A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval
This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xinyi Shen [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on geoscience and remote sensing - New York, NY : IEEE, 1964, 53(2015), 7, Seite 4079-4090 |
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| Übergeordnetes Werk: |
volume:53 ; year:2015 ; number:7 ; pages:4079-4090 |
| Links: |
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| DOI / URN: |
10.1109/TGRS.2015.2390219 |
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| Katalog-ID: |
OLC1965776442 |
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| 520 | |a This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R 2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R 2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., kh RMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP. | ||
| 650 | 4 | |a Microwave theory and techniques | |
| 650 | 4 | |a semiphysical microwave surface emission model | |
| 650 | 4 | |a soil roughness effects | |
| 650 | 4 | |a Microwave radiometry | |
| 650 | 4 | |a advanced integral equation model | |
| 650 | 4 | |a soil moisture effects | |
| 650 | 4 | |a Mathematical model | |
| 650 | 4 | |a Soil Moisture Experiment 2003 campaign data | |
| 650 | 4 | |a Advanced integral equation model (AIEM) | |
| 650 | 4 | |a geophysical techniques | |
| 650 | 4 | |a total least squares method | |
| 650 | 4 | |a microwave remote sensing | |
| 650 | 4 | |a L-band-V-polarization radiometer data | |
| 650 | 4 | |a Oklahoma | |
| 650 | 4 | |a Surface roughness | |
| 650 | 4 | |a Rough surfaces | |
| 650 | 4 | |a soil moisture retrieval | |
| 650 | 4 | |a least squares approximations | |
| 650 | 4 | |a Soil moisture | |
| 650 | 4 | |a rough surface emission | |
| 650 | 4 | |a soil | |
| 700 | 0 | |a Yang Hong |4 oth | |
| 700 | 0 | |a Qiming Qin |4 oth | |
| 700 | 1 | |a Basara, Jeffrey B |4 oth | |
| 700 | 0 | |a Kebiao Mao |4 oth | |
| 700 | 1 | |a Wang, D |4 oth | |
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10.1109/TGRS.2015.2390219 doi PQ20160617 (DE-627)OLC1965776442 (DE-599)GBVOLC1965776442 (PRQ)c1551-55b3f5e2d56cb81e38354b87a9f3d5e3e99d5d568505151bb8d233d0548cc8980 (KEY)0048677920150000053000704079semiphysicalmicrowavesurfaceemissionmodelforsoilmo DE-627 ger DE-627 rakwb eng 620 550 DNB Xinyi Shen verfasserin aut A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R 2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R 2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., kh RMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP. Microwave theory and techniques semiphysical microwave surface emission model soil roughness effects Microwave radiometry advanced integral equation model soil moisture effects Mathematical model Soil Moisture Experiment 2003 campaign data Advanced integral equation model (AIEM) geophysical techniques total least squares method microwave remote sensing L-band-V-polarization radiometer data Oklahoma Surface roughness Rough surfaces soil moisture retrieval least squares approximations Soil moisture rough surface emission soil Yang Hong oth Qiming Qin oth Basara, Jeffrey B oth Kebiao Mao oth Wang, D oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 7, Seite 4079-4090 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:7 pages:4079-4090 http://dx.doi.org/10.1109/TGRS.2015.2390219 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7050308 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 7 4079-4090 |
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10.1109/TGRS.2015.2390219 doi PQ20160617 (DE-627)OLC1965776442 (DE-599)GBVOLC1965776442 (PRQ)c1551-55b3f5e2d56cb81e38354b87a9f3d5e3e99d5d568505151bb8d233d0548cc8980 (KEY)0048677920150000053000704079semiphysicalmicrowavesurfaceemissionmodelforsoilmo DE-627 ger DE-627 rakwb eng 620 550 DNB Xinyi Shen verfasserin aut A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R 2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R 2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., kh RMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP. Microwave theory and techniques semiphysical microwave surface emission model soil roughness effects Microwave radiometry advanced integral equation model soil moisture effects Mathematical model Soil Moisture Experiment 2003 campaign data Advanced integral equation model (AIEM) geophysical techniques total least squares method microwave remote sensing L-band-V-polarization radiometer data Oklahoma Surface roughness Rough surfaces soil moisture retrieval least squares approximations Soil moisture rough surface emission soil Yang Hong oth Qiming Qin oth Basara, Jeffrey B oth Kebiao Mao oth Wang, D oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 7, Seite 4079-4090 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:7 pages:4079-4090 http://dx.doi.org/10.1109/TGRS.2015.2390219 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7050308 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 7 4079-4090 |
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10.1109/TGRS.2015.2390219 doi PQ20160617 (DE-627)OLC1965776442 (DE-599)GBVOLC1965776442 (PRQ)c1551-55b3f5e2d56cb81e38354b87a9f3d5e3e99d5d568505151bb8d233d0548cc8980 (KEY)0048677920150000053000704079semiphysicalmicrowavesurfaceemissionmodelforsoilmo DE-627 ger DE-627 rakwb eng 620 550 DNB Xinyi Shen verfasserin aut A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R 2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R 2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., kh RMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP. Microwave theory and techniques semiphysical microwave surface emission model soil roughness effects Microwave radiometry advanced integral equation model soil moisture effects Mathematical model Soil Moisture Experiment 2003 campaign data Advanced integral equation model (AIEM) geophysical techniques total least squares method microwave remote sensing L-band-V-polarization radiometer data Oklahoma Surface roughness Rough surfaces soil moisture retrieval least squares approximations Soil moisture rough surface emission soil Yang Hong oth Qiming Qin oth Basara, Jeffrey B oth Kebiao Mao oth Wang, D oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 7, Seite 4079-4090 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:7 pages:4079-4090 http://dx.doi.org/10.1109/TGRS.2015.2390219 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7050308 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 7 4079-4090 |
