Harmful algal blooms of cyanobacteria are increasing in magnitude and frequency globally, degrading inland and coastal aquatic ecosystems and adversely affecting public health. Efforts to understand the structure and natural variability of these blooms range from point sampling methods to a wide array of remote sensing tools. This study aims to provide a comprehensive view of cyanobacterial blooms in Clear Lake, California — a shallow, polymictic, naturally eutrophic lake with a long record of episodic cyanobacteria blooms. To understand the spatial heterogeneity and temporal dynamics of cyanobacterial blooms, we evaluated a satellite remote sensing tool for estimating coarse cyanobacteria distribution with coincident, in situ measurements at varying scales and resolutions. The Cyanobacteria Index (CI) remote sensing algorithm was used to estimate cyanobacterial abundance in the top portion of the water column from data acquired from the Ocean and Land Color Instrument (OLCI) sensor on the Sentinel-3a satellite. We collected hyperspectral data from a handheld spectroradiometer; discrete 1 m integrated surface samples for chlorophyll-a and phycocyanin; multispectral imagery from small Unmanned Aerial System (sUAS) flights (∼12 cm resolution); Autonomous Underwater Vehicle (AUV) measurements of chlorophyll-a, turbidity, and colored dissolved organic matter (∼10 cm horizontal spacing, 1 m below the water surface); and meteorological forcing and lake temperature data to provide context to our cyanobacteria measurements. A semivariogram analysis of the high resolution AUV and sUAS data found the Critical Scale of Variability for cyanobacterial blooms to range from 70 to 175 m, which is finer than what is resolvable by the satellite data. We thus observed high spatial variability within each 300 m satellite pixel. Finally, we used the field spectroscopy data to evaluate the accuracy of both the original and revised CI algorithm. We found the revised CI algorithm was not effective in estimating cyanobacterial abundance for our study site. Satellite-based remote sensing tools are vital to researchers and water managers as they provide consistent, high-coverage data at a low cost and sampling effort. The findings of this research support continued development and refinement of remote sensing tools, which are essential for satellite monitoring of harmful algal blooms in lakes and reservoirs.

Quantifying and predicting the drawdown of dissolved oxygen (DO) in lakes is important to ensuring healthy ecosystems and safe water sources. The characterization of DO depletion using available definitions and models requires extensive monitoring to obtain reliable results. These approaches have been mostly developed for seasonally stratified monomictic and dimictic lakes, limiting their applicability for polymictic systems which stratify and mix repeatedly. We compared the predicted duration of hypoxic events in the three basins of a large eutrophic polymictic lake (Clear Lake, CA, USA) by using three different one‐dimensional (1‐D) analytical approaches; two being original and requiring minimal input variables. Data on meteorology, lake temperature, and DO were all measured at multiple locations for one year and used to develop a novel method, named the Birge‐Winkler method. The daily net surface heat fluxes provided an energy term from which estimates of the onset and length of hypoxic periods could be made with minimal calibration. We used the Lake Number method to evaluate the level of accuracy of the Birge‐Winkler method. Finally, we estimated DO values next to the sediments using a Buoyancy Frequency method. Our results from the two original methods provided simple and effective tools for early warning of onset and duration of hypoxia, with a maximum of ±3–5 days uncertainty in the predictions. These indices can become powerful decision support tools for addressing aquatic ecological challenges triggered by hypoxia, including fish kills, internal nutrient loading, heavy metals release, and harmful algal blooms.

The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide considerations for future field campaigns to improve coordination with remote sensing data collection and ensure that field data is well suited for use in model or algorithm development.

Characterizing the vertical structure of phytoplankton biomass is key to understanding the light, nutrient, and mixing dynamics driving lake ecosystems. In situ fluorometry is widely used in limnology to obtain chlorophyll a (Chl a) measurements as proxies for phytoplankton biomass. Unfortunately, daytime fluorometry signals are biased by non‐photochemical quenching, limiting the value of these measurements. Phytoplankton utilize this quenching process to dissipate excess light energy as heat, which contaminates daytime fluorometry measurements with reductions in measured Chl a. Despite the ubiquitous impacts of non‐photochemical quenching on fluorometer measurements, there is no universal correction method for inland waters. We propose a novel model for correcting non‐photochemical quenching impacts in lake systems as a simple exponential function of available light in the water column. This model was developed from data collected from two lakes representing the endmembers in terms of lake productivity and clarity, thus producing a model with possible application to other systems. The study sites are ultraoligotrophic Lake Tahoe, CA‐NV, and hypereutrophic Clear Lake, CA. Our proposed non‐photochemical quenching correction model demonstrates good performance (R² = 0.74) when tested on an independent dataset from Lake George, NY. We applied the model to vertical data profiles from Lake Tahoe and Clear Lake to more accurately evaluate the vertical distribution of Chl a in these lakes. The results of this research have wide‐reaching benefits by enabling more accurate interpretation and application of Chl a fluorescence measurements in lakes with a range of conditions.