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遥感技术与应用  2021, Vol. 36 Issue (6): 1236-1246    DOI: 10.11873/j.issn.1004-0323.2021.6.1236
冰雪遥感专栏     
基于AMSR2和MODIS数据融合的雪深降尺度算法研究—以北疆地区为例
胡晓静1,2,3,4(),郝晓华2(),王建2,戴礼云2,赵宏宇2,李弘毅2
1.兰州交通大学 测绘与地理信息学院,甘肃 兰州 730070
2.中国科学院西北生态环境资源研究院,甘肃 兰州 730000
3.地理国情监测技术应用国家地方联合工程研究中心,甘肃 兰州 730070
4.甘肃省地理国情监测工程实验室,甘肃 兰州 730070
Snow Depth Downscaling Algorithm based on the Fusion of AMSR2 and MODIS Data:A Case Study in Northern Xinjiang, China
Xiaojing Hu1,2,3,4(),Xiaohua Hao2(),Jian Wang2,Liyun Dai2,Hongyu Zhao2,Hongyi Li2
1.Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China
2.Northwest Insttitute of Eco-environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China
3.National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China
4.Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China
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摘要:

高空间分辨率雪深数据对于区域气候、水文研究具有重要的意义。利用10 km空间分辨率的AMSR2 L1B亮度温度数据,结合500 m空间分辨率的MODIS逐日无云积雪面积比例数据,发展了一种多源数据融合的空间动态降尺度雪深反演算法(SDD)。基于该算法获取了北疆地区500 m空间分辨率的雪深数据(SDDsd),并利用研究区30个气象台站和野外实测的雪深数据对该算法反演雪深的精度进行了评估。结果表明:基于SDD方法获取的雪深数据与实测雪深数据之间的决定系数R2为0.74,均方根误差RMSE为3.47 cm;雪深反演的精度与下垫面类型密切相关,草地精度最高,城镇和建设用地次之,耕地相对较差;雪深反演的精度也会受到地形的影响,精度随坡度的增加而降低。相对于微波遥感雪深数据直接重采样结果,新的算法有效提高了浅雪区雪深反演精度,同时能更精细地描述积雪的空间分布,为理解区域气候变化、水文循环提供了可靠的数据支撑。此外,随着长时间序列全球尺度逐日无云FSC数据的生产,结合现有的长时间序列全球尺度AMSR2数据,该算法有望制备全球的降尺度雪深产品。

关键词: 雪深AMSR2MODIS积雪面积比例SSEmod降尺度算法    
Abstract:

High spatial resolution snow depth data is very important to study regional climate changes and hydrological cycle. We have developed a spatial dynamic downscaling snow depth retrieval algorithm (SDD), which retrieves the snow depth by fusing AMSR2 L1B brightness temperature data (10 km) and MODIS daily cloud-free fractional snow cover data (500 m). By using the SDD algorithm, snow depth data (SDDsd) with a spatial resolution of 500 m in the northern Xinjiang region was obtained, and the results were verified and evaluated by comparison with snow depth provided by 30 meteorological stations and field work. The results show that: the snow depth derived from SDD algorithm and in situ snow depth are in good agreement,the R2 is 0.74, and the RMSE is 3.47 cm. After further analysis, it is found that the snow depth inversion accuracy of different land cover types is different. The accuracy of grassland is the best, followed by urban and built-up Lands, and cultivated land is relatively poor. The accuracy of snow depth is also affected by the terrain, and it decreases as the slope increases. Compared with the results of direct re-sampling of microwave remotely sensed snow depth data, the SDD algorithm effectively improves the accuracy of snow depth in shallow snow areas, the spatial distribution of snow is also more perfectly reflected. SDDsd data provides reliable data support for understanding regional climate change and hydrological cycle. In addition, with the global-scale production of long-time series of cloud-free fractional snow cover products, combined with the existing long-time series global-scale AMSR2 data, the SDD algorithm is expected to produce global downscale snow depth products.

Key words: Snow depth    AMSR2    MODIS    FSC    SSEmod    Downscaling algorithm
收稿日期: 2020-12-14 出版日期: 2022-01-26
ZTFLH:  P426.63+5  
基金资助: 国家重点研发计划(2019YFC1510503);国家自然科学基金项目(41971325);中国科学技术基础资源调查计划(2017 FY100502);兰州交通大学优秀平台支持(201806)
通讯作者: 郝晓华     E-mail: 0218769@stu.lzjtu.edu.cn;haoxiaohua@lzb.ac.cn
作者简介: 胡晓静(1994-),女,陕西渭南人,硕士研究生,主要从事积雪遥感方向研究。E?mail: 0218769@stu.lzjtu.edu.cn
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引用本文:

胡晓静,郝晓华,王建,戴礼云,赵宏宇,李弘毅. 基于AMSR2和MODIS数据融合的雪深降尺度算法研究—以北疆地区为例[J]. 遥感技术与应用, 2021, 36(6): 1236-1246.

Xiaojing Hu,Xiaohua Hao,Jian Wang,Liyun Dai,Hongyu Zhao,Hongyi Li. Snow Depth Downscaling Algorithm based on the Fusion of AMSR2 and MODIS Data:A Case Study in Northern Xinjiang, China. Remote Sensing Technology and Application, 2021, 36(6): 1236-1246.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.6.1236        http://www.rsta.ac.cn/CN/Y2021/V36/I6/1236

参数SMMRSSM/IAMSR-EAMSR 2
频率/GHz6.6/10.7/18/21/3719.3/22.3/37/85.56.9/10.65/18.7/23.8/36.5/89.46.9/7.3/10.65/18.7/23.8/36.5/89.3
高度/km955860705700
入射角/°50.353.15555
刈幅/km7801 4001 4551 450
发射日期/年1978~19871987~20072002~20122012年至今
分辨率/km2525/12.525/12.510/5
表1  被动微波传感器主要参数
图1  北疆区域实测雪深数据以及FSC分布图审图号:GS(2019)1815
图2  多源数据融合的空间动态降尺度雪深反演算法(SDD)总体流程图
图3  Grody决策树
图4  北疆区域积雪衰退曲线
图5  降尺度雪深反演流程图
图6  降尺度雪深方法展示
图7  SDD方法获取的雪深积雪季平均雪深空间分布图审图号:GS(2019)1815
图8  实测雪深与反演雪深的比较
雪深验证R2RMSE /cm验证数据来源R2

RMSE

/cm

不同下垫面和地形

精度分析

RMSE /cm
气象站数据和积雪调查数据整体验证0.743.47气象站实测雪深0.743.25下垫面草地3.01
建设用地3.35
耕地3.71
积雪调查野外实测雪深0.66.3坡度0°~0.5°3.4
0.5°~2°6
2°~5°6.3
大于5°7.1
表2  整体和不同分类下的精度评价
图9  不同土地类型和地形的实测雪深数据审图号:GS(2019)1815
图10  积雪识别精度和样本量随雪深的变化关系
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