# Passive Microwave Remote Sensing of Snow Depth and Snow Water Equivalent: Overview

Xiao Xiongxin, Zhang Tingjun*

Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth Environmental Sciences, Lanzhou University, Lanzhou 730000,China

First author:Xiao Xiongxin (1991-), male, Fuping County, Shaanxi Province, Master student. Research areas include remote sensing of snow. E-mail:xiaoxiongxin5118@126.com

Abstract

Snow cover is an informative indicator of climate change because it affects local and regional surface energy and water balance, hydrological processes and climate. Passive Microwave (PM) works all weather and round the clock and penetrates clouds and snow. Passive microwave remote sensing data have been widely applied to retrieving snow depth and snow water equivalent in the past few decades. Recently, the snow depth retrieval study has rapidly developed. This paper reviewed the research progress of snow depth and snow water equivalent inversion algorithm using PM data at home and abroad. Firstly, the basic theory of passive microwave remote sensing snow monitoring and passive microwave remote sensing data were introduced. Then, the current snow depth and snow water equivalent inversion algorithm were summarized into four categories: ① A statistically based linear inversion algorithm; ② An inversion algorithm based on microwave transmission snow model; ③ A nonlinear inversion algorithm based on prior knowledge; ④ Data fusion and data assimilation. Afterwards, the commonly used seven kinds of snow data products were introduced, and several factors affecting the snow depth and the snow water inversion accuracy were discussed. Finally, the possible direction of future snow parameter inversion research was prospected.

Keywords： Passive microwave remote sensing ; Snow depth ; Snow water equivalent ; Snow products.

0

Xiao Xiongxin, Zhang Tingjun. Passive Microwave Remote Sensing of Snow Depth and Snow Water Equivalent: Overview[J]. Advances in Earth Science, 2018, 33(6): 590-605 https://doi.org/10.11867/j.issn.1001-8166.2018.06.0590

## 2 数据和理论基础

### 2.1 被动微波遥感监测积雪的理论原理

Tb=Tb1+Tb2+Tb3+Tb4, (1)

Fig.1   Microwave radiation of the surface with snow cover

### 2.2 被动微波遥感数据介绍

Table 1   Parameters summary of passive microwave sensors

SMMRNimbus 71978.10-1987.086.6H,V136×89
10.7H,V87×57
18.0H,V54×35
21.0H,V47×30
37.0H,V47×30
SSM/IDMSP1987.07-2009.1119.35H,V70×45
22.24V60×40
37.0H,V38×30
85.5H,V16×14
SSMISDMSP2006.12-19.35H,V70×45
22.24V60×40
37.0H,V38×30
91.66H,V38×30
AMSR-EAqua2002.06-2011.106.93H,V75×43
10.65H,V51×39
18.7H,V27×16
23.8H,V32×18
36.5H,V14×8
89.0H,V6×3
AMSR2GCOM-W12012.05-6.93H,V62×35
7.3H,V62×35
10.65H,V42×24
18.7H,V22×14
23.8H,V26×15
36.5H,V12×7
89.0H,V5×3
MWRIFY-3B/3C2010.11-10.65H,V51×85
18.70H,V50×30
23.80H,V27×45
36.50H,V18×30
89.00H,V9×15

## 3 积雪深度和雪水当量反演算法

SWE=SD×ρsnow,(2)

### 3.1 基于统计的线性反演算法

3.1.1 静态反演算法

Table 2   The static snow depth retrieval algorithm based on brightness temperature gradient

Chang算法SD=1.59×(Tb18h-Tb37h)[24](3)
SPD算法SPD=(Tb19v-Tb37v)+Tb19v-Tb19h)
SD=A0×SPD-A1
[45](4)
(5)

SD=0.66×(Tb19h-Tb37h)+b
[49](6)
(7)

Table 3   The static snow depth retrieval algorithm based on forest fraction

Forest 算法SD=0.78×(Tb18h-Tb37h)/(1-f)[50](8)
AMSR-E 算法SD=f×SDf+(1-f)×SD0[41,52](9)

SD=(0.66×(Tb19h-Tb37h)+b)/(1-f)
[54](10)
(11)

