Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning

• Published in: Journal of hydrology (Amsterdam) Vol. 600; p. 126698
• Main Authors: Li, Qingliang, Wang, Ziyu, Shangguan, Wei, Li, Lu, Yao, Yifei, Yu, Fanhua
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: China; data collection; Deep learning; ecosystem management; Machine learning; neural networks; precision agriculture; prediction; sample size; satellites; Small sample; SMAP; Soil moisture; soil temperature; soil water; Transfer learning; China; Deep learning; Small sample; Soil moisture; Transfer learning; Machine learning; SMAP

•Deep learning models predicted soil moisture well with limited SMAP samples.•Transfer learning improved predictions with additional samples from ERA5-land.•Transfer ConvLSTM performed the best with over 90% variation explained.•The predictive ability of different factors was widely investigated.•Tr...