High-Resolution Remote Sensing of Water Quality in the San Francisco Bay-Delta Estuary

Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty

Two fundamental problems have inhibited progress in the simulation of river water quality under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing models to account for the complexity of factors (e.g., hydro-climatic, basin characteristics, land use features) affecting river water quality. To address these concerns this study presents a technique for augmenting limited ground-based observations of water quality variables with remote-sensed surface reflectance data by leveraging a machine learning model capable of accommodating the multidimensionality of water quality influences. Total Suspended Solids (TSS) can serve as a surrogate for chemical and biological pollutants of concern in surface water bodies. Historically, TSS data collection in the United States has been limited to the location of water treatment plants where state or federal agencies conduct regularly-scheduled water sampling. Mathematical models relating riverine TSS concentration to the explanatory factors have therefore been limited and the relationships between climate extremes and water contamination events have not been effectively diagnosed. This paper presents a method to identify these issues by utilizing a Long Short-Term Memory Network (LSTM) model trained on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance data, which is calibrated to TSS data collected by the Ohio River Valley Water Sanitation Commission (ORSANCO). The methodology developed enables a thorough empirical analysis and data-driven algorithms able to account for spatial variability within the watershed and provide effective water quality prediction under uncertainty.

An improved hyperspectral sensing approach for the rapid determination of copper ion concentrations in water environment using short-wavelength infrared spectroscopy

Copper ion is one of the hazardous pollutants often present in industrial wastewater or acid mine drainage that is regarded as a primary environmental challenge. Hyperspectral remote sensing has a long tradition in water quality monitoring. However, its application in heavy metal detection is relatively similar, and the detection is highly influenced by water turbidity or total suspended matter (TSM), requiring research efforts to improve accuracy and generalize the applicability of this technique. In this study, the use of simple filtration (pore size of 0.7 μm) for sample pretreatment to improve hyperspectral remote sensing of copper ion concentrations (Cu, 100-1000 mg/L) in water samples is proposed. A wide variety of water samples, including as-prepared and field (fish pond and river water) samples, were investigated to validate the developed method. Spectral data containing sensitive bands characterized in the range of 900-1100 nm were first preprocessed with logarithm transformation, followed by quantitative prediction model development using stepwise multivariate linear regression (SMLR) with the most sensitive wavebands at around 900 nm and 1080 nm. Satisfactory prediction performance for Cu ions was found for turbid water samples (TSM greater than approximately 200 mg/L) after simple filtration pretreatment, suggesting that pretreatment removed suspended solids in the mixtures and enhanced the spectral features of Cu ions in the model. Moreover, good agreement between the laboratory results and the field samples (adjusted R2 > 0.95 and NRMSE <0.15) highlights the suitability of the developed model and filtration pretreatment for obtaining relevant information for the rapid determination of Cu ion concentrations in complex water samples.

Advances in Catchment Science, Hydrochemistry, and Aquatic Ecology Enabled by High-Frequency Water Quality Measurements

High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function.

A Multi–Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing

Water is a fundamental resource for human survival but the consumption of water that is unfit for drinking leads to serious diseases. Access to high–resolution satellite imagery provides an opportunity for innovation in the techniques used for water quality monitoring. With remote sensing, water quality parameter concentrations can be estimated based on the band combinations of the satellite images. In this study, a hybrid remote sensing and deep learning approach for forecasting multi–step parameter concentrations was investigated for the advancement of the traditionally employed water quality assessment techniques. Deep learning models, including a convolutional neural network (CNN), fully connected network (FCN), recurrent neural network (RNN), multi–layer perceptron (MLP), and long short term memory (LSTM), were evaluated for multi–step estimations of an optically active parameter, i.e., electric conductivity (EC), and an inactive parameter, i.e., dissolved oxygen (DO). The estimation of EC and DO concentrations can aid in the analysis of the levels of impurities and oxygen in water. The proposed solution will provide information on the necessary changes needed in water management techniques for the betterment of society. EC and DO parameters were taken as independent variables with dependent parameters, i.e., pH, turbidity, total dissolved solids, chlorophyll–α, Secchi disk depth, and land surface temperature, which were extracted from Landsat–8 data from the years 2014–2021 for the Rawal stream network. The bi–directional LSTM obtained better results with a root mean square error (RMSE) of 0.2 (mg/L) for DO and an RMSE of 281.741 (μS/cm) for EC, respectively. The results suggest that a hybrid approach provides efficient and accurate results in feature extraction and evaluation of multi–step forecast of both optically active and inactive water quality parameters.

