Snow parameters are important physical quantities of climatology and hydrology research, improving the accuracy of snow parameters is important for climatology, hydrology and disaster prevention and reduction. The western Jilin Province of China has obvious salinization problem. Meanwhile, it belongs to a typical snow-covered area. In this paper, the western Jilin Province is selected as the study area and the main research focuses on analyzing the snow cover conditions. The FY3B-MWRI passive microwave remote sensing data from year 2011 to 2016 are selected as experimental data. Compared with optical remote sensing data, using MWRI data can better obtain snow information, and it is also the preliminary work to retrieve snow depth and snow water equivalent. Furthermore, a new decision tree algorithm for snow cover identification was built to distinguish different snow cover conditions. Compared with the existing three algorithms reported in other literatures, the proposed algorithm improves the identification accuracy of snow cover up to 95.06%. While the accuracy for Singh’s algorithm, Pan’s algorithm and Li’s algorithm were about 80.19%, 78.79% and 90.13%, respectively. This study provides important information to the research of snow cover in saline-alkali land.
The western region of Jilin Province is an important part of fragile ecological environment in Northeast China where the soil salinization problem is particularly obvious. Meanwhile, it belongs to a typical snow-covered area and has a northerly continental monsoon climate, with long, cold winters and short, warm summers. It has one single large snowfall period of six month. Therefore, in this paper, the western Jilin Province was selected as the study area and divided into five land surface types including water bodies, grassland, farmland, slight saline-alkali land, moderate and severe saline-alkali land. Furthermore, the two snow depth retrieval algorithms of Chang algorithm and FY3B operational retrieval algorithm were validated and analyzed by using FY-3B/MWRI passive microwave remote sensing data. The main research focused on the analysis of the snow depth covered on the other four different land surface types except water bodies. Based on the five years' observation data from 2011 to 2015, the changes of snow depth on the four land surface types were analyzed and compared with that of MODIS 09A1 snow cover data. The analysis results demonstrated that the snow depth in farmland type is greater than that in grassland type. In addition, the snow depth in slight saline-alkali land type is greater than that in the moderate and severe saline-alkali land type. The study results also showed that the snow depth of Chang's algorithm is more accurate than that of FY3B operational retrieval algorithm in the study area. This research provided important information to the research of snow depth in saline-alkali land area.