Wang Xiaoyi, Lü Haishen, Crow Wade T, Corzo Gerald, Zhu Yonghua, Su Jianbin, Zheng Jingyao, Gou Qiqi
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.
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。