Monitoring Turbidity in San Francisco Estuary and Sacramento-San Joaquin Delta Using Satellite Remote Sensing

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 R ² = 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.

Remote sensing of CDOM and DOC in alpine lakes across the Qinghai-Tibet Plateau using Sentinel-2A imagery data

As important components of dissolved organic matter (DOM) in an aquatic environment, colored DOM (CDOM) and dissolved organic carbon (DOC) play an essential role in the carbon cycle of an inland aquatic system. Traditionally, CDOM and DOC in inland waters have been primarily determined using in situ observations and laboratory measurements. Most of past lake investigations on CDOM and DOC focused on easily accessible regions and covered a small fraction of lakes worldwide. To our knowledge, little is known about lakes in less accessible areas like the Qinghai-Tibet Plateau (QTP). To address this challenge, optical satellite remote sensing might be useful for capturing a synoptic view of CDOM and DOC with high frequency at large scales, complementing in situ sampling methods for inland waters. In this study, 216 samples collected from 36 lakes across the QTP (2014-2017) were examined to determine the relationships between CDOM absorption coefficient at 350 nm (a₃₅₀) and Sentinel-2A Multi Spectral Instrument (MSI) imagery reflectance data. A strong positive linear correlation with a₃₅₀ was observed with B4/B2 (R² = 0.78, p < 0.01) and with B4/B3 (R² = 0.62). A multi-step regression model was established for estimating a₃₅₀ with B4/B2 and B4/B3 as input variables (R² = 0.81, p < 0.01). A scattered CDOM-DOC relationship was revealed (R² = 0.34, p < 0.05) using a pooled dataset. By dividing the inland waters into four separate groups in accordance with their salinity gradients, we were able to develop much stronger relationships (R² > 0.8, p < 0.01) for CDOM-DOC. Significant differences between fresh and saline waters were demonstrated using satellite-derived CDOM and DOC, where high CDOM (0.86 ± 0.67 m^-1) and low DOC (3.76 ± 4.92 mg L^-1) concentrations were observed for freshwaters, while inverse trends of CDOM (0.53 ± 0.72 m^-1) and DOC (15.76 ± 17.07 mg L^-1) were demonstrated for saline lakes in the Tibetan Plateau. This study confirmed that satellite optical imagery can be used for the monitoring of CDOM and DOC of the lakes of the Tibetan Plateau, which are sensitive to a changing climate and are infrequently investigated due to the harsh environment and poor accessibility. Moreover, it highlighted the importance of combining salinity and remote sensing data in the process of estimating lake DOC.

Remote estimates of CDOM using Sentinel-2 remote sensing data in reservoirs with different trophic states across China

Chromophoric dissolved organic matter (DOM) is called as CDOM which could affect the optical properties of surface waters, and is a useful parameter for monitoring complex inland aquatic systems. Large-scale monitoring of CDOM using remote-sensing has been a challenge due to the poor transferability of CDOM retrieval models across regions. To overcome these difficulties, a study is conducted using Sentinel-2 images, in situ reflectance spectral data, and water chemical parameters at 93 water reservoirs across China classified by trophic state. Empirical algorithms are established between CDOM absorption coefficient a_CDOM(355) and reflectance band ratio (B5/B2,vegetation Red Edge/Blue) acquired in situ and via Sentinel-2 MSI sensors. Relationships are stronger (r² > 0.7, p < 0.05) when analysis is conducted separately by trophic states. Validation models show that, by accounting for trophic state of reservoirs and using B5/B2 band ratios, it is possible to expand the geographical range of remote sensing-based models to determine CDOM. However, the accuracy of model validation decreased from oligotrophic (r²: 0.86) to eutrophic reservoirs (r²: 0.82), likely due to increased complexity of CDOM sources in nutrient-rich systems. This study provides a strategy for using local and remote-sensing data to monitor the spatial variations of CDOM in reservoirs based on different trophic states, and will contribute to water resources management.

