Passive Microwave Brightness Temperature Assimilation to Improve Snow Mass Estimation Across Complex Terrain in Pakistan, Afghanistan, and Tajikistan

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Jawairia A. Ahmad¹  Barton A. Forman¹  Edward H. Bair²  Sujay V. Kumar³ 

[1] Department of Civil and Environmental Engineering, University of Maryland, College Park,, USA;Earth Research Institute, University of California, Santa Barbara,, USA;Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA;

关键词: Brightness temperature;    high mountain Asia;    hydrology;    land surface modeling;    NASA Land Information System;    passive microwave;   

DOI  :  10.1109/JSTARS.2021.3102965

来源: DOAJ

【 摘 要 】

An ensemble Kalman filter is used to assimilate Advanced Microwave Scanning Radiometer-2 (AMSR2) observations of passive microwave (PMW) brightness temperatures (spectral differences, $\Delta T_b$) into land surface model estimates of snow mass over northwestern high mountain Asia (HMA). Trained support vector machines serve as the observation operator and map the geophysical modeled variables into $\Delta T_b$ space within the assimilation framework. Evaluation of the assimilation routine is carried out through comparison of assimilated snow mass estimates with an in situ dataset. The assimilation framework helps improve the land surface model estimates through PMW $\Delta T_b$ assimilation, particularly in terms of decreasing the domain-wide bias. The assimilation framework proved more effective during the (dry) snow accumulation season and decreased the bias and root-mean-square error (RMSE) in snow mass estimates at 76% and 58% of the comparative pixels, respectively. During the snow ablation season, the PMW brightness temperature signal contained less information related to snow mass due to the presence of other concurrent geophysical features that effectively serve as noise during the snow mass update. The utilization of PMW $\Delta T_b$ for accurate snow mass estimation in complex terrain such as HMA is dependent on a multitude of factors for optimal results; however, it does add utility to the land surface model if the relevant pitfalls are taken into consideration prior to the state variable update.

【 授权许可】

Unknown