Remote-sensing monitoring of colored dissolved organic matter in the Arctic Ocean

In the Arctic Ocean, variations in the colored dissolved organic matter (CDOM) have important value and significance. This study proposed and evaluated a novel method by combining the Google Earth Engine with a multilayer back-propagation neural network to retrieve CDOM concentration. This model performed well on the testing data and independent validation data (R2 = 0.76, RMSE = 0.37 m-1, MAPD = 35.43 %), and it was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) images. The CDOM distribution in the Arctic Ocean and its main sea areas was first depicted during the ice-free period from 2002 to 2021, with average CDOM concentration in the range of 0.25 and 0.31 m-1. High CDOM concentration appeared in coastal areas affected by rivers on the Siberian side. The CDOM concentration was highly correlated with salinity (r = -0.92) and discharge (r > 0.68), while melting sea ice diluted seawater and CDOM concentration.

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.

Characteristics of Dissolved Organic Matter Impacted by Different Land Use in Haihe River Watershed, China

It is important to explore characteristics of dissolved organic matter (DOM) in the riverine system due to its critical role in the carbon cycle. This study investigated the distribution characteristics and sources of DOM based on excitation emission matrix three-dimensional fluorescence technology and parallel factor (EEM-PARAFAC) analysis at two rivers in northern China strongly impacted by human activities. The results show that the fluorescence intensity of terrestrial humic-like substances increased during summer in Haihe River. The intensity was significantly higher than in spring due to terrestrial detritus from runoff conveyance. The fluorescence intensity of protein-like substances in spring was the highest and decreased in summer. This feature of DOM in the Duliujian River was related to the increase in precipitation and surface runoff in the wet season and the rapid degradation of mixed DOM in the dry season. An analysis of HIX, BIX and FI showed a low degree of DOM humification and more endogenous contributions from microbial and phytoplankton degradation. Seasonal variations of dissolved organic carbon (DOC) and chromophoric DOM (CDOM, a_335, thereinto C1) suggest that chromophores, particularly terrestrial substances, regulate the temporal patterns of DOM in the two rivers. Combined with the analysis of the proportion of land use types in riparian buffers, tillage had a great impact on DOM content and hydrophobicity in Haihe River watershed. Domestic wastewater and industrial sewage discharge contribute more DOM to Duliujian River watershed, which was indicated by more abundant protein-like components (212.17 ± 94.63 QSU in Duliujian River;186.59 ± 238.72 QSU in Haihe River). This study highlights that different land use types resulted in distinctive sources and seasonal dynamics of DOM in rivers. Meanwhile, it should be considered that the estimation of carbon cycling should involve monitoring and evaluating anthropogenic inputs into rivers.

Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images

Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m−1. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R2 = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R2 = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R2 = 0.71 and MRE = 24.4% for random forest regression (RFR) and R2 = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R2 less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters.

Trends in Satellite Earth Observation for Permafrost Related Analyses—A Review

Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio-temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented. New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.) and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet) will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales.

Chromophoric dissolved organic matter in inland waters: Present knowledge and future challenges

Chromophoric dissolved organic matter (CDOM) plays an important role in the biogeochemical cycle and energy flow of aquatic ecosystems. Thus, systematic and comprehensive understanding of CDOM dynamics is critically important for aquatic ecosystem management. CDOM spans multiple study fields, including analytical chemistry, biogeochemistry, water color remote sensing, and global environmental change. Here, we thoroughly summarize the progresses of recent studies focusing on the characterization, distribution, sources, composition, and fate of CDOM in inland waters. Characterization methods, remote sensing estimation, and biogeochemistry cycle processes were the hotspots of CDOM studies. Specifically, optical, isotope, and mass spectrometric techniques have been widely used to characterize CDOM abundance, composition, and sources. Remote sensing is an effective tool to map CDOM distribution with high temporal and spatial resolutions. CDOM dynamics are mainly determined by watershed-related processes, including rainfall discharge, groundwater, wastewater discharges/effluents, and biogeochemical cycling occurring in soil and water bodies. We highlight the underlying mechanisms of the photochemical degradation and microbial decomposition of CDOM, and emphasize that photochemical and microbial processes of CDOM in inland waters accelerate nutrient cycling and regeneration in the water column and also exacerbate global warming by releasing greenhouse gases. Future study directions to improve the understanding of CDOM dynamics in inland waters are proposed. This review provides an interdisciplinary view and new insights on CDOM dynamics in inland waters.

