The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning
The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning
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DOI：
10.1109/tgrs.2018.2872131
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发表时间：
2019-04-01
影响因子：
8.2
通讯作者：
Shen, Chaopeng
中科院分区：
文献类型：
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作者：
Fang, Kuai;Pan, Ming;Shen, Chaopeng
土壤水分主动被动(SMAP)任务测量全球重要的土壤水分数据。当根据现场测量进行评估时，SMAP的产品可能并不总是比陆地表面模型(LSM)表现更好。然而，我们假设SMAP在数据融合环境中为长期土壤水分估计提供了附加值，正如现场数据所评估的那样。这里，在时间序列深度学习(DL)方法的帮助下，我们创建了一个无缝扩展的SMAP数据集来测试这一假设，并重要的是，衡量这种好处是否会延伸到SMAP有限寿命之外的年份。我们首先证明了被称为长短期记忆(LSTM)的DL模型可以外推几年的SMAP，结果与训练周期相似。我们得到了低性能退化的延长结果，其中SMAP本身与现场数据匹配得很好。令人惊讶的是，LSTM很好地捕捉到了根区土壤水分的年际趋势。在某些情况下，LSTM的性能受到SMAP的限制，SMAP的主要问题似乎是其浅层传感深度。尽管有这个限制，LSTM和LSM Noah之间的简单平均值往往比单独使用Noah更好。此外，Noah与LSTM相结合比与另一种LSM相结合时更有技巧。在仪器稀少的站点上，NOAH-LSTM组合显示出更强的优势。结果验证了LSTM扩展SMAP数据的价值。此外，DL完全是数据驱动的，不需要结构假设。因此，它有其独特的长期预测潜力，并可与其他模型数据集成技术协同应用。
The Soil Moisture Active Passive (SMAP) mission measures important soil moisture data globally. SMAP's products might not always perform better than land surface models (LSM) when evaluated against in situ measurements. However, we hypothesize that SMAP presents added value for long-term soil moisture estimation in a data fusion setting as evaluated by in situ data. Here, with the help of a time series deep learning (DL) method, we created a seamlessly extended SMAP data set to test this hypothesis and, importantly, gauge whether such benefits extend to years beyond SMAP's limited lifespan. We first show that the DL model, called long short-term memory (LSTM), can extrapolate SMAP for several years and the results are similar to the training period. We obtained prolongation results with low-performance degradation where SMAP itself matches well with in situ data. Interannual trends of root-zone soil moisture are surprisingly well captured by LSTM. In some cases, LSTM's performance is limited by SMAP, whose main issue appears to be its shallow sensing depth. Despite this limitation, a simple average between LSTM and an LSM Noah frequently outperforms Noah alone. Moreover, Noah combined with LSTM is more skillful than when it is combined with another LSM. Over sparsely instrumented sites, the Noah-LSTM combination shows a stronger edge. Our results verified the value of LSTM-extended SMAP data. Moreover, DL is completely data driven and does not require structural assumptions. As such, it has its unique potential for long-term projections and may be applied synergistically with other model-data integration techniques.