Inversion of snow depth and snow water equivalent based on passive microwave remote sensing and its application progress

WANG Zekun; GAN Fuping; YAN Bokun; LI Xianqing; LI Hemou

doi:10.6046/zrzyyg.2021322

2022 Vol. 34, No. 3

Article Contents

Article navigation > Remote Sensing for Natural Resources > 2022 > 34(3): 1-9

Next Article Previous Article

WANG Zekun, GAN Fuping, YAN Bokun, LI Xianqing, LI Hemou. 2022. Inversion of snow depth and snow water equivalent based on passive microwave remote sensing and its application progress. Remote Sensing for Natural Resources, 34(3): 1-9. doi: 10.6046/zrzyyg.2021322

Citation:

WANG Zekun, GAN Fuping, YAN Bokun, LI Xianqing, LI Hemou. 2022. Inversion of snow depth and snow water equivalent based on passive microwave remote sensing and its application progress. Remote Sensing for Natural Resources, 34(3): 1-9. doi: 10.6046/zrzyyg.2021322

Inversion of snow depth and snow water equivalent based on passive microwave remote sensing and its application progress

1. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology(Beijing), Beijing 100083, China；
2. College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China；
3. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China

Received Date: 11 October 2021

Revised Date: 15 September 2022

Publish Date: 21 September 2022

Abstract

Abstract

Snow depth and snow water equivalent are critical elements for snow cover observation and are greatly significant in fields such as cryosphere, global climate change, and water resource surveys. Microwave remote sensing is superior to both visible-light and near-infrared remote sensing in snow cover observation. This study systematically summarized the research results of the passive microwave remote sensing in the inversion of snow depth and snow water equivalent. It organized three types of snow cover observation methods, i.e., field surveys, long-term observations at ground stations, and regional observations based on satellite remote sensing, as well as major snow cover parameters to be observed. Furthermore, it summarized and evaluated three inversion algorithms, i.e., semi-empirical method, physical model, and machine learning. Finally, this study presented the results of the snow cover in the Qinghai-Xizang Plateau observed using passive microwave remote sensing, predicted the future development trend of remote sensing-based inversion of snow cover parameters, and put forward scientific suggestions for the in-depth implementation of the inversion of snow depth and snow water equivalent passive microwave remote sensing.
- snow depth and snow water equivalent /
- passive microwave /
- inversion algorithm /
- snow cover observation /
- Qinghai-Xizang Plateau

FullText(HTML)

