DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP
Soil moisture as a variable parameter of the Earth's surface plays a very important role in many applications such as meteorology, climatology, water resources management and hydrology. Therefore, access to soil surface moisture product with high spatial resolution is very important. Due to the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-01-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W2-2022/53/2023/isprs-archives-XLVIII-4-W2-2022-53-2023.pdf |
| _version_ | 1797954266297532416 |
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| author | F. Imanpour M. Dehghani M. Yazdi |
| author_facet | F. Imanpour M. Dehghani M. Yazdi |
| author_sort | F. Imanpour |
| collection | DOAJ |
| description | Soil moisture as a variable parameter of the Earth's surface plays a very important role in many applications such as meteorology, climatology, water resources management and hydrology. Therefore, access to soil surface moisture product with high spatial resolution is very important. Due to the lack of access to soil moisture information with high spatial resolution, the main goal in this article is to downscale the existing soil moisture products and improve their spatial resolution into 1 square kilometer. For this purpose, two methods based on regression and neural network have been used for downscaling the 3 km soil moisture products of SMAP satellite. To this end, other available satellite data and products including various combinations of land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperature in different polarizations of (TBH and TBV) passive microwave sensor data, digital elevation model (DEM) and short-wavelength infrared (SWIR) data of MODIS and Sentinel 3 have been used. In this study, two regions in the north and south of Iran, Golestan and Fars provinces, have been examined, due to the lack of ground measurements of soil moisture, the SMAP product with the resolution of 1 km which has been already downscaled by exploiting the Sentinel-1 radar data, was used to evaluate the results. The evaluation results in Golestan and Fars provinces showed the correlation coefficient of 0.82 to 0.93 and 0.72 to 0.77, respectively, and the average percentage of absolute error in both regression and neural network methods was less than 21 to 30 and 42 to 46 percent. |
| first_indexed | 2024-04-10T23:14:58Z |
| format | Article |
| id | doaj.art-e6e5ba8e101746318c6efba7248e9bbd |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-10T23:14:58Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-e6e5ba8e101746318c6efba7248e9bbd2023-01-12T21:56:08ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-01-01XLVIII-4-W2-2022535810.5194/isprs-archives-XLVIII-4-W2-2022-53-2023DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAPF. Imanpour0M. Dehghani1M. Yazdi2Dept. of Civil and Environmental Engineering, School of Eng., Shiraz University, Shiraz, IranDept. of Civil and Environmental Engineering, School of Eng., Shiraz University, Shiraz, IranDept. of Electrical and Computer Engineering, School of Eng., Shiraz University, Shiraz, IranSoil moisture as a variable parameter of the Earth's surface plays a very important role in many applications such as meteorology, climatology, water resources management and hydrology. Therefore, access to soil surface moisture product with high spatial resolution is very important. Due to the lack of access to soil moisture information with high spatial resolution, the main goal in this article is to downscale the existing soil moisture products and improve their spatial resolution into 1 square kilometer. For this purpose, two methods based on regression and neural network have been used for downscaling the 3 km soil moisture products of SMAP satellite. To this end, other available satellite data and products including various combinations of land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperature in different polarizations of (TBH and TBV) passive microwave sensor data, digital elevation model (DEM) and short-wavelength infrared (SWIR) data of MODIS and Sentinel 3 have been used. In this study, two regions in the north and south of Iran, Golestan and Fars provinces, have been examined, due to the lack of ground measurements of soil moisture, the SMAP product with the resolution of 1 km which has been already downscaled by exploiting the Sentinel-1 radar data, was used to evaluate the results. The evaluation results in Golestan and Fars provinces showed the correlation coefficient of 0.82 to 0.93 and 0.72 to 0.77, respectively, and the average percentage of absolute error in both regression and neural network methods was less than 21 to 30 and 42 to 46 percent.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W2-2022/53/2023/isprs-archives-XLVIII-4-W2-2022-53-2023.pdf |
| spellingShingle | F. Imanpour M. Dehghani M. Yazdi DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP |
| title_full | DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP |
| title_fullStr | DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP |
| title_full_unstemmed | DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP |
| title_short | DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP |
| title_sort | downscaling of soil moisture product of smap |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W2-2022/53/2023/isprs-archives-XLVIII-4-W2-2022-53-2023.pdf |
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