A 1&thinsp;km soil moisture dataset over eastern CONUS generated by assimilating SMAP data into the Noah-MP land surface model

Tai, Sheng-Lun; Yang, Zhao; Gaudet, Brian; Sakaguchi, Koichi; Berg, Larry; Kaul, Colleen; Qian, Yun; Liu, Ye; Fast, Jerome

doi:https://doi.org/10.5194/essd-17-4587-2025

Articles | Volume 17, issue 9

https://doi.org/10.5194/essd-17-4587-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-17-4587-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 9

Data description paper

|

19 Sep 2025

Data description paper |

| 19 Sep 2025

A 1 km soil moisture dataset over eastern CONUS generated by assimilating SMAP data into the Noah-MP land surface model

Sheng-Lun Tai, Zhao Yang, Brian Gaudet, Koichi Sakaguchi, Larry Berg, Colleen Kaul, Yun Qian, Ye Liu, and Jerome Fast

Supplement

https://doi.org/10.5194/essd-17-4587-2025-supplement

Data sets

A 1 km soil moisture data over eastern CONUS generated through assimilating SMAP data into the Noah-MP land surface model Sheng-Lun Tai et al. https://doi.org/10.5281/zenodo.14370563

Oklahoma Mesonet Soil Moisture (OKMSOIL), 1998-01-01 to 2020-10-22, Southern Great Plains (SGP) External Data (satellites and others) (X1) S. Giangrande et al. https://doi.org/10.5439/1027361

Soil Temperature and Moisture Profile (STAMP) J. Kyrouac et al. https://doi.org/10.5439/1238260

Eddy Correlation Flux Measurement System (ECOR) K. Gaustad https://doi.org/10.5439/1097546

Model code and software

LISF NASA-LIS https://github.com/NASA-LIS/LISF

Download

Article (7325 KB)
Full-text XML

Short summary

Our study created a high-resolution soil moisture dataset for the eastern US by integrating satellite data with a land surface model and advanced algorithms, achieving 1 km scale analyses. Validated against multiple in situ networks and analysis datasets, it demonstrated superior accuracy. This dataset is vital for understanding soil moisture dynamics, especially during droughts, and highlights the need to mitigate soil-type-dependent biases in the model.