+fforest×SDforest+ffarmland×SDfarmland
[37](12)

3.1.2 动态反演算法

$TGI=1C∫Tground-TairD(t)dt,$(13)

$D(t)=-αTairCddt(ΔTb19h-37h),$(14)

3.3.1 神经网络

3.3.2 支持向量机

3.3.3 其他机器学习算法

### 3.4 数据融合与数据同化

3.4.1 观测资料与模型模拟

Liston等[78]采用直接将积雪深度转化后的雪水当量值同化到区域大气模型系统(Regional Atmospheric Modeling System)模拟中,即把积雪的观测值直接代入到耦合的陆地—大气模型模拟结果中,改进雪水当量的估算结果,研究结果显示该方法可以显著提高积雪模型的积雪过程模拟。加拿大气象中心使用积雪深度实时实测数据和简化的积雪融化与积累期积雪模型,生成了全球逐日积雪深度再分析数据集[79],与北半球积雪深度实测值相比每个月估算值的RMSE在10~20 cm,偏差一般小于5 cm。而且在北美陆地数据同化系统(North American Land Data Assimilation System,NLDAS)中,也是将地面站点实测积雪数据同化到陆面过程模式中[80],结果显示在所有区域内模型估算都会低估最大雪水当量值,估算值与实测值的差异最大可以达到1 000 mm,东部地区一般会在100 mm以内。

3.4.2 观测资料与遥感资料

Takala等[40]提出了一种数据同化算法,同化地面实测积雪深度数据和被动微波辐射数据,并计算北半球30年长时间序列季节性的雪水当量数据,该数据集使用前苏联、芬兰以及加拿大的雪水当量观测值作为验证数据集。研究结果显示该算法具有较高的可行性,可以提高估算雪水当量的精度,在芬兰地区雪水当量估计值的RMSE均小于40 mm;欧亚大陆区域的RMSE和偏差会随着时间而变化,当雪水当量小于150 mm时RMSE和偏差分别在30~40 mm和-3~9 mm变化;在加拿大地区RMSE大概为40 mm。赵亮等[81]提出了一种融合被动微波遥感数据和地面测站数据动态反演积雪深度的方法,该反演方法可以在不同时空条件下根据测站观测雪深,灵活调整积雪深度反演系数,而且该算法在观测站稀少的西部高山地区的反演结果具有较高空间分辨率,与站点实测相比、判识有无雪的准确率高于80%。

3.4.3 遥感资料与模型模拟

Andreadis等[82]运用集合卡尔曼滤波(Ensemble Kalman Filter,EnKF)将遥感观测数据同化到水文变量渗透能力模型(Variable Infiltration Capacity hydrological model,VIC)中,使用1999—2003年4个冬季的MODIS积雪覆盖范围产品更新VIC模拟的雪水当量。在此过程中应用简单的积雪消融模型反演雪水当量—积雪面积,研究发现在深雪部分估算的雪水当量误差更大,对低海拔高度积雪融化期地区的雪水当量精度提高幅度大,对高海拔积雪积累期精度提高幅度不是很大。此外,为了更新积雪的状态信息,Durand等[83]将被动微波传感器SSM/I和AMSR-E多个波段的亮温以及宽波段albedo观测数据使用EnKF同时同化至陆面过程模式中估算雪水当量,其中该研究中使用集成陆面过程模式SSiB3中简单积雪—大气—土壤传输模型(Simple Snow-Atmosphere-Soil,SAST),研究结果显示,该方法对研究积雪属性和观测值之间的复杂关系具有很好的效果,实验结果显示经同化后的结果对积雪面积识别率可达到90%以上,显著提高了雪水当量估算的精度,雪水当量估算值的RMSE可以低至32 mm。车涛等[84]使用EnkF将被动微波亮温数据同化至积雪过程模型中,使用积雪深度地面实测数据作为同化反演结果的验证数据,通过对实验区7个站点的数据分析,结果显示该同化算法在积雪积累期可以提高估算积雪深度的精度,同化后结果的偏差和RMSE分别是-1.13 cm和4.60 cm,但是在积雪融化期并不能提高积雪深度估算精度,森林、降水以及模型所需的驱动数据都会影响同化结果的精度。

