Improving NDVI by removing cirrus clouds with optical remote sensing data from Landsat-8 – A case study in Quito, Ecuador

The Andean region has a high cloud density throughout the year. The use of optical remote sensing data in the computation of environmental indices of this region has been hampered by the presence of clouds. To maximize accuracy in the computation of several environmental indices including the normalized difference vegetation index (NDVI), we compared the performance of two algorithms in removing clouds in Landsat-8 Operational Land Imager (OLI) data of a high-elevation area. The study area was Quito, Ecuador, which is a city located close to the equator and in a high-elevation area crossed by the Andes Mountains. The first algorithm was the automatic cloud removal method (ACRM), which employs a linear regression between the different spectral bands and the cirrus band. The second algorithm was independent component analysis (ICA), which considers the noise (clouds) as part of independent components applied over the study area. These methods were evaluated based on several images from different years with different cloud conditions. The results indicate that neither algorithm is effective over this region for the removal of clouds or for NDVI computation. However, after improving ACRM, the NDVI computed using ACRM showed a better correlation than ICA with the MODIS NDVI product.