Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation
作者机构：Institute of Remote Sensing ApplicationsChinese Academy of SciencesBeijingChina
出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)
年 卷 期：2013年第6卷第3期
页 面：203-218页
核心收录：
学科分类：0710[理学-生物学] 071001[理学-植物学] 07[理学] 0708[理学-地球物理学] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：The research was supported by National Natural Science Foundation of China,Nos.40801144 and 41171331 Knowledge Innovation Program of CAS,No.KSCX1-YW-09-01 the National Key Technology R&D Program,No.2008BADA8B02
主 题：high spatial and temporal resolution NDVI crop biomass Digital Earth data fusion
摘 要：While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud *** contrast,although Moderate Resolution Imaging Spectroradiometer(MODIS)can only achieve a spatial resolution of 250 m in its normalised difference vegetation index(NDVI)product,it has a high temporal resolution,covering the Earth up to multiple times per *** combine the high spatial resolution and high temporal resolution of different data sources,a new method(Spatial and Temporal Adaptive Vegetation index Fusion Model[STAVFM])for blending NDVI of different spatial and temporal resolutions to produce high spatialtemporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM).STAVFM defines a time window according to the temporal variation of crops,takes crop phenophase into consideration and improves the temporal weighting *** result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal *** application of the generated NDVI dataset in crop biomass estimation was *** average absolute error of 17.2%was *** estimated winter wheat biomass correlated well with observed biomass(R^(2) of 0.876).We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in *** is potential to apply the approach in many other studies,including crop production estimation,crop growth monitoring and agricultural ecosystem carbon cycle research,which will contribute to the implementation of Digital Earth by describing land surface processes in detail.
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