3.4.4 观测资料、遥感资料和模型模拟

Pulliainen[85]同化卫星微波辐射数据和地面观测数据来估算积雪深度和雪水当量。该同化算法基于HUT模型,并运用贝叶斯方法对卫星数据与地面实测数据赋权重。实验结果显示通过数据同化技术并使用SSM/I和AMSR-E数据估算北欧和芬兰地区积雪深度和雪水当量时,与实测插值后结果相比数据同化可以提高积雪深度和雪水当量的反演精度,但是由于该算法对积雪粒径很敏感,所以该模型在长时间序列或大空间尺度上应用时存有一定的不足。

## 4 被动微波卫星遥感积雪产品

Table 4   Snow product dataset

1中国雪深长时间序列

2FY3-MWRI雪深雪水

3AMSR-E积雪产品全球25 km2002.6.19-2011.11.3逐日http:∥nsidc.org/
4SMMR、SSM/I 和
SSMIS积雪产品

5GlobSnow-2积雪产品北半球25 km1979—2012年逐日http:∥www.globsnow.info/
6加拿大气象中心逐日

7兰德公司月平均积雪

4°×5°1950—1976年逐月http:∥nsidc.org/

### 4.1 积雪产品生成的算法

FY3-MWRI雪深雪水当量数据产品使用的积雪深度反演算法有2种:一种是全球积雪深度反演算法,该算法是半经验型算法,与AMSR-E积雪深度反演算法相同(公式(9));另一种是区域积雪深度反演算法,该算法也是一种半经验型算法,但是该算法只适用于中国区域(公式(12))。

AMSR-E的积雪产品可以提供全球逐日、5天和逐月3种类型的积雪产品,该数据集的积雪反演算法是在Chang算法的基础上发展起来的,数据处理时首先会剔除湿雪像元,然后使用MODIS数据将非积雪像元掩膜掉。因为算法考虑了森林的影响,所以会基于森林和非森林区域分别建立反演算法。最后使用公式(9)估算积雪深度。

SMMR积雪产品空间覆盖南、北纬85°之间区域,SMMR积雪产品是在时空上做了插值处理,以极地方位立体投影形式显示,数据限制在0.5°×0.5°经纬格网内,数据中只有陆地区域,其中海洋和海湾地区数据被掩膜。SMMR,SSM/I和SSMIS积雪产品是美国NSIDC依据NASA算法[50,88]使用SMMR,SSM/I 和SSMIS被动微波传感器亮温数据生成的长时间序列(1978年至今)全球积雪数据产品。

GlobSnow-2数据集是欧洲太空局(European Space Agency,ESA)提供的积雪产品,包括积雪面积和雪水当量,雪水当量反演算法——数据同化方法[85]使用到了NSIDC的被动微波亮温数据(SMMR,SSM/I,AMSR-E)辅助以时间序列融化探测算法,整体算法在文献[40]中有详细介绍。

## 6 结 语

(1)微波传感器接收到的亮温对积雪粒径、含水量、积雪密度、植被、地形等变化敏感,浓密的植被大大减弱了向上微波辐射信号,地形的高低起伏增大了卫星传感器接受到的积雪辐射信号的复杂性,而目前所使用的积雪反演方法在某种程度上经常会低估实际值,因而利用被动微波积雪监测中如何减小植被、地形等地表属性以及积雪的物理属性的影响有待进一步研究。

(2)因被动微波遥感数据的粗分辨率而带来的混合像元问题,导致了基于被动微波遥感反演积雪参数时并不能准确描述地表真实积雪信息。所以将其他高分辨率遥感影像数据或产品,如MODIS,Landsat,Lidar和SAR等数据,与被动微波遥感数据融合处理,使得融合处理后的数据更好地表达被动微波遥感数据粗分辨率像元内的异质性信息以及提高多源数据的利用率,实现优势互补,弥补各自传感器数据的缺陷。使用多源遥感数据融合可以更加准确地了解积雪属性(密度、粒径、深霜层、雪层结构)的特征,经数据融合处理生成的高精度积雪遥感产品可用于小区域尺度的高精度的水资源评估、雪灾监测与气象预报等领域。

