首页|基于L1正则化的MODIS地表反射率数据时域重建方法

基于L1正则化的MODIS地表反射率数据时域重建方法

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  • NETL
  • NSTL
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MODIS地表反射率数据被广泛应用于陆地表面的动态监测,但云覆盖等因素的影响使得数据中存在时空缝隙,从而影响了数据可用性.对此,提出一种基于L1正则化的时域重建方法,能有效修复MODIS地表反射率数据中的缺失,实现高精度的长时序数据重建.该方法首先识别时序数据中因自然因素及系统因素产生的噪声,然后基于噪声检测对信息缺失区域进行年际预填补,在此基础上引入对突变噪声更为稳健的L1正则化模型,并结合噪声标记构建时域重建变分模型,还原地表的时序变化趋势.实验结果表明:相比于SG滤波、HP滤波、L1滤波、谐波分析方法,在不同百分比10%、25%、50%、75%的像元缺失情况下,该方法都取得了最高的重建精度;在不同地表场景下,该方法也取得了更好的重建结果.因此,该方法在时序曲线重建和空间细节修复上都更具有优势,表现出较高的实用价值.
L1 Regularization based Temporal Reconstruction Method for MODIS Surface Reflectance Data
MODIS time series surface reflectance data is widely used in the dynamic monitoring of land surface,but the influence of factors such as cloud cover causes spatial and temporal gaps in the data,which affects the da-ta availability.In this paper,we propose a time-domain reconstruction method based on L1 regularization,which can effectively repair the gaps in MODIS surface reflectance data and realize the reconstruction of long time-series data with high accuracy.The proposed method firstly identifies the noise generated by natural and systematic factors in the time-series data,and then pre-fills the missing information region inter-annually based on noise detection.On this basis,we introduce a L1 regularization model that is more robust to abrupt noise,and construct a variational model combining the noise masks to restore the time series trend of land surface.The experimental results show that compared with SG filtering,HP filtering,L1 filtering and HANTS,the method in this paper achieves the highest reconstruction accuracy at different percentages of missing pixels of 10%,25%,50% and 75%,and also achieves better reconstruction results under different ground surface scenes.Therefore,this method has more advantages in both time series curves reconstruction and spatial details restora-tion,which shows a high practical value.

Time series data reconstructionMODIS surface reflectance dataL1 regularizationvariational model

汪宇浩、沈焕锋、李志伟

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武汉大学 资源与环境科学学院,湖北 武汉 430079

时序数据重建 MODIS地表反射率数据 L1正则化 变分模型

国家自然科学基金项目国家自然科学基金项目中国博士后科学基金项目中国博士后科学基金项目中央高校基本科研业务费专项资金项目

41971303421013572021M6924622020TQ02292042021KF0078

2024

10.11873/j.issn.1004-0323.2024.3.0603

遥感技术与应用
中国科学院遥感联合中心

遥感技术与应用

CSTPCD北大核心
影响因子：0.961
ISSN：1004-0323
年,卷(期)：2024.39(3)
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