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Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll‐a (chl‐a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl‐a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl‐a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel‐2 (S2) and Sentinel‐3 (S3) to map chl‐a and cyanobacteria, respectively, classified chl‐a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%–79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%–80% false positive rate and 0%–38% false negative rate, depending on algorithm. Using SRS‐based chl‐a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone. Plain Language Summary: Lakes often have algal blooms that create a water quality concern, especially when they contain cyanobacteria, which can be toxic to both humans and animals. These harmful algal blooms are of great concern in areas with limited water supply in states such as California. While it is often difficult and costly to collect and monitor toxin concentrations, monitoring concentrations of chlorophyll‐a (chl‐a) –a measure of how much algae are present—is relatively common and can even be accomplished using satellite remote sensing. There have been multiple studies that have found a relationship between toxins produced by cyanobacteria and chl‐a. The World Health Organization (WHO) has recently released (2021) an updated release of their previous 1999 guidance values for toxin monitoring based on chl‐a concentration. With satellite data, we were able to measure chl‐a concentration in a major reservoir in California, and then classify the chl‐a measurements into the WHO's guidance values for toxins. We compared the satellite‐based guidance values to the public advisory levels currently set by the California Department of Water Resources. Our results indicate that SRS of chl‐a is a reasonable substitute for cyanobacteria toxin advisories, and our framework can be applied to similar cyanobacteria dominated lakes. Key Points: The World Health Organization (WHO) updated cyanobacteria harmful algal blooms (cyanoHABs) guidelines for chlorophyll‐a (chl‐a) as a proxyWith satellite remote sensing (SRS), we estimated and classified chl‐a to compare cyanotoxins advisories used by CaliforniaThis study provides a framework for evaluating public health utility of SRS for enhancing cyanotoxin monitoring globally [ABSTRACT FROM AUTHOR]

As temperatures rise, it is increasingly important to monitor changes in habitat conditions for aquatic species and mitigate emerging stressors. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) instrument provides temperature products at a spatial and temporal scale not previously available. Here, we utilize ECOSTRESS temperature products to assess fluctuations in thermal habitat suitability for an endangered fish species and we also demonstrate the utility of the data set to assess water surface and bulk temperature changes following completion of two tidal wetland restoration efforts in the San Francisco Estuary and Sacramento–San Joaquin River Delta (Bay Delta). During all hours of the day throughout the 2018–2022 study period, key Delta Smelt habitat areas including Honker Bay, West Suisun Bay, and Mid Suisun Bay had greater than 75% of their areas suitable; there were several instances during which less than 25% of the area was thermally suitable generally coinciding with heatwave events. Overall, midday hours are less frequently suitable than morning and evening hours. Early evidence from ECOSTRESS indicates that there may be a decrease in surface water temperature of up to several degrees in tidal restoration areas, but more data is needed to show statistically significant outcomes. The ECOSTRESS record began in summer of 2018, and a longer record is needed to fully capture temperature changes associated with wetland processes and restoration efforts. Future applications of ECOSTRESS products can the capacity to help further understand ecosystem conditions and how restoration efforts affect water temperature, informing decisions that benefit Delta Smelt and other at‐risk aquatic species. Plain Language Summary: Water temperatures are rising in the San Francisco Estuary and Sacramento–San Joaquin River Delta (Bay Delta), negatively affecting endangered fish survivability. An instrument aboard the International Space Station called the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can give vital information about Bay Delta water temperatures at a broader scale than field measurements with reasonable spatial resolution and on a more frequent basis than previously available to understand where habitat management efforts would be most useful and even assess the impacts of previous restoration efforts on temperature. Here, we look at how favorable the Bay Delta water temperatures are for endangered Delta Smelt and assess the effects of two completed restoration efforts on water temperature (The Tule Red and Winter Island tidal wetland restoration projects). As managers and decision makers continue to evaluate actions toward protecting endangered aquatic species, ECOSTRESS can provide information to support the decision making process. Key Points: Aquatic thermal habitat conditions vary diurnally and spatiallyMidday periods and inland regions have unsuitable habitat for the Delta Smelt most frequentlyECOsystem Spaceborne Thermal Radiometer Experiment on Space Station shows early evidence of surface water temperature cooling benefits from tidal wetland restoration projects of up to 5.4°C [ABSTRACT FROM AUTHOR]