(3)现有主流的积雪深度和雪水当量反演算法采用的微波频率主要是18 GHz 或19 GHz以及37 GHz,而现有研究结果显示目前的反演算法存在一个反演积雪深度阈值的瓶颈,即当雪深达到50 cm左右时亮温差逐渐趋于饱和,需要尝试探索除以上常用频率波段以外频率下的数据,如可以采用穿透深雪能力较强的10 GHz波段以及利用监测浅雪较好的89 GHz 或更高频波段的亮温数据。

(4)数据同化可以破解积雪物理过程不明确、被动微波数据分辨率低以及无法长时间持续监测积雪变化等问题。微波辐射传输模型在积雪参数反演估算过程中的使用和发展,更加有利于了解积雪物理特性和提高积雪参数反演的精度。在数据同化方法体系下同化积雪过程模型、站点观测数据、卫星遥感观测(主被动,光学,雷达等遥感观测)等多源数据,可为提高积雪深度和雪水当量的估算精度提供新的方法。

The authors have declared that no competing interests exist.

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MODIS是新一代图谱合一的光谱成像仪，适合进行雪情监测。概要介绍了MODIS积雪产品及NDSI算法在积雪制图方面的应用，也介绍了MODIS积雪产品在国内外研究应用的现状和今后的发展趋势。并且给出了应用MODIS数据制作内蒙古雪盖图的实例。

[14] Li Jinya, Yang Xiuchun, Xu Bin, et al. Snow monitoring using MODIS and AMSR-E in six main pastoral areas of China[J]. Scientia Geographica Sinica,2011, 31(9): 1 097-1 104. [李金亚, 杨秀春, 徐斌, 等. 基于MODIS 与 AMSR-E 数据的中国 6 大牧区草原积雪遥感监测研究[J]. 地理科学, 2011, 31(9): 1 097-1 104.] [15] Che Tao, Li Xin.The development and prospect of estimating snow water equivalent using passive microwave remote sensing data[J]. Advances in Earth Science, 2004, 19(2): 204-210. [车涛, 李新. 被动微波遥感估算雪水当量研究进展与展望[J]. 地球科学进展, 2004, 19(2): 204-210.]

