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Abstract
Long-term Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data have inherent noise due to clouds and poor atmospheric conditions that limit its applicability for environmental applications. This study was carried out with an objective of noise removal and reconstruction of time series MODIS EVI data (16 day) for the period 2010–2014 using de-noising algorithms. Relative evaluation of de-noising algorithms for smoothing temporal data with ideal noise free data is not possible in actual scenario. Hence, synthetic signals were generated and introduced Gaussian noise at different variance levels for evaluation purpose. Spatial analysis was carried out by introducing noise at different variance levels into the noise free EVI images from the raw EVI stacked image. Spatio-temporal analyses of noise signals in the reconstructed EVI images were evaluated in terms of performance indicators, namely Peak Signal-to-Noise Ratio and Mean Square Error.
Acknowledgements
The authors thank GM, RRSC-E, CGM, NRSC and Director, NRSC for encouraging this work. We acknowledge NASA Earth observing system (http://e4ftl01.cr.usgs.gov/MOLT/MOD13Q1.005/) for providing free MOD13Q1 products. We also acknowledge Climate forecast system reanalysis (CFSR) global meteorological dataset (http://globalweather.tamu.edu) for providing free historical rainfall and temperature data.