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.
Leaf area index (LAI) remote sensing data products with a high resolution (HR) and long time series are in demand in a wide variety of applications. Compared with long time series LAI products with 1 km resolution, LAI products with high spatial resolution are difficult to acquire because of the lack of remote sensing observations in long-term sequences and the lack of estimation methods applicable to highly variable land-cover types. To address these problems, we proposed a recursive update model to estimate 30 m resolution LAI based on the updated Nonlinear Auto-Regressive with Exogenous Inputs (NARX) neural network and MODIS time series. First, we used a variety of HR satellite remote sensing observations to produce HR datasets for recent years. Historical low spatial resolution MODIS products were employed as background information and used to calculate the initial parameters of the NARX neural network for each pixel. Subsequently, one year’s reflectance from the HR dataset was used as the new observation that was input into the NARX model to estimate the HR LAI of that year, and the background and HR data were then used for remodeling to update the NARX model parameters. This procedure was recursively repeated year by year until both MODIS background data and all HR data were involved in the modeling. Finally, we obtained an LAI time series with 30 m resolution. In the cropland study area in Hebei Province, China, the results were compared with LAI measurements from ground sites in 2013 and 2014. A high degree of similarity existed between the results for the two study years (RMSE2013=0.288 and RMSE2014=0.296). The HR LAI estimates showed favorable spatiotemporal continuity and were in good agreement with the multisample ground survey LAI measurements. The results indicated that for data with a rapid revisit cycle and high spatial resolution, the recursive update model based on the NARX neural network has excellent LAI estimation performance and fairly strong fault-tolerance capability.
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