Novel Snow Depth Retrieval Method Using Time Series Ssmi Passive Microwave Imagery

Abstract

Abstract: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 2 = 0.82 and RMSE = 6.3 cm).

Estimating Water Level in the Urmia Lake Using Satellite Data: A Machine Learning Approach

“…Because of these reasons, SC monitoring is an important tool for analyzing this lake's water level, particularly because satellite data provide timely and efficient snow cover information for large areas. Estimation of SC and water stored in the snowpack has great importance in climate changes researches and water resources management (Nikraftar et al, 2016), (Johnson et al, 1984), (Yasunari et al, 1990), (Cohen, 1994). According to the studies, snow is a controlling parameter over the regional weather and climate patterns (Cohen et al, 1999).…”

Estimating Snow Depth Using Multi-Source Data Fusion Based on the D-InSAR Method and 3DVAR Fusion Algorithm

“…Due to the broad application of satellite remote sensing data in ice and snow research since the 1970s, researchers worldwide have utilized different remote sensing data to conduct studies on snow depth inversion to determine the spatial distribution of snow depth. There will be access to snow depth monitoring in areas with sparse observation stations [7][8][9][10][11][12][13][14][15][16][17]. However, the relationship between the snow depth and the brightness value of the optical and near infrared bands is not clear yet [7].…”

“…Microwave remote sensing can penetrate the clouds to obtain information about snow depth, which is irreplaceable in the remote sensing of snow [8]. There have been many important academic achievements in the study of using remote sensing of passive/active microwaves to estimate the snow depth/snow water equivalent [9][10][11][12]. Generally, the spatial resolution obtained from the remote sensing of passive microwave is rather coarse, and it is on the level of kilometres, which cannot satisfy the requirement of snow hydrological process research at a watershed scale.…”