Prediction of Photochemically Produced Reactive Intermediates in Surface Waters via Satellite Remote Sensing

Absorption of solar radiation by colored dissolved organic matter (CDOM) in surface waters results in the formation of photochemically produced reactive intermediates (PPRIs) that react with pollutants in water. Knowing the steady-state concentrations of PPRIs ([PPRI]_ss) is critical to predicting the persistence of pollutants in sunlit surface waters. CDOM levels (a₄₄₀) can be measured remotely for lakes over large areas using satellite imagery. Laboratory measurements of [PPRI]_ss and apparent quantum yields (Φ) of three PPRIs (³DOM*, ¹O₂, and ^•OH) were made for 24 lake samples under simulated sunlight. The total rate of light absorption by the water samples (R_a), the rates of formation (R_f), and [PPRI]_ss of ³DOM* and ¹O₂ linearly increased with increasing a₄₄₀. The production rate of ^•OH was linearly correlated with a₄₄₀, but the steady-state concentration was best fit by a logarithmic function. The relationship between measured a₄₄₀ and Landsat 8 reflectance was used to map a₄₄₀ for more than 10 000 lakes across Minnesota. Relationships of a₄₄₀ with R_f, [PPRIs]_ss, and R_a were coupled with satellite-based a₄₄₀ assessments to map reactive species production rates and concentrations as well as contaminant transformation rates. This study demonstrates the potential for using satellite imagery for estimating contaminant loss via indirect photolysis in lakes.

Spectral and spatial kernel water quality mapping

An empirical approach through remote sensing generally produces a robust data model of water quality for inland and coastal water. Traditional regressions in water quality mapping fail because the bio-optical relationship of turbid water exhibits nonlinear and heterogeneous patterns. In addition, in situ data are generally insufficient in the water quality mapping. Mapping based on a relatively small amount of water quality samples is considered a practical issue in environmental monitoring. Learning-based algorithms that require a large amount of data are inapplicable in this case. According to the concept of Nadaraya-Watson estimator, the kernel model can estimate nonlinear and spatially varying water quality maps effectively in turbid water.Experiments indicate that the kernel estimator provides better goodness-of-fit between the observed and derived concentrations of water quality parameter, e.g., chlorophyll-a in turbid water. The kernel estimator is feasible for a relatively small size of ground observations. Approximately 30% improvement of cross-validation error was identified in this approach when compared with traditional regressions. The model offers a robust approach without further calibrations for estimating the spatial patterns of water quality by using remote sensing reflectance and a small set of observations, considering spatial and spectral information, e.g., multiple bands and band ratios.

Retrieval and Validation of Water Turbidity at Metre-Scale Using Pléiades Satellite Data: A Case Study in the Gironde Estuary