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10.1109/TGRS.2015.2390219 doi PQ20160617 (DE-627)OLC1965776442 (DE-599)GBVOLC1965776442 (PRQ)c1551-55b3f5e2d56cb81e38354b87a9f3d5e3e99d5d568505151bb8d233d0548cc8980 (KEY)0048677920150000053000704079semiphysicalmicrowavesurfaceemissionmodelforsoilmo DE-627 ger DE-627 rakwb eng 620 550 DNB Xinyi Shen verfasserin aut A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R 2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R 2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., kh RMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP. Microwave theory and techniques semiphysical microwave surface emission model soil roughness effects Microwave radiometry advanced integral equation model soil moisture effects Mathematical model Soil Moisture Experiment 2003 campaign data Advanced integral equation model (AIEM) geophysical techniques total least squares method microwave remote sensing L-band-V-polarization radiometer data Oklahoma Surface roughness Rough surfaces soil moisture retrieval least squares approximations Soil moisture rough surface emission soil Yang Hong oth Qiming Qin oth Basara, Jeffrey B oth Kebiao Mao oth Wang, D oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 7, Seite 4079-4090 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:7 pages:4079-4090 http://dx.doi.org/10.1109/TGRS.2015.2390219 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7050308 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 7 4079-4090 |
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10.1109/TGRS.2015.2390219 doi PQ20160617 (DE-627)OLC1965776442 (DE-599)GBVOLC1965776442 (PRQ)c1551-55b3f5e2d56cb81e38354b87a9f3d5e3e99d5d568505151bb8d233d0548cc8980 (KEY)0048677920150000053000704079semiphysicalmicrowavesurfaceemissionmodelforsoilmo DE-627 ger DE-627 rakwb eng 620 550 DNB Xinyi Shen verfasserin aut A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R 2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R 2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., kh RMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP. Microwave theory and techniques semiphysical microwave surface emission model soil roughness effects Microwave radiometry advanced integral equation model soil moisture effects Mathematical model Soil Moisture Experiment 2003 campaign data Advanced integral equation model (AIEM) geophysical techniques total least squares method microwave remote sensing L-band-V-polarization radiometer data Oklahoma Surface roughness Rough surfaces soil moisture retrieval least squares approximations Soil moisture rough surface emission soil Yang Hong oth Qiming Qin oth Basara, Jeffrey B oth Kebiao Mao oth Wang, D oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 7, Seite 4079-4090 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:7 pages:4079-4090 http://dx.doi.org/10.1109/TGRS.2015.2390219 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7050308 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 7 4079-4090 |
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Xinyi Shen |
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Xinyi Shen ddc 620 misc Microwave theory and techniques misc semiphysical microwave surface emission model misc soil roughness effects misc Microwave radiometry misc advanced integral equation model misc soil moisture effects misc Mathematical model misc Soil Moisture Experiment 2003 campaign data misc Advanced integral equation model (AIEM) misc geophysical techniques misc total least squares method misc microwave remote sensing misc L-band-V-polarization radiometer data misc Oklahoma misc Surface roughness misc Rough surfaces misc soil moisture retrieval misc least squares approximations misc Soil moisture misc rough surface emission misc soil A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval |
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620 550 DNB A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval Microwave theory and techniques semiphysical microwave surface emission model soil roughness effects Microwave radiometry advanced integral equation model soil moisture effects Mathematical model Soil Moisture Experiment 2003 campaign data Advanced integral equation model (AIEM) geophysical techniques total least squares method microwave remote sensing L-band-V-polarization radiometer data Oklahoma Surface roughness Rough surfaces soil moisture retrieval least squares approximations Soil moisture rough surface emission soil |
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ddc 620 misc Microwave theory and techniques misc semiphysical microwave surface emission model misc soil roughness effects misc Microwave radiometry misc advanced integral equation model misc soil moisture effects misc Mathematical model misc Soil Moisture Experiment 2003 campaign data misc Advanced integral equation model (AIEM) misc geophysical techniques misc total least squares method misc microwave remote sensing misc L-band-V-polarization radiometer data misc Oklahoma misc Surface roughness misc Rough surfaces misc soil moisture retrieval misc least squares approximations misc Soil moisture misc rough surface emission misc soil |
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A Semiphysical Microwave Surface Emission Model for Soil Moisture Retrieval |
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This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R 2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R 2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., kh RMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP. |
| abstractGer |
This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R 2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R 2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., kh RMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP. |
| abstract_unstemmed |
This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and R 2 of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and R 2 of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., kh RMS <; 3, of the AIEM narrows. Furthermore, the model can be readily extended to broader regions than the investigated case study on field scale in this paper by nesting the model in the τ - ω model and using satellite data from SMOS or SMAP. |
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