Frequent Aureoumbra lagunensis blooms in the Indian River Lagoon (IRL), Florida, have devastated populations of seagrass and marine life and threaten public health. To substantiate a more reliable remote sensing early‐warning system for harmful algal blooms, we apply varimax‐rotated principal component analysis (VPCA) to 12 images spanning ~1.5 years. The method partitions visible‐NIR spectra into independent components related to algae, cyanobacteria, suspended minerals, and pigment degradation products. The components extracted by VPCA are diagnostic for identifiable optical constituents, providing greater specificity in the resulting data products. We show that VPCA components retrieved from Sentinel‐3A Ocean and Land Colour Instrument (OLCI) and a field‐based spectroradiometer are consistent despite vast differences in spatial resolution (~50 cm vs. 300 m). Furthermore, the VPCA components associated with A. lagunensis in both spectral datasets indicate high correlations to Ochrophyta cell counts (R² ≥ 0.92, p < 0.001). Recombining components exhibiting a red‐edge response produces a Chl a algorithm that outperforms empirical band ratio algorithms and preforms as well or better than a variety of semianalytical algorithms. The results from the VPCA spectral decomposition method are more efficient than traditional Empirical Orthogonal Function or PCA, requiring fewer components to explain as much or more variance. Overall, our observations provide excellent validation for Sentinel‐3A OLCI‐based VPCA spectral identification and indicate A. lagunensis was highly concentrated within the Banana River region of the IRL during the study. These results enable improved brown tide monitoring to identify blooms at an early stage, allowing more time for stakeholder response to this public health problem.

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.

Traditional remote sensing retrieval models for water quality have historically relied on limited, localized data sets due to the prohibitive costs of extensive field campaigns and logistical challenges of collecting match‐up data with satellite overpasses. As a result, these models often lack generalizability across seasons, tides, and sites. Furthermore, small field data sets limit the utility of modern machine learning techniques to advance remote sensing retrieval models. In situ optical sensors deployed in a sensor network to continuously monitor larger water bodies can drastically increase the number of measurements, providing the opportunity to develop new approaches for building robust remote sensing retrieval models by leveraging both remote sensing data and in situ networks as an integrated monitoring system. This study leverages a large “ground‐to‐space” sensor network that combines an in situ optical sensor network with satellite‐based remote sensing to overcome these limitations. Utilizing a large‐scale data set from the U.S. Geological Survey's Sacramento—San Joaquin River Delta monitoring network, of dissolved organic matter fluorescence measurements, and remote sensing data from the European Space Agency's Sentinel‐2A and ‐2B satellites, this study implemented a data driven approach for dissolved organic matter models. The data set, consisting of 982 samples collected between 2018 and 2021 was used to train and validate a random forest model (R² = 0.76, RMSE = 6.1 Quinine Sulfate Equivalents), with demonstrated applicability across diverse site conditions, tidal stages, and seasons. This work provides a scalable solution to address critical challenges in water quality monitoring and offers a replicable framework for global water quality management.

Satellite‐based earth observation is a robust tool for tracking change in ecosystems. While terrestrially focused applications of remote sensing have empowered wide adoption for research and management, remote sensing of inland aquatic ecosystems remains comparably nascent. This divergence, in part, stems from the lack of standardized, accessible, and near real‐time remotely sensed surface reflectance, atmospherically corrected for aquatic environments. To date, surface reflectance products at national scales and with minimal latency are typically designed exclusively for terrestrial environments. Rectifying this situation can be accomplished by applying aquatic‐focused atmospheric correction algorithms independent of those used for terrestrial ecosystems. As a first step to filling this data gap, we present the first national scale, dynamically updated, analysis‐ready, aquatic reflectance dataset for inland water derived from Sentinel‐2 for the conterminous United States.

Coastal watersheds impacted by wildfires experience higher erosion resulting in increased sediment delivery to the ocean that alters limiting factors (i.e., light) for marine organisms. With increasing wildfire magnitude and severity, it is critical to explore changes in riverine discharges to the ocean to assess cascading hazards associated with wildfires. In situ data, remotely sensed turbidity data, and hydrological model (Soil and Water Assessment Tool “SWAT”) simulations have been adapted to capture and investigate fire‐related land use change impacts on Malibu Creek, California, USA. Modifying SWAT land cover inputs using burn severity data had minimal impact on simulations, requiring additional parameterization for acceptable model performance. Remotely sensed turbidity, in situ discharge, rating curve sediment loads, and SWAT simulated discharge and sediment loads increased following the Woolsey Fire. When compared to in situ and rating curve data in similar non‐fire water years, the 2019 Woolsey Fire water year in situ discharge was 1.8 times higher, SWAT simulated discharges were 1.4–1.7 times higher, and rating curve sediment load was 1.3 times higher. However, the SWAT simulated sediment loads were slightly lower (0.8–0.9 times) than rating curve sediment loads in similar non‐fire water years. Mean coastal turbidity increased to 18.2 Formazin Nephelometric Unit (FNU) during the first storm post‐fire (mean background value of 4.3 FNU). Synergies between methods demonstrated rapid coastal sediment exports (remote sensing) and ongoing erosion post‐fire (SWAT). These data are essential to understanding fire‐related marine ecological changes and implementing effective management and conservation initiatives.

The San Francisco Bay and Sacramento-San Joaquin Delta (Bay-Delta) is a vital estuarine system increasingly impacted by harmful algal blooms (HABs) and cyanobacterial harmful algal blooms (cyanoHABs). Nutrient-rich waters and climate change drive these events, which disrupt ecosystems, degrade water quality, and threaten fisheries, drinking water supplies, and recreational activities. Within the Bay-Delta, recurring cyanoHABs in the Port of Stockton since 2007 and Franks Tract during 2021 and 2022, demonstrate the pervasive nature of these events. Additionally, a severe HAB dominated by Heterosigma akashiwo in San Francisco Bay in August 2022 underscores the urgent need for advanced monitoring and management strategies to address these escalating challenges. Recent advancements in hyperspectral remote sensing and spectral unmixing tools, such as the Software Application for Spectral Mixture Analysis for Surveillance of Harmful Algal Blooms (SMASH), provide innovative capabilities to detect and quantify HABs.This study evaluates the effectiveness of SMASH in identifying and quantifying phytoplankton genera in the Bay-Delta using hyperspectral imagery acquired by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS-3), machine learning, and multivariate analysis. AVIRIS-3 was flown over the Bay-Delta in September 2023, and provides hyperspectral imagery with a meter-scale spatial resolution. The high spectral and spatial resolution of this imagery allowed us to investigate the performance of the SMASH software for five areas of interest (AOIs) within the Bay-Delta: Frank's Tract, Port of Stockton, Mildred Island, Discovery Bay, and Clifton Court Forebay. SMASH relies on a hyperspectral library of phytoplankton reflectance and a representative water spectrum for the AOIs. These areas were analyzed using combinations of two spectral end-member libraries and three water spectra. A field campaign conducted concurrently with AVRIS-3 flights in September 2023 provided phytoplankton enumeration data to confirm SMASH results. Under various configurations, SMASH accurately detected and quantified the genera Microcystis, Epithemia, and Dolichospermum, closely aligning with field observations. This study is the first to employ AVIRIS-3 imagery with SMASH software to map cyanoHAB genera in the Bay-Delta, demonstrating its potential for advanced monitoring and early detection.