This study investigated the use of frequent metre-scale resolution Pléiades satellite imagery to monitor water quality parameters in the highly turbid Gironde Estuary (GE, SW France). Pléiades satellite data were processed and analyzed in two representative test sites of the GE: 1) the maximum turbidity zone and 2) the mouth of the estuary. The main objectives of this study were to: (i) validate the Dark Spectrum Fitting (DSF) atmospheric correction developed by Vanhellemont and Ruddick (2018) applied to Pléiades satellite data recorded over the GE; (ii) highlight the benefits of frequent metre-scale Pléiades observations in highly turbid estuaries by comparing them to previously validated satellite observations made at medium (250/300 m for MODIS, MERIS, OLCI data) and high (20/30 m for SPOT, OLI and MSI data) spatial resolutions. The results show that the DSF allows for an accurate retrieval of water turbidity by inversion of the water reflectance in the near-infrared (NIR) and red wavebands. The difference between Pléiades-derived turbidity and field measurements was proven to be in the order of 10%. To evaluate the spatial variability of water turbidity at metre scale, Pléiades data at 2 m resolution were resampled to 20 m and 250 m to simulate typical coarser resolution sensors. On average, the derived spatial variability in the GE is lower than or equal to 10% and 26%, respectively, in 20-m and 250-m aggregated pixels. Pléiades products not only show, in great detail, the turbidity features in the estuary and river plume, they also allow to map the turbidity inside ports and capture the complex spatial variations of turbidity along the shores of the estuary. Furthermore, the daily acquisition capabilities may provide additional advantages over other satellite constellations when monitoring highly dynamic estuarine systems.

Spatial variability of phytoplankton in a shallow tidal freshwater system reveals complex controls on abundance and community structure

Estuaries worldwide are undergoing changes to patterns of aquatic productivity because of human activities that alter flow, impact sediment delivery and thus the light field, and contribute nutrients and contaminants like pesticides and metals. These changes can influence phytoplankton communities, which in turn can alter estuarine food webs. We used multiple approaches-including high-resolution water quality mapping, synoptic sampling, productivity and nitrogen uptake rates, Lagrangian parcel tracking, enclosure experiments and bottle incubations-over a short time period to take a "spatial snapshot" of conditions in the northern region of the San Francisco Estuary (California, USA) to examine how environmental drivers like light availability, nutrients, water residence time, and contaminants affect phytoplankton abundance and community attributes like size distribution, taxonomic structure, and nutrient uptake rates. Zones characterized by longer residence time (15-60 days) had higher chlorophyll-a concentrations (9 ± 4 µg L^-1) and were comprised primarily of small phytoplankton cells (<5 µm, 74 ± 8%), lower ammonium concentrations (1 ± 0.8 µM), higher nitrate uptake rates, and higher rates of potential carbon productivity. Conversely, zones characterized by shorter residence time (1-14 days) had higher ammonium concentration (13 ± 5 µM) and lower chlorophyll-a concentration (5 ± 1 µg L^-1) with diatoms making up a larger percent contribution. Longer residence time, however, did not result in the accumulation of large (>5 µm) cells considered important to pelagic food webs. Rather, longer residence time zones had a phytoplankton community comprised primarily of small cells, particularly picocyanobacteria that made up 38 ± 17% of the chlorophyll-a - nearly double the concentration seen in shorter residence time zones (22 ± 7% picocyanobacterial of chlorophyll-a). Our results suggest that water residence time in estuaries may have an effect as large or larger than that experimentally demonstrated for light, contaminants, or nutrients.

Multiplatform Earth Observation Systems for Monitoring Water Quality in Vulnerable Inland Ecosystems: Maspalomas Water Lagoon

The accurate monitoring of water quality indicators, bathymetry and distribution of benthic habitats in vulnerable ecosystems is key to assessing the effects of climate change, the quality of natural areas and to guide appropriate biodiversity, tourism or fisheries policies. Coastal and inland water ecosystems are very complex but crucial due to their richness and primary production. In this context, remote sensing can be a reliable way to monitor these areas, mainly thanks to satellite sensors’ improved spatial and spectral capabilities and airborne or drone instruments. In general, mapping bodies of water is challenging due to low signal-to-noise (SNR) at sensor level, due to the very low reflectance of water surfaces as well as atmospheric effects. Therefore, the main objective of this work is to provide a robust processing framework to estimate water quality parameters in inland shallow waters using multiplatform data. More specifically, we measured chlorophyll concentrations (Chl-a) from multispectral and hyperspectral sensors on board satellites, aircrafts and drones. The Natural Reserve of Maspalomas, Canary Island (Spain), was chosen for the study because of its complexity as well as being an inner lagoon with considerable organic and inorganic matter and chlorophyll concentration. This area can also be considered a well-known coastal-dune ecosystem attracting a large amount of tourists. The water quality parameter estimated by the remote sensing platforms has been validated using co-temporal in situ measurements collected during field campaigns, and quite satisfactory results have been achieved for this complex ecosystem. In particular, for the drone hyperspectral instrument, the root mean square error, computed to quantify the differences between the estimated and in situ chlorophyll-a concentrations, was 3.45 with a bias of 2.96.

Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.

Human activities determine quantity and composition of dissolved organic matter in lakes along the Yangtze River

Dissolved organic matter (DOM) plays important roles in the aquatic biogeochemical cycle and the global carbon cycle. However, it is highly spatially and temporally varied due to complex sources from the catchment (allochthonous) and from within the system (autochthonous). Satellite remote sensing provides the ability to monitor DOM and identify the spatio-temporal variations in lakes on a global or regional scale. In this study, field work was conducted in 55 lakes in August 2012 along the middle and lower reaches of the Yangtze River (MLR-YR), where most lakes were characterized by eutrophication due to intense human activities. The results showed that both colored DOM (CDOM) and total DOM differed significantly by and were linearly related to the human-induced trophic state index (TSI), with R² = 0.41 and 0.61, respectively. Autochthonous substances by phytoplankton contributed to 38.5% of CDOM and 35.2% of DOM, and allochthonous terrestrial substance indexed by land cover change and aquaculture contributed to almost half, with 49.7% of CDOM and 49.8% of DOM. In total, human activities explained as much as 81.7% and 87.5% of the variations in CDOM and DOM, respectively. Finally, a flowchart for estimating DOM from satellite-derived TSI was proposed. This study has great significance for synchronously monitoring and managing aquatic environment quality in regional eutrophic lakes around the world.

Assessment of the chlorine demand and disinfection byproduct formation potential of surface waters via satellite remote sensing

The ability of satellites to assess surface water quality indicators such as colored dissolved organic matter (CDOM) suggests that remote sensing could be a useful tool for evaluating water treatability metrics in considering potential drinking water supplies. To explore this possibility, 24 surface water samples were collected throughout Minnesota, USA with wide ranging values of CDOM (a₄₄₀; 0.41-27.9 m^-1), dissolved organic carbon (DOC; 5.5-47.6 mg/L) and specific ultraviolet absorbance at 254 nm (SUVA₂₅₄; 1.3-5.1 L/mg-M). Laboratory experiments were performed to quantify chlorine demand and the formation of two classes of halogenated disinfection byproducts (DBPs), trihalomethanes (THMs) and haloacetic acids (HAAs), using the uniform formation conditions (UFC) test. Chlorine demand and THM_UFC were linearly correlated with CDOM (R² = 0.97 and 0.91, respectively), indicating that CDOM is a useful predictor of these parameters. On the other hand, data comparing di- and tri-HAA_UFC with CDOM were better fit by a logarithmic relationship (R² = 0.73 and 0.87, respectively), while mono-HAA_UFC was linearly correlated with CDOM (R² = 0.46) but only for low-to moderately-colored waters (a₄₄₀ ≤ 11 m^-1). The correlations relating chlorine demand and DBP_UFC values with CDOM were coupled with satellite CDOM assessments to estimate chlorine demand and DBP_UFC values for all surface waters larger than 0.05 km² in the state of Minnesota, USA. The resulting maps suggest that only 21.8% of Minnesota lakes would meet both the THM and HAA maximum contaminant levels, but only when pre-disinfection treatment removes 75% of DBP precursors. There are limitations to determining CDOM using satellites for high color surface waters (a₄₄₀ > 11 m^-1), however, leading to underpredicted values for CDOM, chlorine demand, and DBP_UFC. Overall, the results demonstrate the potential benefits of satellite remote sensing for assessing potential drinking water sources and water treatability metrics.

