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Mature 5-year-old Prunus domestica trees were used as research objects in plantation areas in arid and semi-arid regions of northern China in Hohhot city; three irrigation gradients and a natural control were set up according to the determination of wilting coefficient and field capacity, and FluorCam 1000-H chlorophyll fluorescence parameters of plant leaves were determined by using a portable chlorophyll fluorescence imager. The results showed that the effects of irrigation gradients on potential photochemical efficiency, maximum photochemical efficiency and non-photochemical burst were not significant (p > 0.05), and the effects on photochemical burst were significant (p < 0.05) at different fertility periods. The potential photochemical efficiency, maximum photochemical efficiency and photochemical burst basically showed a trend of increasing and then decreasing, and the non-photochemical burst showed a trend of decreasing and then increasing during different fertility periods. During the same fertility period, the potential and maximum photochemical efficiencies were significantly increased, and the photochemical and non-photochemical bursts were somewhat reduced under the full irrigation gradient. Therefore, the chlorophyll fluorescence characteristics of Prunus domestica trees at different fertility stages can be regulated by controlling the irrigation gradient to increase the activity of photosystem II (PSII), which in turn improves the photosynthesis of the plants. [ABSTRACT FROM AUTHOR]

The diel vertical variations of chlorophyll a (Chl-a) concentration are thought of primarily as an external manifestation of regulating phytoplankton's biomass, which is essential for dynamically estimating the biogeochemical cycle in inland waters. However, information on these variations is limited due to insufficient measurements. Undersampled observations lead to delayed responses in phytoplankton assessment, impacting accurate evaluations of carbon export and water quality in dynamic inland waters. Here, we report the first lidar-observed diel vertical variations of inland Chl-a concentration. Strong agreement with r2 of 0.83 and a root mean square relative difference (RMSRD) of 9.0% between the lidar-retrieved and in situ measured Chl-a concentration verified the feasibility of the Mie–fluorescence–Raman lidar (MFRL). An experiment conducted at a fixed observatory demonstrated the lidar-observed diel Chl-a concentration variations. The results showed that diel variations of Chl-a and the formation of subsurface phytoplankton layers were driven by light availability and variations in water temperature. Furthermore, the facilitation from solar radiation-regulated water temperature on the phytoplankton growth rate was revealed by the high correlation between water temperature and Chl-a concentration anomalies. Lidar technology is expected to provide new insights into continuous three-dimension observations and be of great importance in dynamic inland water ecosystems. [ABSTRACT FROM AUTHOR]

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R2 = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)2, consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R2 = 0.96; MSE = 0.33 (µg/L)2; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management. [ABSTRACT FROM AUTHOR]

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management. [ABSTRACT FROM AUTHOR]

DNA-binding one zinc finger (DOF) transcription factors are crucial plant-specific regulators involved in growth, development, signal transduction, and abiotic stress response generation. However, the genome-wide identification and characterization of AcDOF genes and their regulatory elements in kiwifruit (Actinidia chinensis) has not been thoroughly investigated. In this study, we screened the kiwifruit genome database and identified 42 AcDOF genes (AcDOF1 to AcDOF42). Phylogenetic analysis facilitated the categorization of these genes into five subfamilies (DOF-a, DOF-b, DOF-c, DOF-d, and DOF-e). We further analyzed the motifs, conserved domains, gene structures, and collinearity of the AcDOFgene family. Gene ontology (GO) enrichment analysis indicated significant enrichment in the "flower development" term and the "response to abiotic stress" category. Promoter prediction analysis revealed numerous cis-regulatory elements related to responses to light, hormones, and low-temperature and drought stress in AcDOF promoters. RNA-seq expression profiles demonstrated the tissue-specific expression of AcDOF genes. Quantitative real-time PCR results showed that six selected genes (AcDOF04, AcDOF09, AcDOF11, AcDOF13, AcDOF21, and AcDOF22) were differentially induced by abscisic acid (ABA), methyl jasmonate (MeJA), and cold, salt, and drought stresses, with AcDOF22 specifically expressed at high levels in drought-tolerant cultivars. Further experiments indicated that transient AcDOF22 overexpression in kiwifruit leaf disks reduced water loss and chlorophyll degradation. Additionally, AcDOF22 was localized to the nucleus and exhibited transcriptional activation, enhancing drought resistance by activating the downstream drought marker gene AcDREB2A. These findings lay the foundation for elucidating the molecular mechanisms of drought resistance in kiwifruit and offer new insights into drought-resistant breeding. [ABSTRACT FROM AUTHOR]

