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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Earth Sci. | doi: 10.3389/feart.2019.00212

Analyzing machine learning predictions of passive microwave brightness temperature spectral difference over snow-covered terrain in High Mountain Asia

  • 1Department of Civil and Environmental Engineering, University of Maryland, College Park, United States
  • 2Laboratory of Hydrological Sciences, Goddard Space Flight Center, United States
  • 3Earth System Science Interdisciplinary Center, University of Maryland, College Park, United States

“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 (∆Tb) as a function of geophysical state 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 states 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 (∆Tb 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.”

Keywords: Brightness temperature, Land surface model (LSM), High Mountain Asia, passive microwave, Support vector machine regression, radiometer, sensitivity analysis, Snow

Received: 21 Mar 2019; Accepted: 31 Jul 2019.

Edited by:

Summer Rupper, The University of Utah, United States

Reviewed by:

Adina E. Racoviteanu, Aberystwyth University, United Kingdom
Matthew J. Heaton, Brigham Young University, United States  

Copyright: © 2019 Ahmad, Forman and Kwon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ms. Jawairia A. Ahmad, University of Maryland, College Park, Department of Civil and Environmental Engineering, College Park, United States,