基于多源数据的青藏高原雪深重建

doi:10.3724/SP.J.1047.2016.00941

摘要/Abstract

摘要：

青藏高原地形复杂,积雪时空分布异质性较强且大部分地区积雪较薄,而被动微波遥感因其空间分辨率低以及雪深反演中的不确定性,极大地限制了其反演青藏高原雪深的精度。本文尝试将多源遥感数据以及与积雪模型（SnowModel）相结合,来重建更高质量的青藏高原雪深数据。首先,利用MODIS积雪面积比例产品,根据构建的积雪衰减曲线以及经验的融合规则对低分辨率被动微波雪深进行了降尺度;然后,结合MODIS/被动微波融合雪深数据和SnowModel对研究区进行雪深数据同化实验;最后,利用地面站实测雪深数据对MODIS/被动微波融合雪深以及同化输出雪深的精度进行了分析和对比。结果表明,基于数据同化方法得到的雪深数据更接近地面观测雪深值,通过均方根误差以及相关系数的对比,同化雪深结果优于MODIS/被动微波融合雪深结果。

关键词: 青藏高原, 多源遥感数据, 雪深, 重建

Abstract:

Due to the variability and complexity of the topography in the Tibetan Plateau, the snow cover over most area of Tibetan Plateau is thin and revealing a high temporal and spatial heterogeneity. The passive microwave remote sensing greatly limits the precision of retrieved snow depth over the Tibetan Plateau, on account of its low spatial resolution and the uncertainty existed during snow depth retrieval. This paper attempts to reconstruct higher quality of snow depth data over the Tibetan Plateau through the fusion of multi-source remote sensing data, combining with a physic based snow model (SnowModel). This research mainly includes the following aspects: first of all, using the in-situ observed snow depth data and corresponding MODIS fractional snow cover data, the snow depletion curve of the study area is established. The MODIS fractional snow cover products (500 m) and passive microwave snow depth products (0.25°) are combined to produce the downscaled snow depth data (0.1°) using an empirical combination rule and the established snow depletion curve. Then, the downscaled snow depth data are assimilated into the SnowModel using the ensemble Kalman filter (EnKF) method. The accuracy of the downscaled snow depth data and the assimilated snow depth are analyzed through comparing them with the in situ observed snow depth data. The results show that there is an obvious depletion curving relationship between the snow depth and fractional snow cover area in the Tibetan Plateau. Using the root mean square error (RMSE) and correlation coefficients (R) as the evaluation standard, the assimilated snow depth is evaluated to be closer to the in-situ observed snow depth than the downscaled snow depth data.

Key words: Tibet Plateau, muti-source remote sensing data, snow depth, reconstruction

唐志光, 李弘毅, 王建, 梁继, 李朝奎, 车涛, 王欣. 基于多源数据的青藏高原雪深重建[J]. 地球信息科学学报, 2016, 18(7): 941-950.

TANG Zhiguang,LI Hongyi,WANG Jian,LIANG Ji,LI Chaokui,CHE Tao,WANG Xin. Reconstruction of Snow Depth over the Tibetan Plateau Based on Muti-source Data[J]. Journal of Geo-information Science, 2016, 18(7): 941-950.

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年份	MAE/cm	RMSE/cm	R
2001	0.31	0.77	0.74
2003	0.27	0.63	0.81
2005	0.37	0.95	0.67
平均值	0.32	0.78	0.74

	变量	扰动方法	标准差	相关系数矩阵
驱动	气温	加法	0.5	1.0	-0.5	0.3	0.6
	降水	乘法	0.5	-0.5	1.0	-0.5	0.5
	短波辐射	乘法	0.1	0.3	-0.5	1.0	-0.3
	长波辐射	加法	15	0.6	0.5	-0.3	1.0
模型状态	雪深	乘法	0.01	-	-	-	-
观测	融合雪深	乘法	0.2	-	-	-	-

年份	融合雪深			同化雪深
年份	RMSE/cm		R	RMSE/cm	R
2000	0.51	0.51		0.20	0.73
2001	0.43	0.45		0.23	0.66
2002	0.40	0.47		0.35	0.81
2003	0.50	0.55		0.31	0.61
2004	0.80	0.40		0.34	0.79
2005	1.79	0.27		0.82	0.79
2006	1.02	0.34		0.44	0.82
2007	1.11	0.37		0.42	0.76
2008	1.05	0.41		0.34	0.87