Invasive floating aquatic vegetation negatively impacts wetland ecosystems and mapping this vegetation through space and time can aid in designing and assessing effective control strategies. Current remote sensing methods for mapping floating aquatic vegetation at the genus level relies on airborne imaging spectroscopy, resulting in temporal gaps because routine hyperspectral satellite coverage is not yet available. Here we achieved genus level and species level discrimination between two invasive aquatic vegetation species using Sentinel 2 multispectral satellite data and machine-learning classifiers in summer and fall. The species of concern were water hyacinth (Eichornia crassipes) and water primrose (Ludwigia spp.). Our classifiers also identified submerged and emergent aquatic vegetation at the community level. Random forest models using Sentinel-2 data achieved an average overall accuracy of 90%, and class accuracies of 79–91% and 85–95% for water hyacinth and water primrose, respectively. To our knowledge, this is the first study that has mapped water primrose to the genus level using satellite remote sensing. Sentinel-2 derived maps compared well to those derived from airborne imaging spectroscopy and we also identified misclassifications that can be attributed to the coarser Sentinel-2 spectral and spatial resolutions. Our results demonstrate that the intra-annual temporal gaps between airborne imaging spectroscopy observations can be supplemented with Sentinel-2 satellite data and thus, rapidly growing/expanding vegetation can be tracked in real time. Such improvements have potential management benefits by improving the understanding of the phenology, spread, competitive advantages, and vulnerabilities of these aquatic plants. [ABSTRACT FROM AUTHOR]

The San Francisco Estuary and Sacramento–San Joaquin River Delta (Bay Delta) is a highly sensitive and critical habitat for the Delta Smelt, an endangered endemic fish, with water temperature being a key determinant of habitat suitability. This study investigates the relationship between open water surface and subsurface conditions from spaceborne thermal measurements (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Landsat-8) and in situ sensor data from the California Data Exchange Center (CDEC) to produce estimates of spatially continuous bulk temperature in the Bay Delta. We found that ECOSTRESS and Landsat-8 surface temperature measurements are well-correlated with bulk water temperatures ($N =236$ and $r = 0.907$ and $N = 226$ and $r = 0.976$ , respectively). For the ECOSTRESS-in situ comparison, accounting for time of day improved the correlation between surface and subsurface conditions ($r = 0.946$ , 0.881, and 0.944 for morning, midday, and evening, respectively). We found that ECOSTRESS surface temperatures were warmer than bulk temperatures in the midday period (2 °C peak at 2 P.M.) and cooler in the morning and evening periods (−1°C peak at 6 A.M.). We also found that a simple harmonic regression model can capture the diurnal variability of the skin effect to predict bulk water temperature (root-mean-square error (RMSE) = 0.809°C). With ECOSTRESS, we found that across the Bay Delta, including open waters and pelagic bays, temperature conditions causing stress and mortality for the Delta Smelt were persistent throughout the day during summer months. ECOSTRESS is a unique dataset capable of informing conservation efforts in the Bay Delta. [ABSTRACT FROM AUTHOR]

