Snow depth and snow cover retrieval from FengYun3B microwave radiation imagery based on a snow passive microwave unmixing method in Northeast China

Gu, Lingjia; Ren, Ruizhi; Zhao, Kai; Li, Xiaofeng

doi:10.1117/1.jrs.8.084682

Cited by 16 publications

(9 citation statements)

References 23 publications

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“…For each grid cell, the SD is estimated from the sum of SD values from each land cover algorithm, weighted by the percentage of the land cover type within each grid cell. The application of this algorithm is described in [45,46].…”

Section: Empirical Sd Retrieval Algorithmmentioning

confidence: 99%

Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

Fan

et al. 2019

Remote Sensing

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At present, passive microwave remote sensing is the most efficient method to estimate snow depth (SD) at global and regional scales. Farmland covers 46% of Northeast China and accurate SD retrieval throughout the whole snow season has great significance for the agriculture management field. Based on the results of the statistical analysis of snow properties in Northeast China from December 2017 to January 2018, conducted by the China snow investigation project, snow characteristics such as snow grain size (SGS), snow density, snow thickness, and temperature of the layered snowpack were measured and analyzed in detail. These characteristics were input to the microwave emission model of layered snowpacks (MEMLS) to simulate the brightness temperature (TB) time series of snow-covered farmland in the periods of snow accumulation, stabilization, and ablation. Considering the larger SGS of the thick depth hoar layer that resulted in a rapid decrease of simulated TBs, effective SGS was proposed to minimize the simulation errors and ensure that the MEMLS can be correctly applied to satellite data simulation. Statistical lookup tables (LUTs) for MWRI and AMSR2 data were generated to represent the relationship between SD and the brightness temperature difference (TBD) at 18 and 36 GHz. The SD retrieval results based on the LUT were compared with the actual SD and the SD retrieved by Chang’s algorithm, Foster’s algorithm, the standard MWRI algorithm, and the standard AMSR2 algorithm. The results demonstrated that the proposed algorithm based on the statistical LUT achieved better accuracy than the other algorithms due to its incorporation of the variation in snow characteristics with the age of snow cover. The average root mean squared error of the SD for the whole snow season was approximately 3.97 and 4.22 cm for MWRI and AMSR2, respectively. The research results are beneficial for monitoring SD in the farmland of Northeast China.

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Section: Empirical Sd Retrieval Algorithmmentioning

confidence: 99%

Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

Fan

et al. 2019

Remote Sensing

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“…The root-mean-square error (rmse) of the snow depth retrieval was approximately 5.6 cm. Gu et al [14] proposed a passive microwave unmixing method for snow based on the Chinese Land Use/Land Cover data with a 1-km spatial resolution and an antenna gain function for passive microwaves. The land cover type of Northeast China was partitioned into 0196-2892 © 2016 IEEE.…”

Section: Introductionmentioning

confidence: 99%

“…The mixed pixels problem directly affects the retrieval accuracy of snow parameters. In our paper published in 2014 [14], the types of land cover over Northeast China were partitioned into grass, farmland, bare soil, forest, and water body. In this previous research on the passive microwave unmixing algorithm, the needleleaf forest, broadleaf forest, and mixed forest types defined in the original algorithm were unified into a single "pure" forest type.…”

Section: Introductionmentioning

confidence: 99%

Snow Depth Retrieval Based on a Multifrequency Dual-Polarized Passive Microwave Unmixing Method From Mixed Forest Observations

Ren

2016

IEEE Trans. Geosci. Remote Sensing

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Due to their low spatial resolution (≥ 10 km), passive microwave data from satellites often contain many mixed pixels. This is one of the main reasons that the retrieval accuracy of snow depth data is unsatisfactory for current practical demands. This paper proposes a multifrequency dual-polarized passive microwave unmixing method for snow. The land cover type of the observation area is partitioned into broadleaf forest, needleleaf forest, annual grass vegetation, and urban types. The component brightness temperature (CBT) of each land cover type, that is, unmixed data, with a 500-m data resolution, is obtained using the proposed unmixing method. Considering that the actual snow grain size was larger than 0.4 mm in the study area, Foster's snow depth retrieval algorithm, which refines the empirical coefficient, is employed in this paper. Compared with Chang's snow depth retrieval algorithm, the overall accuracy of snow depth data improved by approximately 29.2% using Foster's algorithm. Based on Foster's algorithm, the obtained results indicate that the overall accuracy of the snow depth data improved by approximately 22.9% when using the CBT compared with the original mixed-pixel method. The accuracy of snow depth data in annual grass vegetation is improved by approximately 38%, whereas those in needleleaf forest and broadleaf forest are improved by approximately 4.6% and 4.3%, respectively. The experimental results demonstrate that the CBT effectively improves the retrieval accuracy of snow depth data when compared with the case of using only the mixed-pixel method.Index Terms-Broadleaf forest, component brightness temperature (CBT), Fengyun-3B (FY3B) microwave radiation imagery, input data selection strategy, mixed pixels, needleleaf forest, snow depth, snow grain size (SGS).

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“…The overall impact of snow cover on the ground thermal regime depends on the complex factors, which include timing, duration, accumulation, and melting processes of snow cover, density, structure, and thickness of snow cover and interactions of snow cover with micrometeorological conditions, etc. (Zhang, 2005;Li et al, 2014). If the other environment conditions are almost similar, snow properties themselves (e.g., depth, density and timing) will have a significant impact on the ground temperature (Ling and Zhang, 2003;2006;2007).…”

Section: Introductionmentioning

confidence: 99%

Effects of snow cover on ground thermal regime: A case study in Heilongjiang Province of China

Zheng

et al. 2016

Chin. Geogr. Sci.

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The important effects of snow cover to ground thermal regime has received much attention of scholars during the past few decades. In the most of previous research, the effects were usually evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial (the ground profile) and time series analysis in the extremely snowy winter of 2012-2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011-2012 based on the measured data collected by 63 meteorological stations. Our results illustrate the positive (warmer) effect of snow cover on the ground temperature (GT) on the daily basis, the highest difference between GT and daily mean air temperature (DGAT) is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the response depth of ground to the alteration of snow depth is far more than 40 cm.

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Snow depth and snow cover retrieval from FengYun3B microwave radiation imagery based on a snow passive microwave unmixing method in Northeast China

Cited by 16 publications

References 23 publications

Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

Snow Depth Retrieval Based on a Multifrequency Dual-Polarized Passive Microwave Unmixing Method From Mixed Forest Observations

Effects of snow cover on ground thermal regime: A case study in Heilongjiang Province of China

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