Abstract
It has a great significance to combine multi-source with different spatial resolution and temporal resolution to produce high spatiotemporal resolution Normalized Difference Vegetation Index (NDVI) time series data sets. In this study, four spatiotemporal fusion models were analyzed and compared with each other. The models included the spatial and temporal adaptive reflectance model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion model (FSDAF), and a spatiotemporal vegetation index image fusion model (STVIFM). The objective of is to: 1) compare four fusion models using Landsat-MODIS NDVI image from the Banan district, Chongqing Province; 2) analyze the prediction accuracy quantitatively and visually. Results indicate that STVIFM would be more suitable to produce NDVI time series data sets.
Highlights
The Normalized Difference Vegetation Index (NDVI) is a widely used vegetation index (VI) and provides a way of evaluating the biophysical or biochemical information related to vegetation growth [1]
Results indicate that spatiotemporal vegetation index image fusion model (STVIFM) would be more suitable to produce NDVI time series data sets
All the predicted NDVI images are consistent with the actual image from visual comparison, and water boundaries and clear land can be predicted obviously, which demonstrate the practicality of these spatiotemporal models
Summary
The Normalized Difference Vegetation Index (NDVI) is a widely used vegetation index (VI) and provides a way of evaluating the biophysical or biochemical information related to vegetation growth [1]. Long term NDVI time-series datasets have been widely used for monitoring ecosystem dynamics to understand the responses of climate change [2] [3]. Y. Zhao constraints, it is difficult to obtain NDVI data with both high spatial and high temporal resolution on the same remote sensing instrument [4]. Spatiotemporal fusion techniques which combine NDVI date from multi-sensors with high spatial and temporal resolution is feasible solution to acquire remote sensing time series for monitoring surface vegetations dynamics [6] [7]
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