Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia
Snow is an important component of the terrestrial freshwater budget in high mountain Asia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despite the importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equiv...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-08-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/article/10.3389/feart.2019.00212/full |
| _version_ | 1818256279410311168 |
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| author | Jawairia A. Ahmad Barton A. Forman Yonghwan Kwon Yonghwan Kwon Yonghwan Kwon |
| author_facet | Jawairia A. Ahmad Barton A. Forman Yonghwan Kwon Yonghwan Kwon Yonghwan Kwon |
| author_sort | Jawairia A. Ahmad |
| collection | DOAJ |
| description | Snow is an important component of the terrestrial freshwater budget in high mountain Asia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despite the importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (ΔTb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASA Land Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMs serve as a nonlinear map between the geophysical space (modeled in Noah-MP) and the observation space (ΔTb as measured by the radiometer). Advanced Microwave Scanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm. |
| first_indexed | 2024-12-12T17:25:14Z |
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| id | doaj.art-cd1c3849f3da4b4eae9e282baf0e24d8 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-12-12T17:25:14Z |
| publishDate | 2019-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-cd1c3849f3da4b4eae9e282baf0e24d82022-12-22T00:17:33ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632019-08-01710.3389/feart.2019.00212461371Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain AsiaJawairia A. Ahmad0Barton A. Forman1Yonghwan Kwon2Yonghwan Kwon3Yonghwan Kwon4Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, United StatesDepartment of Civil and Environmental Engineering, University of Maryland, College Park, MD, United StatesDepartment of Civil and Environmental Engineering, University of Maryland, College Park, MD, United StatesHydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United StatesEarth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United StatesSnow is an important component of the terrestrial freshwater budget in high mountain Asia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despite the importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (ΔTb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASA Land Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMs serve as a nonlinear map between the geophysical space (modeled in Noah-MP) and the observation space (ΔTb as measured by the radiometer). Advanced Microwave Scanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.https://www.frontiersin.org/article/10.3389/feart.2019.00212/fullsensitivity analysissupport vector machinebrightness temperatureland surface modelhigh mountain Asia |
| spellingShingle | Jawairia A. Ahmad Barton A. Forman Yonghwan Kwon Yonghwan Kwon Yonghwan Kwon Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia Frontiers in Earth Science sensitivity analysis support vector machine brightness temperature land surface model high mountain Asia |
| title | Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia |
| title_full | Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia |
| title_fullStr | Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia |
| title_full_unstemmed | Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia |
| title_short | Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia |
| title_sort | analyzing machine learning predictions of passive microwave brightness temperature spectral difference over snow covered terrain in high mountain asia |
| topic | sensitivity analysis support vector machine brightness temperature land surface model high mountain Asia |
| url | https://www.frontiersin.org/article/10.3389/feart.2019.00212/full |
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