Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra (Rrsδ) from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, Rrsδ-fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra (Rrsδ) from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, Rrsδ-fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

Instrumented buoys are used to monitor water quality, yet there remains a need to evaluate whether in vivo fluorometric measures of chlorophyll a (Chl a) produce accurate estimates of phytoplankton abundance. Here, 6 years (2014–2019) of in vitro measurements of Chl a by spectrophotometry were compared with coeval estimates from buoy-based fluorescence measurements in eutrophic Buffalo Pound Lake, Saskatchewan, Canada. Analysis revealed that fluorometric and in vitro estimates of Chl a differed both in terms of absolute concentration and patterns of relative change through time. Three models were developed to improve agreement between metrics of Chl a concentration, including two based on Chl a and phycocyanin (PC) fluorescence and one based on multiple linear regressions with measured environmental conditions. All models were examined in terms of two performance metrics; accuracy (lowest error) and reliability (% fit within confidence intervals). The model based on PC fluorescence was most accurate (error = 35%), whereas that using environmental factors was most reliable (89% within 3σ of mean). Models were also evaluated on their ability to produce spatial maps of Chl a using remotely sensed imagery. Here, newly developed models significantly improved system performance with a 30% decrease in Chl a errors and a twofold increase in the range of reconstructed Chl a values. Superiority of the PC model likely reflected high cyanobacterial abundance, as well as the excitation–emission wavelength configuration of fluorometers. Our findings suggest that a PC fluorometer, used alone or in combination with environmental measurements, performs better than a single-excitation-band Chl a fluorometer in estimating Chl a content in highly eutrophic waters.

Chlorophyll-a concentration (chla) is a useful indicator of harmful algal blooms in early warning systems that use remote sensing data as input. However, its retrieval is challenging in small waterbodies due to the lack of high spatial-resolution water-color sensors and the substantial optical interference of other water constituents. Here, we demonstrate the potential of support vector machines and Sentinel-2 images to retrieve chla in a shallow eutrophic lake (Buffalo Pound Lake, Saskatchewan, Canada). Following validation against in-situ chla measurements over three open water seasons (2017–2019), our proposed method based on Support Vector Regression (SVR) outperforms the most common semi-empirical models, i.e., locally-tuned indices (normalized difference chlorophyll index, 2band, and 3band), as well as the state-of-the-art global empirical model (Mixture Density Network). The superiority of SVR is shown in terms of overall and stratified accuracy, as well as spatial validity. We argue that for small waterbodies where numerous matched pairs of in-situ chla and reflectance are not available, SVR might retrieve chla more accurately than locally-tuned indices and global empirical models.

Chlorophyll-a concentration (chla) is a useful indicator of harmful algal blooms in early warning systems that use remote sensing data as input. However, its retrieval is challenging in small waterbodies due to the lack of high spatial-resolution water-color sensors and the substantial optical interference of other water constituents. Here, we demonstrate the potential of support vector machines and Sentinel-2 images to retrieve chla in a shallow eutrophic lake (Buffalo Pound Lake, Saskatchewan, Canada). Following validation against in-situ chla measurements over three open water seasons (2017–2019), our proposed method based on Support Vector Regression (SVR) outperforms the most common semi-empirical models, i.e., locally-tuned indices (normalized difference chlorophyll index, 2band, and 3band), as well as the state-of-the-art global empirical model (Mixture Density Network). The superiority of SVR is shown in terms of overall and stratified accuracy, as well as spatial validity. We argue that for small waterbodies where numerous matched pairs of in-situ chla and reflectance are not available, SVR might retrieve chla more accurately than locally-tuned indices and global empirical models.

Haig HA, Chegoonian AM, Davies J-M, Bateson D, Leavitt PR. 2021. Marked blue discoloration of late winter ice and water due to autumn blooms of cyanobacteria. Lake Reserv Manage. XX:XXX–XXX.Continu...

Haig HA, Chegoonian AM, Davies J-M, Bateson D, Leavitt PR. 2021. Marked blue discoloration of late winter ice and water due to autumn blooms of cyanobacteria. Lake Reserv Manage. XX:XXX–XXX.Continu...

This work describes a pilot study in southern Ontario, Canada evaluating the use of the ‘Headwall Nano-Hyperspec’ hyperspectral imager onboard a Remotely Piloted Aircraft System (RPAS). Hyperspectral imagers are extremely useful for monitoring vegetation health and water quality, among other environmental parameters. However, guidelines on the use of this specific instrument for these applications are not yet available. As such, recommended operational settings and calibration procedures are presented here, based on nearly 50 flight campaigns over water bodies and vineyards. Using these procedures, spectral reflectance was successfully captured using an RPAS.