Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping
Soil moisture is one of the core hydrological and climate variables that crucially influences water and energy budgets. The spatial resolution of available soil moisture products is generally coarser than 25 km, which limits their hydro-meteorological and eco-hydrological applications and the manage...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/14/3373 |
| _version_ | 1797443957753380864 |
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| author | Zebin Zhao Rui Jin Jian Kang Chunfeng Ma Weizhen Wang |
| author_facet | Zebin Zhao Rui Jin Jian Kang Chunfeng Ma Weizhen Wang |
| author_sort | Zebin Zhao |
| collection | DOAJ |
| description | Soil moisture is one of the core hydrological and climate variables that crucially influences water and energy budgets. The spatial resolution of available soil moisture products is generally coarser than 25 km, which limits their hydro-meteorological and eco-hydrological applications and the management of water resources at watershed and agricultural scales. A feasible solution to overcome these limitations is to downscale coarse soil moisture products with the support of higher-resolution spatial information. Although many auxiliary variables have been used for this purpose, few studies have analyzed their applicability and effectiveness in arid regions. To this end, we comprehensively evaluated four commonly used auxiliary variables, including NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature), TVDI (Temperature Vegetation Dryness Index), and SEE (Soil Evaporative Efficiency), against ground-based soil moisture observations during the vegetation growing season in the Heihe River Basin, China. Performance metrics indicated that SEE is most sensitive (R<sup>2</sup> ≥ 0.67) to soil moisture because it is controlled by soil evaporation limited by the available soil moisture. The similarity of spatial patterns also showed that SEE best captures soil moisture changes, with the STD (standard deviation) of the HD (Hausdorff Distance) less than 0.058 when compared with PLMR (Polarimetric L-band Multi-beam Radiometer) soil moisture products. In addition, soil moisture was mapped by RF (Random Forests) using both single auxiliary variables and 11 types of multiple auxiliary variable combinations. SEE was found to be the best auxiliary variable for scaling and mapping soil moisture with accuracy of 0.035 cm<sup>3</sup>/cm<sup>3</sup>. Among the multiple auxiliary variables, the combination of LST, NDVI, and SEE was found to best enhance the scaling and mapping accuracy of soil moisture with 0.034 cm<sup>3</sup>/cm<sup>3</sup>. |
| first_indexed | 2024-03-09T13:05:37Z |
| format | Article |
| id | doaj.art-4f8d48b7b4b442fbbf6496b28d709c85 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T13:05:37Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4f8d48b7b4b442fbbf6496b28d709c852023-11-30T21:49:06ZengMDPI AGRemote Sensing2072-42922022-07-011414337310.3390/rs14143373Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and MappingZebin Zhao0Rui Jin1Jian Kang2Chunfeng Ma3Weizhen Wang4Heihe Remote Sensing Experimental Research Station, Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSoil moisture is one of the core hydrological and climate variables that crucially influences water and energy budgets. The spatial resolution of available soil moisture products is generally coarser than 25 km, which limits their hydro-meteorological and eco-hydrological applications and the management of water resources at watershed and agricultural scales. A feasible solution to overcome these limitations is to downscale coarse soil moisture products with the support of higher-resolution spatial information. Although many auxiliary variables have been used for this purpose, few studies have analyzed their applicability and effectiveness in arid regions. To this end, we comprehensively evaluated four commonly used auxiliary variables, including NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature), TVDI (Temperature Vegetation Dryness Index), and SEE (Soil Evaporative Efficiency), against ground-based soil moisture observations during the vegetation growing season in the Heihe River Basin, China. Performance metrics indicated that SEE is most sensitive (R<sup>2</sup> ≥ 0.67) to soil moisture because it is controlled by soil evaporation limited by the available soil moisture. The similarity of spatial patterns also showed that SEE best captures soil moisture changes, with the STD (standard deviation) of the HD (Hausdorff Distance) less than 0.058 when compared with PLMR (Polarimetric L-band Multi-beam Radiometer) soil moisture products. In addition, soil moisture was mapped by RF (Random Forests) using both single auxiliary variables and 11 types of multiple auxiliary variable combinations. SEE was found to be the best auxiliary variable for scaling and mapping soil moisture with accuracy of 0.035 cm<sup>3</sup>/cm<sup>3</sup>. Among the multiple auxiliary variables, the combination of LST, NDVI, and SEE was found to best enhance the scaling and mapping accuracy of soil moisture with 0.034 cm<sup>3</sup>/cm<sup>3</sup>.https://www.mdpi.com/2072-4292/14/14/3373soil moistureauxiliary variableHausdorff DistanceRandom Forestsscalingmapping |
| spellingShingle | Zebin Zhao Rui Jin Jian Kang Chunfeng Ma Weizhen Wang Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping Remote Sensing soil moisture auxiliary variable Hausdorff Distance Random Forests scaling mapping |
| title | Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping |
| title_full | Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping |
| title_fullStr | Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping |
| title_full_unstemmed | Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping |
| title_short | Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping |
| title_sort | using of remote sensing based auxiliary variables for soil moisture scaling and mapping |
| topic | soil moisture auxiliary variable Hausdorff Distance Random Forests scaling mapping |
| url | https://www.mdpi.com/2072-4292/14/14/3373 |
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