A method to extract cyanobacteria blooms from satellite imagery with no requirements for any prior atmospheric correction or cloud-masking

Abstract

Lake Chaohu has been suffering from harmful cyanobacteria blooms, while the clouds pixels in satellite images are usually mistaken as cyanobacteria blooms by some traditional indicators, leading to the need for cloud masking in advance. In addition, atmospheric correction is another challenge due to lack of a general atmospheric correction method and the difficulties in evaluating its accuracy without in situ investigations. Fortunately, tasseled cap transformation (TCT) allows to extract vegetation properties directly from satellite imagery digital numbers (DN), which provides a perspective for extracting cyanobacteria blooms independent from atmospheric correction. This study focuses on how to use TCT to establish an indicator, which allows to extract cyanobacteria blooms directly from image DN values without conducting any atmospheric correction or cloud-masking. Training and test sets containing over 200,000 pixels are constructed from 18 Sentinel-2A/B MSI images acquired in different seasons in recent three years. Four components are derived from TCT and they could form up to 81 linear combinations. Experimental results performed on the training set show that the candidate, which combines the last three components with the coefficients of 1,-1 and 0, assigns cyanobacteria blooms pixels in a completely separated value range from water, cloud, cloud shadow and cloud edge pixels. The candidate is defined as ICW3C index. Its threshold value range of (175 330) is given and the pixels with ICW3C values greater than its threshold could be classified as cyanobacteria blooms. Comparisons between ICW3C and the floating algae index (FAI) on the test set show that ICW3C misclassifies 0.02% of cloud pixels and 1.55% of yellow cloud edge pixels as cyanobacteria blooms, however, 19.18% clouds, 13.74% yellow cloud edges and 19.34% blue-green cloud edges are incorrectly identified as cyanobacteria blooms by FAI. Comparisons between ICW3C and FAI performed on image regions over time show that, in clear-sky regions with cyanobacteria blooms, FAI extracts 5.81% more pixels, which mainly lay in the edge of cyanobacteria blooms. In cloud-covered image regions without cyanobacteria blooms, FAI misclassifies over 608 times as many cloud and cloud edge pixels as ICW3C. Sensitivity test results suggest that the change of ICW3C threshold within its value range (175 330) will not lead to serious increase in misclassification, and ICW3C performs stable to variations of viewing geometry. Extension tests indicate that ICW3C is applicable for several other sensors. Further researches are still needed to test whether ICW3C is suitable for other inland lakes or seas.