Estuaries worldwide are experiencing stress due to increased droughts, which often prompt intervention by environmental managers and government agencies. Effective management of water resources in estuarine systems can be enhanced by new technologies and methodologies to support decision-making processes. Here, we evaluate the use of high-frequency, high-resolution satellite remote sensing within two managementrelevant case studies in the San Francisco Estuary and the Sacramento--San Joaquin River Delta. We used a remote sensing-derived time series of turbidity maps to (1) identify favorable turbidity conditions for the endangered fish species, delta smelt (Hypomesus transpacificus), during the height of the great California drought in the dry season of 2015, and (2) evaluate changes in turbidity following the installation of an emergency saltwater intrusion barrier. The mapping results indicate several persistent areas of turbidity refugia throughout the summer in the north and west Delta; however, there was infrequent connectivity. A comparison of images captured during ebb tides before and after barrier installation showed a mean increase of 6.6 Nephelometric Turbidity Units (NTU) in the San Joaquin River and 4 NTU in Fisherman's Cut. Our assessment of the barrier supports previous findings, which used field samples to conclude barrier installation may have resulted in increased turbidity near the barrier and enhances these findings by providing spatial context. [ABSTRACT FROM AUTHOR]

This study utilizes satellite data to investigate water quality conditions in the San Francisco Estuary and its upstream delta, the Sacramento--San Joaquin River Delta. To do this, this study derives turbidity from the European Space Agency satellite Sentinel-2 acquired from September 2015 to June 2019 and conducts a rigorous validation with in situ measurements of turbidity from optical sensors at continuous monitoring stations. This validation includes 965 matchup comparisons between satellite and in situ sensor data across 22 stations, yielding R² = 0.63 and 0.75 for Nephelometric Turbidity Unit and Formazin Nephelometric Unit (FNU) stations, respectively. This study then applies remote sensing to evaluate patterns in turbidity during the Suisun Marsh Salinity Control Gates Action ("Gates action"), a pilot study designed to increase habitat access and quality for the endangered Delta Smelt. The basic strategy was to direct more freshwater into Suisun Marsh, creating more low salinity habitat that would then have higher (and more suitable) turbidity than upstream river channels. For all seven acquisitions considered from June 29 to September 27, 2018, turbidity conditions in Bays and Sloughs subregions were consistently higher (and more suitable) (26-47 FNU) than what was observed in the upstream River region (13-25 FNU). This overall pattern was observed when comparing images acquired during similar tidal stages and heights. [ABSTRACT FROM AUTHOR]

Arctic wetlands store nearly half of the world's soil organic carbon and are crucial to the global carbon cycle. However, most Earth system models fail to account for wetlands given heterogeneity and uncertainty surrounding the response of different wetland types to climate change.We analyzed summer (June-August) carbon dioxide (CO2) exchange, meteorological conditions, and ecological conditions at four Arctic wetlands. Micrometeorological flux tower data were used to assess CO2 exchange and meteorological conditions across 7 to 9 years of data. Climate data were used to assess meteorological conditions across 30 years of data. Satellite data described ecological conditions. Each site acted as a CO2 sink, but strength varied, ranging from -49 to -93 g C/m² per summer. A trend analysis was used to investigate how CO2 balance and surrounding conditions may have changed. CO2 exchange significantly changed at one site, becoming a stronger sink. Changes in meteorological conditions were variable across sites with the exception of air temperature which, when considering significant trends, unanimously increased across sites. Ecological conditions suggested increased vegetation cover regardless of statistical significance at all sites. A conditional random forest algorithm was used to evaluate drivers of CO2 exchange. Cumulative precipitation and evapotranspiration were the main drivers of net ecosystem exchange and gross primary productivity, while ecosystem respiration was primarily driven by air temperature, suggesting that projected changes in temperature and precipitation patterns will influence the carbon balance of Arctic wetlands. Variability across sites emphasizes the need for long-term observations across wetland types and climatic gradients. [ABSTRACT FROM AUTHOR]

