Generation of global 1&thinsp;km daily soil moisture product from 2000 to 2020 using ensemble learning

Zhang, Yufang; Liang, Shunlin; Ma, Han; He, Tao; Wang, Qian; Li, Bing; Xu, Jianglei; Zhang, Guodong; Liu, Xiaobang; Xiong, Changhao

doi:https://doi.org/10.5194/essd-15-2055-2023

Articles | Volume 15, issue 5

https://doi.org/10.5194/essd-15-2055-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-15-2055-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 5

Data description paper

|

23 May 2023

Data description paper |

| 23 May 2023

Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning

Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong

Data sets

A global 1-km surface soil moisture product from 2000 to 2020 Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, and Bing Li https://doi.org/10.5281/zenodo.7172664

Short summary

Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.