[16] Li Xin, Che Tao.A review on passive microwave remote sensing of snow cover[J] Journal of Glaciology and Geocryology, 2007, 29(3): 487-496. [李新, 车涛. 积雪被动微波遥感研究进展[J]. 冰川冻土, 2007, 29(3):487-496.] 积雪是冰冻圈中最活跃的要素之一,被动微波遥感具有高时间分辨率且能够迅速覆盖全球,在积雪时空变化监测中作用突出.总结分析了积雪被动微波遥感的主要模型,并对其方法、特点和适用性进行了较详细评述,重点介绍了NASA算法在雪深和雪水当量反演中的应用、反演结果的不确定性以及对它的改进.讨论新兴的积雪数据同化方法,介绍了同化被动微波观测以改进雪深和雪水当量反演精度的研究案例.评述了我国积雪被动微波遥感的进展,并且对未来可能的研究方向做出展望. [17] Wu Yang, Zhang Jiahua, Xu Haiming, et al. Advances in study of snow-cover from remote sensing data[J]. Meteorological Monthly,2007, 33(6): 3-10. [吴杨, 张佳华, 徐海明, 等. 卫星反演积雪信息的研究进展[J]. 气象, 2007, 33(6): 3-10.] 综合分析了积雪信息反演的主要遥感信息源和提取方法。在光学遥感方面,应用较广的主要是改进型甚高分辨率扫描辐射仪(AVHRR)资料和中分辨率成像光谱仪(MODIS)资料;提取积雪信息大多是根据积雪在可见光波段的高反射率和近红外波段的低反射率,并通过建立回归模型反演积雪面积和深度。由于传感器的改进,MODIS卫星资料在空间分辨率、积雪反演算法等方面明显优于AVHRR资料。光学仪器受云层和大气的影响很大,由于云和积雪在可见光和近红外波段上都具有高反射率。并且由于云层的遮挡,云下的地表信息不能被光学遥感仪器所接收到。微波遥感方面,被动微波遥感仪如微波辐射计成像仪(SSM/I)、高级微波扫描辐射计(AMS [18] Zhao Yingshi.Principle and Method of Remote Sensing Application Analysis (Second Edition)[M].Beijing: Science Press, 2013. [赵英时. 遥感应用分析原理与方法 (第二版)[M]. 北京:科学出版社, 2013.] [19] Sun Zhiwen,Shi Jiancheng,Jiang Lingmei, et al. Development of snow depth and snow water equivalent algorithm in western China using passive microwave remote sensing data[J]. Advances in Earth Science,2006,21(12):1 363-1 369. [孙之文,施建成,蒋玲梅,等. 被动微波遥感反演中国西部地区雪深、雪水当量算法初步研究[J].地球科学进展,2006,21(12):1 363-1 369.] 雪深、雪水当量是积雪研究中重要参数,其在流域水量平衡和融雪径流预报以及雪灾监测与评价中起着重要作用。Chang等(1987)以辐射传输理论和米氏散射为理论基础,假定积雪密度和颗粒大小为常数,利用实测雪深数据和SMMR的亮温数据,通过统计回归方法,建立了雪深与18GHz和37GHz水平极化的亮温梯度之间的关系,发展了SMMR半经验的反演雪深的算法。后在此基础上又发展了针对SSM/I的半经验反演雪深算法。2002年发射的装载于Aqua卫星上的AM-SR-E是新一代的被动微波辐射计,性能较以往星载被动微波辐射计有较大提高,采用了改进后的SSM/I的半经验算法作为其估算全球雪水当量的反演算法。将AMS [20] Jin Rui, Li Xin.A review on the algorithm of frozen/thaw boundary detection by using passive microwave remote sensing[J]. Remote Sensing Technology and Application, 2002, 17(6):370-375. [晋锐, 李新. 被动微波遥感监测土壤冻融界限的研究综述[J]. 遥感技术与应用, 2002, 17(6): 370-375.]

[21] Zhang Tingjun, Jin Rui, Gao Feng.Overview of the satellite remote sensing of frozen ground: Passive microwave sensors[J]. AdvanceS in Earth Science, 2009,24(10):1 073-1 083. [张廷军, 晋锐, 高峰. 冻土遥感研究进展:被动微波遥感[J]. 地球科学进展, 2009, 24(10): 1 073-1 083.]           摘要 多年冻土和季节冻土分别占北半球裸露地表的24％和55％。近地表土壤冻融的范围、冻结起始 日期、持续时间及冻融深度对寒季／寒区植物生长、大气与土壤间能量、水分及温室气体交换都具有极其重要的影响。自20世纪70年代以来，应用卫星遥感结合 地面观测资料研究局地到区域尺度的季节冻土和多年冻土已取得诸多成果，而遥感在冻土研究中的最直接应用是利用微波探测近地表土壤冻融状态。相对于主动 SAR，星载被动微波传感器具有多通道观测且重访周期较高，空间分辨率很低的特点。重点评述了近几十年来被动微波辐射计在冻土研究中的算法发展及其应用前 景，主要包括双指标算法、时间序列变化检测算法及判别树算法3类，其核心均是基于冻土的低温特征和“体散射变暗”效应。发展可靠实用的微波遥感土壤冻融状 态判别算法，提供区域和全球尺度上的土壤冻融状态信息，对水文学、气象学以及农业科学、工程地质研究与应用都具有重要意义。 [22] Zhang Tingjun, Jin Rui, Gao Feng.Overview of the satellite remote sensing of frozen ground: Visile thermal infrared and radar sensor[J]. Advances in Earth Science, 2009,24(9):963-972. [张廷军, 晋锐, 高峰. 冻土遥感研究进展——可见光、红外及主动微波卫星遥感方法[J]. 地球科学进展, 2009, 24(9): 963-972.]