Processes of spread and patterns of persistence of invasive species affect species and communities in the new environment. Predicting future rates of spread is of great interest for timely management decisions, but this depends on models that rely on understanding the processes of invasion and historic observations of spread and persistence. Unfortunately, the rates of spread and patterns of persistence are difficult to model or directly observe, especially when multiple rates of spread and diverse persistence patterns may be co-occurring over the geographic distribution of the invaded ecosystem. Remote sensing systematically acquires data over large areas at fine spatial and spectral resolutions over multiple time periods that can be used to quantify spread processes and persistence patterns. We used airborne imaging spectroscopy data acquired once a year for 5 years from 2004 to 2008 to map an invaded submerged aquatic vegetation ( SAV) community across 2220 km2 of waterways in the Sacramento-San Joaquin River Delta, California, USA, and measured its spread rate and its persistence. Submerged aquatic vegetation covered 13-23 km2 of the waterways (6-11%) every year. Yearly new growth accounted for 40-60% of the SAV area, ~50% of which survived to following year. Spread rates were overall negative and persistence decreased with time. From this dataset, we were able to identify both radial and saltatorial spread of the invaded SAV in the entire extent of the Delta over time. With both decreasing spread rate and persistence, it is possible that over time the invasion of this SAV community could decrease its ecological impact. A landscape-scale approach allows measurements of all invasion fronts and the spatial anisotropies associated with spread processes and persistence patterns, without spatial interpolation, at locations both proximate and distant to the focus of invasion at multiple points in time. [ABSTRACT FROM AUTHOR]

Optical remote sensing in complex waters is challenging because the optically active constituents may vary independently and have a combined and interacting influence on the remote sensing signal. Additionally, the remote sensing signal is influenced by noise and spectral contamination by confounding factors, resulting in ill-posedness and ill-conditionedness in the inversion of the model. There is a need for inversion methods that are less sensitive to these changing or shifting spectral features. We propose WaveIN, a wavelet-enhanced inversion method, specifically designed for complex waters. It integrates wavelet-transformed high-spectral resolution reflectance spectra in a multiscale analysis tool. Wavelets are less sensitive to a bias in the spectra and can avoid the changing or shifting spectral features by selecting specific wavelet scales. This paper applied WaveIN to simulated reflectance spectra for the Scheldt River. We tested different scenarios, where we added specific noise or confounding factors, specifically uncorrelated noise, contamination due to spectral mixing, a different sun zenith angle, and specific inherent optical property (SIOP) variation. WaveIN improved the constituent estimation in case of the reference scenario, contamination due to spectral mixing, and a different sun zenith angle. WaveIN could reduce, but not overcome, the influence of variation in SIOPs. Furthermore, it is sensitive to wavelet edge effects. In addition, it still requires in situ data for the wavelet scale selection. Future research should therefore improve the wavelet scale selection. [ABSTRACT FROM PUBLISHER]

This letter presents an application of continuous wavelet analysis, providing a new semi-empirical approach to estimate Chlorophyll-a (Chl-a) in optically complex inland waters. Traditionally spectral narrow band ratios have been used to quantify key diagnostic features in the remote sensing signal to estimate concentrations of optically active water quality constituents. However, they cannot cope easily with shifts in reflectance features caused by multiple interactions between variable absorption and backscattering effects that typically occur in optically complex waters. We use continuous wavelet analysis to detect Chl-a features at various wavelengths and frequency scales. Using the wavelet decomposition, we build a 2-D correlation scalogram between in situ pond reflectance spectra and in situ Chl-a concentration. By isolating the most informative wavelet regions via thresholding, we could relate all five regions to known inherent optical properties. We select the optimal feature per region and compare them to three well-known narrow band ratio models. For this experimental application, the wavelet features outperform the NIR-red models, while fluorescence line height (FLH) yield comparable results. Because wavelets analyze the signal at different scales and synthesize information across bands, we hypothesize that the wavelet features are less sensitive to confounding factors, such as instrument noise, colored dissolved organic matter, and suspended matter. [ABSTRACT FROM PUBLISHER]

