Studies On Snow Parameters Estimation Based On Active Microwave Remote Sensing

Snow cover is an important part of the global terrestrial ecosystem,water cycle and energy cycle.Snow cover has a very important influence on surface energy,landsurface moisture and gas flow,and these feedback intensities also bring great uncertainty to the climate prediction system.Snow cover plays an important role in the climate system and ecosystem.Snow accumulation is also an important water resource.The interaction between snow cover and climate is very complicated,and the effect of snow on the climate also has a great impact on the ecosystem.The measurement of water storage in snow and the prediction of the rate of thawing are very necessary for both water resources management and flood control systems.Therefore,how to quickly obtain a wide range of high-precision snow information to meet the needs of these fields has become a hot issue in current research.Due to the variability of snow cover in space and time,many snow cover parameters are difficult to detect without the aid of remote sen sing techniques.The development of satellite remote sensing technology provides a new technical measure for the large-scale detection of snow on the surface of the earth.It extends the traditional "point" measurement method to "surface" observation and o vercomes the shortcomings of traditional snow measurement.Because visible and near-IR signals can not penetrate the snow and the optical properties of ice and snow are similar,snow reflectivity is insensitive to information such as depth of snow,water content in the snow(except for very shallow snow).The high sensitivity of microwave to the scattering of objects makes it have more advantages than the visible and near-infrared bands in obtaining information of snow depth and snow internal properties,which makes it possible to invert the snow parameters by microwave.Because of coarse resolution,passive microwave signals can not be used to invert the change of snow parameters on small-scale.Because highresolution radar data is not only expensive but also time-consuming,large-scale monitoring of surface water parameters,such as soil moisture and snow equivalent,mainly uses active microwave scatterometer and passive microwave radiometer.WCOM is a space-earth scientific exploration satellite driven by the Earth science target.The scatterometer carried on this satellite is applied to the inversion study of snow parameters.This study is aimed at the characteristics of WCOM and the corresponding scientific goals to develop methods of wet snow discrimination,snow wetness estimation and snow water equivalent estimation.The main content of this article is summarized as follows:1.Due to the weak penetration of microwaves into water,it is not possible to estimate the SWE information when the snow contains water.Therefore,before SWE inversion,wet snow needs to be removed.The presence of liquid water in the snow will increase the absorption of microwaves sharply and reduce the volume scattering effect.Therefore when dry snow becomes wet,there will be a sudden drop in trend of the backsscattering signals time series.The previous research indicates that the discrimination threshold of dry-wet snow at C-band is 3d B.Based on the forward model,we explore the applicability of the threshold and develop a new method of wet-snow discrimination based on time-series of snow backscattering signals.2.Snow water equivalent is the product of snow density and snow depth,which indicates the potential water content in snowmelt.It plays an important role in forecasting the water balance and snowmelt runoff and in monitoring and evaluating snow disaster.X and Ku bands have been proved to be the best combination bands for inversion of SWE.Therefore,this paper develops snow water equivalen t inversion algorithm based on active microwave database simulated by Bicontinuous-VRT model at X and Ku bands.First of all,the accuracy of the forward model is verified and the sensitivity of backscattering at X and Ku band to snow particles is demonstrated.Then,the signal of snow-volume is parameterized.The cost function is constructed and used to estimate the snow water equivalent.Finally,,The inversion results are verified with the in situ data.The results show that the estimated snow water equivalent by this algorithm has a root mean square error(RMSE)of 16.59 mm for the winter of 2009–2010 and 19.70 mm for the winter of 2010–2011.3.Snow wetness is the main source of water supply,but also the main indicators of weather forecasting,handover of climate change.Therefore,after the classification of wet and dry snow,it is very necessary to inverse snow wetness.Firstly,the influencing factors of wetsnow backscattering signals are analyzed by using the simulation database.Then,the relationship of scattering characteristics between frequency and polarization is explored respectively from surface and volume backscattering of wet snow.Based on these relationships,the inversion algorithm of snow wetness is developed.