Life Detection on Icy Moons Using Flow Cytometry and Intrinsically Fluorescent Biomolecules.

In a previous experiment, we demonstrated the capability of flow cytometry as a potential life detection technology for icy moons using exogenous fluorescent stains (Wallace et al., 2023). In this companion experiment, we demonstrated the capability of flow cytometry to detect life using intrinsically fluorescent biomolecules in addition to exogenous stains. We used a method similar to our previous work to positively identify six classes of intrinsically fluorescent biomolecules: flavins, carotenoids, chlorophyll, tryptophan, NAD+, and NAD(P)H. We demonstrated the effectiveness of this method with six known organisms and known abiotic material and showed that the cytometer is easily able to distinguish the known organisms and the known abiotic material by using the intrinsic fluorescence of these six biomolecules. To simulate a life detection experiment on an icy moon lander, we used six natural samples with unknown biotic and abiotic content. We showed that flow cytometry can identify all six intrinsically fluorescent biomolecules and can separate the biotic material from the known abiotic material on scatter plots. The use of intrinsically fluorescent biomolecules in addition to exogenous stains will potentially cast a wider net for life detection on icy moons using flow cytometry.

Effects of environmental variability on phytoplankton structure, diversity and biomass at the Brazil-Malvinas Confluence (BMC).

The Brazil-Malvinas Confluence (BMC) is a significant biological frontier where distinct currents meet, fostering optimal conditions for phytoplankton development. In this study we tested the hypothesis that eddys promote an increase in phytoplankton biomass at the Brazil-Malvinas Confluence (BMC), altering species diversity. Phytoplankton were collected with Niskin bottles and nutrient concentrations assessed at two depths (Surface and Deep Chlorophyll Maximum Layer - DCML) in areas outside and under the influence of Cold-Core (CCE) and Warm-Core (WCE) Eddies. Environmental variables were determined in situ using a CTD profiler. Four regions were separated based on environmental variables and phytoplankton species, namely, the Brazil Current (BC), Malvinas Current (MC), CCE, and WCE. Species diversity was higher in the eddies. The conditions of the WCE were different from those of the CCE, with low temperature and salinity and high cell density values in the latter. The phylum Bacillariophyta was predominant in terms of species richness in all regions and was responsible for the higher cell density in the MC, while dinoflagellates were dominant in the BC and eddies. Therefore, eddy activity alters the structure, diversity and biomass of the phytoplankton community in the BMC.

Climate change triggered planktonic cyanobacterial blooms in a regulated temperate river.

Harmful algae blooms are a rare phenomenon in rivers but seem to increase with climate change and river regulation. To understand the controlling factors of cyanobacteria blooms that occurred between 2017 and 2020 over long stretches (> 250 km) of the regulated Moselle River in Western Europe, we measured physico-chemical and biological variables and compared those with a long-term dataset (1997-2016). Cyanobacteria (Microcystis) dominated the phytoplankton community in the late summers of 2017-2020 (cyano-period) with up to 110 µg Chlorophyll-a/L, but had not been observed in the river in the previous 20 years. From June to September, the average discharge in the Moselle was reduced to 69-76% and water temperature was 0.9-1.8 °C higher compared to the reference period. Nitrogen (N), phosphorus (P) and silica (Si) declined since 1997, albeit total nutrient concentrations remained above limiting conditions in the study period. Cyanobacterial blooms correlated best with low discharge, high water temperature and low nitrate. We conclude that the recent cyanobacteria blooms have been caused by dry and warm weather resulting in low flow conditions and warm water temperature in the regulated Moselle. Under current climate projections, the Moselle may serve as an example for the future of regulated temperate rivers.

Low-Cost Imaging to Quantify Germination Rate and Seedling Vigor across Lettuce Cultivars.

The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce (Lactuca sativa) cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the 'Muir' cultivar to have a larger canopy size and better germination, though 'Sparx' and 'Crispino' surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests (R2 = 0.88, RMSE = 0.27, p < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap.

