assimilate the most recent SMAP observations as soon as they become available. The kernel is adaptive in that it can accommodate irregular observational schedules. Testing over the CONUS, this NRT forecast product showcases predictions with unprecedented accuracy when evaluated against subsequent SMAP retrievals. It showed smaller error than NRT forecasts reported in the literature, especially at longer forecast latency. The comparative advantage was due to LSTM's structural improvements, as well as its ability to utilize more input variables and more training data. The DI-LSTM was compared to the original LSTM model that runs without data integration, referred to as the projection model here. We found that the DI procedure removed the autocorrelated effects of forcing errors and errors due to processes not represented in the inputs, for example, irrigation and floodplain/lake inundation, as well as mismatches due to unseen forcing conditions. The effects of this purely data-driven DI kernel are discussed for the first time in the geosciences. Furthermore, this work presents an upper-bound estimate for the random component of the SMAP retrieval error.