Remote Sensing of CDOM, CDOM Spectral Slope, and Dissolved Organic Carbon in the Global Ocean

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.

Informing ocean color inversion products by seeding with ancillary observations

Ocean reflectance inversion algorithms provide many products used in ecological and biogeochemical models. While a number of different inversion approaches exist, they all use only spectral remote-sensing reflectances (Rrs(λ)) as input to derive inherent optical properties (IOPs) in optically deep oceanic waters. However, information content in Rrs(λ) is limited, so spectral inversion algorithms may benefit from additional inputs. Here, we test the simplest possible case of ingesting optical data ('seeding') within an inversion scheme (the Generalized Inherent Optical Property algorithm framework default configuration (GIOP-DC)) with both simulated and satellite datasets of an independently known or estimated IOP, the particulate backscattering coefficient at 532 nm (bbp(532)). We find that the seeded-inversion absorption products are substantially different and more accurate than those generated by the standard implementation. On global scales, seasonal patterns in seeded-inversion absorption products vary by more than 50% compared to absorption from the GIOP-DC. This study proposes one framework in which to consider the next generation of ocean color inversion schemes by highlighting the possibility of adding information collected with an independent sensor.

Remote sensing for chromophoric dissolved organic matter (CDOM) monitoring research 2003-2022: A bibliometric analysis based on the web of science core database

Chromophoric dissolved organic matter (CDOM) occupies a critical part in biogeochemistry and energy flux of aquatic ecosystems. CDOM research spans in many fields, including chemistry, marine environment, biomass cycling, physics, hydrology, and climate change. In recent years, a series of remarkable research milestone have been achieved. On the basis of reviewing the research process of CDOM, combined with a bibliometric analysis, this study aims to provide a comprehensive review of the development and applications of remote sensing in monitoring CDOM from 2003 to 2022. The findings show that remote sensing data plays an important role in CDOM research as proven with the increasing number of publications since 2003, particularly in China and the United States. Primary research areas have gradually changed from studying absorption and fluorescence properties to optimization of remote sensing inversion models in recent years. Since the composition of oceanic and freshwater bodies differs significantly, it is important to choose the appropriate inversion method for different types of water body. At present, the monitoring of CDOM mainly relies on a single sensor, but the fusion of images from different sensors can be considered a major research direction due to the complex characteristics of CDOM. Therefore, in the future, the characteristics of CDOM will be studied in depth inn combination with multi-source data and other application models, where inversion algorithms will be optimized, inversion algorithms with low dependence on measured data will be developed, and a transportable inversion model will be built to break the regional limitations of the model and to promote the development of CDOM research in a deeper and more comprehensive direction.

Semi-empirical models for remote estimating colored dissolved organic matter (CDOM) in a productive tropical estuary

Inland waters are important components of the global carbon cycle as they regulate the flow of terrestrial carbon to the oceans. In this context, remote monitoring of Colored Dissolved Organic Matter (CDOM) allows for analyzing the carbon content in aquatic systems. In this study, we develop semi-empirical models for remote estimation of the CDOM absorption coefficient at 400 nm (a_CDOM) in a tropical estuarine-lagunar productive system using spectral reflectance data. Two-band ratio models usually work well for this task, but studies have added more bands to the models to reduce interfering signals, so in addition to the two-band ratio models, we tested three- and four-band ratios. We used a genetic algorithm (GA) to search for the best combination of bands, and found that adding more bands did not provide performance gains, showing that the proper choice of bands is more important. NIR-Green models outperformed Red-Blue models. A two-band NIR-Green model showed the best results (R² = 0.82, RMSE = 0.22 m^-1, and MAPE = 5.85%) using field hyperspectral data. Furthermore, we evaluated the potential application for Sentinel-2 bands, especially using the B5/B3, Log(B5/B3) and Log(B6/B2) band ratios. However, it is still necessary to further explore the influence of atmospheric correction (AC) to estimate the a_CDOM using satellite data.