Climate change leads to a doubling of turbidity in a rapidly expanding Tibetan lake

Recent climate change is causing most lakes on the Tibetan Plateau to grow at an unprecedented rate. Changes in the physical properties and water storage of the lakes are now relatively well documented. Yet the impacts on their water quality remain poorly understood. Turbidity is a well-established optical water-quality indicator related to suspended particulate matter concentration which can affect vertical light attenuation and ecosystem functioning. Here, we use remotely sensed data to assess the seasonal and long-term variations in turbidity in Siling Lake, one of the fastest growing lakes on the Tibetan Plateau, and to identify potential driving mechanisms of this change. The lake experiences two distinct peaks of turbidity during the year: one in August (warm season) caused by the seasonal influx of sediments from the Zagya Zangbo River, and one in December (cold season) caused by the wind-driven resuspension of sediments along the lakes' shorelines. The analysis further revealed a persistent increasing trend that doubled the average lake turbidity between 2000 and 2017. Evidence suggests this rise in turbidity results from a climate-driven increase in sediment supply from the Zagya Zangbo River, and from sediment resuspension associated with the erosion of shorelines recently submerged during the rapid expansion of the lake (paleoshorelines). Our results highlight the vulnerability of the Tibetan Lakes' water quality to climate change.

The Influence of Signal to Noise Ratio of Legacy Airborne and Satellite Sensors for Simulating Next-Generation Coastal and Inland Water Products

Presently, operational ocean color satellite sensors are designed with a legacy perspective for sampling the open ocean primarily in the visible domain, while high spatial resolution sensors such as Sentinel-2, Sentinel-3, and Landsat8 are increasingly used for observations of coastal and inland water quality. Next-generation satellites such as the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) and Surface Biology and Geology (SBG) sensors are anticipated to increase spatial and/or spectral resolution. An important consideration is determining the minimum signal-to-noise ratio (SNR) needed to retrieve typical biogeochemical products, such as biomass, in aquatic systems, and whether legacy sensors can be used for algorithm development. Here, we evaluate SNR and remote-sensing reflectance (Rrs) uncertainty for representative bright and dim targets in coastal California, USA. The majority of existing sensors fail to meet proposed criteria. Despite these limitations, uncertainties in retrieved biomass as chlorophyll or normalized difference vegetation index (NDVI) remain well below a proposed threshold of 17.5%, suggesting that existing sensors can be used in coastal systems. Existing commercially available in-water and airborne instrument suites can exceed all proposed thresholds for SNR and Rrs uncertainty, providing a path forward for collection of calibration and validation data for future satellite missions.

Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach

The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability in space and time due to variability in water constituent compositions, mixtures, and inherent optical properties. This study used in situ spectral reflectances and their first derivatives to compare empirical algorithms for estimating TSS using hyperspectral and multispectral data. These algorithms were applied to imagery collected by NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over coastal Louisiana, USA, and validated with a multiyear in situ dataset. The best performing models were then applied to independent spectroscopic data collected in the Peace–Athabasca Delta, Canada, and the San Francisco Bay–Delta Estuary, USA, to assess their robustness and transferability. A derivative-based partial least squares regression (PLSR) model applied to simulated AVIRIS-NG data showed the most accurate TSS retrievals (R2 = 0.83) in these contrasting deltaic environments. These results highlight the potential for a more broadly applicable generalized algorithm employing imaging spectroscopy for estimating suspended solids.

Earth observations and integrative models in support of food and water security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.

