Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data
Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature veg...
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| Autor*in: |
Kwon, Young-Joo [verfasserIn] Ryu, Sumin [verfasserIn] Cho, Jaeil [verfasserIn] Lee, Yang-Won [verfasserIn] Park, No-Wook [verfasserIn] Chung, Chu-Yong [verfasserIn] Hong, Sungwook [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Asia-Pacific journal of atmospheric sciences - Berlin : Springer, 2010, 56(2020), 2 vom: 21. Jan., Seite 275-289 |
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| Übergeordnetes Werk: |
volume:56 ; year:2020 ; number:2 ; day:21 ; month:01 ; pages:275-289 |
| Links: |
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| DOI / URN: |
10.1007/s13143-020-00174-6 |
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| Katalog-ID: |
SPR039698807 |
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| 520 | |a Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products. | ||
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| 650 | 4 | |a TVDI |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a MODIS |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Elevation effect |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a GLDAS |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ryu, Sumin |e verfasserin |4 aut | |
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| 700 | 1 | |a Park, No-Wook |e verfasserin |4 aut | |
| 700 | 1 | |a Chung, Chu-Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Sungwook |e verfasserin |4 aut | |
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10.1007/s13143-020-00174-6 doi (DE-627)SPR039698807 (SPR)s13143-020-00174-6-e DE-627 ger DE-627 rakwb eng 550 ASE Kwon, Young-Joo verfasserin aut Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products. Soil moisture (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Elevation effect (dpeaa)DE-He213 GLDAS (dpeaa)DE-He213 Ryu, Sumin verfasserin aut Cho, Jaeil verfasserin aut Lee, Yang-Won verfasserin aut Park, No-Wook verfasserin aut Chung, Chu-Yong verfasserin aut Hong, Sungwook verfasserin aut Enthalten in Asia-Pacific journal of atmospheric sciences Berlin : Springer, 2010 56(2020), 2 vom: 21. Jan., Seite 275-289 (DE-627)623178869 (DE-600)2545937-5 1976-7951 nnns volume:56 year:2020 number:2 day:21 month:01 pages:275-289 https://dx.doi.org/10.1007/s13143-020-00174-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2020 2 21 01 275-289 |
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10.1007/s13143-020-00174-6 doi (DE-627)SPR039698807 (SPR)s13143-020-00174-6-e DE-627 ger DE-627 rakwb eng 550 ASE Kwon, Young-Joo verfasserin aut Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products. Soil moisture (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Elevation effect (dpeaa)DE-He213 GLDAS (dpeaa)DE-He213 Ryu, Sumin verfasserin aut Cho, Jaeil verfasserin aut Lee, Yang-Won verfasserin aut Park, No-Wook verfasserin aut Chung, Chu-Yong verfasserin aut Hong, Sungwook verfasserin aut Enthalten in Asia-Pacific journal of atmospheric sciences Berlin : Springer, 2010 56(2020), 2 vom: 21. Jan., Seite 275-289 (DE-627)623178869 (DE-600)2545937-5 1976-7951 nnns volume:56 year:2020 number:2 day:21 month:01 pages:275-289 https://dx.doi.org/10.1007/s13143-020-00174-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2020 2 21 01 275-289 |
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10.1007/s13143-020-00174-6 doi (DE-627)SPR039698807 (SPR)s13143-020-00174-6-e DE-627 ger DE-627 rakwb eng 550 ASE Kwon, Young-Joo verfasserin aut Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products. Soil moisture (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Elevation effect (dpeaa)DE-He213 GLDAS (dpeaa)DE-He213 Ryu, Sumin verfasserin aut Cho, Jaeil verfasserin aut Lee, Yang-Won verfasserin aut Park, No-Wook verfasserin aut Chung, Chu-Yong verfasserin aut Hong, Sungwook verfasserin aut Enthalten in Asia-Pacific journal of atmospheric sciences Berlin : Springer, 2010 56(2020), 2 vom: 21. Jan., Seite 275-289 (DE-627)623178869 (DE-600)2545937-5 1976-7951 nnns volume:56 year:2020 number:2 day:21 month:01 pages:275-289 https://dx.doi.org/10.1007/s13143-020-00174-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2020 2 21 01 275-289 |
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10.1007/s13143-020-00174-6 doi (DE-627)SPR039698807 (SPR)s13143-020-00174-6-e DE-627 ger DE-627 rakwb eng 550 ASE Kwon, Young-Joo verfasserin aut Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products. Soil moisture (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Elevation effect (dpeaa)DE-He213 GLDAS (dpeaa)DE-He213 Ryu, Sumin verfasserin aut Cho, Jaeil verfasserin aut Lee, Yang-Won verfasserin aut Park, No-Wook verfasserin aut Chung, Chu-Yong verfasserin aut Hong, Sungwook verfasserin aut Enthalten in Asia-Pacific journal of atmospheric sciences Berlin : Springer, 2010 56(2020), 2 vom: 21. Jan., Seite 275-289 (DE-627)623178869 (DE-600)2545937-5 1976-7951 nnns volume:56 year:2020 number:2 day:21 month:01 pages:275-289 https://dx.doi.org/10.1007/s13143-020-00174-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2020 2 21 01 275-289 |