The canopy of perennial evergreen fruit trees in southern China has a unique Bidirectional Reflectance Factor (BRF) due to its complex multi-branch structure and density changes. This study aimed to address the lack of clarity regarding the changes in BRF of evergreen fruit trees in southern China. Litchi, a typical fruit tree in this region, was chosen as the subject for establishing a three-dimensional (3D) real structure model. The canopy BRF of litchi was simulated under different leaf components, illumination geometry, observed geometry, and leaf area index (LAI) using a 3D radiation transfer model. The corresponding changes in characteristics were subsequently analyzed. The findings indicate that the chlorophyll content and equivalent water thickness of leaves exert significant influences on canopy BRF, whereas the protein content exhibit relatively weak effects. Variation in illumination and observation geometry results in the displacement of hotspots, with the solar zenith angle and view zenith angle exerting significant influence on the BRF. As the LAI of the litchi orchard increases, the distribution of hotspots becomes more concentrated, and the differences in angle information are relatively smaller when observed from multiple angles. With the increase in LAI in litchi orchards, the BRF on the principal plane would be saturated, but observation at hotspots could alleviate this phenomenon. The above analysis provides a reference for quantitative inversion of vegetation parameters using remote sensing monitoring information of typical perennial evergreen fruit trees. [ABSTRACT FROM AUTHOR]

Numerous policies have been proposed by international and supranational institutions, such as the European Union, to surveil Earth from space and furnish indicators of environmental conditions across diverse scenarios. In tandem with these policies, different initiatives, particularly on both sides of the Atlantic, have emerged to provide valuable data for environmental management such as the concept of essential climate variables. However, a key question arises: do the available data align with the monitoring requirements outlined in these policies? In this paper, we concentrate on Earth Observation (EO) optical data applications for environmental monitoring, with a specific emphasis on ocean colour. In a rapidly changing climate, it becomes imperative to consider data requirements for upcoming space missions. We place particular significance on the application of these data when monitoring lakes and marine protected areas (MPAs). These two use cases, albeit very different in nature, underscore the necessity for higher-spatial-resolution imagery to effectively study these vital habitats. Limnological ecosystems, sensitive to ice melting and temperature fluctuations, serve as crucial indicators of a climate in change. Simultaneously, MPAs, although generally small in size, play a crucial role in safeguarding marine biodiversity and supporting sustainable marine resource management. They are increasingly acknowledged as a critical component of global efforts to conserve and manage marine ecosystems, as exemplified by Target 3 of the Kunming–Montreal Global Biodiversity Framework (GBF), which aims to effectively conserve 30% of terrestrial, inland water, coastal, and marine areas by 2030 through protected areas and other conservation measures. In this paper, we analysed different policies concerning EO data and their application to environmental-based monitoring. We also reviewed and analysed the existing relevant literature in order to find gaps that need to be bridged to effectively monitor these habitats in an ecosystem-based approach, making data more accessible, leading to the generation of water quality indicators derived from new high- and very high-resolution satellite monitoring focusing especially on Chlorophyll-a concentrations. Such data are pivotal for comprehending, at small and local scales, how these habitats are responding to climate change and various stressors. [ABSTRACT FROM AUTHOR]

