Analysis and prediction of MODIS LAI time series with Dynamic Harmonic Regression model

JIANG Bo~

doi:10.11834/jrs.20100102

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Analysis and prediction of MODIS LAI time series with Dynamic Harmonic Regression model
Vol. 14, Issue 1, Pages: 13-32(2010)
Published： 2010 ，
DOI： 10.11834/jrs.20100102

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[1]江波,梁顺林,王锦地,肖志强.运用动态谐波回归模型对MODIS叶面积指数时间序列产品的分析与预测[J].遥感学报,2010,14(01):13-32.

JIANG Bo~. Analysis and prediction of MODIS LAI time series with Dynamic Harmonic Regression model. [J]. Journal of Remote Sensing 14(1):13-32(2010)
[1]江波,梁顺林,王锦地,肖志强.运用动态谐波回归模型对MODIS叶面积指数时间序列产品的分析与预测[J].遥感学报,2010,14(01):13-32. DOI： 10.11834/jrs.20100102.

JIANG Bo~. Analysis and prediction of MODIS LAI time series with Dynamic Harmonic Regression model. [J]. Journal of Remote Sensing 14(1):13-32(2010) DOI： 10.11834/jrs.20100102.

摘要

运用动态谐波回归模型（Dynamic Harmonic Regression

DHR）对MODIS的长时间序列的LAI产品进行分析

可以从中分离出LAI随时间变化的多年趋势、季节变化及残差等主要成分

通过建立的模型实现LAI年间变化的短时预测。本文将所述DHR模型分析方法试用于遥感数据产品随时间变化的信息提取

对LAI年间变化的预测结果证明该方法用于遥感像元尺度LAI产品的时间序列分析与预测的效果良好。

Abstract

Leaf Area Index（LAI） is one of the most important parameters in describing the dynamics of vegetation on land surfaces.LAI products have been produced from data of many remote sensing satellite sensors

such as the Moderate Resolution Imaging Spectroradiometer（MODIS）.In this paper

we used the Dynamic Harmonic Regression（DHR） model to analyze the LAI time series products.The model can decompose the trend

seasonal and residuals components from the original time series

and predict the short-time LAI values.We use the DHR model to extract the time change information from the MODIS LAI time series products.The results show this method to be very effective in predicting the short-term LAI on the pixel basis.

关键词

叶面积指数时间序列MODISDHR模型

Keywords

leaf area index（LAI）time seriesMODISDHR

references

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Comparison analysis based on different inverse algorithms of surface albedo products

Crop area estimation based on MODIS-EVI time series according to distinct characteristics of key phenology phases:a case study of winter wheat area estimation in small-scale area

Time-series accuracy validation and variation characteristic analysis of MODIS leaf-area index products for crop in the middle reaches of the Heihe River

High-precision monitoring of green tide biomass in the Yellow Sea of China through optical remote sensing

High spatial and temporal resolution monitoring of water area changes of Dongting Lake by joint Sentinel satellite series of radar and optical images

相关机构

College of Global Change and Earth System Science, Beijing Normal University

School of land science and technology, China University of Geosciences

State Key Laboratory of Remote Sensing Science,Jointly Sponsored by Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences and Beijing Normal University

北京师范大学资源学院地表过程与资源生态国家重点实验室

Beijing Normal University, State Key Laboratory of Remote Sensing Science

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