Application and recent progress of inland water monitoring using remote sensing techniques

Hyperspectral remote sensing, which retrieves the water quality parameters by direct high-resolution analysis of the electromagnetic spectrum reflected from the water surface, has been widely applied for inland water quality detection. Such a new approach provides an opportunity to generate real-time data from water with the noncontact method, largely improving working efficiency. By summarizing the development and current applications of hyperspectral remote sensing, we compare the relative merits of varying remote sensing platforms, popular inversion models, and the application of hyperspectral monitoring of chlorophyll-a (Chl-a), transparency, total suspended solids (TSS), colored dissolved organic matter (CDOM), phycocyanin (PC), total phosphorus (TP), and total nitrogen (TN) water quality parameters. Most studies have focused on spaceborne remote sensing, which is usually used to monitor large waterbodies for Chl-a and other water quality parameters with optical properties; semiempirical, bio-optical, and semianalytical models are frequently used. With the rapid development of aerospace technology and near-surface remote sensing, the spectral resolution of remote sensing imaging technology has been dramatically improved and has begun to be applied to small waterbodies. In the future, the multiplatform linkage monitoring approach may become a new research direction. Advanced computer technology has also enabled machine learning models to be applied to water quality parameter inversion, and machine learning models have higher robustness than the three commonly used models mentioned above. Although nitrogen and phosphorus, with nonoptical properties, have also received attention and research from some scholars in recent years, the uncertainty of their mechanisms makes it necessary to maintain a cautious attitude when treating such research.

Ocean water quality monitoring using remote sensing techniques: A review

Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring.

Challenges in quantifying and characterizing dissolved organic carbon: Sampling, isolation, storage, and analysis

Despite the advancements in analytical techniques, there are still great challenges and difficulties in accurately and effectively quantifying and characterizing dissolved organic carbon (DOC) in environmental samples. The objectives of this review paper are (a) to understand the roles and variability of DOC along the water continuum; (b) to identify the constraints, inconsistences, limitations, and artifacts in DOC characterization; and (c) to provide recommendations and remarks to improve the analytical accuracy. For the first objective, we summarize the four ecological and engineering roles of DOC along the water continuum from source water to municipal utility, including nutrients and energy sources, controlling the fates of micropollutants, buffering capacity, and treatability and precursors of disinfection byproducts. We also discuss three major challenges in DOC analysis, including spatial and temporal variations, degradability and stability, and unknown structures and formulas. For the second objective, we review the procedures and steps in DOC analysis, including sampling in diverse environmental matrices, isolation of DOC fraction, storage and preservation techniques, and analyses on bulk chemical characteristics. We list and discuss the available options and evaluate the advantages and disadvantages of each choice. Last, we provide recommendations and remarks for each stage: sampling, isolation, storage, and analysis.

Using CDOM spectral shape information to improve the estimation of DOC concentration in inland waters: A case study of Andean Patagonian Lakes

For the last two decades different scientific disciplines have focused on lacustrine dissolved organic matter (DOM) given its importance in the biogeochemistry of carbon and in ecosystem functioning. New satellites supply the appropriate resolutions to evaluate chromophoric dissolved organic matter (CDOM) in inland waters, opening the possibility to estimate DOM at appropriate spatiotemporal scales. This requires, however, a robust relationship between CDOM and dissolved organic carbon (DOC). In this work, we evaluated the use of CDOM as a proxy of DOC in 7 Andean Patagonian lakes. Considering the entire data set, CDOM absorption coefficients (a₃₅₅ and a₄₄₀) were linearly related with DOC. Shallow lakes, however, drove this relationship showing a moderate relationship, whereas, deep lakes with lower colour presented a weaker relationship. Therefore, we assessed the use of CDOM spectral shape information to improve DOC estimates regardless of observed DOM differences due to climatic seasonality and lakes' morphometry. The use of well-known CDOM spectral shape metrics (i.e., S_275-295 and a₂₅₀:a₃₆₅ ratio) significantly improved DOC estimation. Particularly, using a Gaussian decomposition approach we found that much of the variation in the spectral shape, associated with the variability of CDOM:DOC ratio, was explained by differences in two dynamic regions centred at 270 and 320 nm. A strong nonlinear relationship was found between the a₂₇₀:a₃₂₀ ratio and the DOC-specific absorption coefficients a*₃₅₅ and a*₄₄₀. This was translated into a further improvement in DOC estimation yielding the higher R² and lower mean absolute differences (MAPD < 16%), either considering the entire data set or shallow and deep lakes separately. Our results highlight that incorporating the CDOM spectral shape information improves the characterization of the DOC pool of inland waters, which is particularly relevant for remote and/or inaccessible sites and has significant implications for the environmental management, biogeochemical studies and future remote sensing applications.

