A Comparative Evaluation of Snow Depth and Snow Water Equivalent Using Empirical Algorithms and Multivariate regressions

  • Arash Adib Shahid Chamran University, Ahvaz, Iran
  • Arash Zaerpour Shahid Chamran University, Ahvaz, Iran
  • Ali Motamedi Khuzestan Water and Power Authority (KWPA), Ahvaz, Iran
Keywords: Passive microwave, brightness temperature, special sensor microwave imager, multivariate regression

Abstract

Space-borne passive microwave (PM) radiometers have provided an opportunity to estimate Snow water equivalent (SWE) and Snow depth (SD) at both regional and global scales. This study attempts to employ empirical algorithms and multivariate regressions (MRs) using Special Sensor Microwave Imager (SSM/I) brightness temperature (TB) in order to achieve an accurate assessment of SD and SWE which well suited for the interest study area. The SSM/I data consist of Pathfinder Daily EASE-Grid TB supplied by the National Snow and Ice Data Centre (NSIDC). For the present study, satellite-based data were gathered from 1992 through 2015 in two versions (v1: 09 July 1987 to 29 April 2009; v2: 14 December 2006 up to now). The results indicated that a stepwise multivariate nonlinear regression (MNLR) outperformed (r = 0.41, and 0.344 for SD and SWE, respectively) other methods. However, a fairly unsatisfactory correlation between ground-based and satellite derived data has been confirmed due to the sparse ground based data and not considering other parameters (snow density, moisture, etc.)
Published
2018-03-10
How to Cite
Adib, A., Zaerpour, A., & Motamedi, A. (2018). A Comparative Evaluation of Snow Depth and Snow Water Equivalent Using Empirical Algorithms and Multivariate regressions. International Journal of Integrated Engineering, 10(1). Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/1870
Section
Articles