Quality of Mezilaurus itauba seedlings inoculated with Trichoderma harzianum under doses of organomineral fertilizer from cupuaçu residues.

Fungi of the genus Trichoderma spp have been related to the production of hormones or correlated with growth factors, promoting greater efficiency in the use of some nutrients, thus allowing greater availability and absorption by plants. In this context, the objective of this study was to determine the dose of organomineral fertilizer from cupuaçu (Theobroma grandiflorum) residues and the efficiency of Trichoderma harzianum on the initial growth and morphophysiological quality of Mezilaurus itauba seedlings in the northern Amazon. Dose of 50% of the organomineral fertilizer from cupuaçu residues (ORFCup) with Trichoderma harzianum promotes better quality and robustness in Mezilaurus itauba seedlings. The presence of Trichoderma harzianum + 50% ORFCup promotes positive gains in the root biomass of Mezilaurus itauba seedlings. The presence of Trichoderma harzianum promotes an increase in chlorophylls a and b contents in Mezilaurus itauba seedlings. For the production of Mezilaurus itauba seedlings, it is recommended to use Trichoderma harzianum + 50% ORFCup, as it promoted increments in all physiological and morphological indices under the conditions of the present study.

Traditional and new trend strategies to enhance pigment contents in microalgae.

Microalgae are a source of a wide variety of commodities, including particularly valuable pigments. The typical pigments present in microalgae are the chlorophylls, carotenoids, and phycobiliproteins. However, other types of pigments, of the family of water-soluble polyphenols, usually encountered in terrestrial plants, have been recently reported in microalgae. Among such microalgal polyphenols, many flavonoids have a yellowish hue, and are used as natural textile dyes. Besides being used as natural colorants, for example in the food or cosmetic industry, microalgal pigments also possess many bioactive properties, making them functional as nutraceutical or pharmaceutical agents. Each type of pigment, with its own chemical structure, fulfills particular biological functions. Considering both eukaryotes and prokaryotes, some species within the four most promising microalgae groups (Cyanobacteria, Rhodophyta, Chlorophyta and Heterokontophyta) are distinguished by their high contents of specific added-value pigments. To further enhance microalgae pigment contents during autotrophic cultivation, a review is made of the main related strategies adopted during the last decade, including light adjustments (quantity and quality, and the duration of the photoperiod cycle), and regard to mineral medium characteristics (salinity, nutrients concentrations, presence of inductive chemicals). In contrast to what is usually observed for growth-related pigments, accumulation of non-photosynthetic pigments (polyphenols and secondary carotenoids) requires particularly stressful conditions. Finally, pigment enrichment is also made possible with two new cutting-edge technologies, via the application of metallic nanoparticles or magnetic fields.

Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel - 2 and Google Earth engine - Bhadra and Tungabhadra, India.

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (µg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

Assessing Chlorophyll-a Variations in Caspian Sea during the COVID-19 Pandemic.

The COVID-19 pandemic's disruptions to human activities prompted serious environmental changes. Here, we assessed the variations in coastal water quality along the Caspian Sea, with a focus on the Iranian coastline, during the lockdown. Utilizing Chlorophyll-a data from MODIS-AQUA satellite from 2015 to 2023 and Singular Spectrum Analysis for temporal trends, we found a 22% Chlorophyll-a concentration decrease along the coast, from 3.2 to 2.5 mg/m³. Additionally, using a deep learning algorithm known as Long Short-Term Memory Networks, we found that, in the absence of lockdown, the Chlorophyll-a concentration would have been 20% higher during the 2020-2023 period. Furthermore, our spatial analysis revealed that 98% of areas experienced about 18% Chlorophyll-a decline. The identified improvement in coastal water quality presents significant opportunities for policymakers to enact regulations and make local administrative decisions aimed at curbing coastal water pollution, particularly in areas experiencing considerable anthropogenic stress.

Variability of hydrographic and biogeochemical properties in the North-western Adriatic coastal waters in relation to river discharge and climate changes.