One- and Two-Band Sensors and Algorithms to Derive aCDOM(440) from Global Above- and In-Water Optical Observations

The colored (or chromophoric, depending on the literature) dissolved organic matter (CDOM) spectral absorption coefficient, aCDOM(λ), is a variable of global interest that has broad application in the study of biogeochemical processes. Within the funding for scientific research, there is an overarching trend towards increasing the scale of observations both temporally and spatially, while simultaneously reducing the cost per sample, driving a systemic shift towards autonomous sensors and observations. Legacy aCDOM(λ) measurement techniques can be cost-prohibitive and do not lend themselves toward autonomous systems. Spectrally rich datasets carefully collected with advanced optical systems in diverse locations that span a global range of water bodies, in conjunction with appropriate quality assurance and processing, allow for the analysis of methods and algorithms to estimate aCDOM(440) from spectrally constrained one- and two-band subsets of the data. The resulting algorithms were evaluated with respect to established fit-for-purpose criteria as well as quality assured archival data. Existing and proposed optical sensors capable of exploiting the algorithms and intended for autonomous platforms are identified and discussed. One-band in-water algorithms and two-band above-water algorithms showed the most promise for practical use (accuracy of 3.0% and 6.5%, respectively), with the latter demonstrated for an airborne dataset.

Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning

This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and it is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points. This training data allowed for our machine learning algorithms to rapidly learn by example and provide wide area maps of the composition of the environment. Along side these larger autonomous robots two smaller robots that can be deployed by a single individual were also deployed (a walking robot and a robotic hover-board), observing significant small scale spatial variability.

Singlet Oxygen Photogeneration in Coastal Seawater: Prospect of Large-Scale Modeling in Seawater Surface and Its Environmental Significance

Chromophoric-dissolved organic matter (CDOM) acts as the precursor to singlet oxygen (¹O₂) in natural waters, while water acts as the main scavenger. In this study, we showed that ¹O₂ in coastal seawater can be successfully predicted from CDOM parameters. The ¹O₂ steady-state concentration [¹O₂]_ss and photoformation rate (R¹O₂) varied by a factor of 6 across 13 sampling stations in the Seto Inland Sea, Japan, ranging from 1.2 to 8.2 × 10^-14 M and 3.32 to 22.7 × 10^-9 M s^-1, respectively. Investigation of CDOM optical properties revealed that CDOM abundance measured as the absorption coefficient at 300 nm (a₃₀₀) had the strongest correlation (r = 0.96, p < 0.001) with [¹O₂]_ss, while parameters indicative of CDOM quality (e.g., spectral slope) did not influence [¹O₂]_ss. A linear relationship between [¹O₂]_ss and a₃₀₀, normalized to a sunlight intensity of 0.91 kW/m², was derived as [¹O₂]_ss (10^-14 M) = 2.12(a₃₀₀) + 0.48. This was then used to predict [¹O₂]_ss using a₃₀₀ values from a subsequent, independent sampling exercise conducted 2 years after the first sampling. There was a good agreement (r = 0.93, p < 0.001) between the predicted values and the experimentally determined values based on a 95% prediction interval plot. Kinetic estimations using [¹O₂]_ss suggest that ¹O₂ mediates the degradation of tetrabromobisphenol A in surface seawater (t_1/2 = 0.63 days) while also contributing to the indirect photolysis of methyl mercury. The findings from this study suggest that large-scale modeling of ¹O₂ generation in surface seawater from CDOM parameters is possible with useful environmental significance for determining the fate of pollutants.

Model for separating the contributions of non-algal particles and colored dissolved organic matter to light absorption by seawater

We evaluated the performance of a recently developed absorption partitioning model [J. Geophys. Res. Oceans120, 2601 (2015)JGRCEY0148-022710.1002/2014JC010604] that derives the spectral absorption coefficients of non-algal particles, a _{N A P} (λ), and colored dissolved organic matter, a _g (λ), from the total absorption coefficient of seawater. The model's performance was found unsatisfactory when the model was tested with a large dataset of absorption measurements from diverse open-ocean and coastal aquatic environments. To address these limitations, we developed a new model based on a different approach for estimating a _{N A P} (λ) and a _g (λ) from the sum of these two coefficients, a _{d g} (λ), within the visible spectral region. The very good overall performance of the model is demonstrated, with no tendency for bias and relatively small absolute differences (the median ≤20%) between the model-derived and measured values of a _{N A P} (λ) and a _g (λ) over a wide range of aquatic environments.