Evaluation of MODIS-Landsat and AVHRR-Landsat NDVI data fusion using a single pair base reference image:a case study in a tropical upstream catchment on Java,Indonesia

作     者：Andry Rustanto Martijn J.Booij 

作者机构：Department of Water Engineering and ManagementFaculty of Engineering TechnologyUniversity of TwenteEnschedethe Netherlands Department of GeographyUniversitas IndonesiaDepokIndonesia 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2022年第15卷第1期

页      面：164-197页

核心收录：

学科分类：08[工学] 0708[理学-地球物理学] 0815[工学-水利工程] 0804[工学-仪器科学与技术] 0816[工学-测绘科学与技术] 0706[理学-大气科学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：supported by the Ministry of Higher Education and Research of the Republic of Indonesia. 

主　　题：Image blending Landsat NDVI MODIS NDVI AVHRR NDVI tropical upstream catchment 

摘      要：Image blending is one of the alternative methods to fill temporal gaps in the monitoring of historical vegetation properties using continuous NDVI derived from Landsat 5 TM/7 ETM+and 8 OLI images.Frequent cloud occurrence in the tropical upstream catchment limits the use of image blending methods that allow to employment of a single pair base reference.This study aims to evaluate two image blending methods with nine input data configurations to select the most applicable one.Scatter plots and statistical indices such as ME,RMSE,model efficiency and structure similarity showed FSDAF outperforms STARFM in generating both synthetic Landsat 8 OLI NDVI and Landsat 5 TM/7 ETM+NDVI when employing unsupervised and supervised classification images,respectively,where both were applied along with MODIS NDVI 250 m v.005.When generating synthetic Landsat 5 TM/7 ETM+NDVI using AVHRR NDVI,both algorithms performed similarly.However,when considering the temporal over spatial variance ratio between base reference and predictor images,both algorithms performed almost similar when the value close to minimum.This study shows that selection of image blending algorithm with use single pair base reference image should consider input data configuration and temporal over spatial variance ratio.