Study on the Factors that Influencing High Spatio-temporal Resolution NDVI Fusion Accuracy in Tropical Mountainous Area

As an important data source in remote sensing application, high spatiotemporal resolution NDVI time series data is of great significance for dynamic change monitoring of land cover, especially in tropical mountainous areas, where the surface elevation changes significantly, climate conditions are complex and spatiotemporally heterogeneous. Many multi-spatiotemporal data fusion models have been proposed by scholars. However, it is rare to analyze the fusion accuracy of these models and their influencing factors in tropical mountainous areas. This study takes the Naban River Watershed in the tropical mountainous area of Southwest China as the study area. Four representative models have been selected from three types of spatiotemporal data fusion methods, namely weight function-based method, Bayesian-based method, and Hybrid method. The four models are Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Spatial and Temporal Adaptive Reflectance Fusion Model (RASTFM), and Bayesian Spatiotemporal Fusion Model (BSFM). Among them, STARFM and ESTARFM are weight function-based method, BSFM is Bayesian-based method, and RASTFM is Hybrid method. This study carries out analysis of data source selection, terrain of the study area, landscape spatial heterogeneity, pixel numerical accuracy of fusion model, and atmospheric conditions such as thin clouds and haze. The results show that, firstly, the fusion accuracy decreases with the increase of time interval and its relative variation. A better match in sensor spectrum between the two input data results in a higher fusion accuracy. OLI is better than Sentinel-2 while MODIS is better than VIIRS. Compared with unadjusted data, data adjusted by the Bidirectional Reflectance Distribution Function (BRDF) can effectively improve fusion accuracy.Secondly, fusion accuracy is negatively correlated with spatial heterogeneity. Fusion accuracy decreases when spatial heterogeneity increases. There is a strong negative correlation between fusion accuracy and spatial heterogeneity at elevations. Fusion accuracy decreases when slope increases. In comparison, slope aspect has little influence on fusion accuracy. The influence of terrain on RASTFM is smaller when compared with models. Thirdly, the more high-quality high-resolution raw data as input data for the model, the higher the fusion accuracy will be. Fourthly, thin clouds and haze have a significant negative impact on the fusion accuracy. The results have important values as references for improving the high spatial-temporal data fusion model in tropical mountainous areas and establishing high spatiotemporal resolution NDVI data sets in complex geographical environment. © 2022, Science Press. All right reserved.