Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture
<p>The National Aeronautics and Space Administration (NASA) Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal (<span class="inline-formula"><i>T</i>&...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-09-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/25/5029/2021/hess-25-5029-2021.pdf |
| _version_ | 1819115670518890496 |
|---|---|
| author | B. Li B. Li S. P. Good S. P. Good |
| author_facet | B. Li B. Li S. P. Good S. P. Good |
| author_sort | B. Li |
| collection | DOAJ |
| description | <p>The National Aeronautics and Space Administration (NASA) Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal (<span class="inline-formula"><i>T</i><sub>Bh</sub></span>) and vertical (<span class="inline-formula"><i>T</i><sub>Bv</sub></span>) polarized microwave brightness temperatures through a modeled mechanistic relationship between vegetation opacity, surface scattering albedo, and soil effective temperature (<span class="inline-formula"><i>T</i><sub>eff</sub></span>). Although this model has been validated against in situ soil moisture, there is a lack of systematic characterization of where and why SMAP estimates deviate from the in situ observations. Here, we assess how the information content of in situ soil moisture observations from the US Climate Reference Network contrasts with (1) the information contained within raw SMAP observations (i.e., “informational random uncertainty”) derived from <span class="inline-formula"><i>T</i><sub>Bh</sub></span>, <span class="inline-formula"><i>T</i><sub>Bv</sub></span>, and <span class="inline-formula"><i>T</i><sub>eff</sub></span> themselves and with (2) the information contained in SMAP's dual-channel algorithm (DCA) soil moisture estimates (i.e., “informational model uncertainty”) derived from the model's inherent structure and parameterizations. The results show that, on average, 80 % of the information in the in situ soil moisture is unexplained by SMAP DCA soil moisture estimates. Loss of information in the DCA modeling process contributes 35 % of the unexplained information, while the remainder is induced by a lack of additional explanatory power within <span class="inline-formula"><i>T</i><sub>Bh</sub></span>, <span class="inline-formula"><i>T</i><sub>Bv</sub></span>, and <span class="inline-formula"><i>T</i><sub>eff</sub></span>. Overall, retrieval quality of SMAP DCA soil moisture, denoted as the Pearson correlation coefficient between SMAP DCA soil moisture and in situ soil moisture, is negatively correlated with the informational uncertainties, with slight differences across different land covers. The informational model uncertainty (Pearson correlation of <span class="inline-formula">−0.59</span>) was found to be more influential than the informational random uncertainty (Pearson correlation of <span class="inline-formula">−0.34</span>), suggesting that the poor performance of SMAP DCA at some locations is driven by model parameterization and/or structure and not underlying satellite measurements of <span class="inline-formula"><i>T</i><sub>Bh</sub></span> and <span class="inline-formula"><i>T</i><sub>Bv</sub></span>. A decomposition of mutual information between <span class="inline-formula"><i>T</i><sub>Bh</sub></span>, <span class="inline-formula"><i>T</i><sub>Bv</sub></span>, and DCA soil moisture shows that on average 58 % of information provided by <span class="inline-formula"><i>T</i><sub>Bh</sub></span> and <span class="inline-formula"><i>T</i><sub>Bv</sub></span> to DCA estimates is redundant. The amount of information redundantly and synergistically provided by <span class="inline-formula"><i>T</i><sub>Bh</sub></span> and <span class="inline-formula"><i>T</i><sub>Bv</sub></span> was found to be closely related (Pearson correlations of 0.79 and <span class="inline-formula">−0.82</span>, respectively) to the retrieval quality of SMAP DCA. <span class="inline-formula"><i>T</i><sub>Bh</sub></span> and <span class="inline-formula"><i>T</i><sub>Bv</sub></span> tend to contribute large redundant information to DCA estimates under surfaces or conditions where DCA makes better retrievals. This study provides a baseline approach that can also be applied to evaluate other remote sensing models and understand informational loss as satellite retrievals are translated to end-user products.</p> |
| first_indexed | 2024-12-22T05:04:53Z |
| format | Article |
| id | doaj.art-ccc9ecb9ae2542208853dcab5b61acce |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-22T05:04:53Z |
