Monitoring cyanobacterial harmful algal blooms at high spatiotemporal resolution by fusing Landsat and MODIS imagery

Abstract

Toxic Cyanobacteria-rich Harmful Algal Blooms (CyanoHABs) are severe environmental issues impacting water environment, aquatic life populations, and surrounding wild and human lives. Given their fast-changing variations in space and time, frequent monitoring of CyanoHABs with sufficient spatial details and coverage is needed to understand their impacts. However, current monitoring activities based on in situ or satellite data do not meet this requirement due to the limited spatial coverages of buoy measurements and the compromise between spatial resolution and temporal frequency of satellite observations. In this study, we develop a Spatial-Temporal Image Fusion (STIF) approach to enable high spatial-temporal resolution monitoring of CyanoHABs. The proposed approach consists of two steps: (1) a new CyanoHAB spectral index called Broad Wavelength Algae Index (BWAI) is developed for fine-but-sparse (Landsat) and coarse-but-frequent (MODIS) satellite images, and (2) the Landsat and MODIS derived BWAI images are fused by the Robust Adaptive Spatial and Temporal Fusion Model (RASTFM) to generate fine-and-frequent Landsat-like BWAI images. Our results show that the proposed BWAI index is with higher similarity and correlation with the reference Cyanobacteria Index (CI) images and in situ observations than the comparative algae indices devised for broad wavelength sensors. Moreover, the 30-m Landsat-like BWAI image series provide more accurate and detailed results than their 500-m MODIS counterparts and greatly improve the temporal frequency over Landsat-based algae indices, demonstrating the contribution of the improved spatial-temporal resolution achieved by the proposed STIF approach. Consequently, this research fills the gap of high spatiotemporal resolution monitoring of CyanoHABs and paves a new way of assessing water environment in a timely and detailed manner.