Satellite‐derived chlorophyll estimates from the Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) and Coastal Zone Color Scanner (CZCS), a large archive of in situ near‐surface chlorophyll data, and satellite sea surface temperature (SST) measurements were used to quantify spatial and seasonal variability of near‐surface chlorophyll and SST in middle shelf to slope waters off the coast of the U.S. Northeast. The results of empirical orthogonal function (EOF) analysis on normalized monthly fields (after temporal and spatial means were removed) show that all three chlorophyll climatologies have similar mode 1 temporal and spatial patterns in these waters. Mode 1, which explains about half of the total variability in monthly climatological images, shows that shelf waters in the Gulf of Maine (GOM) are out of phase with the mid‐Atlantic bight (MAB), with seasonally high chlorophyll concentrations in winter in the MAB and in summer in the GOM. The three chlorophyll climatologies begin to differ at higher modes (modes 2 and 3), although SeaWiFS and in situ climatologies keep similar features through mode 3. Higher modes in both SST and chlorophyll are related to the effects of tidal mixing on Georges Bank, differences in seasonal stratification between the southwestern and northeastern GOM, and the importance of the spring bloom in MAB outer shelf waters and western GOM. SST patterns during the CZCS and SeaWiFS eras are very similar, and this indicates that the observed differences between results obtained with these two sensors are probably not caused by differences in physical processes during the two satellite eras.

Harmful algal bloom (HAB) species in the Chesapeake Bay can negatively impact fish, shellfish, and human health via the production of toxins and the degradation of water quality. Due to the deleterious effects of HAB species on economically and environmentally important resources, such as oyster reef systems, Bay area resource managers are seeking ways to monitor HABs and water quality at large spatial and fine temporal scales. The use of satellite ocean color imagery has proven to be a beneficial tool for resource management in other locations around the world where high-biomass, nearly monospecific HABs occur. However, remotely monitoring HABs in the Chesapeake Bay is complicated by the presence of multiple, often co-occurring, species and optically-complex waters. Here we present a summary of common marine and estuarine HAB species found in the Chesapeake Bay, Alexandrium monilatum, Karlodinium veneficum, Margalefidinium polykrikoides, and Prorocentrum minimum, that have been detected from space using multispectral data products from the Ocean and Land Color Imager (OLCI) sensor on the Sentinel-3 satellites and identified based on historic phytoplankton data, in situ sampling, and ecological associations. We review how future hyperspectral instruments will improve discrimination of potentially harmful species from other phytoplankton communities and present a framework in which satellite data products could aid Chesapeake Bay resource managers with monitoring water quality and protecting shellfish resources.

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6 reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped over time for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1 reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.

Within the next decade, NASA plans to launch three new missions with imaging spectrometers for aquatic science and applications: Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) in 2024, Geostationary Littoral Imaging Radiometer (GLIMR) in 2026, and Surface Biology and Geology (SBG) in 2028. Taken together, these missions will evaluate long‐term trends in phytoplankton biomass linked to climate change, and provide new spectral capabilities to assess aquatic biogeochemistry, biophysics, and habitats. Hyperspectral measurements of ocean color, paired with advanced retrieval algorithms, can provide new information on phytoplankton community composition and water quality. We compare the mission architecture and sensor characteristics to identify the synergistic opportunities to merge algorithms, field data, and calibration and validation techniques. Each mission has unique temporal and spatial characteristics to monitor the aquatic transitions from watershed to open ocean ecosystems. SBG provides observations at high spatial scales to monitor emergent, floating, submerged, and benthic habitats from inland to coastal waters. With global daily coverage, PACE can track the fate of material as it meanders offshore and provides an enhanced context for phytoplankton diversity and global biogeochemical cycling. GLIMR is optimized to resolve temporal processes that give rise to aquatic rates and fluxes including phytoplankton growth rates, physiology, and episodic events such as storms. Applications with high spectral, spatial, and temporal resolution from these NASA missions include assessing carbon dynamics and biogeochemical cycling across the land‐ocean continuum, harmful algal blooms, and oil spills.