Validation And Improvement Of The Series Of NASA Snow Parameter Inversion Algorithms In Time And Spatial Series

The series of NASA algorithms(Chang algorithm, NASA 96 algorithm and Foster algorithm)are the simple, practical empirical algorithms of passive microwave remote sensing for snow depth and snow water equivalent inversion. A wide range of algorithm verification and improvement has been put forward by many scholars. The NASA algorithm also inevitably has regional applicability, In order to evaluate the applicability of the Series of NASA algorithms in Northeast China on space and time further, then develop a localization algorithm to adapt northeast of China. In this paper, we first select a10km×10km mixed pixel of passive microwave remote sensing in which farmland and forest as the main parts in Changchun Jingyuetan area. Continuous observation of snow parameters and meteorological data on time through the entire dry snow season(December 2014 to the next February)have been done, and combined with FY3B- MWRI light temperature during this period. Valuating and analyzing the accuracy of the NASA series algorithm. Secondly, we select 71 meteorological stations in Heilongjiang Province, using method of binary tree classification of seasonal snow cover for three kinds of weather conditions to make classification, then use the classification results to verify the series of NASA algorithms and improve the Foster algorithm. The results show that, in the time series, Chang algorithm and NASA 96 algorithm perform better in snow depth Inversion in the former half of the period. With time going, the trend of overestimate becomes more obvious. Considering the influence of forest coverage, the inversion precision of NASA 96 algorithm is higher. Their largest overestimated value is 24.46 cm and 14.62 cm respectively. This may caused by changing snow properties, especially particle size increasing during the dry snow season. Foster algorithm can seriously overvalued snow water equivalent, the snow type classification system may not be suitable for Northeast China. In order to further improve the Foster algorithm, the types of snow cover in Heilongjiang province were divided.The localized dynamic coefficients of various types are obtained by regression, and the inversion algorithm is applied to the northeast region, which greatly improves the accuracy of the inversion. In this article, the continuous observation data of snow lay the foundation for understanding the properties of snow in northeast region. Time series verification and analysis towards algorithms provide a reliable basis for the further improvement of snow parameter inversion algorithms.