Abstract
Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. Therefore, several methods are developed to produce high resolution (spatial and temporal) NDVI time-series datasets, which face some limitations including high computation loads and unreasonable assumptions. In this study, an unmixing-based method, NDVI Linear Mixing Growth Model (NDVI-LMGM), is proposed to achieve the goal of accurately and efficiently blending MODIS NDVI time-series data and multi-temporal Landsat TM/ETM+ images. This method firstly unmixes the NDVI temporal changes in MODIS time-series to different land cover types and then uses unmixed NDVI temporal changes to predict Landsat-like NDVI dataset. The test over a forest site shows high accuracy (average difference: −0.0070; average absolute difference: 0.0228; and average absolute relative difference: 4.02%) and computation efficiency of NDVI-LMGM (31 seconds using a personal computer). Experiments over more complex landscape and long-term time-series demonstrated that NDVI-LMGM performs well in each stage of vegetation growing season and is robust in regions with contrasting spatial and spatial variations. Comparisons between NDVI-LMGM and current methods (i.e., Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM) and Weighted Linear Model (WLM)) show that NDVI-LMGM is more accurate and efficient than current methods. The proposed method will benefit land surface process research, which requires a dense NDVI time-series dataset with high spatial resolution.
Highlights
The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used vegetation indices to characterize the absorptive and reflective features of vegetation, which represent vegetation greenness and vigor [1,2,3]
In order to test the performance of the proposed method, NDVI-LMGM, three typical methods for downscaling Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI with the help of Landsat TM/ETM+ imagery were selected for comparison, Enhanced STARFM (ESTARFM), Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the WLM
This study proposed a new method, NDVI-LMGM, to produce high spatial resolution NDVI
Summary
The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used vegetation indices to characterize the absorptive and reflective features of vegetation, which represent vegetation greenness and vigor [1,2,3]. NDVI time-series data derived from multi-temporal satellite images are an appropriate data source for studying the spatial and temporal dynamics of ecosystem responses to climate change [4]. The available NDVI time-series products are generated from sensors with frequent observations (e.g., daily) but coarse spatial resolutions, ranging from 250 m to 8000 m, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and NOAA Advanced Very High. As a result, these products are incapable of capturing spatial details necessary for monitoring land cover and ecosystem changes in heterogeneous areas [5]. Combining NDVI data from multi-sensors is a possible solution for producing high spatial and temporal resolution NDVI data, which is a critical requirement for monitoring vegetation dynamics
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