GNSS-IR model of snow depth estimation combining wavelet transform with sliding window

doi:10.11947/j.AGCS.2020.20200268

Abstract

Abstract: Currently, GNSS interferometric reflectometry technology has become a high-precision method for monitoring land surface snow depth. Aiming at the problems of signal separation and random estimation biases, we developed a GNSS-IR refined model with multi-satellite fusion for snow depth estimation combining wavelet transform with sliding window. The common polynomial method was replaced by discrete wavelet transform to obtain the high-quality SNR sequences of the reflected signals which can calculate the reflected height of GPS antenna. Then, these reflected heights from SNR observations of multi-satellite were effectively selected and averaged using the sliding window under a constrained threshold. The refined model was established using GNSS observations for snow season from 2016 to 2017, and then the snow depth datasets of both PBO H₂O and SNOTEL were regarded as reference to verify the performance of the refined model. The results show that there is a high agreement between snow depths derived from the refined model and in situ measurements, and the RMSE is 10 cm. Compared with the results of a single satellite, the accuracy and the stability of the refined model with multi-satellite fusion are obviously better. In terms of RMSE, the accuracy of the refined model has been improved by 50% when compared with PBO H₂O dataset. In addition, taking into consideration that land surface roughness is an error factor, a relative RMSE value of snow depth estimations corrected by a new datum of the reflection height is approximately 4 cm, and the correlation coefficient between snow depth estimations and in situ measurements reaches 0.98.

Key words: global navigation satellite system interferometric reflectometry, wavelet transform, sliding window, snow depth estimation, land surface roughness

CLC Number:

P228

BIAN Shaofeng, ZHOU Wei, LIU Lilong, LI Houpu, LIU Bei. GNSS-IR model of snow depth estimation combining wavelet transform with sliding window[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(9): 1179-1188.

