Brightness temperature simulation of snow cover based on snow grain size evolution using in situ data

Lili Wu; Xiaofeng Li; Kai Zhao; Xingming Zheng; Tao Jiang

doi:10.1117/1.JRS.10.036016

18 August 2016 Brightness temperature simulation of snow cover based on snow grain size evolution using in situ data

Lili Wu, Xiaofeng Li, Kai Zhao, Xingming Zheng, Tao Jiang

Author Affiliations +

Funded by: National Natural Science Foundation of China, Natural Science Foundation of China, National Natural Science Foundation of China (NSFC), Science Technology Research Foundation of Jilin Provincial, Jilin Province Natural Science Foundation

Journal of Applied Remote Sensing, Vol. 10, Issue 3, 036016 (August 2016). https://doi.org/10.1117/1.JRS.10.036016

Abstract

Snow depth parameter inversion from passive microwave remote sensing is of great significance to hydrological process and climate systems. The Helsinki University of Technology (HUT) model is a commonly used snow emission model. Snow grain size (SGS) is one of the important input parameters, but SGS is difficult to obtain in broad areas. The time series of SGS are first evolved by an SGS evolution model (Jordan 91) using in situ data. A good linear relationship between the effective SGS in HUT and the evolution SGS was found. Then brightness temperature simulations are performed based on the effective SGS and evolution SGS. The results showed that the biases of the simulated brightness temperatures based on the effective SGS and evolution SGS were −6.5 and −3.6 K, respectively, for 18.7 GHz and −4.2 and −4.0 K for 36.5 GHz. Furthermore, the model is performed in six pixels with different land use/cover type in other areas. The results showed that the simulated brightness temperatures based on the evolution SGS were consistent with those from the satellite. Consequently, evolution SGS appears to be a simple method to obtain an appropriate SGS for the HUT model.

Citation Download Citation

Lili Wu, Xiaofeng Li, Kai Zhao, Xingming Zheng, and Tao Jiang "Brightness temperature simulation of snow cover based on snow grain size evolution using in situ data," Journal of Applied Remote Sensing 10(3), 036016 (18 August 2016). https://doi.org/10.1117/1.JRS.10.036016

Published: 18 August 2016

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