The dynamics of hydrographic and biogeochemical properties in a Northwestern coastal area of the Adriatic Sea were investigated. The time series data from continuous observation (2007-2022) allowed the investigation of annual trends and seasonal cycles along a coastal transect influenced by local river discharge. Various statistical models were used to investigate water temperature, salinity, chlorophyll a, dissolved organic, inorganic and particulate nutrients, precipitation and river discharge. It was found that the local river discharge regime played an essential role in interannual, and seasonal biogeochemical dynamics associated with global climate change in the Mediterranean region. A significant trend towards oligotrophic conditions was detected, as evidenced by the downward trend in the river mouth and on the sea of chlorophyll a (-0.2 µg L-1 in the sea), dissolved organic and inorganic nitrogen and phosphorus (i.e., -0.43 µM yr-1 of DON in the sea and -6.67 of DIN µM yr-1 in the river mouth or -0.07 µM yr-1 of DOP and -0.02 µM yr-1 of DIP in the river mouth) and silicate (-2.47 µM yr-1 in the river mouth) concentrations. Salinity showed a long-term increase in the sea (0.08 yr-1), corresponding to a significant decrease in water discharge from the local river (-0.27 m3 s-1 yr-1) and precipitation (-0.06 mm yr-1). The dissolved organic and inorganic nutrients highlighted a different seasonal accumulation under the river runoff regime. The nutrient enrichment was predominantly driven by river contribution. Data analysis showed that the coastal biogeochemical properties dynamics were mostly influenced by river discharge and precipitation regimes, which in turn are driven by climate change variability in the North-western Adriatic Sea.

Primary production in the bays of the novaya zemlya archipelago (Kara Sea) in the contrasting glacial and non-glacial environmental conditions.

Ongoing warming is leading to the accelerated shrinkage of glaciers located on Arctic islands. Consequently, the influence of glacial meltwater on phytoplankton primary production in Arctic bays becomes critically important in an era of warming. This work studies the spatiotemporal variation of primary production and chlorophyll a concentration in the bays along the eastern coast of the Novaya Zemlya archipelago. Data were collected during nine cruises performed from July to October (2013-2022). The effect of underwater photosynthetically available radiation (PAR) and nutrients on primary production was assessed separately for bays influenced by glacial meltwater (glacial bays) and those without such influence (non-glacial bays). The median value of water column-integrated primary production (IPP) for all bays was 38 mgC m-2 d-1, characterizing them as oligotrophic areas. IPP in non-glacial bays was found to be 2.3-fold and 1.4-fold higher than that in glacial bays during summer and autumn, respectively. Underwater PAR was the main abiotic factor determining IPP during the ice-free period. In the entire bays nutrient concentrations were high, exceeding the limiting values for growth and photosynthesis of phytoplankton. It was concluded that the high turbidity from glacial meltwater runoff leads to decreased underwater PAR and, consequently, to a decline in IPP. This study demonstrates that rapid warming could have a negative impact on the productivity of high Arctic bays and their adjacent areas.

Change in water column total chlorophyll-a in the Mediterranean revealed by satellite observation.

Chlorophyll-a (Chl-a) is a crucial pigment in algae and macrophytes, which makes the concentration of total Chl-a in the water column (total Chl-a) an essential indicator for estimating the primary productivity and carbon cycle of the ocean. Integrating the Chl-a concentration at different depths (Chl-a profile) is an important way to obtain the total Chl-a. However, due to limited cost and technology, it is difficult to measure Chl-a profiles directly in a spatially continuous and high-resolution way. In this study, we proposed an integrated strategy model that combines three different machine learning methods (PSO-BP, random forest and gradient boosting) to predict the Chl-a profile in the Mediterranean by using several sea surface variables (photosynthetically active radiation, spectral irradiance, sea surface temperature, wind speed, euphotic depth and KD490) and subsurface variables (mixed layer depth) observed by or estimated from satellite and BGC-Argo float observations. After accuracy estimation, the integrated model was utilized to generate the time series total Chl-a in the Mediterranean from 2003 to 2021. By analysing the time series results, it was found that seasonal fluctuation contributed the most to the variation in total Chl-a. In addition, there was an overall decreasing trend in the Mediterranean phytoplankton biomass, with the total Chl- decreasing at a rate of 0.048 mg/m2 per year, which was inferred to be related to global warming and precipitation reduction based on comprehensive analysis with sea surface temperature and precipitation data.

