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全文摘要次数: 108 全文下载次数: 79
引用本文:

DOI:

10.11834/jrs.20242483

收稿日期:

2022-09-19

修改日期:

2024-01-08

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基于机器学习的MODIS亚洲高山区积雪面积比例制图研究
高伟强1, 郝晓华2, 和栋材1, 孙兴亮3, 李弘毅2, 任鸿瑞1, 赵琴2
1.太原理工大学;2.中国科学院西北生态环境资源研究院;3.兰州大学
摘要:

积雪面积比例(Fractional Snow Cover, FSC)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(High Mountain Asia, HMA)基于分地类特征选择的多元自适应回归样条(Multivariate Adaptive Regression Splines, MARS)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,比较由 LC-MARS 模型制备的 FSC 与 MOD10A1、SnowCCI 在亚洲高山区精度表现。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,明显提高山林区 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 2000-2021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。

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

Objective: 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. Method: 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. Result:(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 mountain and forest areas. (3) The overall RMSE of MOD10A1 is 0.178 and MAE is 0.096; the overall RMSE of SnowCCI is 0.247 and MAE is 0.131. The accuracy of FSC prepared by LC-MARS is higher than that of MOD10A1 and SnowCCI, indicating that FSC inversion by LC-MARS has some application value. Conclusion: The LC-MARS model can fit high-dimensional nonlinear relationships and significantly improve the inversion accuracy of FSC in mountain and forest areas. The computational efficiency of the LC-MARS model based on Google Earth Engine is high, and it is suitable for preparing FSC products of large scale long time series. In this study, the day-by-day MODIS FSC products of HMA from 2000 to 2021 were prepared based on the LC-MARS model, which provides important data support for the study of climate change, hydrological and water resources in HMA

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