The goal of historic image classification using signature extension techniques is of widespread interest for remote sensing applications. Historic ground reference data for classifier training is frequently unavailable, necessitating unsupervised transfer learning techniques. Environmental and data variability over time create variable spectral target classes, creating a challenge for knowledge transfer. There is a need for robust classifiers that account for data with high variability and non-normal distributions, and can be successfully applied to unlabeled data; ensemble classification trees are one solution to this problem. With the goal of detecting submerged aquatic vegetation, we trained an ensemble classifier with one collection of 48 imaging spectroscopy flightlines from a single year (2008). We then tested its performance when applied to four years (2004–2007) of historic imaging spectroscopy of the same size over the same area. We validated the resulting classifications with corresponding historic ground reference data. Knowledge transfer success was varied, depending on which image year dataset was used to train the classifier. The 2008-trained classifier had the most stable performance when applied to historic image datasets: overall accuracies ranged from 78.8% to 85.9%. Detection of submerged aquatic vegetation was limited most by the percent cover of the canopy. Water column depth above the plant canopy did not have an effect on detection. The classifier was successful because the training dataset encompassed the range of variability of the study area and the historic datasets. [ABSTRACT FROM PUBLISHER]

Greenberg, Jonathan Asher, Erin L. Hestir, David Riano, George J. Scheer, and Susan L. Ustin, 2012. Using LiDAR Data Analysis to Estimate Changes in Insolation Under Large-Scale Riparian Deforestation. Journal of the American Water Resources Association (JAWRA) 48(5): 939-948. DOI: 10.1111/j.1752-1688.2012.00664.x Abstract: Riparian vegetation provides shade from insolation to stream channels. A consequence of removing vegetation may be an increase in insolation that can increase water temperatures and negatively impact ecosystem health. Although the mechanisms of riparian shading are well understood, spatially explicit, mechanistic models of shading have been limited by the data requirements of precisely describing the three-dimensional structure of a riparian corridor. Remotely acquired, high spatial resolution LiDAR data provide detailed three-dimensional vegetation structure and terrain topography over large regions. By parameterizing solar radiation models that incorporate terrain shadowing with LiDAR data, we can produce spatially explicit estimates of insolation. As a case study, we modeled the relative change in insolation on channels in the Sacramento-San Joaquin River Delta under current conditions and under a hypothesized deforested Delta using classified LiDAR, rasterized at a 1-m resolution. Our results suggest that the removal of levee vegetation could result in a 9% increase in solar radiation incident on Delta waters, and may lead to water temperature increases. General, coarse-scale channel characteristics (reach width, azimuth, levee vegetation cover, and height) only accounted for 72% of the variation in the insolation. This indicates that the detailed information derived from LiDAR data has greater explanatory power than coarser reach-scale metrics often used for insolation estimates. [ABSTRACT FROM AUTHOR]

Summary [ABSTRACT FROM AUTHOR]

Invasive plants are non-native species that can spread rapidly, leading to detrimental economic, ecological, or environmental impact. In aquatic systems such as the Sacramento-San Joaquin River Delta in California, USA, management agencies use manned aerial vehicles (MAV) imaging spectroscopy missions to map and track annual changes in invasive aquatic plants. Advances in unmanned aerial vehicles (UAV) and sensor miniaturization are enabling higher spatial resolution species mapping, which is promising for early detection of invasions before they spread over larger areas. This study compared maps made from UAV-based imaging spectroscopy with the manned airborne imaging spectroscopy-derived maps that are currently produced for monitoring invasive aquatic plants in the Sacramento-San Joaquin Delta. Concurrent imagery was collected using the MAV mounted HyMap sensor and the UAV mounted Nano-Hyperspec at a wetland study site and classification maps generated using random forest models were compared. Classification accuracies were comparable between the Nano- and HyMap-derived maps, with the Nano-derived map having a slightly higher overall accuracy. Additionally, the higher resolution of the Nano imagery allowed detection of patches of water hyacinth present in the study site that the HyMap could not. However, it would not be feasible to operate the Nano as a replacement to HyMap at scale despite its improved detection capabilities due to the high costs associated with overcoming area coverage limitations. Overall, UAV-based imaging spectroscopy provides comparable or improved capability, and we suggest it could be used to supplement existing monitoring programs by focusing on target areas of high ecologic or economic priority. [ABSTRACT FROM AUTHOR]