TY - JOUR
T1 - Global daily 9 km remotely sensed soil moisture (2015–2025) with microwave radiative transfer-guided learning
AU - Feng, Sijia
AU - Li, Aoyang
AU - Zhou, Rui
AU - Butterbach-Bahl, Klaus
AU - Guan, Kaiyu
AU - Jin, Zhenong
AU - Looms, Majken C.
AU - Wang, Sherrie
AU - Igel, Christian
AU - Treat, Claire
AU - Olesen, Jørgen Eivind
AU - Wang, Sheng
N1 - This work was supported by the Danish Data Science Academy, which is funded by the Novo Nordisk Foundation (NNF21SA0069429) and VILLUM FONDEN (40516). This work was also supported by the VILLUM Young Investigator 2024 project (00072051), the Novo Nordisk Starting Grant (NNF23OC0087612), the SCALE project (AgriFoodTure, Innovation Fund Denmark), NASA ECOSTRESS Science and Applications Program (80NSSC23K0308), NASA Early Career Investigator Program in Earth Science (80NSSC24K1057), and the Global Wetland Center (NNF23OC0081089, Novo Nordisk Foundation). Additional funding was provided by the Pioneer Center for Research in Sustainable Agricultural Futures (Land-CRAFT), DNRF grant number P2, and Aarhus University. The author acknowledge NASA, ESA, INRAE and ECMWF for providing global SM products and land surface features, and thank ISMN, AmeriFlux, JapanFlux, ICOS and previous studies for providing available in-situ SM measurements.
This work was supported by the Danish Data Science Academy, which is funded by the Novo Nordisk Foundation (NNF21SA0069429) and VILLUM FONDEN (40516). This work was also supported by the VILLUM Young Investigator 2024 project (00072051), the Novo Nordisk Starting Grant (NNF23OC0087612), the SCALE project (AgriFoodTure, Innovation Fund Denmark), NASA ECOSTRESS Science and Applications Program (80NSSC23K0308), NASA Early Career Investigator Program in Earth Science (80NSSC24K1057), and the Global Wetland Center (NNF23OC0081089, Novo Nordisk Foundation). Additional funding was provided by the Pioneer Center for Research in Sustainable Agricultural Futures (Land-CRAFT), DNRF grant number P2, and Aarhus University. The author acknowledge NASA, ESA, INRAE and ECMWF for providing global SM products and land surface features, and thank ISMN, AmeriFlux, JapanFlux, ICOS and previous studies for providing available in-situ SM measurements.
PY - 2026/2/12
Y1 - 2026/2/12
N2 - Accurate estimation of surface soil moisture (SM) in terrestrial ecosystems is essential for understanding hydroclimate dynamics. The L-band Soil Moisture Active Passive (SMAP) mission provides 9-km global daily surface SM by using a microwave radiative transfer model (RTM)-based algorithm. However, the accuracy of SMAP SM is limited in regions with dense vegetation cover and complex surface conditions, due to the empirical parameterization and oversimplified radiative transfer processes. To overcome the limitations, we developed a Process-Guided Machine Learning (PGML) framework to integrate RTM theories and deep learning to predict global daily surface 9-km SM from April 2015 to June 2025. Informed by domain knowledge, we developed the PGML model structure using RTM and hydrological theories, designed a Kling-Gupta efficiency-based cost function, pretrained it with RTM simulations, and fine-tuned it with in-situ measurements. The independent validation shows that PGML SM has strong agreement with in-situ measurements (R = 0.868 and unbiased RMSE = 0.054 m3/m3). This study highlights the potential of PGML to enhance the accuracy of satellite SM, thereby supporting improved water resources and ecosystem management.
AB - Accurate estimation of surface soil moisture (SM) in terrestrial ecosystems is essential for understanding hydroclimate dynamics. The L-band Soil Moisture Active Passive (SMAP) mission provides 9-km global daily surface SM by using a microwave radiative transfer model (RTM)-based algorithm. However, the accuracy of SMAP SM is limited in regions with dense vegetation cover and complex surface conditions, due to the empirical parameterization and oversimplified radiative transfer processes. To overcome the limitations, we developed a Process-Guided Machine Learning (PGML) framework to integrate RTM theories and deep learning to predict global daily surface 9-km SM from April 2015 to June 2025. Informed by domain knowledge, we developed the PGML model structure using RTM and hydrological theories, designed a Kling-Gupta efficiency-based cost function, pretrained it with RTM simulations, and fine-tuned it with in-situ measurements. The independent validation shows that PGML SM has strong agreement with in-situ measurements (R = 0.868 and unbiased RMSE = 0.054 m3/m3). This study highlights the potential of PGML to enhance the accuracy of satellite SM, thereby supporting improved water resources and ecosystem management.
UR - https://www.scopus.com/pages/publications/105034307062
UR - https://www.scopus.com/pages/publications/105034307062#tab=citedBy
U2 - 10.1038/s41597-026-06721-6
DO - 10.1038/s41597-026-06721-6
M3 - Article
C2 - 41680217
AN - SCOPUS:105034307062
SN - 2052-4463
VL - 13
JO - Scientific Data
JF - Scientific Data
IS - 1
M1 - 435
ER -