We analyzed 27 established and new simple and therefore perhaps portable satellite phycocyanin pigment reflectance algorithms for estimating cyanobacterial values in a temperate 8.9 km² reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident water surface observations collected from 44 sites within 1 h of image acquisition. The algorithms were adapted to real Compact Airborne Spectrographic Imager (CASI), synthetic WorldView-2, Sentinel-2, Landsat-8, MODIS and Sentinel-3/MERIS/OLCI imagery resulting in 184 variants and corresponding image products. Image products were compared to the cyanobacterial coincident surface observation measurements to identify groups of promising algorithms for operational algal bloom monitoring. Several of the algorithms were found useful for estimating phycocyanin values with each sensor type except MODIS in this small lake. In situ phycocyanin measurements correlated strongly (r² = 0.757) with cyanobacterial sum of total biovolume (CSTB) allowing us to estimate both phycocyanin values and CSTB for all of the satellites considered except MODIS in this situation.

Various empirical algorithms have been developed to retrieve chlorophyll-a (Chl-a) from multispectral and hyperspectral images as a proxy variable for algal blooms in inland waters. In most previous studies, a single empirical model (global model) was calibrated for the entire water body under study. Our analysis shows that the performance of a global model is limited for optically complex inland waters. We discovered that the global model tends to overestimate in some regions and underestimate in other regions, and that the model residuals (errors) display an apparent spatial autocorrelation pattern. To address the inadequacy of the global empirical model, this paper presents regionally or locally adaptive models to better estimate Chl-a concentrations for the first time. We collected two dense sets of Chl-a measurements over Harsha Lake in Ohio during a Sentinel-2A satellite overpass and a dedicated airborne hyperspectral flight. Based on the atmospherically corrected multispectral and hyperspectral images and concurrent in situ measurements, we implemented and evaluated the performance of regionally and locally adaptive models in comparison with the single global model. Among a number of candidate empirical algorithms, the two-band algorithm produces the best global model for Chl-a retrievals for both the multispectral and hyperspectral image sources. By subdividing the water body under investigation into several regions or a set of local areas, we demonstrate that regionally and locally adaptive models can improve Chl-a estimate accuracy by 13%-28% for the multispectral image and by 33%-47% for the hyperspectral image, in comparison with the best global Chl-a model.

Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.