Limitations on using CDOM as a proxy for DOC in temperate lakes

Colored dissolved organic matter (CDOM) has been widely studied as part of efforts to improve understanding of the aquatic carbon cycle, by laboratory, in situ, and remote sensing methods. We studied ecoregion-scale differences in CDOM and dissolved organic carbon (DOC) to understand variability in organic matter composition and the use of CDOM as a proxy for DOC. Data from 299 lakes across the U.S. Upper Midwest showed that CDOM, measured as absorptivity at 440 nm (a₄₄₀), correlated strongly with DOC (R² = 0.81, n = 412). Colored lakes in the Northern Lakes and Forests (NLF) ecoregion drove this relationship. Lakes in the North Central Hardwood Forests (NCHF) had low color (most had a₄₄₀ < 3 m^-1) and weaker CDOM-DOC relationships (R² = 0.47). Spectral slopes and specific ultraviolet absorbance (SUVA), indicated relatively low aromaticity and non-terrestrial DOM sources in low color lakes. Multiple regression analyses that included total dissolved nitrogen (TDN) and CDOM, but not chlorophyll a, improved DOC estimates in low color lakes, suggesting a dominant contribution of non-planktonic sources of low color DOM in these lakes. Our results show that CDOM is a reliable, regional proxy for DOC in lakes where forests and wetlands dominate the landscape and the DOM is primarily terrestrial in origin. Mapping of lake DOC at broad spatial scales by satellite-derived CDOM has lower accuracy in low color lakes.

Quantification of Polychlorinated Biphenyl (PCB) Concentration in San Francisco Bay Using Satellite Imagery

The U.S. Environmental Protection Agency banned the use of polychlorinated biphenyls (PCBs) in 1979, due to the high environmental and public health risks with which they are associated. However, PCBs continue to persist in the San Francisco Bay (SFB), often at concentrations deemed unsafe for humans. In situ PCB monitoring within the SFB is extremely limited, due in large part to the high monetary costs associated with sampling. Here we offer a cost effective alternative to in situ PCB monitoring by demonstrating the feasibility of indirectly quantifying PCBs in the SFB via satellite remote sensing using a two-step approach. First, we determined the relationship between in situ PCB concentrations and suspended sediment concentrations (SSC) in the SFB. We then correlated in situ SSC with spatially and temporally consistent Landsat 8 and Sentinel 2A reflectances. We demonstrate strong relationships between SSC and PCBs in all three SFB sub-embayments (R2 > 0.28–0.80, p < 0.01), as well as a robust relationship between SSC and satellite measurements for both Landsat 8 and Sentinel 2A (R2 > 0.72, p < 0.01). These relationships held regardless of the atmospheric correction regime that we applied. The end product of these relationships is an empirical two-step relationship capable of deriving PCBs from satellite imagery. Our approach of estimating PCBs in the SFB by remotely sensing SSC is extremely cost-effective when compared to traditional in situ techniques. Moreover, it can also be utilized to generate PCB concentration maps for the SFB. These maps could one day serve as an important tool for PCB remediation in the SFB, as they can provide valuable insight into the spatial distribution of PCBs throughout the bay, as well as how this distribution changes over time.

Commercial silicate phosphate sequestration and desorption leads to a gradual decline of aquatic systems

Laboratory desorption behaviour, function and elemental composition of commercially marketed silicate minerals used to sequester phosphorus pollution as well as Zeolite, Smectite, and Kaolinite were determined to see whether their use by environmental scientists and water managers in eutrophic waterways has the potential to contribute to longer-term environmental impacts. As expected, lower phosphorus concentrations were observed, following treatment. However, data relating to desorption, environmental fate and bioavailability of phospho-silicate complexes (especially those containing rare earth elements) appear to be underrepresented in product testing and trial publications. Analysis of desorption of phosphate (P) was > 5 μg[P]/L for all three non-commercial samples and 0 > μg[P]/L > 5 for all commercial silicates for a range of concentrations from 0 to 300 μg[P]/L. Based on a review of bioaccumulation data specific to the endangered Cherax tenuimanus (Hairy Marron) and other endemic species, this is significant considering anything > 20 μg[La]/L is potentially lethal to the hairy marron, other crustaceans and even other phyla. Where prokaryotic and eukaryotic effects are underreported, this represents a significant challenge. Especially where product protocols recommend continual reapplication, this is significant because both the forward and reverse reactions are equally important. The users of silicate minerals in water columns should accept the dynamic nature of the process and pay equal attention to both adsorption and desorption because desorption behaviour is an inherent trait. Even if broader desorption experimentation is difficult, expensive and time-consuming, it is a critical consideration nonetheless.

