In Scientific data ; h5-index 0.0
Surface soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, and critical to meteorology, hydrology, and ecology. The Tibetan Plateau (TP), known as "The third pole of the world" and "Asia's water towers", exerts huge influences on and sensitive to global climates. In this situation, longer time series of soil moisture can provide sufficient information to understand the role of the TP. This paper presents the first comprehensive dataset (2002-2015) of spatio-temporal continuous soil moisture at 0.25° resolution, based on satellite-based optical (i.e. MODIS) and microwave (ECV) products using a machine learning method named general regression neural network (GRNN). The dataset itself reveals significant information on the soil moisture and its changes over the TP, and can aid to understand the potential driven mechanisms for climate change over the TP.
Cui Yaokui, Zeng Chao, Zhou Jie, Xie Hongjie, Wan Wei, Hu Ling, Xiong Wentao, Chen Xi, Fan Wenjie, Hong Yang