一种使用随机森林回归的用于土壤湿度主动被动探测卫星（SMAP）的低延迟区域填补方法
A reduced latency regional gap-filling method for SMAP using random forest regression.
作者信息
Wang Xiaoyi, Lü Haishen, Crow Wade T, Corzo Gerald, Zhu Yonghua, Su Jianbin, Zheng Jingyao, Gou Qiqi
机构信息
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, National Cooperative Innovation Center for Water Safety and Hydro-science, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China.
出版信息
iScience. 2022 Dec 22;26(1):105853. doi: 10.1016/j.isci.2022.105853. eCollection 2023 Jan 20.
The soil moisture active/passive (SMAP) mission represents a significant advance in measuring soil moisture from satellites. However, its large spatial-temporal data gaps limit the use of its values in near-real-time (NRT) applications. Considering this, the study uses NRT operational metadata (precipitation and skin temperature), together with some surface parameterization information, to feed into a random forest model to retrieve the missing values of the SMAP L3 soil moisture product. This practice was tested in filling the missing points for both SMAP descending (6:00 AM) and ascending orbits (6:00 PM) in a crop-dominated area from 2015 to 2019. The trained models with optimized hyper-parameters show the goodness of fit (R ≥ 0.86), and their resulting gap-filled estimates were compared against a range of competing products with and triple collocation validation. This gap-filling scheme driven by low-latency data sources is first attempted to enhance NRT spatiotemporal support for SMAP L3 soil moisture.
土壤湿度主动/被动探测任务（SMAP）在卫星测量土壤湿度方面取得了重大进展。然而，其较大的时空数据空白限制了其数据在近实时（NRT）应用中的使用。考虑到这一点，本研究使用近实时运行元数据（降水量和地表温度）以及一些地表参数化信息，输入到随机森林模型中，以填补SMAP L3土壤湿度产品的缺失值。这种做法在2015年至2019年一个以农作物为主的地区，对SMAP降轨（上午6:00）和升轨（下午6:00）的缺失点进行填补时进行了测试。经过超参数优化训练的模型显示出良好的拟合度（R≥0.86），并将其填补空白后的估计值与一系列竞争产品进行比较，并采用双配置和三配置验证。首次尝试这种由低延迟数据源驱动的填补空白方案，以增强对SMAP L3土壤湿度的近实时时空支持。
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