A global dataset of remote sensing-based soil critical point and permanent wilting point
Abstract The critical point (CP) and permanent wilting point (PWP) are key soil hydraulic characteristics that control the land surface energy budget and water balance. There is a lack of available data for these parameters on the global scale. This study extracts CP and PWP through soil moisture dr...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05048-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850204045297319936 |
|---|---|
| author | Yawei Xu Qing He Hui Lu Kun Yang Dara Entekhabi Daniel J. Short Gianotti |
| author_facet | Yawei Xu Qing He Hui Lu Kun Yang Dara Entekhabi Daniel J. Short Gianotti |
| author_sort | Yawei Xu |
| collection | DOAJ |
| description | Abstract The critical point (CP) and permanent wilting point (PWP) are key soil hydraulic characteristics that control the land surface energy budget and water balance. There is a lack of available data for these parameters on the global scale. This study extracts CP and PWP through soil moisture drydown (SMD) and provides global yearly soil hydraulic properties from a long-term (2002–2023) remote-sensing soil moisture product (Neural Network-based Soil Moisture, NNsm). Validated against 1334 stations from the International Soil Moisture Network (ISMN), the results show that the global medians of CP and PWP based on the NNsm are robust over time, and outperform the Soil Moisture Active and Passive (SMAP) dataset in accuracy due to the advantage of daily temporal resolution. Furthermore, this dataset holds an advantage over existing products, as it is derived from a multi-year climatological mean state and solely from satellite-based soil moisture observation. The derived dataset is useful for those who wish to connect land-atmosphere characteristics with their interests, as well as calibrate land surface models. |
| format | Article |
| id | doaj-art-0a71526d8e7c4c67b2a3144ca339473a |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-0a71526d8e7c4c67b2a3144ca339473a2025-08-20T02:11:22ZengNature PortfolioScientific Data2052-44632025-04-0112111310.1038/s41597-025-05048-yA global dataset of remote sensing-based soil critical point and permanent wilting pointYawei Xu0Qing He1Hui Lu2Kun Yang3Dara Entekhabi4Daniel J. Short Gianotti5Department of Earth System Science, Institute for Global Change Studies, Tsinghua UniversityDepartment of Civil Engineering, The University of TokyoDepartment of Earth System Science, Institute for Global Change Studies, Tsinghua UniversityDepartment of Earth System Science, Institute for Global Change Studies, Tsinghua UniversityDepartment of Civil and Environmental Engineering, Massachusetts Institute of TechnologyDepartment of Civil and Environmental Engineering, Massachusetts Institute of TechnologyAbstract The critical point (CP) and permanent wilting point (PWP) are key soil hydraulic characteristics that control the land surface energy budget and water balance. There is a lack of available data for these parameters on the global scale. This study extracts CP and PWP through soil moisture drydown (SMD) and provides global yearly soil hydraulic properties from a long-term (2002–2023) remote-sensing soil moisture product (Neural Network-based Soil Moisture, NNsm). Validated against 1334 stations from the International Soil Moisture Network (ISMN), the results show that the global medians of CP and PWP based on the NNsm are robust over time, and outperform the Soil Moisture Active and Passive (SMAP) dataset in accuracy due to the advantage of daily temporal resolution. Furthermore, this dataset holds an advantage over existing products, as it is derived from a multi-year climatological mean state and solely from satellite-based soil moisture observation. The derived dataset is useful for those who wish to connect land-atmosphere characteristics with their interests, as well as calibrate land surface models.https://doi.org/10.1038/s41597-025-05048-y |
| spellingShingle | Yawei Xu Qing He Hui Lu Kun Yang Dara Entekhabi Daniel J. Short Gianotti A global dataset of remote sensing-based soil critical point and permanent wilting point Scientific Data |
| title | A global dataset of remote sensing-based soil critical point and permanent wilting point |
| title_full | A global dataset of remote sensing-based soil critical point and permanent wilting point |
| title_fullStr | A global dataset of remote sensing-based soil critical point and permanent wilting point |
| title_full_unstemmed | A global dataset of remote sensing-based soil critical point and permanent wilting point |
| title_short | A global dataset of remote sensing-based soil critical point and permanent wilting point |
| title_sort | global dataset of remote sensing based soil critical point and permanent wilting point |
| url | https://doi.org/10.1038/s41597-025-05048-y |
| work_keys_str_mv | AT yaweixu aglobaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT qinghe aglobaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT huilu aglobaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT kunyang aglobaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT daraentekhabi aglobaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT danieljshortgianotti aglobaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT yaweixu globaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT qinghe globaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT huilu globaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT kunyang globaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT daraentekhabi globaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint AT danieljshortgianotti globaldatasetofremotesensingbasedsoilcriticalpointandpermanentwiltingpoint |