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Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R 2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 μg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

Three different LED spectra (W: White light; WFR: W + far-red light; WB: W + blue light) with similar photosynthetic photon flux density (PPFD) were designed to explore the effects of supplementary far-red and blue lights on leaf color, biomass and phytochemicals of two cultivars of red-leaf lettuce (“Yanzhi” and “Red Butter”) in an artificial lighting plant factory. Lettuce plants under WB had redder leaf color and significantly higher contents of pigments, such as chlorophyll a, chlorophyll b, chlorophyll (a + b) and anthocyanins. The accumulation of health-promoting compounds, such as vitamin C, vitamin A, total phenolic compounds, total flavonoids and anthocyanins in the two lettuce cultivars were obviously enhanced by WB. Lettuce under WFR showed remarkable increase in fresh weight and dry weight; meanwhile, significant decreases of pigments, total phenolic compounds, total flavonoids and vitamin C were found. Thus, in the plant factory system, the application of WB can improve the coloration and quality of red leaf lettuce while WFR was encouraged for the purpose of elevating the yield of lettuce.

The effect of foliar application of polyamines on roses ( Rosa hybrida cv. ‘Herbert Stevens’) was investigated in a factorial experiment based on a completely randomized design with three replications in a greenhouse. Two factors were applied including polyamine type (putrescine, spermidine, and spermine) and polyamine concentration (0, 1, 2 and 4 mM). The recorded traits included root fresh and dry weight, root length, number of flowers, flower longevity, chlorophyll content, carotenoids, antioxidant enzymes activity (catalase, ascorbate peroxidase and guaiacol peroxidase) and some macronutrients such as nitrogen, phosphorus and potassium. The results showed that among polyamines, putrescine had the greatest effect on root dry weight; spermidine showed the greatest effect on root length, chlorophyll content, plant phosphorus and spermine affected root fresh weight and flower longevity most strongly. Polyamine concentration of 1 mM had the strongest effect on flower longevity, carotenoids, nitrogen and phosphorus content. The highest potassium rate was observed in treatments with the concentration of 4 mM. Polyamine treatments had no significant effect on the number of flowers per plant and antioxidant enzymes.

Phytoplankton blooms above the seamount Maud Rise in the Antarctic Ocean have been reported but their emerging mechanisms and their importance for the wider Southern Ocean are not well known. We use satellite data spanning over the last two  decades and in-situ data collected from a ship, an underwater glider and Biogeochemical-Argo profiling floats to understand the processes involved in the formation of Maud Rise phytoplankton blooms. We find that the seamount generates upwelling of warm deep water that transports heat, and likely dissolved iron, to the surface via diapycnal mixing. This creates a recurring annular structure of chlorophyll concentration (or chlorophyll halo) in correspondence with the previously observed warm water and sea ice halo over Maud Rise. The in-situ observations reveal integrated chlorophyll-a concentrations of up to 100 mg·m −2 , which suggests exceptionally high phytoplankton biomass within the Southern Ocean, thus making the seamount a regional phytoplankton hotspot. Annular phytoplankton blooms over the seamount Maud Rise in the Antarctic Ocean are linked with the presence of heat and dissolved iron upwelled from the deep sea through interactions between the ocean circulation and the topography of the seamount.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R 2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R 2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R 2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710 , 600 , and R 730 , 650 showed the greatest R 2 values with respect to Chl a (R 2 = 0.80) for mandarin fruits while the GI had the greatest R 2 with Chl a (R 2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R 2 of 0.993 with an RMSE of 0.149 for the training set, and an R 2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R 2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R 2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

Phytoplankton is a fundamental component of marine food webs and play a crucial role in marine ecosystem functioning. The phenology (timing of growth) of these microscopic algae is an important ecological indicator that can be utilized to observe its seasonal dynamics, and assess its response to environmental perturbations. Ocean colour remote sensing is currently the only means of obtaining synoptic estimates of chlorophyll-a (a proxy of phytoplankton biomass) at high temporal and spatial resolution, enabling the calculation of phenology metrics. However, ocean colour observations have acknowledged weaknesses compromising its reliability, while the scarcity of long-term in situ data has impeded the validation of satellite-derived phenology estimates. To address this issue, we compared one of the longest available in situ time series (20 years) of chlorophyll-a concentrations in the Eastern Mediterranean Sea (EMS), along with concurrent remotely-sensed observations. The comparison revealed a marked coherence between the two datasets, indicating the capability of satellite-based measurements in accurately capturing the phytoplankton seasonality and phenology metrics (i.e., timing of initiation, duration, peak and termination) in the studied area. Furthermore, by studying and validating these metrics we constructed a satellite-derived phytoplankton phenology atlas, reporting in detail the seasonal patterns in several sub-regions in coastal and open seas over the EMS. The open waters host higher concentrations from late October to April, with maximum levels recorded during February and lowest during the summer period. The phytoplankton growth over the Northern Aegean Sea appeared to initiate at least a month later than the rest of the EMS (initiating in late November and terminating in late May). The coastal waters and enclosed gulfs (such as Amvrakikos and Maliakos), exhibit a distinct seasonal pattern with consistently higher levels of chlorophyll-a and prolonged growth period compared to the open seas. The proposed phenology atlas represents a useful resource for monitoring phytoplankton growth periods in the EMS, supporting water quality management practices, while enhancing our current comprehension on the relationships between phytoplankton biomass and higher trophic levels (as a food source).

