被动微波遥感反演雪水当量不确定性因素分析

Uncertainty Analysis for Retrieving Snow Water Equivalent based on Passive Microwave Remote Sensing

雪水当量定义为积雪融化后液态水的高度,是描述季节性积雪储量的关键参数。星载被动微波遥感适用于长时间序列、全球尺度的雪水当量监测。但目前的微波辐射传输模型大多忽略或简化了自然界垂直分层结构中的土壤、植被和大气等要素对积雪辐射亮温的影响,特别是植被参数(例如透过率、覆盖度、单次散射反照率)引起的微波亮温变化仍然不清晰。本研究通过构建土壤—积雪—森林—大气微波辐射模型,重点开展被动微波遥感反演雪水当量的不确定性机理研究。通过模型敏感性分析发现:①冠层透过率是森林参数中影响微波亮温最敏感的因子,其次是森林覆盖度,而单次散射反照率影响最小;②微波亮温随着森林覆盖度的增加而升高,但随着冠层透过率和雪粒径参数的增加而降低,即三者之间存在“抵偿效应”。通过构建的模型模拟数据库和卫星观测对风云三号B星(FY-3B)和The Advanced Microwave Scanning Radiometer 2(AMSR2)雪水当量反演算法进行亮温噪声测试发现:①亮温噪声对AMSR2雪水当量反演算法影响较大,特别是在森林像元尤为严重,与算法中表征积雪参数演化的极化因子和森林下雪深校正方法不确定性有关;②亮温噪声对FY-3B算法的影响较小,甚至可以忽略,主要是由于亮温差的形式削减了亮温不确定性。本研究基于构建的辐射传输模型定量分析了森林和积雪参数对微波亮温的影响,确定了影响雪水当量反演精度的关键参数;同时表明雪水当量反演方法对亮温噪声的鲁棒性也是影响算法精度的重要方面,为未来发展普适性的雪水当量反演算法奠定了理论基础和参考。

Snow Water Equivalent(SWE)is defined as the height of melting snow,regarded as the most impor⁃tant variable describing the amount of snow.Space-borne passive microwave(PMW)remote sensing is current⁃ly viewed as a attractive option for SWE estimation at global scale.One challenge for SWE estimation is that most inverse algorithms were built based on a non-comprehensive RTM in which it neglects other components,e.g.,forest.Forest canopy not only attenuates the microwave radiation from soil,but also emits some radiation into the sensor.Therefore,forest canopy increases the uncertainness of SWE retrievals.This paper aims to study the sensitivity of forest parameters(transmissivity,cover fraction,and single albedo)to brightness tem⁃perature based on a proposed systematic RTM,and further analysis the robust of SWE algorithms to brightness temperature noise.To better describe the transmission of electromagnetic wave in a real complex contracture,a systematic RTM was built firstly,considering the soil,snow,forest and atmosphere.The Advanced Integral Equation Model(AIEM)model was applied to simulate soil-snow boundary reflectivity.A semi-empirical radi⁃ative transfer theory(HUT)model was applied to simulate snow microwave emission.A zero-order tau-omega model was used to describe the interactions between snow and forest canopy.Then a database based the built RTM was generated,and applied to conduct sensitivity analysis of forest and snow parameters to brightness temperature.Meanwhile,noise tests of brightness temperature to SWE retrieving algorithms were done based modeling data and satellite observations.The results indicate that canopy transmissivity is the most sensitive fac⁃tor among three forest parameters,secondly forest fraction,and lastly single albedo.Meanwhile,the brightness temperatures raise with increasing forest fraction,but decline with increasing canopy transmissivity and snow grain size.Namely,there is‘neutralize effect’among these three parameters(forest fraction,canopy transmis⁃sivity,and snow grain size).This is because forest canopy attenuates the radiation from snow and the brightness temperature(Tb)of snow is typically lower than canopy radiation.Thus,the Tbs in forested areas are higher than those in open areas.The noise analysis based on modelling data and satellite observation shows that the in⁃fluence of brightness temperature uncertainness on AMSR2 SWE retrieving algorithm is more serious than FY-3B method,especially in forest areas.This is maybe because a polariton index charactering grain size evolution is empirical.The influence of brightness temperature uncertainness on FY-3B algorithm is very small,even can be negligible.The paper first quantitatively investigated the sensitivity of forest(transmissivity,fraction,single albedo)and snow(grain size)parameters to microwave brightness temperature based on the built systematic ourselves.What's more,this study determined the key parameters affecting SWE retrievals,including canopy transmissivity,forest fraction and snow grain size.Additionally,the noise analysis reminds us the robust of algo⁃rithms to brightness temperature uncertainness must be considered,not just their accuracy.This study provides the theoretical basis and direction for improving SWE in the near future.