Abstract
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.
Highlights
Normalized Difference Vegetation Index (NDVI) conveys valuable information relating to vegetation properties on the land surface [1,2]
The current study developed an Optical Cloud Pixel Recovery (OCPR) method based on the Random Forest algorithm and assessed its performance for NDVI recovery beneath cloud obscured or otherwise faulty pixels
The method is based on three assumptions: (1) NDVI data are a proxy for vegetation vigor, a monthly NDVI time series will follow the annual cycle of growth and decline; (2) The “cfmask” product provided with Landsat Maximum Value Composite (MVC) imagery accurately identifies clouds and cloud shadows [43]; (3) coastal NDVI dynamics are related to local hydrologic variables rainfall, temperature, and water level [18,19]
Summary
Normalized Difference Vegetation Index (NDVI) conveys valuable information relating to vegetation properties on the land surface [1,2]. NDVI is a vegetation index derived from optical remote sensors and represents the reflective and absorptive characteristics of vegetation in the red and near infrared (NIR) bands of the electromagnetic spectrum. For this reason, a chronological analysis of NDVI can indicate changes in vegetation conditions proportional to the absorption of photo-synthetically active radiation [3]. A chronological analysis of NDVI can indicate changes in vegetation conditions proportional to the absorption of photo-synthetically active radiation [3] Such time series analyses of NDVI can detect the impact of natural events or anthropogenic disturbances on vegetation and can play an important role in natural resource management [4]. NDVI change detection can provide multi-dimensional information such as differences in urban land use/land cover changes [5], vegetation dynamics, surface elevation and floodplain dynamics [6].
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