As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of eutrophication problems in water bodies has been caused by changes in the climate environment and overexploitation of natural resources by humans. Chlorophyll-a, as a key indicator for water body assessment, plays an important role in eutrophication research and has a profound impact on the global biogeochemical cycle of the climate process. Studies have shown that temperature can directly or indirectly affect the concentration of chlorophyll-a by influencing the growth of algae and water quality indicators in water bodies. Considering the temperature factor in the inversion of chlorophyll-a concentration is a novel research approach. Based on the influence of temperature on chlorophyll-a concentration, we propose the idea of inverting global ocean chlorophyll-a concentration based on temperature zoning. Using monthly average remote sensing reflectance data from VIIRS (Visible and Infrared Imaging Radiometer Suite), combined with the results of temperature zoning, the OC3V(SST) model was constructed to invert the monthly average chlorophyll-a concentration in the global ocean in October 2018. The OC3V(SST) model has been validated by applying it to the remaining 11 months of January, April, July, and October in 2017, 2018, and 2019, as well as the entire 31-day dataset of October 2018. The results indicate that temperature zonation can significantly improve the inversion accuracy of chlorophyll-a and further explore the spatial distribution patterns of global chlorophyll-a concentrations across various temperature ranges based on monthly averages from the global ocean. Additionally, the study investigates the continuity issues of various models and the correlation between temperature and chlorophyll-a. [ABSTRACT FROM AUTHOR]

Water deficit is the main limiting factor constraining sugarcane productivity, and its impact is expected to increase due to climate changes. During prolonged drought periods, most plants become extremely vulnerable to ROS accumulation, which can severely damage their photosynthetic apparatus. The PetC gene, encoding a Rieske FeS protein (ISP), has been shown to regulate the electron transport chain and protect photosystems (PSs) under drought conditions in some plant species. In sugarcane, transcriptome analysis revealed that ScPetC is repressed during drought stress in the field. In this study, we have overexpressed ScPetC in tobacco plants and evaluated its role in water-deficit tolerance. Our results indicate that the ScPetC protein structure is conserved when compared to other species. ScPetC overexpression reduced the negative impact of water deficit on plant development. This effect was associated with a positive impact on ScPetC quantum efficiency and the electron transport rate of PSII, the photosynthetic rate, and water use efficiency. The total chlorophyll content under water deficit was higher in plants overexpressing ScPetC, and this was correlated with less chlorophyll degradation from oxidative damage. Together, these results demonstrate that ScPetC confers tolerance to water deficit and oxidative stresses, making it a candidate gene for crop improvement. [ABSTRACT FROM AUTHOR]

This paper presents the results of a mesocosm experiment for the evaluation of remote sensing chlorophyll-a (chl-a) concentration estimations in an anthropogenic water reservoir. The chl-a presence in the water causes changes in the water surface reflectance spectrum, especially in the green and red part, but many factors could affect the remote measurements of chl-a content. The in situ mesocosm method of the experiment was used for investigating the spectral reflectance of the inland water surface in a wide range of chl-a concentrations. Eight specially designed measurement boxes were placed into the water. In half of the boxes, the devices to support the development of the submerged water plant were installed. During the experiment, simultaneously, spectral data from the water surface were gathered and physical–chemical analyses of water were carried out. The obtained results confirm the usefulness of the mesocosm experiment for the remote sensing chl-a concentration algorithms being developed. The concentration of dissolved organic carbon was identified as a key factor that interfered with remote chl-a estimations in the analyzed reservoir. [ABSTRACT FROM AUTHOR]