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10.1007/s13143-020-00174-6 doi (DE-627)SPR039698807 (SPR)s13143-020-00174-6-e DE-627 ger DE-627 rakwb eng 550 ASE Kwon, Young-Joo verfasserin aut Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products. Soil moisture (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Elevation effect (dpeaa)DE-He213 GLDAS (dpeaa)DE-He213 Ryu, Sumin verfasserin aut Cho, Jaeil verfasserin aut Lee, Yang-Won verfasserin aut Park, No-Wook verfasserin aut Chung, Chu-Yong verfasserin aut Hong, Sungwook verfasserin aut Enthalten in Asia-Pacific journal of atmospheric sciences Berlin : Springer, 2010 56(2020), 2 vom: 21. Jan., Seite 275-289 (DE-627)623178869 (DE-600)2545937-5 1976-7951 nnns volume:56 year:2020 number:2 day:21 month:01 pages:275-289 https://dx.doi.org/10.1007/s13143-020-00174-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2020 2 21 01 275-289 |
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Enthalten in Asia-Pacific journal of atmospheric sciences 56(2020), 2 vom: 21. Jan., Seite 275-289 volume:56 year:2020 number:2 day:21 month:01 pages:275-289 |
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Enthalten in Asia-Pacific journal of atmospheric sciences 56(2020), 2 vom: 21. Jan., Seite 275-289 volume:56 year:2020 number:2 day:21 month:01 pages:275-289 |
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Soil moisture TVDI MODIS Elevation effect GLDAS |
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Asia-Pacific journal of atmospheric sciences |
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Kwon, Young-Joo @@aut@@ Ryu, Sumin @@aut@@ Cho, Jaeil @@aut@@ Lee, Yang-Won @@aut@@ Park, No-Wook @@aut@@ Chung, Chu-Yong @@aut@@ Hong, Sungwook @@aut@@ |
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Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. 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Kwon, Young-Joo |
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Kwon, Young-Joo ddc 550 misc Soil moisture misc TVDI misc MODIS misc Elevation effect misc GLDAS Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data |
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550 ASE Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data Soil moisture (dpeaa)DE-He213 TVDI (dpeaa)DE-He213 MODIS (dpeaa)DE-He213 Elevation effect (dpeaa)DE-He213 GLDAS (dpeaa)DE-He213 |
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Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data |
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10.1007/s13143-020-00174-6 |
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infrared soil moisture retrieval algorithm using temperature-vegetation dryness index and moderate resolution imaging spectroradiometer data |
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Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data |
| abstract |
Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products. |
| abstractGer |
Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products. |
| abstract_unstemmed |
Abstract Most infrared satellite remote sensors have a higher spatial resolution than microwave satellite sensors. Microwave satellite remote sensing has proven successful for the retrieval of soil moisture (SM) information. In this study, we propose a SM retrieval algorithm based on temperature vegetation dryness index (TVDI), a function of land surface temperature (LST), and the normalized difference vegetative index (NDVI) provided by Moderate Resolution Imaging Spectroradiometer (MODIS) data. We implemented the LST correction with elevation effect. Conversion relationships between TVDI and SM content for a variety of land types were obtained from spatial and temporal collocation of TVDI and Global Land Data Assimilation System (GLDAS) SM content for 2014. From the comparison with the GLDAS SM for 2015, the proposed TVDI-based SM algorithm showed good performance with CC = 0.609, bias = −0.035 $ m^{3} $/$ m^{3} $, and root-mean-square-error (RMSE) = 0.047 $ m^{3} $/$ m^{3} $, while the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) SMs present CC = 0.637 and 0.741, bias = 0.042 and 0.010 $ m^{3} $/$ m^{3} $, and RMSE = 0.152 and 0.103 $ m^{3} $/$ m^{3} $, respectively. For the in situ SM measurements of the Korea Rural Development Administration (RDA), the proposed TVDI-based SM algorithm yielded CC = 0.556, bias = −0.039 $ m^{3} $/$ m^{3} $, and RMSE = 0.051 $ m^{3} $/$ m^{3} $ excluding the winter season. Consequently, the proposed SM algorithm could contribute to complementing the low spatial resolutions of microwave satellite SM products and low temporal resolutions of GLDAS SM products. |
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Infrared Soil Moisture Retrieval Algorithm Using Temperature-Vegetation Dryness Index and Moderate Resolution Imaging Spectroradiometer Data |
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https://dx.doi.org/10.1007/s13143-020-00174-6 |
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Ryu, Sumin Cho, Jaeil Lee, Yang-Won Park, No-Wook Chung, Chu-Yong Hong, Sungwook |
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| score |
7.400589 |