Direct versus indirect effects of human activities on dissolved organic matter in highly impacted lakes

Human activities can alter dissolved organic matter (DOM) in lakes through both direct (i.e., exporting DOM of anthropogenic sources) and indirect effects (i.e., enhancing the autochthonous production of DOM via nutrient loading). Distinguishing between the direct and indirect effects is important to better understand human impacts on aquatic systems, but it remains highly challenging due to the interdependence of associated environmental variables. Here, we demonstrated that disentangling the direct and indirect effects can be achieved through combining large-scale environmental monitoring with the Partial Least Squares Path Modeling (PLS-PM). We presented DOM data from 61 lakes within the floodplain of the Yangtze River (Lakes-YR), China, a region that has been subjected to intense anthropogenic disturbances. We analyzed the amount and composition of DOM through dissolved organic carbon (DOC), chromophoric DOM (CDOM), and fluorescent DOM (FDOM). Four fluorescence components were identified, including one tyrosine-like component, one tryptophan-like component, and two humic-like components. Most of the lakes were dominated by freshly produced DOM with small molecular weights and low humification. Results from the PLS-PM models showed that the autochthonous production was more important than anthropogenic inputs in mediating DOC and CDOM. In contrast, FDOM parameters in lakes were more sensitive to the direct, anthropogenic sources, including treated domestic, industrial wastewater, and the effluents of aquaculture. These sources can be identified by elevated FDOM content per DOC (FDOM: DOC ratio) relative to autochthonous DOM, suggesting the potential of using FDOM as a tracer to identify and monitor the contribution of anthropogenic organic matter to inland waters.

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

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.

Quantifying DOC and Its Controlling Factors in Major Arctic Rivers during Ice-Free Conditions using Sentinel-2 Data

The six largest Arctic rivers (Yenisey, Lena, Ob’, Kolyma, Yukon, and Mackenzie) drain the organic-rich Arctic watersheds and serve as important pools in the global carbon cycle. Satellite remote sensing data are considered to be a necessary supplement to the ground-based monitoring of riverine organic matter circulation, especially for the ice-free periods in high-latitudes. In this study, we propose a remote sensing retrieval algorithm to obtain the chromophoric dissolved organic matter (CDOM) levels of the six largest Arctic rivers using Sentinel-2 images from 2016 to 2018. These CDOM results are converted to dissolved organic carbon (DOC) concentrations using the strong relationship (R2 = 0.89) between the field measurements of these two water constituents. The temporal-spatial distributions of the DOC in the six largest Arctic rivers during ice-free conditions are depicted. The performance of the retrieval algorithm verifies the capacity of using Sentinel-2 data to monitor riverine DOC variations due to its improved spatial resolution, better band placement, and increased observation frequency. River discharge, watershed slopes, human activities, and land use/land cover change drove much of the variation in the satellite-derived DOC. The seasonality, geography, and scale would affect the correlation between DOC concentration and these influence factors. Our results could improve the ability to monitor DOC fluxes in Arctic rivers and advance our understanding of the Earth’s carbon cycle.

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.