| publishDate | 2021-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-ccc9ecb9ae2542208853dcab5b61acce2022-12-21T18:38:08ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382021-09-01255029504510.5194/hess-25-5029-2021Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moistureB. Li0B. Li1S. P. Good2S. P. Good3Department of Biological & Ecological Engineering, Oregon State University, Corvallis, OR 97331, USAWater Resources Graduate Program, Oregon State University, Corvallis, OR 97331, USADepartment of Biological & Ecological Engineering, Oregon State University, Corvallis, OR 97331, USAWater Resources Graduate Program, Oregon State University, Corvallis, OR 97331, USA<p>The National Aeronautics and Space Administration (NASA) Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal (<span class="inline-formula"><i>T</i><sub>Bh</sub></span>) and vertical (<span class="inline-formula"><i>T</i><sub>Bv</sub></span>) polarized microwave brightness temperatures through a modeled mechanistic relationship between vegetation opacity, surface scattering albedo, and soil effective temperature (<span class="inline-formula"><i>T</i><sub>eff</sub></span>). Although this model has been validated against in situ soil moisture, there is a lack of systematic characterization of where and why SMAP estimates deviate from the in situ observations. Here, we assess how the information content of in situ soil moisture observations from the US Climate Reference Network contrasts with (1) the information contained within raw SMAP observations (i.e., “informational random uncertainty”) derived from <span class="inline-formula"><i>T</i><sub>Bh</sub></span>, <span class="inline-formula"><i>T</i><sub>Bv</sub></span>, and <span class="inline-formula"><i>T</i><sub>eff</sub></span> themselves and with (2) the information contained in SMAP's dual-channel algorithm (DCA) soil moisture estimates (i.e., “informational model uncertainty”) derived from the model's inherent structure and parameterizations. The results show that, on average, 80 % of the information in the in situ soil moisture is unexplained by SMAP DCA soil moisture estimates. Loss of information in the DCA modeling process contributes 35 % of the unexplained information, while the remainder is induced by a lack of additional explanatory power within <span class="inline-formula"><i>T</i><sub>Bh</sub></span>, <span class="inline-formula"><i>T</i><sub>Bv</sub></span>, and <span class="inline-formula"><i>T</i><sub>eff</sub></span>. Overall, retrieval quality of SMAP DCA soil moisture, denoted as the Pearson correlation coefficient between SMAP DCA soil moisture and in situ soil moisture, is negatively correlated with the informational uncertainties, with slight differences across different land covers. The informational model uncertainty (Pearson correlation of <span class="inline-formula">−0.59</span>) was found to be more influential than the informational random uncertainty (Pearson correlation of <span class="inline-formula">−0.34</span>), suggesting that the poor performance of SMAP DCA at some locations is driven by model parameterization and/or structure and not underlying satellite measurements of <span class="inline-formula"><i>T</i><sub>Bh</sub></span> and <span class="inline-formula"><i>T</i><sub>Bv</sub></span>. A decomposition of mutual information between <span class="inline-formula"><i>T</i><sub>Bh</sub></span>, <span class="inline-formula"><i>T</i><sub>Bv</sub></span>, and DCA soil moisture shows that on average 58 % of information provided by <span class="inline-formula"><i>T</i><sub>Bh</sub></span> and <span class="inline-formula"><i>T</i><sub>Bv</sub></span> to DCA estimates is redundant. The amount of information redundantly and synergistically provided by <span class="inline-formula"><i>T</i><sub>Bh</sub></span> and <span class="inline-formula"><i>T</i><sub>Bv</sub></span> was found to be closely related (Pearson correlations of 0.79 and <span class="inline-formula">−0.82</span>, respectively) to the retrieval quality of SMAP DCA. <span class="inline-formula"><i>T</i><sub>Bh</sub></span> and <span class="inline-formula"><i>T</i><sub>Bv</sub></span> tend to contribute large redundant information to DCA estimates under surfaces or conditions where DCA makes better retrievals. This study provides a baseline approach that can also be applied to evaluate other remote sensing models and understand informational loss as satellite retrievals are translated to end-user products.</p>https://hess.copernicus.org/articles/25/5029/2021/hess-25-5029-2021.pdf |
| spellingShingle | B. Li B. Li S. P. Good S. P. Good Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture Hydrology and Earth System Sciences |
| title | Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture |
| title_full | Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture |
| title_fullStr | Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture |
| title_full_unstemmed | Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture |
| title_short | Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture |
| title_sort | information based uncertainty decomposition in dual channel microwave remote sensing of soil moisture |
| url | https://hess.copernicus.org/articles/25/5029/2021/hess-25-5029-2021.pdf |
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