Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index
Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency’s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such pro...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5366 |
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| author | Xin Lu Hongli Zhao Yanyan Huang Shuangmei Liu Zelong Ma Yunzhong Jiang Wei Zhang Chuan Zhao |
| author_facet | Xin Lu Hongli Zhao Yanyan Huang Shuangmei Liu Zelong Ma Yunzhong Jiang Wei Zhang Chuan Zhao |
| author_sort | Xin Lu |
| collection | DOAJ |
| description | Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency’s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such products are low (e.g., >3 km). In recent years, using auxiliary data to downscale the spatial resolutions of SM products has been a hot research topic in the remote sensing research area. A new method, which spatially downscalesan SM product to generate a daily SM dataset at a 16 m spatial resolution based on a spatiotemporal fusion model (STFM) and modified perpendicular drought index (MPDI), was proposed in this paper. (1) First, a daily surface reflectance dataset with a 16 m spatial resolution was produced based on an STFM. (2) Then, a spatial scale conversion factor (SSCF) dataset was obtained by an MPDI dataset, which was calculated based on the dataset fused in the first step. (3) Third, a downscaled daily SM product with a 16 m spatial resolution was generated by combining the SSCF dataset and the original SM product. Five cities in southern Hebei Province were selected as study areas. Two 16 m GF6 images and nine 500 m MOD09GA images were used as auxiliary data to downscale a timeseries 25 km CCI SM dataset for nine dates from May to June 2019. A total of 151 in situ SM observations collected on 1 May, 21 May, 1 June, and 11 June were used for verification. The results indicated that the downscaled SM data with a 16 m spatial resolution had higher correlation coefficients and lower RMSE values compared with the original CCI SM data. The correlation coefficients between the downscaled SM data and in situ data ranged from 0.45 to 0.67 versus 0.33 to 0.54 for the original CCI SM data; the RMSE values ranged from 0.023 to 0.031 cm<sup>3</sup>/cm<sup>3</sup> versus 0.027 to 0.032 cm<sup>3</sup>/cm<sup>3</sup> for the original CCI SM data. The findings described in this paper can ensure effective farmland management and other practical production applications. |
| first_indexed | 2024-03-09T13:02:39Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T13:02:39Z |
| publishDate | 2022-07-01 |
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| spelling | doaj.art-4ff765a053ec4675b43b62e220efa8212023-11-30T21:52:23ZengMDPI AGSensors1424-82202022-07-012214536610.3390/s22145366Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought IndexXin Lu0Hongli Zhao1Yanyan Huang2Shuangmei Liu3Zelong Ma4Yunzhong Jiang5Wei Zhang6Chuan Zhao7Sichuan Research Institute of Water Conservancy, Chengdu 610072, ChinaDepartment of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaSchool of Software Engineering, Chengdu University of Information Technology, Chengdu 610200, ChinaSichuan Research Institute of Water Conservancy, Chengdu 610072, ChinaSichuan Research Institute of Water Conservancy, Chengdu 610072, ChinaDepartment of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaChina Electronics Technology Group Corporation (CETC), Big Data Research Institute Chengdu Branch Co., Ltd., Chengdu 610093, ChinaSichuan Research Institute of Water Conservancy, Chengdu 610072, ChinaSoil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency’s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such products are low (e.g., >3 km). In recent years, using auxiliary data to downscale the spatial resolutions of SM products has been a hot research topic in the remote sensing research area. A new method, which spatially downscalesan SM product to generate a daily SM dataset at a 16 m spatial resolution based on a spatiotemporal fusion model (STFM) and modified perpendicular drought index (MPDI), was proposed in this paper. (1) First, a daily surface reflectance dataset with a 16 m spatial resolution was produced based on an STFM. (2) Then, a spatial scale conversion factor (SSCF) dataset was obtained by an MPDI dataset, which was calculated based on the dataset fused in the first step. (3) Third, a downscaled daily SM product with a 16 m spatial resolution was generated by combining the SSCF dataset and the original SM product. Five cities in southern Hebei Province were selected as study areas. Two 16 m GF6 images and nine 500 m MOD09GA images were used as auxiliary data to downscale a timeseries 25 km CCI SM dataset for nine dates from May to June 2019. A total of 151 in situ SM observations collected on 1 May, 21 May, 1 June, and 11 June were used for verification. The results indicated that the downscaled SM data with a 16 m spatial resolution had higher correlation coefficients and lower RMSE values compared with the original CCI SM data. The correlation coefficients between the downscaled SM data and in situ data ranged from 0.45 to 0.67 versus 0.33 to 0.54 for the original CCI SM data; the RMSE values ranged from 0.023 to 0.031 cm<sup>3</sup>/cm<sup>3</sup> versus 0.027 to 0.032 cm<sup>3</sup>/cm<sup>3</sup> for the original CCI SM data. The findings described in this paper can ensure effective farmland management and other practical production applications.https://www.mdpi.com/1424-8220/22/14/5366remote sensingsoil moisture (SM)physical modelspatial downscalingmodified perpendicular drought index (MPDI)European Space Agency’s Climate Change Initiative (ESA CCI) |
| spellingShingle | Xin Lu Hongli Zhao Yanyan Huang Shuangmei Liu Zelong Ma Yunzhong Jiang Wei Zhang Chuan Zhao Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index Sensors remote sensing soil moisture (SM) physical model spatial downscaling modified perpendicular drought index (MPDI) European Space Agency’s Climate Change Initiative (ESA CCI) |
| title | Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index |
| title_full | Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index |
| title_fullStr | Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index |
| title_full_unstemmed | Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index |
| title_short | Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index |
| title_sort | generating daily soil moisture at 16 m spatial resolution using a spatiotemporal fusion model and modified perpendicular drought index |
| topic | remote sensing soil moisture (SM) physical model spatial downscaling modified perpendicular drought index (MPDI) European Space Agency’s Climate Change Initiative (ESA CCI) |
| url | https://www.mdpi.com/1424-8220/22/14/5366 |
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