Predictive performance of regression models to estimate Chlorophyll-a concentration based on Landsat imagery

Chlorophyll-a (Chl-a) concentration is a key parameter to describe water quality in marine and freshwater environments. Nowadays, several products with Chl-a have derived from satellite imagery, but they are not available or reliable sometimes for coastal and/or small water bodies. Thus, in the last decade several methods have been described to estimate Chl-a with high-resolution (30 m) satellite imagery, such as Landsat, but a standardized method to estimate Chl-a from Landsat imagery has not been accepted yet. Therefore, this study evaluated the predictive performance of regression models (Simple Linear Regression [SLR], Multiple Linear Regression [MLR] and Generalized Additive Models [GAMs]) to estimate Chl-a based on Landsat imagery, using in situ Chl-a data collected (synchronized with the overpass of Landsat 8 satellite) and spectral reflectance in the visible light portion (bands 1–4) and near infrared (band 5). These bands were selected because of Chl-a absorbance/reflectance properties in these wavelengths. According to goodness of fit, GAM outperformed SLR and MLR. However, the model validation showed that MLR performed better in predicting log-transformed Chl-a. Thus, MLR, constructed by using four spectral bands (1, 2, 3, and 5), was considered the best method to predict Chl-a. The coefficients of this model suggested that log-transformed Chl-a concentration had a positive linear relationship with bands 1 (coastal/aerosol), 3 (green), and 5 (NIR). On the other hand, band 2 (blue) suggested a negative relationship, which implied high coherence with Chl-a absorbance/reflectance properties measured in the laboratory, indicating that Landsat 8 images could be applied effectively to estimate Chl-a concentrations in coastal environments.

Toward Long-Term Aquatic Science Products from Heritage Landsat Missions

This paper aims at generating a long-term consistent record of Landsat-derived remote sensing reflectance (Rrs) products, which are central for producing downstream aquatic science products (e.g., concentrations of total suspended solids). The products are derived from Landsat-5 and Landsat-7 observations leading to Landsat-8 era to enable retrospective analyses of inland and nearshore coastal waters. In doing so, the data processing was built into the SeaWiFS Data Analysis System (SeaDAS) followed by vicariously calibrating Landsat-7 and -5 data using reference in situ measurements and near-concurrent ocean color products, respectively. The derived Rrs products are then validated using (a) matchups using the Aerosol Robotic Network (AERONET) data measured by in situ radiometers, i.e., AERONET-OC, and (b) ocean color products at select sites in North America. Following the vicarious calibration adjustments, it is found that the overall biases in Rrs products are significantly reduced. The root-mean-square errors (RMSE), however, indicate noticeable uncertainties due to random and systematic noise. Long-term (since 1984) seasonal Rrs composites over 12 coastal and inland systems are further evaluated to explore the utility of Landsat archive processed via SeaDAS. With all the qualitative and quantitative assessments, it is concluded that with careful algorithm developments, it is possible to discern natural variability in historic water quality conditions using heritage Landsat missions. This requires the changes in Rrs exceed maximum expected uncertainties, i.e., 0.0015 [1/sr], estimated from mean RMSEs associated with the matchups and intercomparison analyses. It is also anticipated that Landsat-5 products will be less susceptible to uncertainties in turbid waters with Rrs(660) > 0.004 [1/sr], which is equivalent of ~1.2% reflectance. Overall, end-users may utilize heritage Rrs products with “fitness-for-purpose” concept in mind, i.e., products could be valuable for one application but may not be viable for another. Further research should be dedicated to enhancing atmospheric correction to account for non-negligible near-infrared reflectance in CDOM-rich and extremely turbid waters.

Characterization of CDOM absorption of reservoirs with its linkage of regions and ages across China

