MODIS亚洲高山区积雪面积比例制图  

Machine learning-based mapping of fraction snow cover in High Mountain Asia by MODIS

作　　者：高伟强 郝晓华 和栋材[1] 孙兴亮 李弘毅 任鸿瑞[1] 赵琴 GAO Weiqiang;HAO Xiaohua;HE Dongcai;SUN Xingliang;LI Hongyi;REN Hongrui;ZHAO Qin(College of Mining Engineering,Taiyuan University of Technology,Taiyuan 030000,China;Northwest Institute of Eco-environment and Resources Chinese Academy of Sciences,Lanzhou 730000,China;College of Resources and Environment,Lanzhou University,Lanzhou 730000,China)

机构地区：[1]太原理工大学矿业工程学院,太原030000 [2]中国科学院西北生态环境资源研究院,兰州730000 [3]兰州大学资源环境学院,兰州730000

出　　处：《遥感学报》2024年第9期2223-2239,共17页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:U22A20564,41971325);国家重点研发计划(编号:2022YFF0711702-05)。

摘　　要：积雪面积比例FSC(Fractional Snow Cover)能在亚像元尺度上定量描述积雪的覆盖程度,相比二值积雪更适合反映复杂山区积雪的分布情况,是山区融雪径流模拟,气候变化预测的重要输入参数。本研究在亚洲高山区HMA(High Mountain Asia)基于分地类特征选择的多元自适应回归样条MARS(Multivariate Adaptive Regression Splines)模型LC-MARS发展了MODIS FSC反演算法,并制备了亚洲高山区FSC产品。以Landsat 8提取的FSC为参考真值验证LC-MARS模型反演FSC精度,对比相同训练样本下LC-MARS模型与线性回归模型反演FSC精度,比较LC-MARS模型制备的FSC与MOD10A1、SnowCCI在亚洲高山区的精度表现。结果表明:(1) LC-MARS模型反演的FSC总Accuracy、Recall分别为93.4%、97.1%,总体RMSE为0.148,MAE为0.093,总体精度较高。(2)相同训练样本下LC-MARS模型在林区、植被和裸地反演FSC精度均高于线性回归模型,表明LC-MARS模型更适用于山林区FSC反演。(3) MOD10A1总体RMSE为0.178,MAE为0.096;SnowCCI总体RMSE为0.247,MAE为0.131,LC-MARS制备的FSC精度均高于MOD10A1、SnowCCI,表明由LC-MARS反演的FSC具有一定的应用价值。总体而言,LC-MARS模型可以拟合高维非线性关系,显著提高山林区FSC的反演精度且模型运算效率高,适用于制备大尺度长时间序列的FSC产品。本研究基于LC-MARS模型制备了2000年—2021年亚洲高山区逐日MODIS FSC产品,为亚洲高山区气候变化、水文水资源研究提供重要的数据支撑。High Mountain Asia(HMA) is the richest high altitude region in the world except for the poles in terms of glacier and snow resources,The accurate monitoring of HMA snowpack distribution is important for HMA snowmelt runoff simulation,climate change prediction and ecosystem evolution.Fractional Snow Cover(FSC) can quantitatively describe the extent of snow cover at the sub-image scale,and is more suitable for reflecting the distribution of snow in complex mountainous areas than binary snow.The objective of this study is to develop a new HMA snow area ratio inversion algorithm and integrate the algorithm into Google Earth Engine to prepare a set of long time series HMA snow area ratio products.Considering the influence of HMA topography and sub-bedding type on the accuracy of snow accumulation information extraction,this paper proposes a Multivariate Adaptive Regression Splines(MARS) model LC-MARS to invert the proportion of snow accumulation area in Asia by integrating topography correction and subland class feature extraction.The FSC extracted by Landsat 8 is used as the true value,and the LC-MARS model is tested for inversion FSC accuracy using binary and error validation methods,and the performance of linear regression models trained with the same training samples and the LC-MARS model for inversion HMAFSC accuracy is compared,and the accuracy of the FSC inversion of the LC-MARS model with SnowCCI and MOD10A1 is also compared.(1) The overall accuracy of FSC binary validation of LC-MARS model inversion showed that Accuracy and Recall were 93.4% and 97.1%,respectively,and the overall accuracy of error validation showed that RMSE was 0.148 and MAE was 0.093,both binary validation and error validation indicated that the FSC accuracy of LC-MARS model inversion was higher.(2) The LC-MARS model trained based on the same training samples has higher FSC accuracy than the linear regression model in forest area,vegetation and bare land inversions,indicating that the LC-MARS model is more suitable for FSC inversions in mou

关 键 词：遥感 亚洲高山区 积雪面积比例 MODIS MARS 地形校正 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置] P2[自动化与计算机技术—控制科学与工程]