Potable source-water reservoirs are the main water supplies in many urbanizing regions, yet their long-term responses to cultural eutrophication are poorly documented in comparison with natural lakes, creating major management uncertainties. Here, long-term discrete data (June 2006–June 2018) for classical eutrophication water quality indicators, continuous depth-profile data for dissolved oxygen (DO), and an enhanced hybrid statistical trend analysis model were used to evaluate the eutrophication status of a potable source-water reservoir. Based on classical indicators (nitrogen, N and phosphorus, P concentrations and ratios; phytoplankton biomass as chlorophyll 𝑎, chl 𝑎; and trophic state indices), the reservoir was eutrophic to hypereutrophic and stoichiometrically imbalanced. Anoxia/hypoxia occurred for 7–8 months annually systemwide, even throughout the water column for days to weeks in some years; and elevated total ammonia (up to ~900 μg tNH3 L⁻¹) in surface waters from late summer/fall through late winter/early spring suggested substantial internal legacy nutrient loading. These surprising DO and tNH3 phenomena may characterize many reservoirs in urbanizing areas, and the associated cascade of negative impacts may increasingly affect them under global warming. Total organic carbon (TOC), seasonally influenced by phytoplankton biomass, commonly exceeded 6 mg L⁻¹, which is problematic for potable-water treatment, and significantly trended up over time. Wet-year inflow dilution influenced an apparent decreasing trend in nutrients within the hypereutrophic upper reservoir, which receives most tributary inputs. Nevertheless, significant reservoirwide trends (increasing total phosphorus [TP], phytoplankton chl 𝑎, TOC) and mid- and/or lower region trends (increasing total nitrogen [TN], tNH3, decreasing TN:TP ratios) suggest that water quality degradation from eutrophication has worsened over time. These findings support broadly applicable recommendations to strengthen protection of potable source-water reservoirs in urbanizing watersheds: (1) protective numeric water quality criteria are needed for TOC as well as TN, TP, and chl 𝑎; (2) continuous diel data capture more realistic DO conditions than traditional sampling, and can provide important insights for water treatment managers; and (3) assessment of reservoir eutrophication status to track management progress over time should emphasize classic indicators equally as statistical trends, which are highly sensitive to short-term meteorological forcing

The main influencing factors of water environment and the spatiotemporal differences of eutrophication were identified in the Dianchi Lake, the results indicated a comparatively poor and fluctuating the water environmental condition in the Caohai compared to the Waihai, and differences in the correlation between water environment indicators were observed in Caohai and Waihai. The absolute contribution rates of the inner sources to water temperature, pH, electrical conductance, total nitrogen, chlorophyll a and algal density were the largest in Caohai, while offshore sources are pH, electrical conductance, permanganate index, total phosphorus and chlorophyll a in Waihai. The eutrophication level is relatively high near the Xiyuan Suidao section, and the comprehensive trophic level indexes are 61.14, 64.45 and 64.45 in spring, summer and autumn, respectively, which all reach the state of moderate eutrophication; the comprehensive trophic level index is 55.72 in winter, which reaches the state of light eutrophication. The algal density near the Xiyuan Suidao and Luojiaying sections exhibited high levels, reaching a state of moderate algal bloom in summer. Spatial autocorrelation analyses highlighted significant positive and negative spatial autocorrelation for comprehensive Trophic Level Index and algal density, respectively, in Dianchi Lake. The High-High aggregation of the comprehensive Trophic Level Index and algal density was mainly concentrated in the Caohai, while the Low-Low aggregation of the comprehensive Trophic Level Index was primarily observed in the Waihai. Consequently, the risk of eutrophication and algal bloom outbreak in Caohai surpassed that in Waihai. Therefore, it is imperative to propose appropriate treatment measures based on the varying eutrophication level and algal bloom outbreak during different time periods and in distinct regions of the aquatic ecological environment in Dianchi Lake.