The absorption of chromophoric dissolved organic matter (CDOM) is an important part of light absorptions in aquatic systems. The increasing eutrophication of reservoirs and regional characteristics would affect the CDOM properties sensitively which would be important for the application of remote sensing monitoring. The highest (4.07 ± 2.31 m^-1) and lowest (0.79 ± 0.67 m^-1) CDOM concentrations of reservoirs were observed in the northeastern lake region (NER) and Tibetan Plateau lake region (TPR), respectively. The differences between S_275-295 among the five lake regions were significant (p < 0.05) in which the steepest S_275-295 (0.0173 ± 0.0026 nm^-1) was observed in TPR and the shallowest (0.0326 ± 0.0152 nm^-1) in Yungui Plateau lake region (YGR). The strong relationships between a_CDOM(355) and DOC appeared in the NER (R² = 0.43), eastern lake region (EAR) (R² = 0.69), Mengxin lake region (MXR) (R² = 0.61), and YGR (R² = 0.79) which would be a good proxy for DOC in regional reservoirs. Most of all, the correlation between reservoir's establishing time and CDOM absorption under oligotrophic states was relatively strong in the EAR and MXR regions. It indicated that the establishing time of reservoirs affected the CDOM absorption to some extent under the oligotrophic states without much human disturbance. Our results indicate CDOM absorption varies with regions, and the relationships between CDOM and DOC are variable for different regions. Therefore, DOC estimation in reservoirs through CDOM absorption needs to be considered according to lake regions and trophic states.

Global distribution of dissolved organic matter along the aquatic continuum: Across rivers, lakes and oceans

Based on an extensive literature survey containing more than 12,000 paired measurements of dissolved organic carbon (DOC) concentrations and absorption of chromophoric dissolved organic matter (CDOM) distributed over four continents and seven oceans, we described the global distribution and transformation of dissolved organic matter (DOM) along the aquatic continuum across rivers and lakes to oceans. A strong log-linear relationship (R²=0.92) between DOC concentration and CDOM absorption at 350nm was observed at a global scale, but was found to be ecosystem-dependent at local and regional scales. Our results reveal that as DOM is transported towards the oceans, the robustness of the observed relation decreases rapidly (R² from 0.94 to 0.44) indicating a gradual decoupling between DOC and CDOM. This likely reflects the decreased connectivity between the landscape and DOM along the aquatic continuum. To support this hypothesis, we used the DOC-specific UV absorbance (SUVA) to characterize the reactivity of the DOM pool which decreased from 4.9 to 1.7m² × gC^-1 along the aquatic continuum. Across the continuum, a piecewise linear regression showed that the observed decrease of SUVA occurred more rapidly in freshwater ecosystems compared to marine water ecosystems, suggesting that the different degradation processes act preferentially on CDOM rather than carbon content. The observed change in the DOM characteristics along the aquatic continuum also suggests that the terrestrial DOM pool is gradually becoming less reactive, which has profound consequences on cycling of organic carbon in aquatic ecosystems.

The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)

Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles.

Dissolved organic carbon fluxes in the Middle Atlantic Bight: An integrated approach based on satellite data and ocean model products

Continental margins play an important role in global carbon cycle, accounting for 15-21% of the global marine primary production. Since carbon fluxes across continental margins from land to the open ocean are not well constrained, we undertook a study to develop satellite algorithms to retrieve dissolved organic carbon (DOC) and combined these satellite data with physical circulation model products to quantify the shelf boundary fluxes of DOC for the U.S. Middle Atlantic Bight (MAB). Satellite DOC was computed through seasonal relationships of DOC with colored dissolved organic matter absorption coefficients, which were derived from an extensive set of in situ measurements. The multiyear time series of satellite-derived DOC stocks (4.9 Teragrams C; Tg) shows that freshwater discharge influences the magnitude and seasonal variability of DOC on the continental shelf. For the 2010-2012 period studied, the average total estuarine export of DOC into the MAB shelf is 0.77 Tg C yr^-1 (year). The integrated DOC tracer fluxes across the shelf boundaries are 12.1 Tg C yr^-1 entering the MAB from the southwest alongshore boundary, 18.5 Tg C yr^-1 entering the MAB from the northeast alongshore boundary, and 29.0 Tg C yr^-1 flowing out of the MAB across the entire length of the 100 m isobath. The magnitude of the cross-shelf DOC flux is quite variable in time (monthly) and space (north to south). The highly dynamic exchange of water along the shelf boundaries regulates the DOC budget of the MAB at subseasonal time scales.