Open AccessNOVEL SNOW DEPTH RETRIEVAL METHOD USING TIME SERIES SSMI PASSIVE MICROWAVE IMAGERY
Author(s) -
Zahir Nikraftar,
Mahdi Hasanlou,
M. Esmaeilzadeh
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b8-525-2016
Subject(s) - snow , weighting , microwave , remote sensing , special sensor microwave/imager , meteorology , algorithm , microwave imaging , mean squared error , range (aeronautics) , computer science , environmental science , geology , geography , mathematics , telecommunications , brightness temperature , statistics , engineering , physics , aerospace engineering , acoustics
The Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSM/IS) are satellites that work in passive microwave range. The SSM/I has capability to measure geophysical parameters which these parameters are key to investigate the climate and hydrology condition in the world. In this research the SSMI passive microwave data is used to study the feasibility of monitoring snow depth during snowfall month from 2010 to 2015 using an algorithm in conjunction with ground depth measured at meteorological stations of the National Centre for Environmental Information (NCEI). The previous procedures for snow depth retrieval algorithms uses only one or two passive bands for modelling snow depth. This study enable us to use of a nonlinear multidimensional regression algorithm which incorporates all channels and their related weighting coefficients for each band. Higher value of these coefficients are indicator of the importance of each band in the regression model. All channels and their combination were used in support of the vector algorithm combined with genetic algorithm (GA) for feature selection to estimate snow depth. The results were compared with those algorithms developed by recent researchers and the results clearly shows the superiority of proposed method (R<sup>2</sup> = 0.82 and RMSE = 6.3 cm).