Novel methods for monitoring low chlorophyll-a concentrations in the large, oligotrophic Lake Malawi/Nyasa/Niassa.

The use of remote sensing for monitoring chlorophyll-a (chla) and modelling eutrophication has advanced over the last decades. Although the application of the technology has proven successful in ocean ecosystems, there is a need to monitor chla concentrations in large, nutrient-poor inland water bodies. The main objective of this study was to explore the utility of publicly available remotely sensed Sentinel-2 (S2) imagery to quantify chla concentrations in the nutrient-deficient Lake Malawi/Niassa/Nyasa (LMNN). A secondary objective was to compare the S2 derived chla with the Global Change Observation Mission-Climate (GCOM-C) chla product that provides uninterrupted data throughout the year. In situ chla data (n = 76) from upper, middle and lower sections of LMNN served as a reference to produce remote sensing-based quantification. The line-height approach method built on color index, was applied for chla concentrations below 0.25 mg/m3. Moderate Resolution Imaging Spectroradiometer 3-band Ocean Color (MODIS-OC3) - was adopted when chla concentration exceeded 0.35 mg/m3. The MODIS-OC3 algorithm had generic model coefficients that were calibrated for each in situ sample by using GCOM-C Level 3 chla product. A weighted sum of the two algorithms was applied for chla concentrations that fell between 0.25 and 0.35 mg/m3. The above methods were then applied to the S2 data to estimate chla at each pixel. S2 showed a promising accuracy in distinguishing chla levels (MSE = 0.18) although the chla range in the lake was relatively narrow, particularly using the locally calibrated coefficients of the OC3 algorithm. Chla distribution maps produced from the S2 data revealed limited spatial variation across the LMNN with higher concentrations identified in the coastal areas. S2-derived chla and GCOM-C chla comparison showed fairly good similarity between the two datasets (MSE = 0.205). Accepting this similarity, monthly chla dynamics of the lake was profiled using the temporally reliable GCOM-C data that showed oligotrophic conditions (1.7 mg/m3 to 3.2 mg/m3) in most parts of the lake throughout the year. The study's findings advance the potential for both remote sensing approaches to provide vital information at the required spatial and temporal resolution for evidence-based policymaking and proactive environmental management in an otherwise very data deficient region.

Response of chlorophyll-a to rainfall event in the basin of the South China sea: Statistical analysis.

Chlorophyll-a (Chl-a) is an essential ecological indicator, and affected by processes such as typhoons, mesoscale eddies, and Rossby waves. However, the impact of more frequent and widespread precipitation events on Chl-a seems to be overlooked. This study utilized remote sensing data and reanalysis data to investigate the response of Chl-a to 240 precipitation events in the central South China Sea from 2005 to 2019. The results indicate that precipitation events have a significant impact on Chl-a concentration. Following a precipitation event in 2019, the Chl-a concentration in the affected area increased by approximately 0.22 mg m-³ from the 3rd to the 7th day. The reasons for the increase in Chl-a concentration were the vertical mixing induced by wind stirring and the upwelling caused by wind stress curl, which transported nutrients to the euphotic zone, lowering the sea surface temperature and triggering a proliferation of phytoplankton. Additionally, dissolved nutrients in precipitation provided a nutrient source for Chl-a growth. The contributions of nutrient supply, wind speed, and wind stress curl to the increase in Chl-a concentration during precipitation events were 18%, 37%, and 45%, respectively. Precipitation events enhanced marine primary productivity, playing a crucial role in deepening our understanding of ocean-atmosphere interactions and their impact on marine ecosystem.

Chlorophyll a in lakes and streams of the United States (2005-2022).

The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005-2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey's National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.

Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China.

Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are significant for water system management. In this study, spatial-temporal analysis and correlation analysis were applied to reveal Chla concentration pattern in the Fuchun River, China. Then four exogenous variables (wind speed, water temperature, dissolved oxygen and turbidity) were used for predicting Chla concentrations by six models (3 traditional machine learning models and 3 deep learning models) and compare the performance in a river with different hydrology characteristics. Statistical analysis shown that the Chla concentration in the reservoir river segment was higher than in the natural river segment during August and September, while the dominant algae gradually changed from Cyanophyta to Cryptophyta. Moreover, air temperature, water temperature and dissolved oxygen had high correlations with Chla concentrations among environment factors. The results of the prediction models demonstrate that extreme gradient boosting (XGBoost) and long short-term memory neural network (LSTM) were the best performance model in the reservoir river segment (NSE = 0.93; RMSE = 4.67) and natural river segment (NSE = 0.94; RMSE = 1.84), respectively. This study provides a reference for further understanding eutrophication and early warning of algal blooms in different type of rivers.

Impacts of a newly formed lava delta on the marine environment: Lava-induced upwelling and abrupt chlorophyll depletion during the Tajogaite eruption (La Palma, 2021).

The 2021 Tajogaite eruption in La Palma (Canary Islands, Spain) emitted vast volumes of lava during 85 days, which reached the ocean in several occasions at the western flank of the island. Most of these flows merged to create a primary lava delta, covering an area of 48 ha, with an additional 30 ha underwater. Here we characterize the effects of the lava-seawater interaction on the surrounding marine environment. The area was sampled during two multidisciplinary oceanographic cruises: the first one comprised the days before the lava reached the ocean and after the first contact; and the second took place a month later, when the lava delta was already formed but still receiving lava inputs. Physical-chemical anomalies were found in the whole water column at different depths up to 300 m in all measured parameters, such as turbidity (+9 NTU), dissolved oxygen concentration (-17.17 µmol kg-1), pHT25 (-0.1), and chlorophyll-a concentration (-0.33 mg m-3). Surface temperature increased up to +2.3 °C (28.5 °C) and surface salinity showed increases and decreases of -1.01 and +0.70, respectively, in a radius of 4 km around the lava delta. In the water column, the heated waters experimented a lava-induced upwelling, bringing deeper, nutrient-rich waters to shallower depths; however, this feature did not trigger any phytoplankton bloom. In fact, integrated chlorophyll-a showed an abrupt decrease of -41 % in just two days and -69 % a month later, compared to prior conditions. The chlorophyll-a depletion reached a distance larger than 2.5 km (not delimited).

Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms.

Frequent coastal harmful algal blooms (HABs) threaten the ecological environment and human health. Biscayne Bay in southeastern Florida also faces algal bloom issues; however, the mechanisms driving these blooms are not fully understood, emphasizing the importance of HAB prediction for effective environmental management. The overarching goal of this study is to offer a robust HAB predictive framework and try to enhance the understanding of HAB dynamics. This study established three scenarios to predict chlorophyll-a concentrations, a recognized representative of HABs: Scenario 1 (S1) using single nonlinear machine learning (ML) algorithms, hybrid Scenario 2 (S2) combining linear models and nonlinear ML algorithms, and hybrid Scenario 3 (S3) combining temporal decomposition and ML (TD-ML) algorithms. The novel-developed S3 TD-ML hybrid models demonstrated superior predictive capabilities, achieving all R2 values above 0.9 and MAPE under 30% in tests, significantly outperforming the S1 with an average R2 of 0.16 and the S2 with an R2 of -0.06. S3 models effectively captured the algal dynamics, successfully predicting complex time series with extremes and noise. In addition, we unveiled the relationship between environmental variables and chlorophyll-a through correlation analysis and found that climate change might intensify the HABs in Biscayne Bay. This research developed a precise predictive framework for early warning and proactive management of HABs, offering potential global applicability and improved prediction accuracy to address HAB challenges.

Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.

Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a CCD capable of a 30 m resolution and has a revisit interval of 2 days, rendering it a superb choice or supplemental sensor for monitoring trophic state of lakes. For effective long-term and regional-scale mapping, both the imagery and the evaluation of machine learning algorithms are essential. The several typical machine learning algorithms, i.e., Support Vector Regression (SVR), Gradient Boosting Decision Trees (GBDT), XGBoost (XGB), Random Forest (RF), K-Nearest Neighbor (KNN), Kernel Ridge Regression (KRR), and Multi-Layer Perception Network (MLP), were developed using our in-situ measured Chl-a. A cross-validation grid to identify the most effective hyperparameter combinations for each algorithm was used, as well as the selected optimal superparameter combinations. In Chl-a mapping of three typical lakes, the R2 of GBDT, XGB, RF, and KRR all reached 0.90, while XGB algorithm also exhibited stable performance with the smallest error (RMSE = 3.11 µg/L). Adjustments were made to align the Chl-a spatial-temporal patterns with past data, utilizing HJ1-A/B CCD images mapping through XGB algorithm, which demonstrates its stability. Our results highlight the considerable effectiveness and utility of HJ-1 A/B CCD imagery for evaluation and monitoring trophic state of lakes in a cold arid region, providing the application cases contribute to the ongoing efforts to monitor water qualities.

Seasonal patterns and bloom dynamics of phytoplankton based on satellite-derived chlorophyll-a in the eastern yellow sea.

Satellite-derived chlorophyll-a concentration (Chl-a) is essential for assessing environmental conditions, yet its application in the optically complex waters of the eastern Yellow Sea (EYS) is challenged. This study refines the Chl-a algorithm for the EYS employing a switching approach based on normalized water-leaving radiance at 555 nm wavelength according to turbidity conditions to investigate phytoplankton bloom patterns in the EYS. The refined Chl-a algorithm (EYS algorithm) outperforms prior algorithms, exhibiting a strong alignment with in situ Chl-a. Employing the EYS algorithm, seasonal and bloom patterns of Chl-a are detailed for the offshore and nearshore EYS areas. Distinct seasonal Chl-a patterns and factors influencing bloom initiation differed between the areas, and the peak Chl-a during the bloom period from 2018 to 2020 was significantly lower than the average year in both areas. Specifically, bimodal and unimodal peak patterns in Chl-a were observed in the offshore and nearshore areas, respectively. By investigating the relationships between environmental factors and bloom parameters, we identified that major controlling factors governing bloom initiation were mixed layer depth (MLD) and suspended particulate matter (SPM) in the offshore and nearshore areas, respectively. Additionally, this study proposed that the recent decrease in the peak Chl-a might be caused by rapid environmental changes such as the warming trend of sea surface temperature (SST) and the limitation of nutrients. For example, external forcing, phytoplankton growth, and nutrient dynamics can change due to increased SST and limitation of nutrients, which can lead to a decrease in Chl-a. This study contributes to understanding phytoplankton dynamics in the EYS, highlighting the importance of region-specific considerations in comprehending Chl-a patterns and bloom dynamics.

Clustering evaluation of water quality for various classes of in-flow rivers in connected brackish lakes.

Brackish waters and estuaries at the lower reaches of rivers accumulate organic matter and nutrients from various sources in the watershed. Sufficient light and shallow water depth stimulate phytoplankton growth, resulting in a more diversified ecosystem with higher trophic levels. For effective watershed management, it is crucial to characterize the water quality of all rivers, including small and medium-sized ones. Our field survey assessed water quality parameters in 26 inflow rivers surrounding Lakes Shinji and Nakaumi, two consolidated brackish lakes in Japan. The parameters included water temperature, salinity, chlorophyll-a, and nutrients. The study used hierarchical clustering. The Silhouette Index was used to assess clustering outcomes and identify any difficulties in dispersion across clusters. The 26 rivers surrounding Lakes Shinji and Nakaumi were classified into six groups based on their water quality characteristics. This classification distinguishes itself from earlier subjective methods that relied on geographical factors. The new approach identifies a need for improved management of river water quality. The results of the cluster analysis provide valuable insights for future management initiatives. It is important to consider these findings alongside established watershed criteria.