References

[1] 柏延臣, 冯学智, 李新, 等. 基于被动微波遥感的青藏高原雪深反演及其结果评价[J]. 遥感学报, 2001, 5(3):161-165. BO Yanchen, FENG Xuezhi, LI Xin, et al. The retrieval of snow depth in Qinghai_Xizang (Tibet) Plateau from passive microwave remote sensing data and its results assessment[J]. Journal of Remote Sensing, 2001, 5(3):161-165.
[2] 张双成, 戴凯阳, 南阳, 等. GNSS-MR技术用于雪深探测的初步研究[J]. 武汉大学学报(信息科学版), 2018, 43(2):234-240. ZHANG Shuangcheng, DAI Kaiyang, NAN Yang, et al. Preliminary research on GNSS-MR for snow depth[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2):234-240.
[3] 杨元喜. 综合PNT体系及其关键技术[J]. 测绘学报, 2016, 45(5):505-510. DOI:10.11947/j.AGCS.2016.20160127. YANG Yuanxi. Concepts of comprehensive PNT and related key technologies[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(5):505-510. DOI:10.11947/j.AGCS.2016.20160127.
[4] 吴泽民, 边少锋. 后验概率与最小均方误差解结合的GNSS部分模糊度解算策略[J]. 测绘学报, 2018, 47(S1):54-60. DOI:10.11947/j.AGCS.2018.20180295. WU Zemin, BIAN Shaofeng. GNSS partial ambiguity resolution based on posterior probability and minimum squares error solution[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(S1):54-60. DOI:10.11947/j.AGCS.2018.20180295.
[5] 万玮, 陈秀万, 彭学峰, 等. GNSS遥感研究与应用进展和展望[J]. 遥感学报, 2016, 20(5):858-874. WAN Wei, CHEN Xiuwan, PENG Xuefeng, et al. Overview and outlook of GNSS remote sensing technology and applications[J]. Journal of Remote Sensing, 2016, 20(5):858-874.
[6] 金双根, 张勤耘, 钱晓东. 全球导航卫星系统反射测量(GNSS+R)最新进展与应用前景[J]. 测绘学报, 2017, 46(10):1389-1398. DOI:10.11947/j.AGCS.2017.20170282. JIN Shuanggen, ZHANG Qinyun, QIAN Xiaodong. New progress and application prospects of global navigation satellite system reflectometry (GNSS+R)[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1389-1398. DOI:10.11947/j.AGCS.2017.20170282.
[7] 陈锐志, 王磊, 李德仁, 等. 导航与遥感技术融合综述[J]. 测绘学报, 2019, 48(12):1507-1522. DOI:10.11947/j.AGCS.2019.20190446. CHEN Ruizhi, WANG Lei, LI Deren, et al. A survey on the fusion of the navigation and the remote sensing techniques[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(12):1507-1522. DOI:10.11947/j.AGCS.2019.20190446.
[8] MARTIN-NEIRA M. A passive reflectometry and interferometry system (PARIS):application to ocean altimetry[J]. ESA Journal, 1993, 17(4):331-355.
[9] 张双成, 南阳, 李振宇, 等. GNSS-MR技术用于潮位变化监测分析[J]. 测绘学报, 2016, 45(9):1042-1049. DOI:10.11947/j.AGCS.2016.20150498. ZHANG Shuangcheng, NAN Yang, LI Zhenyu, et al. Analysis of tide variation monitored by GNSS-MR[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(9):1042-1049. DOI:10.11947/j.AGCS.2016.20150498.
[10] 王娜子, 鲍李峰, 高凡. 逐历元GNSS-R测高单差和双差算法[J]. 测绘学报, 2016, 45(7):795-802. DOI:10.11947/j.AGCS.2016.20150638. WANG Nazi, BAO Lifeng, GAO Fan. Improved water level retrieval from epoch-by-epoch single and double difference GNSS-R algorithms[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(7):795-802. DOI:10.11947/j.AGCS.2016.20150638.
[11] YU Kegen, RIZOS C, DEMPSTER A G. GNSS-based model-free sea surface height estimation in unknown sea state scenarios[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(5):1424-1435.
[12] LARSON K M, NIEVINSKI F G. GPS snow sensing:results from the earth scope plate boundary observatory[J]. GPS Solutions, 2013, 17(1):41-52.
[13] 周威, 刘立龙, 黄良珂, 等. GLONASS卫星SNR信号的雪深探测[J]. 遥感学报, 2018, 22(5):889-899. ZHOU Wei, LIU Lilong, HUANG Liangke, et al. Monitoring snow depth based on the SNR signal of GLONASS satellites[J]. Journal of Remote Sensing, 2018, 22(5):889-899.
[14] TSANG L, KONG J A, SHIN R T. Theory of microwave remote sensing[M]. New York:Wiley-Interscience, 1985.
[15] KAVAK A, VOGEL W J, XU Guanghan. Using GPS to measure ground complex permittivity[J]. Electronics Letters, 1998, 34(3):254-255.
[16] BILICH A, LARSON K M. Mapping the GPS multipath environment using the signal-to-noise ratio (SNR)[J]. Radio Science, 2007, 42(6):RS6003.
[17] BILICH A, LARSON K M, AXELRAD P. Modeling GPS phase multipath with SNR:case study from the Salar de Uyuni, Boliva[J]. Journal of Geophysical Research:Solid Earth, 2008, 113(B4):B04401.
[18] LARSON K M, SMALL E E, GUTMANN E, et al. Using GPS multipath to measure soil moisture fluctuations:initial results[J]. GPS Solutions, 2008, 12(3):173-177.
[19] LARSON K M, GUTMANN E D, ZAVOROTNY V U, et al. Can we measure snow depth with GPS receivers?[J]. Geophysical Research Letters, 2009, 36(17):L17502.
[20] LARSON K M, SMALL E E. Estimation of snow depth using L1 GPS signal-to-noise ratio data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(10):4802-4808.
[21] JIN Shuanggen, QIAN Xiaodong, KUTOGLU H. Snow depth variations estimated from GPS-reflectometry:a case study in Alaska from L2P SNR data[J]. Remote Sensing, 2016, 8(1):63.
[22] 黄良珂, 周威, 刘立龙, 等. 基于GPS新型L5信号的地表雪深反演研究[J]. 测绘通报, 2019(7):1-5, 11. DOI:10.13474/j.cnki.11-2246.2019.0208. HUANG Liangke, ZHOU Wei, LIU Lilong, et al. Research on surface snow depth retrieval of new L5 signals from GPS[J]. Bulletin of Surveying and Mapping, 2019(7):1-5, 11. DOI:10.13474/j.cnki.11-2246.2019.0208.
[23] JACOBSON M D. Dielectric-covered ground reflectors in GPS multipath reception-theory and measurement[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(3):396-399.
[24] JACOBSON M D. Estimating snow water equivalent for a slightly tilted snow-covered prairie grass field by GPS interferometric reflectometry[J]. EURASIP Journal on Advances in Signal Processing, 2014, 2014(1):61.
[25] MALLAT S G. A theory for multiresolution signal decomposition:the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7):674-693.
[26] WANG Xiaolei, ZHANG Qin, ZHANG Shuangcheng. Water levels measured with SNR using wavelet decomposition and Lomb-Scargle periodogram[J]. GPS Solutions, 2017, 22(1):22.
[27] TABIBI S, GEREMIA-NIEVINSKI F, VAN DAM T. Statistical comparison and combination of GPS, GLONASS, and multi-GNSS multipath reflectometry applied to snow depth retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7):3773-3785.
[28] 梁月吉, 任超, 黄仪邦, 等. 多星融合的土壤湿度滚动式估算模型[J]. 遥感学报, 2019, 23(4):648-660. LIANG Yueji, REN Chao, HUANG Yibang, et al. Rolling estimation model of soil moisture based on multi-satellite fusion[J]. Journal of Remote Sensing, 2019, 23(4):648-660.