Evaluation of water quality based on a machine learning algorithm and water quality index for the Ebinur Lake Watershed, China

The water quality index (WQI) has been used to identify threats to water quality and to support better water resource management. This study combines a machine learning algorithm, WQI, and remote sensing spectral indices (difference index, DI; ratio index, RI; and normalized difference index, NDI) through fractional derivatives methods and in turn establishes a model for estimating and assessing the WQI. The results show that the calculated WQI values range between 56.61 and 2,886.51. We also explore the relationship between reflectance data and the WQI. The number of bands with correlation coefficients passing a significance test at 0.01 first increases and then decreases with a peak appearing after 1.6 orders. WQI and DI as well as RI and NDI correlation coefficients between optimal band combinations of the peak also appear after 1.6 orders with R² values of 0.92, 0.58 and 0.92. Finally, 22 WQI estimation models were established by POS-SVR to compare the predictive effects of these models. The models based on a spectral index of 1.6 were found to perform much better than the others, with an R² of 0.92, an RMSE of 58.4, and an RPD of 2.81 and a slope of curve fitting of 0.97.

A large airborne survey of Earth's visible-infrared spectral dimensionality

The intrinsic spectral dimensionality indicates the observable degrees of freedom in Earth's solar-reflected light field, quantifying the diversity of spectral content accessible by visible and infrared remote sensing. The solar-reflected regime spans the 0.38 - 2.5 μm interval, and is captured by a wide range of current and planned instruments on both airborne and orbital platforms. To date there has been no systematic study of its spectral dimensionality as a function of space, time, and land cover. Here we report a multi-site, multi-year statistical survey by NASA's "Classic" Airborne Visible Near InfraRed Spectrometer (AVIRIS-C). AVIRIS-C measured large regions of California, USA, spanning wide latitudinal and elevation gradients containing all canonical MODIS land cover types. The spectral uniformity of the AVIRIS-C design enabled consistent in-scene assessment of measurement noise across acquisitions. The estimated dimensionality as a function of cover type ranged from the low 20s to the high 40s, and was approximately 50 for the combined dataset. This result indicates the high diversity of physical processes distinguishable by imaging spectrometers like AVIRIS-C for one region of the Earth.

Using Continuous Underway Isotope Measurements To Map Water Residence Time in Hydrodynamically Complex Tidal Environments

Stable isotopes present in water (δ²H, δ¹⁸O) have been used extensively to evaluate hydrological processes on the basis of parameters such as evaporation, precipitation, mixing, and residence time. In estuarine aquatic habitats, residence time (τ) is a major driver of biogeochemical processes, affecting trophic subsidies and conditions in fish-spawning habitats. But τ is highly variable in estuaries, owing to constant changes in river inflows, tides, wind, and water height, all of which combine to affect τ in unpredictable ways. It recently became feasible to measure δ²H and δ¹⁸O continuously, at a high sampling frequency (1 Hz), using diffusion sample introduction into a cavity ring-down spectrometer. To better understand the relationship of τ to biogeochemical processes in a dynamic estuarine system, we continuously measured δ²H and δ¹⁸O, nitrate and water quality parameters, on board a small, high-speed boat (5 to >10 m s^-1) fitted with a hull-mounted underwater intake. We then calculated τ as is classically done using the isotopic signals of evaporation. The result was high-resolution (∼10 m) maps of residence time, nitrate, and other parameters that showed strong spatial gradients corresponding to geomorphic attributes of the different channels in the area. The mean measured value of τ was 30.5 d, with a range of 0-50 d. We used the measured spatial gradients in both τ and nitrate to calculate whole-ecosystem uptake rates, and the values ranged from 0.006 to 0.039 d^-1. The capability to measure residence time over single tidal cycles in estuaries will be useful for evaluating and further understanding drivers of phytoplankton abundance, resolving differences attributable to mixing and water sources, explicitly calculating biogeochemical rates, and exploring the complex linkages among time-dependent biogeochemical processes in hydrodynamically complex environments such as estuaries.