References

[1]	Choi G, Robinson D, Kang S. Changing northern hemisphere snow seasons[J]. Journal of Climate, 2010, 23(19):5305-5310. Google Scholar
[2]	车涛, 李新. 1993—2002年中国积雪水资源时空分布与变化特征[J]. 冰川冻土, 2005, 27(1):64-67. Google Scholar
[3]	Che T, Li X. Spatial distribution and temporal variation of snow water resources in China during 1993—2002[J]. Journal of Glaciology and Geocryology, 2005, 27(1):64-67. Google Scholar
[4]	胡汝骥. 中国积雪与雪灾防治[M]. 北京: 中国环境出版社, 2013. Google Scholar
[5]	Hu R J. Snow and snow disaster in China[M]. Beijing: Chinese Environment Science Press, 2013. Google Scholar
[6]	Shi J C, Xiong C, Jiang L M. Review of snow water equivalent microwave remote sensing[J]. Science China, 2016, 59(4):731-745. Google Scholar
[7]	梁顺林, 白瑞, 陈晓娜, 等. 2019年中国陆表定量遥感发展综述[J]. 遥感学报, 2020, 24(6):618-671. Google Scholar
[8]	Liang S L, Bai R, Chen X N, et al. Review of China’s land surface quantitative remote sensing development in 2019[J]. Journal of Remote Sensing, 2020, 24(6):618-671. Google Scholar
[9]	蒋玲梅. 被动微波雪水当量研究[D]. 北京: 北京师范大学, 2005. Google Scholar
[10]	Jiang L M. Passive microwave remote sensing of snow water equivalence study[D]. Beijing: Beijing Normal University, 2005. Google Scholar
[11]	车涛, 李新, 高峰. 青藏高原积雪深度和雪水当量的被动微波遥感反演[J]. 冰川冻土, 2004, 26(3):363-368. Google Scholar
[12]	Che T, Li X, Gao F. Estimation of snow water equivalent in the Xizang Plateau using passive microwave remote sensing data(SSM/I)[J]. Journal of Glaciology and Geocryology, 2004, 26(3):363-368. Google Scholar
[13]	Josberger E G, Mognard N M. A passive microwave snow depth algorithm with a proxy for snow metamorphism[J]. Hydrological Processes, 2002, 16(8):1557-1568. Google Scholar
[14]	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):1097-1104. Google Scholar
[15]	Li J Y, Yang X C, Xu B, et al. Snow monitoring using MODIS and AMSR-E in six main pastoral areas of China[J]. Scientia Geographica Sinica, 2011, 31(9):1097-1104. Google Scholar
[16]	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. Google Scholar
[17]	Che T, Li X. 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. Google Scholar
[18]	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. Google Scholar
[19]	Li X, Che T. A review on passive microwave remote sensing of snow cover[J]. Journal of Glaciology and Geocryology, 2007, 29(3):487-496. Google Scholar
[20]	Pulliainen J T, Grandell J, Hallikainen M T. HUT snow emission model and its applicability to snow water equivalent retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(3):1378-1390. Google Scholar
[21]	Wiesmann A, Matzler C. Microwave emission model of layered snow packs[J]. Remote Sensing of Environment, 1999, 70(3):307-316. Google Scholar
[22]	Tsang L, Chen C T, Chang A T, et al. Dense media radiative transfer theory based on quasicrystalline approximation with applications to passive microwave remote sensing of snow[J]. Radio Science, 2000, 35(3):731-749. Google Scholar
[23]	Du J, Shi J, Rott H. Comparison between a multi-scattering and multi-layer snow scattering model and its parameterized snow backscattering model[J]. Remote Sensing of Environment, 2010, 114(5):1089-1098. Google Scholar
[24]	Ding K H, Xu X, Tsang L. Electromagnetic scattering by Bicontinuous random microstructures with discrete permittivities[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(8):3139-3151. Google Scholar
[25]	Chang A T C, Foster J L, Hall D K. Nimbus-7 SMMR derived global snow cover parameters[J]. Annals of Glaciology, 1987, 9:39-44. Google Scholar
[26]	Foster J L, Chang A T C, Hall D K. Comparison of snow mass estimates from a prototype passive microwave snow algorithm,a revised algorithm and a snow depth climatology[J]. Remote Sensing of Environment, 1997, 62(2):132-142. Google Scholar
