13 citations as recorded by crossref.

1. ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018 F. Cheng et al. 10.5194/essd-15-395-2023
2. Artificial intelligence achieves easy-to-adapt nonlinear global temperature reconstructions using minimal local data M. Wegmann & F. Jaume-Santero 10.1038/s43247-023-00872-9
3. Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. 10.3389/feart.2023.1130853
4. A methodological framework for assessing pastoral socio-ecological system vulnerability: A case study of Altay Prefecture in Central Asia Z. Yang et al. 10.1016/j.scitotenv.2022.160828
5. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
6. The Carbon Transfer From Plant to Soil Is More Efficient in Less Productive Ecosystems X. Fan et al. 10.1029/2023GB007727
7. Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021 H. Yang & Q. Wang 10.1016/j.jhydrol.2023.129579
8. The International Soil Moisture Network: serving Earth system science for over a decade W. Dorigo et al. 10.5194/hess-25-5749-2021
9. SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022 Q. Zhang et al. 10.5194/essd-14-4473-2022
10. The Relationships between Biomass and Soil Respiration across Different Forest Management Practices C. Hu et al. 10.3390/f15040712
11. A stepwise method for downscaling SMAP soil moisture dataset in the CONUS during 2015–2019 H. Yang et al. 10.1016/j.jag.2024.103912
12. Building Trust in Earth Science Findings through Data Traceability and Results Explainability P. Olaya et al. 10.1109/TPDS.2022.3220539
13. Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA D. Warner et al. 10.1016/j.ejrh.2021.100946