Abstract
A new algorithm was developed in this research to minimize aerosol effects on the normalized difference vegetation index (NDVI). Simulation results show that in red-NIR reflectance space, variations in red and NIR channels to aerosol optical depth (AOD) follow a specific pattern. Based on this rational, the apparent reflectance in these two bands of neighboring pixels were used to reduce aerosol effects on NDVI values of the central pixel. We call this method the neighboring pixels (NP) algorithm. Validation was performed over vegetated regions in the border area between China and Russia using Landsat 8 Operational Land Imager (OLI) imagery. Results reveal good agreement between the aerosol corrected NDVI using our algorithm and that derived from the Landsat 8 surface reflectance products. The accuracy is related to the gradient of NDVI variation. This algorithm can achieve high accuracy in homogeneous forest or cropland with the root mean square error (RMSE) being equal to 0.046 and 0.049, respectively. This algorithm can also be applied to atmospheric correction and does not require any information about atmospheric conditions. The use of the moving window analysis technique reduces errors caused by the spatial heterogeneity of aerosols. Detections of regions with homogeneous NDVI are the primary sources of biases. This new method is operational and can prove useful at different aerosol concentration levels. In the future, this approach may also be used to examine other indexes composed of bands attenuated by noises in remote sensing.
Highlights
The normalized difference vegetation index (NDVI) derived from remote sensing data has achieved great success in monitoring global vegetation variations
Results of neighboring pixels (NP) algorithm implemented with multiple window sizes were compared to surface
NDVI derived from the Landsat 8 surface reflectance product generated from the L8SR algorithm
Summary
The normalized difference vegetation index (NDVI) derived from remote sensing data has achieved great success in monitoring global vegetation variations. The observation of NDVI by optical remote sensing is always disturbed by atmosphere among which aerosols are some of the most active components [1,2,3]. The observed NDVI signal drops after aerosol effects and leads to an underestimation of the amount of vegetation at surface [5]. Aerosol correction is of great importance in regions with high aerosol loadings or in biomass burning conditions before vegetation monitoring with NDVI. There are essentially two methods to minimize aerosol effects on vegetation indices (VIs): The first method involves retrieving the ambient aerosol optical depth (AOD) as an input parameter into the atmospheric correction algorithm to generate surface reflectance product.
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