[27]	车涛. 积雪被动微波遥感反演与积雪数据同化方法研究[D]. 兰州: 中国科学院寒区旱区环境与工程研究所, 2006. Google Scholar
[28]	Che T. Study on passive microwave remote sensing of snow and snow data assimilation method[D]. Lanzhou: Cold and Arid Regions Environmental and Engineering Research Institute,CAS, 2006. Google Scholar
[29]	Che T, Xin L, Jin R, et al. Snow depth derived from passive microwave remote-sensing data in China[J]. Annals of Glaciology, 2008, 49(1):145-154. Google Scholar
[30]	Jiang L M, Wang P, Zhang L X, et al. Improvement of snow depth retrieval for FY3B-MWRI in China[J]. Science China Earth Science, 2014, 57(6):1278-1292. Google Scholar
[31]	Kelly R. The AMSR-E snow depth algorithm:Description and initial results[J]. Journal of the Remote Sensing Society of Japan, 2009, 29(1):307-317. Google Scholar
[32]	王建, 钟歆玥, 戴礼云, 等. 中国积雪特性及分布调查[J]. 地球科学进展, 2018, 33(1):12-26. Google Scholar
[33]	Wang J, Zhong X Y, Dai L Y, et al. Investigation on the characteristics and distribution of snow cover in China[J]. Advances in Earth Science, 2018, 33(1):12-26. Google Scholar
[34]	王子龙, 胡石涛, 付强, 等. 积雪参数遥感反演研究进展[J]. 东北农业大学学报, 2016, 47(9):100-106. Google Scholar
[35]	Wang Z L, Hu S T, Fu Q, et al. Research progress in remote sensing inversion of snow cover parameters[J]. Journal of Northeast Agricultural University, 2016, 47(9):100-106. Google Scholar
[36]	马丽娟, 秦大河. 1957—2009年中国台站观测的关键积雪参数时空变化特征[J]. 冰川冻土, 2012, 34(1):1-11. Google Scholar
[37]	Ma L J, Qin D H. Spatial-temporal characteristics of observed key parameters for snow cover in China during 1957—2009[J]. Journal of Glaciology and Geocryology, 2012, 34(1):1-11.[26] 中国气象局. 地面气象观测规范[M]. 北京: 气象出版社, 2003:151-153. Google Scholar [26] China Meteorological Administration. Ground meteorological observation specifications[M]. Beijing: China Meteorological Press, 2003:151-153. Google Scholar
[38]	邱玉宝, 卢洁羽, 石利娟, 等. 高亚洲地区被动微波遥感雪水当量数据集[J]. 中国科学数据(中英文网络版), 2019, 4(1):110-125. Google Scholar
[39]	Qiu Y B, Lu J Y, Shi L J, et al. Passive microwave remote sensing snow water equivalent data set in high Asia area[J]. Scientific Data in China(Chinese-English Online Edition), 2019, 4(1):110-125. Google Scholar
[40]	Dozier J, Painter T. Multispectral and hyperspectral remote sensing of alpine snow properties[J]. Annual Review of Earth and Planetary Sciences, 2004, 21(1):465-494. Google Scholar
[41]	梁顺林, 李小文, 王锦地. 定量遥感:理念与算法(第二版)[M]. 北京: 科学出版社, 2019:718-719. Google Scholar
[42]	Liang S L, Li X W, Wang J D. Quantitative remote sensing:Ideas and algorithms (second edition)[M]. Beijing: Science Press, 2019: 718-719. Google Scholar
[43]	Tedesco M, Pulliainen J, Takala, et al. Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data[J]. Remote Sensing of Environment, 2004, 90(1):76-85. Google Scholar
[44]	Kunkee D B, Swadley S D, Poe G A, et al. Special sensor microwave imager sounder (SSMIS) radiometric calibration anomalies—part I:Identification and characterization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(4):1017-1033. Google Scholar
[45]	Gan Y, Zhang Y, Kongoli C, et al. Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States[J]. Remote Sensing of Environment, 2021, 254:112280. Google Scholar
[46]	孙知文, 施建成, 杨虎, 等. 风云三号微波成像仪积雪参数反演算法初步研究[J]. 遥感技术与应用, 2007, 22(2):264-267. Google Scholar
[47]	Sun Z W, Shi J C, Yang H, et al. A study on snow depth estimating and snow water equivalent algorithm for FY-3 MWRI[J]. Remote Sensing Technology and Application, 2007, 22(2):264-267. Google Scholar
