Validation of Water Quality Monitoring Algorithms for Sentinel-2 and Sentinel-3 in Mediterranean Inland Waters with In Situ Reflectance Data

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.

Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form

Anthropogenic eutrophication is a global environmental problem threatening the ecological functions of many inland freshwaters and diminishing their abilities to meet their designated uses. Water authorities worldwide are being pressed to improve their abilities to monitor, predict, and manage the incidence of harmful algal blooms (HABs). While most water quality management decisions are still based on conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management, recent advances in remote sensing are providing new opportunities towards better understanding water quality variability in these important freshwater systems. This study assessed the potential of using the Sentinel 2 Multispectral Instrument to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive periods of HABs. The work first evaluated the ability to transfer and recalibrate previously developed reservoir-specific Landsat 7 and 8 water quality models when used with Sentinel 2 data. The results showed poor transferability between Landsat and Sentinel 2, with most models experiencing a significant drop in their predictive skill even after recalibration. Sentinel 2 models were then developed for the reservoir based on 153 water quality samples collected over 2 years. The models explored different functional forms, including multiple linear regressions (MLR), multivariate adaptive regression splines (MARS), random forests (RF), and support vector regressions (SVR). The results showed that the RF models outperformed their MLR, MARS, and SVR counterparts with regard to predicting chlorophyll-a, total suspended solids, Secchi disk depth, and phycocyanin. The coefficient of determination (R2) for the RF models varied between 85% for TSS up to 95% for SDD. Moreover, the study explored the potential of quantifying cyanotoxin concentrations indirectly from the Sentinel 2 MSI imagery by benefiting from the strong relationship between cyanotoxin levels and chlorophyll-a concentrations.

SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements

Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea-land segmentation.

Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States

Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. The Medium Resolution Imaging Spectrometer (MERIS) and United States (US) National Lakes Assessments are leveraged to model the probability among lakes of exceeding lower and higher demonstration thresholds for microcystin toxin, cyanobacteria, and chlorophyll a. By leveraging the large spatial variation among lakes using two national-scale data sources, rather than focusing on temporal variability, this approach avoids many of the previous challenges in relating satellite imagery to cyanotoxins. For every satellite-derived lake-level Cyanobacteria Index (CI_cyano) increase of 0.01 CI_cyano/km2, the odds of exceeding six bloom thresholds increased by 23-54 %. When the models were applied to the 2192 satellite monitored lakes in the US, the number of lakes identified with ≥75 % probability of exceeding the thresholds included as many as 335 lakes for the lower thresholds and 70 lakes for the higher thresholds, respectively. For microcystin, the models identified 162 and 70 lakes with ≥75 % probability of exceeding the lower (0.2 μg/L) and higher (1.0 μg/L) thresholds, respectively. This approach represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.

Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things

Monitoring environmental water quality in an efficient, cheap, and sustainable way can better serve the country's strategic requirements for water resources and water ecological protection. This paper takes the Shaanxi section of the Weihe River Basin as a pilot project and aims to use the Internet of Things technology to develop water quality monitoring sensors, so as to realize the construction of low-cost, high-reliability water quality monitoring demonstration applications. First of all, we established the design of the water quality collection terminal, designed the low-power water quality sensor node, supported the Internet of Things protocol and the collection of various water quality parameters, and used networking for data transmission. Secondly, we use the ant colony algorithm-based system clustering model to obtain a cluster map of water quality monitoring tasks in a certain section of the Weihe River Basin. We take the task clustering graph as an example for analysis, optimize the monitoring model through the ant colony algorithm, and obtain the weight of the optimization index. The weight of the scheduled task limit of the monitoring point becomes larger, so the release of the monitoring task mainly affects the limit of the scheduled task of the monitoring point. Through the above work, we designed and implemented a set of online water quality monitoring system based on the Internet of Things and data mining technology. The system can provide reference for large-scale water resource protection and water environment governance.

Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data

Inland waters are dynamic systems that are under pressure from anthropogenic activities, thus constant observation of these waters is essential. Remote sensing provides a great opportunity to have frequent observations of inland waters. The aim of this study was to create a data-driven model that uses a machine learning algorithm and Sentinel-2 data to classify lake observations into four biophysical classes: Clear, Moderate, Chla-dominated, and Turbid. We used biophysical variables such as water transparency, chlorophyll concentration, and suspended matter to define these classes. We tested six machine learning algorithms that use spectral features of lakes as input and chose random forest classifiers, which yielded the most accurate results. We applied our two-step model on 19,292 lake spectra for the years 2015–2020, from 226 lakes. The prevalent class in 67% of lakes was Clear, while 19% of lakes were likely affected by strong algal blooms (Chla-dominated class). The models created in this study can be applied to lakes in other regions where similar lake classes are found. Biophysical lake classification using Sentinel-2 MSI data can help to observe long-term and short-term changes in lakes, thus it can be a useful tool for water management experts and for the public.

Towards the Combination of C2RCC Processors for Improving Water Quality Retrieval in Inland and Coastal Areas

Sentinel-2 offers great potential for monitoring water quality in inland and coastal waters. However, atmospheric correction in these waters is challenging, and there is no standardized approach yet, but different methods coexist under constant development. The atmospheric correction Case 2 Regional Coast Colour (C2RCC) processor has been recently updated with the C2X-COMPLEX (C2XC). This study is one of the first attempts at exploring its performance, in comparison with C2RCC and C2X, in inland and coastal waters in the east of the Iberian Peninsula, in retrieving water surface reflectance and estimating chlorophyll-a ([Chl-a]), total suspended matter ([TSM]), and Secchi disk depth (ZSD). The relationship between in situ ZSD and Kd_z90max product (i.e., the depth of the water column from which 90% of the water-leaving irradiance is derived) of the C2RCC processors demonstrated the potential of this product for estimating water clarity (r > 0.75). However, [TSM] and [Chl-a] derived from the different processors with default calibration factors were not suitable within the targeted scenarios, requiring recalibration based on optical water types or a shift to dynamic algorithm blending approaches. This would benefit from switching between C2RCC and C2XC, which extends the potential for improving surface reflectance estimates to a wide range of scenarios and suggests a promising future for C2-Nets in operational monitoring of water quality.

Estimating Organic and Inorganic Part of Suspended Solids from Sentinel 2 in Different Inland Waters

Inland waters are very sensitive ecosystems that are mainly affected by pressures and impacts within their watersheds. One of water’s dominant constituents is the suspended particulate matter that affects the optical properties of water bodies and can be detected from remote sensors. It is important to know their composition since the ecological role they play in water bodies depends on whether they are mostly organic compounds (phytoplankton, decomposition of plant matter, etc.) or inorganic compounds (silt, clay, etc.). Nowadays, the European Space Agency Sentinel-2 mission has outstanding characteristics for measuring inland waters’ biophysical variables. This work developed algorithms that can estimate the total concentration of suspended matter (TSM), differentiating organic from inorganic fractions, through the combined use of Sentinel-2 images with an extensive database obtained from reservoirs, lakes and marshes within eastern zones of the Iberian Peninsula. For this, information from 121 georeferenced samples collected throughout 40 field campaigns over a 4-year period was used. All possible two-band combinations were obtained and correlated with the biophysical variables by fitting linear regression between the field data and bands combination. The results determined that only using bands 705 or 783 lead to the obtaining the amount of total suspended matter and their organic and inorganic fractions, with errors of 10.3%, 14.8% and 12.2%, respectively. Therefore, remote sensing provides information about total suspended matter dynamics and characteristics as well as its spatial and temporal variation, which would help to study its causes.