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Article

Optical Cloud Pixel Recovery via Machine Learning

Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
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Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Remote Sens. 2017, 9(6), 527; https://doi.org/10.3390/rs9060527
Received: 14 December 2016 / Revised: 19 May 2017 / Accepted: 21 May 2017 / Published: 25 May 2017
Remote sensing derived Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Studies that are heavily dependent on optical sensors are subject to data loss due to cloud coverage. Specifically, cloud contamination is a hindrance to long-term environmental assessment when using information from satellite imagery retrieved from visible and infrared spectral ranges. Landsat has an ongoing high-resolution NDVI record starting from 1984. Unfortunately, this long time series NDVI data suffers from the cloud contamination issue. Though both simple and complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques have limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum using a Random Forest (RF) trained and tested with multi-parameter hydrologic data. The RF-based OCPR model is compared with a linear regression model to demonstrate the capability of OCPR. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance. The RF-based OCPR method achieves a root mean squared error of 0.016 between predicted and observed NDVI reflectance values. The linear regression model achieves a root mean squared error of 0.126. Our findings suggest that the RF-based OCPR method is effective to repair cloudy pixels and provides continuous and quantitatively reliable imagery for long-term environmental analysis. View Full-Text
Keywords: data reconstruction; random forest; NDVI; hydrology; regression; rainfall; temperature data reconstruction; random forest; NDVI; hydrology; regression; rainfall; temperature
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MDPI and ACS Style

Tahsin, S.; Medeiros, S.C.; Hooshyar, M.; Singh, A. Optical Cloud Pixel Recovery via Machine Learning. Remote Sens. 2017, 9, 527. https://doi.org/10.3390/rs9060527

AMA Style

Tahsin S, Medeiros SC, Hooshyar M, Singh A. Optical Cloud Pixel Recovery via Machine Learning. Remote Sensing. 2017; 9(6):527. https://doi.org/10.3390/rs9060527

Chicago/Turabian Style

Tahsin, Subrina, Stephen C. Medeiros, Milad Hooshyar, and Arvind Singh. 2017. "Optical Cloud Pixel Recovery via Machine Learning" Remote Sensing 9, no. 6: 527. https://doi.org/10.3390/rs9060527

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