[48]	Armstrong R L, Brodzik M J. Hemispheric-scale comparison and evaluation of passive-microwave snow algorithms[J]. Annals of Glaciology, 2002, 34(1):38-44. Google Scholar
[49]	Foster J L, Sun C, Walker J P, et al. Quantifying the uncertainty in passive microwave snow water equivalent observations[J]. Remote Sensing of Environment, 2005, 94(2):187-203. Google Scholar
[50]	Tedesco M, Jeyaratnam J. A new operational snow retrieval algorithm applied to historical AMSR-E brightness temperatures[J]. Remote Sensing, 2016, 8(12):1-25. Google Scholar
[51]	Yang J W, Jiang L M, Wu S L, et al. Development of a snow depth estimation algorithm over China for the FY-3D/MWRI[J]. Remote Sensing, 2019, 11(8):1-21. Google Scholar
[52]	孙知文, 于鹏珊, 夏浪, 等. 被动微波遥感积雪参数反演方法进展[J]. 国土资源遥感, 2015, 27(1):9-15.doi: 10.6046/gtzyyg.2015.01.02. Google Scholar
[53]	Sun Z W, Yu P S, Xia L, et al. Progress in study of snow parameter inversion by passive remote sensing[J]. Remote Sensing for Land and Resources, 2015, 27(1):9-15.doi: 10.6046/gtzyyg.2015.01.02. Google Scholar
[54]	Stiles W H, Ulaby F T, Rango A. Microwave measurements of snowpack properties[J]. Nordic Hydrology, 1981, 12(3):143-166. Google Scholar
[55]	Che T, Dai L, Zheng X, et al. Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China[J]. Remote Sensing of Environment, 2016, 183:334-349. Google Scholar
[56]	Sturm M, Brian T, Glen L, et al. Estimating snow water equivalent using snow depth data and climate classes[J]. Journal of Hydrometeorology, 2010, 11(6):1380-1394. Google Scholar
[57]	Jiang L M, Shi J C, Tjuatja S. Estimation of snow water equivalence using the polarimetric scanning radiometer from the Cold Land Processes Experiments(CLPX03)[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(2):359-363. Google Scholar
[58]	Pan J, Durand M T, Jagt B J V, et al. Application of a Markov chain Monte Carlo algorithm for snow water equivalent retrieval from passive microwave measurements[J]. Remote Sensing of Environment, 2017, 192:150-165. Google Scholar
[59]	Royer A, Roy A, Montpetit B, et al. Comparison of commonly-used microwave radiative transfer models for snow remote sensing[J]. Remote Sensing of Environment, 2017, 190:247-259. Google Scholar
[60]	蒋玲梅, 崔慧珍, 王功雪, 等. 积雪、土壤冻融与土壤水分遥感监测研究进展[J]. 遥感技术与应用, 2020, 35(6):1237-1262. Google Scholar
[61]	Jiang L M, Cui H Z, Wang G X, et al. Progress on remote sensing of snow,surface soil frozen/thaw state and soil moisture[J]. Remote Sensing Technology and Application, 2020, 35(6):1237-1262. Google Scholar
[62]	Forman B A, Reichle R H, Derksen C. Estimating passive microwave brightness temperature over snow-covered land in North America using a land surface model and an artificial neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(1):235-248. Google Scholar
[63]	Forman B A, Reichle R H. Using a support vector machine and a land surface model to estimate large-scale passive microwave brightness temperatures over snow covered land in North America[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(9):4431-4441. Google Scholar
[64]	Xiao X, Zhang T, Zhong X, et al. Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data[J]. Remote Sensing of Environment, 2018, 210(7):48-64. Google Scholar
[65]	Yang J, Jiang L, Luojus K, et al. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach[J]. The Cryosphere, 2020, 14(6):1763-1778. Google Scholar
[66]	Bair E H, Andre A C, Karl R. Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan[J]. The Cryosphere, 2018, 12(5):1579-1594. Google Scholar
[67]	Durand M T, Liu D S. The need for prior information in characterizing snow water equivalent from microwave brightness temperatures[J]. Remote Sensing of Environment, 2012, 126(4):248-257. Google Scholar
