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遥感技术与应用  2020, Vol. 35 Issue (6): 1303-1311    DOI: 10.11873/j.issn.1004-0323.2020.6.1303
冰雪遥感专栏     
基于环境信息和回归模型的青藏高原MODIS积雪面积比例产品制备
雷华锦1,2(),李弘毅1,3(),王建1,4,郝晓华1,3,赵宏宇1,2,张娟5
1.中国科学院西北生态环境资源研究院,甘肃 兰州 730000
2.中国科学院大学,北京 100049
3.中国科学院黑河遥感试验研究站,甘肃 张掖 734000
4.江苏省地理信息资源开发与利用协同创新中心,江苏 南京 210023
5.青海省气象科学研究所,青海 西宁 810000
MODIS Fractional Snow Cover Products Preparing on Tibetan Plateau based on Environmental Information and Regression Model
Huajin Lei1,2(),Hongyi Li1,3(),Jian Wang1,4,Xiaohua Hao1,3,Hongyu Zhao1,2,Juan Zhang5
1.Northwest Institute of Eco-Environmental Resources,Chinese Academy of Sciences,Lanzhou 730000,China
2.University of Chinese Academy of Sciences,Beijing 100049,China
3.Heihe Remote Sensing Experimental Research Station,Chinese Academy of Sciences,Zhangye 734000,China
4.Geography of Jiangsu Province Collaborative Innovation Center for Information Resources Development and Utilization,Nanjing 210023,China
5.Qinghai Meteorological Science Research Institute,Xining 810000,China
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摘要:

积雪面积比例(Fractional Snow Cover, FSC)是定量描述单位像元内积雪覆盖面积(Snow Cover Area, SCA)与像元空间范围的比值,可为区域气候模拟、水文模型等提供积雪分布的定量信息。MODIS FSC产品是根据经验模型计算得到,并没有考虑地形、植被和地表温度等环境因素的影响,在青藏高原的验证精度低。针对此问题,考虑青藏高原地区环境因素(地形、植被、地表温度)对FSC制备的影响,基于多元自适应回归模型(Multivariate Adaptive Regression Splines, MARS)和线性回归模型分别建立FSC制备的非参数回归模型和经验回归模型。用Landsat 8地表反射率的数据和SNOMAP算法制备FSC的参考数据集。选取一部分参考数据集作为模型的训练数据集,另一部分作为模型的检验数据集。研究结果表明:MARS方法估计FSC的精度明显高于线性回归模型和原有的MODIS FSC制备方法。MARS的总体R、RMSE、MAE分别为0.791、0.103、0.058。在线性回归模型中精度最高的总体R、RMSE、MAE分别为0.647、0.128、0.072。MODIS 原有FSC制图方法的总体R、RMSE、MAE分别为0.595、0.221、0.170。考虑了环境信息的MARS方法更加适用于青藏高原地区FSC制备。本研究为制备青藏高原地区更高精度的FSC数据提供了新思路。

关键词: 青藏高原线性回归模型积雪面积比例MODIS多元自适应回归模型    
Abstract:

Fractional Snow Cover (FSC) is the ratio of the Snow Cover Area (SCA) to the spatial area in a unit pixel, which can provide quantitative information of snow cover distribution for regional climate simulation and hydrological model. MODIS FSC products are calculated according to the empirical model, without considering the impact of environmental factors such as topography, vegetation and surface temperature. The accuracy in the Tibetan plateau is low. Therefore , the effects of environmental factors (topography, vegetation, and surface temperature) on FSC preparation were taken into account in Tibetan plateau, based on Multivariate Adaptive Regression Splines (MARS) and linear regression model, and established a non-parametric regression model and an empirical regression model respectively based on Multivariate Adaptive Regression Splines (MARS) and linear regression model. The reference dataset of FSC was prepared with Landsat 8 surface reflectance data and SNOMAP algorithm. A part of reference dataset is selected as the training samples of the model, and the other part as the validation dataset of the model. The results show that the accuracy of the MARS method is significantly higher than that of the linear regression model and the original MODIS FSC preparation method. The total R, RMSE and MAE of MARS were 0.791, 0.103 and 0.058, respectively. In the linear regression model, the overall R, RMSE and MAE with the highest accuracy are 0.647, 0.128 and 0.072, respectively. The overall R, RMSE and MAE of the original MODIS FSC mapping method are 0.595, 0.221 and 0.170 respectively. MARS method with environmental information is more suitable for FSC preparation in Tibetan plateau. This study provides a new idea for preparing FSC data with higher accuracy in Tibetan plateau.

Key words: Tibetan plateau    Regression model    Fractional snow cover    MODIS    Multivariate adaptive regression splines
收稿日期: 2019-10-09 出版日期: 2021-01-26
ZTFLH:  TP75  
基金资助: 甘肃省科技计划(17JR5RA296);科技基础资源调查专项(2017FY100503);国家自然科学基金项目(41571371);青海省科技厅重点研发与转化项目(2017?SF?131)
通讯作者: 李弘毅     E-mail: leihuajin@lzb.ac.cn;lihongyi@lzb.ac.cn
作者简介: 雷华锦(1996-),女,四川雅安人,硕士研究生,主要从事寒区遥感水文研究。E?mail:leihuajin@lzb.ac.cn
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引用本文:

雷华锦,李弘毅,王建,郝晓华,赵宏宇,张娟. 基于环境信息和回归模型的青藏高原MODIS积雪面积比例产品制备[J]. 遥感技术与应用, 2020, 35(6): 1303-1311.

Huajin Lei,Hongyi Li,Jian Wang,Xiaohua Hao,Hongyu Zhao,Juan Zhang. MODIS Fractional Snow Cover Products Preparing on Tibetan Plateau based on Environmental Information and Regression Model. Remote Sensing Technology and Application, 2020, 35(6): 1303-1311.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.6.1303        http://www.rsta.ac.cn/CN/Y2020/V35/I6/1303

图1  研究区概况图及样本的空间分布(其中黑色框为用于模型训练的样本空间分布,红色框为验证样本的空间分布)
图2  数据处理流程图
图3  NDSI、NDVI 与FSC之间的关系(其中R为参数之间的相关系数)
样本编号云量/%有效像元个数行/列号日期
Ta1.9857 457131/372016-03-25
Tb1.0539 310133/362018-11-08
Tc0.7444 610136/382014-02-19
Td0.821 039138/382014-12-18
Te1.3620 523141/342013-12-20
Tf1.2652 320150/332017-01-12
表1  检验数据集的元数据信息
M有效像元个数RRMSEMAE
FSCMARS35 876.500.7910.1030.058
FSCa35 876.500.5530.1440.091
FSCb35 876.500.6470.1280.072
FSCc35 876.500.5950.1390.090
FSCMOD35 876.500.5950.2210.170
表2  不同模型制备FSC的精度
图4  检验样本的误差图
图5  Ta(时间为2016年3月25日)用不同模型制备FSC的结果(a)表示Landsat 8的“真值”FSC,(b)~(f)分别表示MARS及4种不同线性回归模型的FSC结果
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