Research Of Snow Depth Retrieval In Northeast China Based On Satellite-borne Passive Microwave Remote Sensing Data

With the rapid development of computer technology,the remote sensing technology has been improved.The study of large range of surface parameters has changed from the field survey mainly relying on manpower and time to the method combining remote sensing and field survey of small range.Seasonal snow cover plays an important role in global climate change and hydrological cycles.Although traditional point observations such as ground station can represent the surface characteristics in a specific area,they are difficult to represent the snow in a large range.Therefore current large range of snow accumulation researches mainly depend on the remote sensing.Snow research field of remote sensing covers many directions,such as optical remote sensing and microwave remote sensing.And with the development of satellite technology,the remote sensing of snow will be refined.On the basis of the research on passive microwave remote sensing of snow home and abroad,in this paper,the main research work and innovation are as follows:(1)Comparison of Snow Depth Retrieval Algorithm in Northeastern China Based on AMSR2 and FY3B-MWRI DataConsidering different types of underlying surface,the results of snow depth(SD)retrieval using AMSR2 Snow Depth Retrieval Algorithm and FY3B-MWRI Snow Depth Retrieval Algorithm for China region are compared in the research.Moreover,to validate the accuracy of these two algorithms,the retrieval results are compared with the SD data observed at the national meteorological stations in Northeastern China.Furthermore,the retrieval SD is also compared with AMSR2 and FY standard SD products,respectively.The root mean square errors(RMSE)results using AMSR2 algorithms and FY algorithm are close in forest,which are 13.64 cm and 13.53 cm,respectively.However,The FY algorithm shows a better result than AMSR2 algorithms in grassland and farmland.The RMSE results using FY algorithm in grassland and farmland are 6.96 cm and 8.88 cm,respectively.(2)Automatic snow grain size measurement method based on adaptive minimum circumscribed rectangleSnow grain size(SGS)affects the brightness temperature of snow.At present,SGS is mainly based on manual measurement,but actually the number of snow particle data that need to be measured is large.Therefore,a method of automatic SGS measurement based on adaptive minimum circumscribed rectangle is proposed.The average SGS of the actual measurement and automatic measurement result are 3.21 mm and 2.98 mm,respectively.The results obtained can meet the requirements of actual research,and it greatly saves manpower and time.It is a quick method for measuring SGS.(3)Snow Depth Retrieved of Farmland in Northeast China Based on Look-up Table of Satellite-borne Passive Microwave Brightness Temperature DifferenceAfter analyzing the accuracy of AMSR2 algorithm and FY algorithm in Northeast China,and on the basis of statistical analysis of snow survey data,the brightness temperature difference(DBT)look-up table(LUT)established by Microwave Emission Model of Layered Snowpacks(MEMLS),is used to retrieve the snow depth(SD)on farmland in Northeast China.After the establishment of LUT,since each DBT in LUT corresponds to a SD,the DBT data provided by satelliteborne microwave radiometer are used to retrieve SD in LUT.And the SD is compared with the in situ measured SD and the SD retrieved by empirical algorithm.The results show the accuracy of LUT established by MEMLS is high.When the in situ average SD of snow accumulation period,stabilization period and ablation period is 6cm,13 cm and 15 cm,respectively,root mean square error(RMSE)in three periods is 3.23 cm,4.24 cm and 4.10 cm,respectively;average bias is 2cm,3cm and 3cm,respectively.With the development of passive microwave remote sensing technology,study of passive microwave remote sensing of snow has become a research hotspot.This paper provides a foundation for the future research of snow in Northeast China.