A global soil moisture product at 1 km resolution based on Sentinel-1 (2016-2022)
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| Format: | Dataset Open Access |
| Langue: | en |
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PANGAEA
2025
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| _version_ | 1867170124822216704 |
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| author | Fan, Dong Zhao, Tianjie Jiang, Xiaoguang García-García, Almudena Schmidt, Toni Samaniego, Luis Attinger, Sabine Wu, Hua Jiang, Yazhen Shi, Jiancheng Fan, Lei Tang, Bohui Wagner, Wolfgang Dorigo, Wouter Gruber, Alexander Mattia, Francesco Balenzano, Anna Brocca, Luca Jagdhuber, Thomas Wigneron, Jean-Pierre Montzka, Carsten Peng, Jian |
| author_facet | Fan, Dong Zhao, Tianjie Jiang, Xiaoguang García-García, Almudena Schmidt, Toni Samaniego, Luis Attinger, Sabine Wu, Hua Jiang, Yazhen Shi, Jiancheng Fan, Lei Tang, Bohui Wagner, Wolfgang Dorigo, Wouter Gruber, Alexander Mattia, Francesco Balenzano, Anna Brocca, Luca Jagdhuber, Thomas Wigneron, Jean-Pierre Montzka, Carsten Peng, Jian |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | Soil moisture, although a small fraction of total water content, plays a critical role in the Earth's surface water-heat cycle by influencing processes such as evaporation, infiltration, and vegetation growth and development. Remote sensing has emerged as a critical method for obtaining global-scale soil moisture data. A dual-polarization algorithm (DPA) is proposed and the dual-polarization (VV+VH) observations from the Sentinel-1 C-band synthetic aperture radar are used to generate a global soil moisture dataset with a spatial resolution of 1 km. Due to the observation mode of Sentinel-1, the temporal resolution of this dataset is higher in European and high latitude regions compared to other continents and lower latitude regions. The dataset is provided in raster format, with one set of ascending and one set of descending data for each day, and to ensure data quality, certain areas not suitable for soil moisture retrieval have been excluded, such as water bodies, permanent wetlands, frozen areas, ice and snow covered surfaces, and urban and built-up areas. Feedback and collaboration to improve the dataset is encouraged. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_968754 |
| institution | PANGAEA |
| language | en |
| publishDate | 2025 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | A global soil moisture product at 1 km resolution based on Sentinel-1 (2016-2022) Fan, Dong Zhao, Tianjie Jiang, Xiaoguang García-García, Almudena Schmidt, Toni Samaniego, Luis Attinger, Sabine Wu, Hua Jiang, Yazhen Shi, Jiancheng Fan, Lei Tang, Bohui Wagner, Wolfgang Dorigo, Wouter Gruber, Alexander Mattia, Francesco Balenzano, Anna Brocca, Luca Jagdhuber, Thomas Wigneron, Jean-Pierre Montzka, Carsten Peng, Jian 1km spatial resolution; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); CLIMATE-Pan-TPE; global; MATLAB ® - modeling and processing; Model-Data fusion for understanding Environmental Variability; MoDEV; Monitoring and Modelling Climate Change in Water, Energy and Carbon Cycles in the Pan-Third Pole Environment; SAR; Sentinel-1; soil moisture/water content Soil moisture, although a small fraction of total water content, plays a critical role in the Earth's surface water-heat cycle by influencing processes such as evaporation, infiltration, and vegetation growth and development. Remote sensing has emerged as a critical method for obtaining global-scale soil moisture data. A dual-polarization algorithm (DPA) is proposed and the dual-polarization (VV+VH) observations from the Sentinel-1 C-band synthetic aperture radar are used to generate a global soil moisture dataset with a spatial resolution of 1 km. Due to the observation mode of Sentinel-1, the temporal resolution of this dataset is higher in European and high latitude regions compared to other continents and lower latitude regions. The dataset is provided in raster format, with one set of ascending and one set of descending data for each day, and to ensure data quality, certain areas not suitable for soil moisture retrieval have been excluded, such as water bodies, permanent wetlands, frozen areas, ice and snow covered surfaces, and urban and built-up areas. Feedback and collaboration to improve the dataset is encouraged. |
| title | A global soil moisture product at 1 km resolution based on Sentinel-1 (2016-2022) |
| topic | 1km spatial resolution; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); CLIMATE-Pan-TPE; global; MATLAB ® - modeling and processing; Model-Data fusion for understanding Environmental Variability; MoDEV; Monitoring and Modelling Climate Change in Water, Energy and Carbon Cycles in the Pan-Third Pole Environment; SAR; Sentinel-1; soil moisture/water content |
| url | https://doi.org/10.1594/PANGAEA.968754 |