增强型植被指数EVI(Enhanced Vegetation Index)综合处理了源于大气、土壤、饱和的问题,比归一化植被指数NDVI(Normalized Difference Vegetation Index)能更好地与植被的生物量、叶面积指数和光合有效辐射分量等建立有效的相关关系.针对EVI产品时间分辨率较低以及受到云覆盖等影响导致大量像元缺失问题,本文基于 MODIS 日 地表反射率产品,提出一种 MVC(Maximum-Value Composite)与 HANTS(Harmonic Analysis of Time Series)算法相结合的日分辨率EVI重建方法,以黄淮海平原为研究区重建了 2021年日分辨率EVI时间序列数据.结果表明,提出的EVI重建算法可用于大面积长时序日分辨率EVI时间序列数据的重建,重建结果纹理丰富,填补了原EVI大量的缺失像元,同时可去除原EVI数据的噪声,且符合各类地物EVI时序曲线的变化规律.此外,在与S-G滤波方法的对比分析中,经HANTS算法重建后的EVI在空间分布合理性以及保真性等方面均优于前者,其重建EVI与优质EVI像元之间的年均R2与RMSE分别为0.94和0.024,优于S-G方法的0.73和0.093.提出的日分辨率EVI重建方法为生成高时间分辨率EVI提供了新思路和技术途径.
A method for reconstructing long-term daily resolution EVIs based on MODIS daily surface reflectance products
The Enhanced Vegetation Index(EVI)combines factors such as atmospheric,soil,and saturation conditions and effectively correlates these data with vegetation biomass,leaf area index,and photosynthetically active radiation.Although the performance of the EVI is better than that of the Normalized Difference Vegetation Index(NDVI),the low temporal resolution of EVI products and the presence of cloud cover often result in a large number of missing pixels.In this study,we propose a daily resolution EVI reconstruction method that combines the Maximum Value Composite(MVC)and harmonic analysis of time series(HANTS)algorithms based on MODIS daily surface reflectance products.Given the spectral response differences of varying sensors carried by different satellites,the comparability of the EVIs calculated based on the Terra and Aqua satellites was analyzed prior to conducting the MVC operation.The analysis revealed a strong spatial linear correlation between the two variables,with R2 and RMSE values ranging from 0.9796-0.9935 and 0.0116-0.0297,respectively.The annual mean R2 and RMSE values were 0.9883 and 0.0196,respectively.The fitted parameters a and b had value ranges of 0.9447 to 1.0420 and-0.0065 to-0.0072,respectively,with annual mean values of 0.9910 and 0.0012.Despite spectral differences,the calculated EVIs based on the two satellite datasets exhibit minimal differences and thus are suitable for further processing via the MVC algorithm.This method was applied to reconstruct daily resolution EVI time series data for the North China Plain in 2021.The proposed EVI reconstruction algorithm is effective for large-scale and long-term reconstructions of daily resolution EVI time series data.The reconstructed EVI yields a rich texture,fills in the missing pixels,removes noise from the original EVI data,and follows the changing patterns of various land cover types.The HANTS method offers three advantages over the S-G filtering algorithm.First,compared with the original EVI,the HANTS method better preserved the spatial distribution patterns of the original EVI during reconstruction;by contrast,the S-G algorithm exhibited larger changes in spatial distribution in the reconstructed EVI.Second,the EVI curves reconstructed using the HANTS algorithm are smoother with minimal noise for typical land cover types;by contrast,the EVI curves reconstructed using the S-G algorithm have more local noise and nondifferentiable points,which hinders the extraction of vegetation phenological characteristics.Third,in terms of fidelity evaluation against high-quality reference EVI pixels,the HANTS algorithm demonstrated a strong linear correlation with the reference EVI pixels.The R2 and RMSE values ranged from 0.91 to 0.97 and from 0.017 to 0.032 across the months,with the strongest and weakest correlations occurring in September and June,respectively.By contrast,the S-G algorithm showed a weaker linear correlation with the reference EVI pixels.The R2 and RMSE values ranged from 0.38 to 0.91 and from 0.055 to 0.206 across the months,with the strongest and weakest correlations occurring in May and August,respectively.Overall,the HANTS method consistently outperformed the S-G method in terms of fidelity,with higher R2 values and lower RMSE values across all months.The proposed daily resolution EVI reconstruction method offers new guidelines and technical approaches for generating high-temporal resolution EVI data.
MODISvegetation indexEVIMVCHANTSdaily resolutionNorth China Plain
NETL
NSTL
万方数据