In order to screen candidate aquaporin genes involved in resisting osmotic stress, we analyzed the physiological responses and the expression levels of aquaporin genes in garlic under drought and salt stress with 'Er Shuizao' as plant material. Different physiological indicators were detected under drought and salt stress treatments. RT-qPCR was used to detect the expression levels of the candidate aquaporin genes in specific tissues. Finally, we screened AsPIP1-3 as a candidate gene and analyzed its function. The results showed that the relative water content and chlorophyll content of leaves decreased, the O2− production rate increased, and H2O2 accumulated in garlic under drought and salt stress. The activities of SOD, POD, and CAT enzymes first increased and then decreased in garlic. The content of soluble sugar and proline increased to maintain cell osmotic balance, and the content of MDA and relative conductivity continued to increase. Most aquaporin gene expression first increased and then decreased in garlic under drought and salt stress. AsPIP1-3 gene expression is up-regulated under drought and salt stress in garlic. The relative expression was the highest on the 6th day of stress, being related to antioxidant enzyme activity and osmotic regulation. The consistent changes in gene expressions and physiological responses indicated that AsPIP1-3 played a role in resisting garlic osmotic stress. AsPIP1-3 was located on the cell membrane, being consistent with the predicted results of subcellular localization. The germination rate and root length of transgenic Arabidopsis under drought stress were significantly different from the wild type. Drought stress reduced the ROS accumulation of transgenic Arabidopsis, and the antioxidant enzyme activity was significantly higher than the wild type. The relative conductivity and MDA content significantly decreased, and the proline content increased under drought stress. The expression level of the genes related to drought stress response (AtRD22, AtP5CS, AtABF3, and AtLEA) significantly increased. The overexpression of AsPIP1-3 genes improved the drought tolerance of transgenic Arabidopsis plants, showing that AsPIP1-3 proteins enhanced drought tolerance. Our study laid a foundation for exploring the regulatory mechanism of garlic to abiotic stress. [ABSTRACT FROM AUTHOR]

Water deficit affects the growth as well as physiological and biochemical processes in plants. The aim of this study was to determine differences in physiological and biochemical responses to drought stress in two wheat cultivars—Chinese Spring (CS) and SQ1 (which are parents of a mapping population of doubled haploid lines)—and to relate these responses to final yield and agronomic traits. Drought stress was induced by withholding water for 14 days, after which plants were re-watered and maintained until harvest. Instantaneous gas exchange parameters were evaluated on the 3rd, 5th, 10th, and 14th days of seedling growth under drought. After 14 days, water content and levels of chlorophyll a+b, carotenoids, malondialdehyde, soluble carbohydrates, phenolics, salicylic acid, abscisic acid (ABA), and polyamines were measured. At final maturity, yield components (grain number and weight), biomass, straw weight, and harvest index were evaluated. Physiological and biochemical parameters of CS responded more than those of SQ1 to the 14-day drought, reflected in a greater reduction in final biomass and yield in CS. Marked biochemical differences between responses of CS and SQ1 to the drought were found for soluble carbohydrates and polyamines. These would be good candidates for testing in the mapping population for the coincidence of the genetic control of these traits and final biomass and yield. [ABSTRACT FROM AUTHOR]

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI > 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations. [ABSTRACT FROM AUTHOR]

Aquaculture plays a vital role in global food production, with fish pond water quality directly impacting aquatic product quality. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) serves as a key producer of aquatic products in South China. Monitoring environmental changes in fish ponds serves as an indicator of their health. This study employed the extreme gradient boosting tree (BST) model of machine learning, utilizing Landsat imagery data, to assess Chlorophyll-a (Chl-a) concentration in GBA fish ponds from 2013 to 2022. The study also examined the corresponding spatiotemporal variations in Chl-a concentration. Key findings include: (1) clear seasonal fluctuations in Chl-a concentration, peaking in summer (56.7 μg·L−1) and reaching lows in winter (43.5 μg·L−1); (2) a slight overall increase in Chl-a concentration over the study period, notably in regions with rapid economic development, posing a heightened risk of eutrophication; (3) influence from both human activities and natural factors such as water cycle and climate, with water temperature notably impacting summer Chl-a levels; (4) elevated Chl-a levels in fish ponds compared to surrounding natural water bodies, primarily attributed to human activities, indicating an urgent need to revise breeding practices and address eutrophication. These findings offer a quantitative assessment of fish pond water quality and contribute to sustainable aquaculture management in the GBA. [ABSTRACT FROM AUTHOR]