[68]	肖雄新, 张廷军. 基于被动微波遥感的积雪深度和雪水当量反演研究进展[J]. 地球科学进展, 2018, 33(6):590-605. Google Scholar
[69]	Xiao X X, Zhang T J. Passive microwave remote sensing of snow depth and snow water equivalent:Overview[J]. Advances in Earth Science, 2018, 33(6):590-605. Google Scholar
[70]	郭建平, 刘欢, 安林昌, 等. 2001—2012年青藏高原积雪覆盖率变化及地形影响[J]. 高原气象, 2016, 35(1):24-33. Google Scholar
[71]	Guo J P, Liu H, An L C, et al. Changes in the snow cover rate of the Xizang Plateau from 2001 to 2012 and the influence of terrain[J]. Plateau Meteorology, 2016, 35(1):24-33. Google Scholar
[72]	郝大磊, 肖青, 闻建光, 等. 定量遥感升尺度转换方法研究进展[J]. 遥感学报, 2018, 22(3):408-423. Google Scholar
[73]	Hao D L, Xiao Q, Wen J G, et al. Advances in up-scaling methods of quantitative remote sensing[J]. Journal of Remote Sensing, 2018, 22(3):408-423. Google Scholar
[74]	李长春, 包安明, 岳继博, 等. 基于地形校正的山区积雪深度反演算法[J]. 干旱区研究, 2016, 33(5):927-933. Google Scholar
[75]	Li C C, Bao A M, Yue J B, et al. Mountain snow depth inversion algorithm based on terrain correction[J]. Arid Zone Research, 2016, 33(5):927-933. Google Scholar
[76]	李晓静, 刘玉洁, 朱小祥, 等. 利用SSM/I数据判识我国及周边地区雪盖[J]. 应用气象学报, 2007, 18(1):12-20. Google Scholar
[77]	Li X J, Liu Y J, Zhu X X, et al. Using SSM/I data to identify snow cover in my country and surrounding areas[J]. Journal of Applied Meteorology, 2007, 18(1):12-20. Google Scholar
[78]	黄晓东, 李旭冰, 刘畅宇, 等. 青藏高原积雪范围和雪深/雪水当量遥感反演研究进展及挑战[J]. 冰川冻土, 2019, 41(5):1138-1149. Google Scholar
[79]	Huang X D, Li X B, Liu C Y, et al, Remote sensing inversion of snow cover extent and snow depth/snow water equivalent on the Qinghai-Xizang Plateau:Advance and challenge[J]. Glaciology and Geocryology, 2019, 41(5):1138-1149. Google Scholar
[80]	Liang T G, Zhang X T, Xie H J, et al. Towards improved daily snow cover map with advanced combination of MODIS and AMSR-E measurements[J]. Remote Sensing of Environment, 2008, 112(10):3750-3761. Google Scholar
[81]	Gao Y, Xie H J, Lu N, et al. Toward advanced daily cloud-free snow cover and snow water equivalent products from Terra Aqua MODIS and Aqua AMSR-E measurements[J]. Journal of Hydrolo-gy, 2010, 385(1):23-35. Google Scholar
[82]	Huang X D, Deng J, Ma X F, et al. Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China[J]. The Cryosphere, 2016, 10(5):2453-2463. Google Scholar
[83]	Wang Y L, Huang X D, Wang J S, et al. AMSR2 snow depth down scaling algorithm based on a multifactor approach over the Xizang Plateau,China[J]. Remote Sensing of Environment, 2019, 231:111268. Google Scholar
[84]	车涛, 郝晓华, 戴礼云, 等. 青藏高原积雪变化及其影响[J]. 中国科学院院刊, 2019, 34(11):1247-1253. Google Scholar
[85]	Che T, Hao X H, Dai L Y, et al. Snowcover variation and its impacts over the Qinghai-Xizang Plateau[J]. Bulletin of Chinese Academy of Sciences, 2019, 34(11):1247-1253. Google Scholar
[86]	Huang X D, Liu C Y, Wang Y L, et al. Snow cover variations across China from 1951—2018[J]. The Cryosphere Discussions, 2020. doi: 10.5194/tc-2020-202. Google Scholar
[87]	Senan R, Orsolini Y J, Weisheimer A, et al. Impact of springtime Himalayan-Xizang Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts[J]. Climate Dynamics, 2016, 47(9):2709-2725. Google Scholar
[88]	王顺久. 青藏高原积雪变化及其对中国水资源系统影响研究进展[J]. 高原气象, 2017, 36(5):1153-1164. Google Scholar
[89]	Wang S J. Progresses in variability of snow cover over the Qinghai-Xizang Plateau and its impact on water resources in China[J]. Plateau Meteorology, 2017, 36(5):1153-1164.[66] Fang Y H, Zhang X, Niu G Y, et al. Study of the spatiotemporal characteristics of meltwater contribution to the total runoff in the upper Changjiang River basin[J]. Water, 2017, 9(3):165. Google Scholar