Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework
Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework
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DOI：
10.1029/2019wr024902
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发表时间：
2019-02
影响因子：
5.4
通讯作者：
Hanzi Mao;D. Kathuria;N. Duffield;B. Mohanty
中科院分区：
文献类型：
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作者：
Hanzi Mao;D. Kathuria;N. Duffield;B. Mohanty
作为土壤湿度主动被动 (SMAP) 任务的最新 3 公里土壤湿度产品，SMAP/Sentinel‐1 L2_SM_SP 产品具有独特的功能，可通过雷达和辐射计微波观测的融合提供全球范围的 3 公里土壤湿度估计。这种高分辨率土壤湿度产品的空间和时间可用性取决于同时进行的雷达和辐射计观测，而这受到 Sentinel-1 雷达的窄测绘带和低重访时间表的显着限制。为了解决这个问题，本文提出了一种新颖的基于机器学习的双层框架，该框架可以预测间隙区域的亮度温度以及随后的土壤湿度。所提出的方法能够在辐射计观测可用而雷达观测缺失的区域令人满意地填充土壤湿度。我们发现，将历史雷达反向散射测量（30 天平均值）纳入机器学习框架可以提高其预测性能。双层框架的有效性根据四个具有不同气候状况的研究区域的区域保留 SMAP/Sentinel-1 3 公里土壤湿度估计进行了验证。结果表明，与 SMAP 33 公里土壤湿度产品相比，我们提出的方法能够重建间隙区域 3 公里土壤湿度，具有高皮尔逊相关系数（亚利桑那州/俄克拉荷马州/爱荷华州/阿肯色州平均 R 提高 47%/35%/20%/80%）和低无偏均方根误差（平均无偏均方根误差提高 20%/10%/7%/26%）。针对机载数据和土壤湿度网络现场数据的额外验证也令人满意。
As the most recent 3‐km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel‐1 L2_SM_SP product has a unique capability to provide global‐scale 3‐km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high‐resolution soil moisture product depends on concurrent radar and radiometer observations which is significantly restricted by the narrow swath and low revisit schedule of the Sentinel‐1 radars. To address this issue, this paper presents a novel two‐layer machine learning‐based framework which predicts the brightness temperature and subsequently the soil moisture at gap areas. The proposed method is able to gap‐fill soil moisture satisfactorily at areas where the radiometer observations are available while the radar observations are missing. We find that incorporating historical radar backscatter measurements (30‐day average) into the machine learning framework boosts its predictive performance. The effectiveness of the two‐layer framework is validated against regional holdout SMAP/Sentinel‐1 3‐km soil moisture estimates at four study areas with distinct climate regimes. Results indicate that our proposed method is able to reconstruct 3‐km soil moisture at gap areas with high Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R, at Arizona/Oklahoma/Iowa/Arkansas) and low unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean unbiased root mean square error) when compared to the SMAP 33‐km soil moisture product. Additional validations against airborne data and in situ data from soil moisture networks are also satisfactory.