Abstract
Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) method) showed that our proposed method has the following advantages: (1) it can obtain more accurate estimates; (2) it can retain more spatial detail; (3) its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4) it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies.
Highlights
The Normalized Difference Vegetation Index (NDVI) derived from satellite imagery is an important vegetation index that represents vegetation greenness and vigor, which is employed widely in many significant areas of research [1,2,3], such as land cover classification and change detection [4,5,6], mapping land surface emissivity [7], and assessing ecological responses to environmental change [8].a trade-off must be made between spatial and temporal resolutions in remote sensing instruments [9]
Measurements, whereas if the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI measurements were noisy signals, they had to be highly restricted by the prior information, more importance was placed on the prior information
The multi-year average MODIS NDVI time series for each land-cover type is used to constrain the unmixing process for the MODIS NDVI observations in a Bayesian framework to obtain the initial downscaled NDVI, before applying a rebuilding model, which uses the relationships between the paired initial NDVI and Landsat NDVI on other dates to generate high spatial and temporal resolution Landsat-like NDVI datasets
Summary
The Normalized Difference Vegetation Index (NDVI) derived from satellite imagery is an important vegetation index that represents vegetation greenness and vigor, which is employed widely in many significant areas of research [1,2,3], such as land cover classification and change detection [4,5,6], mapping land surface emissivity [7], and assessing ecological responses to environmental change [8].a trade-off must be made between spatial and temporal resolutions in remote sensing instruments [9]. The Normalized Difference Vegetation Index (NDVI) derived from satellite imagery is an important vegetation index that represents vegetation greenness and vigor, which is employed widely in many significant areas of research [1,2,3], such as land cover classification and change detection [4,5,6], mapping land surface emissivity [7], and assessing ecological responses to environmental change [8]. No sensor provides NDVI data sets with both adequate spatial resolution and frequent coverage to satisfy the needs of most environmental applications. The well-known economically accessible Landsat mission provides 30-m resolution datasets, which can successfully capture spatial details [10,11], but the revisit time of 16 days and frequent cloud cover make it very difficult to obtain sufficient high-quality data, which severely limit its application to the detection of rapid changes in ecosystems [12]. The Moderate Resolution Imaging Spectroradiometer (MODIS) imagery has a daily revisit cycle, but its coarse-resolution restricts its effectiveness in heterogeneous areas.
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