Assessment of spectral indices and water color combinations for detecting algal blooms in coastal subtropical shallow lakes

Abstract

Algae and cyanobacteria blooms are a growing concern for the quality of aquatic ecosystems, but logistics and cost constraints often limit their monitoring. The use of spectral indices through remote sensing can help detect blooms in places that are difficult to access or have limited available data. However, differences in water optical properties and sensor configuration may affect the accuracy of these indices in inland waters. Here, we assessed whether multiple spectral indices and one colour algorithm based on the International Commission of Illumination colour space (CIE) could increase the accuracy of bloom detection in a shallow coastal lakes system using different satellites. We first calibrated thresholds for the indices against visually detectable blooms and tested the agreement of various algorithm combinations. We found the threshold adjustment did not improve bloom detection for Landsat 8/9 and Sentinel-2, but it is essential for Landsat 5. Bloom areas obtained with CIE combined with the Adjusted Floating Algae Index (AFAI), for the Landsat series, and the Normalized Difference Chlorophyll Index (NDCI), for Sentinel-2, resulted in the best overall accuracy. The CIE algorithm helped reduce false positives in non-blooming lakes. Our results show that using single algorithms with CIE can be applied to retrieve accurate bloom occurrence and areas with multiple sensors; however, these must be tailored according to local characteristics. The methods validated here can be applied to understand the long-term variability of bloom events in lake systems located in regions that are inaccessible or that suffer from a lack of data.