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遥感技术与应用  2022, Vol. 37 Issue (2): 499-506    DOI: 10.11873/j.issn.1004-0323.2022.2.0499
数据论文专栏     
巴音河流域高时空分辨率叶面积指数数据集
傅笛1,2(),金鑫1,2,3(),金彦香1,2,3,毛旭锋1,2,3,翟婧雅1,2
1.青海师范大学 地理科学学院,青海 西宁 810016
2.青海省自然地理与环境过程重点实验室,青海 西宁 810016
3.高原科学与可持续发展研究院,青海 西宁 810016
High Spatial and Temporal Resolution Leaf Area IndexDataset of Bayin River Basin
Di Fu1,2(),Xin Jin1,2,3(),Yanxiang Jin1,2,3,Xufeng Mao1,2,3,Jingya Zhai1,2
1.School of the Geographical Science,Qinghai Normal University,Xining 810016,China
2.Key Laboratory of Physical Geography and Environmental Processes,Xining 810016,China
3.Academy of Plateau Science and Sustainability,Xining 810016,China
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摘要:

叶面积指数(Leaf Area Index,LAI)是表征地表特征变化的重要指标之一,也是陆表、水文等模型的重要参数。本数据集是基于增强型时空自适应反射率融合模型(ESTARFM),将全球陆地表层卫星(GLASS)LAI(8d/500m)、中分辨率成像光谱仪(MODIS)MOD13A1和MYD13A1、陆地卫星Landsat-7 ETM+和Landsat-8 OLI数据,进行融合,得到8 d/30 m分辨率的LAI,通过分段线性内插最终得到巴音河流域高时空分辨率LAI(1 d/30 m)。对比高时空分辨率LAI(1 d/30 m)与GLASS LAI产品的时空特征,验证数据集精度。结果表明:与原始GLASS LAI相比,本数据集在空间上具有与GLASS LAI一致的分布特征,且轮廓与纹理更为清晰。在时间上,二者具有相同的月际变化特征,且由1 d/30 m LAI估算的区域月平均LAI和区域8日平均LAI与原始GLASS LAI存在显著正相关性,R2分别为0.95、0.94,Pearson积矩相关系数均为0.97,P值均小于0.01。此数据集可为陆表过程、水文循环等模拟提供重要的数据支持,为监测植被-陆表-大气循环的变化提供重要依据。

关键词: GLASS LAIESTARFM模型高时空分辨率数据集    
Abstract:

Leaf Area Index (Leaf Area Index, LAI) is one of the important indicators to characterize the changes of land surface characteristics, as well as an important parameter of land surface and hydrological models.This dataset was based on GLASS LAI (8 d/500 m), combined with MOD13A1, MYD13A1 and Landsat 7-ETM+,Landsat 8-OLI data. First, ESTARFM model was used to synthesize LAI at 8 d/30 m resolution, and then LAI with high spatial and temporal resolution (1 d/30 m) was obtained by time linear interpolation. The spatial and temporal characteristics of LAI with high spatial and temporal resolution (1 d/30 m) were compared based on GLASS LAI products to verify the accuracy of the data set. The results show that the distribution features of this data set are basically consistent with that of GLASS LAI in space, and the contour and texture are clearer. In terms of time, they have the same intermonthly variation characteristics, and the regional monthly average LAI and regional 8-day average LAI estimated by 1 d/30 m LAI have a significant positive correlation with the original GLASS LAI, R2 are 0.95 and 0.94, respectively. Pearson product moment correlation coefficients are 0.97, P values are all less than 0.01.

Key words: GLASS LAI    ESTARFM model    High spatial and temporal resolution    Dataset
收稿日期: 2021-08-10 出版日期: 2022-06-17
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目“基于模型改进的高寒内陆河流域地下水补排关系研究”(41801094);青海省科技厅自然科学基金项目“基于模型改进的柴达木盆地植被恢复的土壤水资源响应研究”(2021?ZJ?705)
通讯作者: 金鑫     E-mail: fud2020@163.com;jinx13@lzu.edu.cn
作者简介: 傅笛(1997-),女,重庆荣昌人,硕士研究生,主要从事水文过程方面的研究。E?mail: fud2020@163.com
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引用本文:

傅笛,金鑫,金彦香,毛旭锋,翟婧雅. 巴音河流域高时空分辨率叶面积指数数据集[J]. 遥感技术与应用, 2022, 37(2): 499-506.

Di Fu,Xin Jin,Yanxiang Jin,Xufeng Mao,Jingya Zhai. High Spatial and Temporal Resolution Leaf Area IndexDataset of Bayin River Basin. Remote Sensing Technology and Application, 2022, 37(2): 499-506.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.2.0499        http://www.rsta.ac.cn/CN/Y2022/V37/I2/499

图1  研究区概况图
图2  高时空分辨率LAI计算流程图
图3  原始GLASS LAI与高时空分辨率LAI空间特征对比
图4  2014~2018年季节变化下的高时空分辨率LAI空间分布
图5  2014~2018年GLASS LAI与高时空分辨率LAI对比分析
R2Pearson积矩相关系数P
区域月平均LAI值0.950.97<0.01
区域8日LAI平均值0.940.97<0.01
表1  高时空分辨率LAI与原始GLASS LAI相关系数结果
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