Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm
© 2021 The Author(s) Due to the success of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, new satellite missions are on the horizon. The current and future missions can benefit from investigations that seek to improve retrieval algorithms that quantitat...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/132967 |
| _version_ | 1826207090343936000 |
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| author | Zhao, Tianjie Shi, Jiancheng Entekhabi, Dara Jackson, Thomas J Hu, Lu Peng, Zhiqing Yao, Panpan Li, Shangnan Kang, Chuen Siang |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Zhao, Tianjie Shi, Jiancheng Entekhabi, Dara Jackson, Thomas J Hu, Lu Peng, Zhiqing Yao, Panpan Li, Shangnan Kang, Chuen Siang |
| author_sort | Zhao, Tianjie |
| collection | MIT |
| description | © 2021 The Author(s) Due to the success of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, new satellite missions are on the horizon. The current and future missions can benefit from investigations that seek to improve retrieval algorithms that quantitatively map global soil moisture and vegetation optical depth (tau) from Earth's microwave emissions. In this study, we explore multi-angular and multi-frequency approaches for the retrieval of soil moisture and vegetation tau, considering the payload configurations of current and future satellite missions (such as the Copernicus Imaging Microwave Radiometer, the Water Cycle Observation Mission, and the Terrestrial Water Resources Satellite) using a new set of ground observations. Two ground-based microwave radiometry datasets collected in Inner Mongolia during the Soil Moisture Experiment in the Luan River from July to August 2017 (cropland) and August to September 2018 (grassland) are used for this study. The corn field, which covers an entire growth period, indicated that the degree of information increases linearly as the number of channels (in terms of the incidence angle and frequency) increases, and that the multi-frequency observations contain slightly more independent information than do the multi-angular observations under the same number of channels. The polarization difference in brightness temperature is sensitive to both soil moisture and vegetation water content, especially at L-band due to its penetrating ability. Soil moisture explains most of the variance in frequency differences of brightness temperature at adjacent frequencies (L- & C-bands, C- & X-bands), while the variance in incidence-angle differences of brightness temperature is mostly associated with the vegetation water content. A multi-channel collaborative algorithm (MCCA) is developed based on the two-component version of the omega-tau model, which utilizes information from collaborative channels expressed as an analytical form of brightness temperature at the core channel to rule out the parameters to be retrieved. Results of soil moisture retrieval show that the multi-angular approach used by the MCCA generally has a better performance, unbiased root mean square difference (ubRMSD) varying from 0.028 cm3/cm3 to 0.037 cm3/cm3, than the multi-frequency approach (ubRMSD from 0.028 cm3/cm3 to 0.089 cm3/cm3) for the corn field. This is attributed to the dependence of vegetation tau on the frequency being more significant than that on the incidence angle. Except for in the C- & X-band combination, the multi-frequency approach used by the MCCA performs better (ubRMSD from 0.018 cm3/cm3 to 0.023 cm3/cm3) than the multi-angular version (ubRMSD from 0.026 cm3/cm3 to 0.034 cm3/cm3) for the grass field due to reduced vegetation effects for this type of cover. It is affirmed that increasing the number of observation channels could make the soil moisture retrieval more robust, but might also limit the retrieval performance, as the probability that the model estimations will not match the observations is increased. This study provides new insights into the design of potential satellite missions to improve soil moisture retrieval. A satellite with simultaneous multi-angular and multi-frequency observation capabilities is highly recommended. |
| first_indexed | 2024-09-23T13:44:16Z |
| format | Article |
| id | mit-1721.1/132967 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:44:16Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1329672024-06-05T20:49:37Z Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm Zhao, Tianjie Shi, Jiancheng Entekhabi, Dara Jackson, Thomas J Hu, Lu Peng, Zhiqing Yao, Panpan Li, Shangnan Kang, Chuen Siang Massachusetts Institute of Technology. Department of Civil and Environmental Engineering © 2021 The Author(s) Due to the success of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, new satellite missions are on the horizon. The current and future missions can benefit from investigations that seek to improve retrieval algorithms that quantitatively map global soil moisture and vegetation optical depth (tau) from Earth's microwave emissions. In this study, we explore multi-angular and multi-frequency approaches for the retrieval of soil moisture and vegetation tau, considering the payload configurations of current and future satellite missions (such as the Copernicus Imaging Microwave Radiometer, the Water Cycle Observation Mission, and the Terrestrial Water Resources Satellite) using a new set of ground observations. Two ground-based microwave radiometry datasets collected in Inner Mongolia during the Soil Moisture Experiment in the Luan River from July to August 2017 (cropland) and August to September 2018 (grassland) are used for this study. The corn field, which covers an entire growth period, indicated that the degree of information increases linearly as the number of channels (in terms of the incidence angle and frequency) increases, and that the multi-frequency observations contain slightly more independent information than do the multi-angular observations under the same number of channels. The polarization difference in brightness temperature is sensitive to both soil moisture and vegetation water content, especially at L-band due to its penetrating ability. Soil moisture explains most of the variance in frequency differences of brightness temperature at adjacent frequencies (L- & C-bands, C- & X-bands), while the variance in incidence-angle differences of brightness temperature is mostly associated with the vegetation water content. A multi-channel collaborative algorithm (MCCA) is developed based on the two-component version of the omega-tau model, which utilizes information from collaborative channels expressed as an analytical form of brightness temperature at the core channel to rule out the parameters to be retrieved. Results of soil moisture retrieval show that the multi-angular approach used by the MCCA generally has a better performance, unbiased root mean square difference (ubRMSD) varying from 0.028 cm3/cm3 to 0.037 cm3/cm3, than the multi-frequency approach (ubRMSD from 0.028 cm3/cm3 to 0.089 cm3/cm3) for the corn field. This is attributed to the dependence of vegetation tau on the frequency being more significant than that on the incidence angle. Except for in the C- & X-band combination, the multi-frequency approach used by the MCCA performs better (ubRMSD from 0.018 cm3/cm3 to 0.023 cm3/cm3) than the multi-angular version (ubRMSD from 0.026 cm3/cm3 to 0.034 cm3/cm3) for the grass field due to reduced vegetation effects for this type of cover. It is affirmed that increasing the number of observation channels could make the soil moisture retrieval more robust, but might also limit the retrieval performance, as the probability that the model estimations will not match the observations is increased. This study provides new insights into the design of potential satellite missions to improve soil moisture retrieval. A satellite with simultaneous multi-angular and multi-frequency observation capabilities is highly recommended. 2021-10-13T19:17:48Z 2021-10-13T19:17:48Z 2021-02 2021-01 2021-10-13T18:21:53Z Article http://purl.org/eprint/type/JournalArticle 0034-4257 https://hdl.handle.net/1721.1/132967 Tianjie Zhao, Jiancheng Shi, Dara Entekhabi, Thomas J. Jackson, Lu Hu, Zhiqing Peng, Panpan Yao, Shangnan Li, Chuen Siang Kang, Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm, Remote Sensing of Environment, Volume 257, 2021 en 10.1016/J.RSE.2021.112321 Remote Sensing of Environment Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Elsevier |
| spellingShingle | Zhao, Tianjie Shi, Jiancheng Entekhabi, Dara Jackson, Thomas J Hu, Lu Peng, Zhiqing Yao, Panpan Li, Shangnan Kang, Chuen Siang Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm |
| title | Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm |
| title_full | Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm |
| title_fullStr | Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm |
| title_full_unstemmed | Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm |
| title_short | Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm |
| title_sort | retrievals of soil moisture and vegetation optical depth using a multi channel collaborative algorithm |
| url | https://hdl.handle.net/1721.1/132967 |
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