Optical Cloud Pixel Recovery via Machine Learning

Tahsin, Subrina; Medeiros, Stephen; Hooshyar, Milad; Singh, Arvind

doi:10.3390/rs9060527

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Optical Cloud Pixel Recovery via Machine Learning

2017
By Tahsin, Subrina ; Medeiros, Stephen ; Hooshyar, Milad ; ...
Source: Remote Sens. 9(6), 1-19

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Journal Title:

Remote Sensing
Personal Author:

Tahsin, Subrina ; Medeiros, Stephen ; Hooshyar, Milad ; Singh, Arvind

Tahsin, Subrina ; Medeiros, Stephen ; Hooshyar, Milad ; Singh, Arvind Less -
NOAA Program & Office:

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science)

NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science) Less -
Description:

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.
Keywords:

[+]

Climatic Factors Forest Resilience Optical Images Remote Sensing Vegetation Mapping
Source:

Remote Sens. 9(6), 1-19
DOI:

https://doi.org/10.3390/rs9060527
Document Type:

Journal Article
Funding:

Grant no. NA10NOS4780146
Place as Subject:

Apalachicola Bay (Fla.) ; Mexico, Gulf of ; Gulf coast (U.S.)

Apalachicola Bay (Fla.) ; Mexico, Gulf of ; Gulf coast (U.S.) Less -
Rights Information:

CC BY
Compliance:

PMC
Main Document Checksum:

[+]

urn:sha256:5fa8d270aea2a2bf88b0cdaf702099cf17c241775c54cd173128b778da6eeaa7
Download URL:

https://repository.library.noaa.gov/view/noaa/25270/noaa_25270_DS1.pdf
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Optical Cloud Pixel Recovery via Machine Learning

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