The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) concentration is one of the important indicators in water bodies for assessing the ecological health of water quality. In this paper, an OGolden-DBO-XGBoost Chl-a concentration inversion model is proposed using Wuliangsu Lake as the study area, and by combining the Sentinel-2 remote-sensing satellite images and measured Chl-a concentration data in Wuliangsu Lake, the XGBoost model is optimized using the hybrid-strategy-improved dung beetle optimization algorithm (OGolden-DBO), and an OGolden-DBO-XGBoost Chl-a concentration inversion model. The OGolden-DBO-XGBoost model's coefficients of determination (R2s) were 0.8936 and 0.8850 on the training set and test set, according to the results. The root mean squared errors (RMSEs) were 3.1353 and 2.9659 μg/L, and the mean absolute errors (MAEs) were 1.8918 and 2.4282 μg/L. The model performed well and provided a strong support for the detection of Chl-a concentration in Wuliangsu Lake. [ABSTRACT FROM AUTHOR]

The Humboldt squid (Dosidicus gigas), is a significant economic species off Peru. The abundance and distribution of the species are highly susceptible to fluctuations in marine environmental conditions. The evolution of mesoscale eddies represents one of the dynamic processes in the ocean, exerting varying degrees of influence on regional biogeochemical processes from generation to dissipation. However, the mechanisms governing the regulation of abundance and spatial distribution of D. gigas off Peru during this evolution remain unclear. Therefore, this study employed normalization techniques and the habitat suitability index (HSI) model, utilizing data from the D. gigas fishery, mesoscale eddies, and marine environmental factors (including sea surface temperature, temperature at 50 m depth, and chlorophyll-a concentration) to analyze the environmental changes and the abundance, spatial distribution, and habitat changes of D. gigas during the evolution of mesoscale eddies. The results indicate that eddies undergo four stages: formation, intensification, maturity, and decay. During eddy evolution, the abundance of D. gigas exhibited an initial increase and a subsequent decrease within cyclonic eddies (CEs), whereas, within anticyclonic eddies (AEs), abundance showed a gradual decline. The outcome of the HSI model revealed that, the habitat suitability and the proportion of suitable habitat areas within eddies were highly consistent with the abundance of D. gigas during all stages of the eddies other than the intensification stage of AEs. The study speculated that both CEs and AEs can create favorable environmental conditions during the maturity stage, thereby leading to an increase in suitable habitat and abundance of D. gigas. These findings emphasize the significant impact of mesoscale eddy evolution on the abundance and habitat distribution of D. gigas. [ABSTRACT FROM AUTHOR]

This study utilizes satellite data, including HY-1C/D, along with reanalysis data, to unveil the typical environmental characteristics of the sea surface in the middle of Zhoushan fishery. The article addresses three main issues. The first one is the development of an ocean primary productivity (OPP) inversion algorithm model. The second one is the study of chlorophyll-α (Chl-α) concentration and OPP distribution characteristics in Zhoushan fishery using China's domestically produced ocean satellite HY-1C/D CZI data. The last one is the revelation of the characteristics of typical environmental factors on the sea surface at Zhoushan fishery by combining HY-1C/D with multi-source data. The results show the following: (1) The middle part of Zhoushan fishery exhibits significant seasonal and regional variations in Chl-α concentration and OPP. Chl-α concentration ranges mainly between 0.2 and 2.9 µg/L, with higher concentrations in spring and summer and lower Chl-α concentrations in autumn and winter. Spatially, Chl-α concentration gradually decreases from west to east. The OPP in the study area ranges from 100 mg·m−2d−1 to 1000 mg·m−2d−1, with high OPP values distributed on the western side, ranging from 400 mg·m−2d−1 to 1000 mg·m−2d−1, and gradually decreasing seaward. The highest OPP occurs in summer and the lowest in winter. (2) The correlation analysis between Chl-α concentration and OPP revealed a strong positive relationship. Consequently, this study developed an empirical model for estimating OPP based on Chl-α concentration and validated its feasibility. The model applies to areas with Chl-α concentrations ranging from 0.2 to 4 µg/L. (3) The convergence of freshwater injection, multiple ocean currents, and seasonal upwelling in the study area brings about a rich supply of nutrients. Additionally, the region is characterized by suitable conditions, including optimal Chl-α concentrations, OPP, SST, salinity, currents, and geological water depths. The synergistic effect of these factors together contributed to the formation of Zhoushan fishery. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate different L. corniculatus L. ecotypes under water-deficit conditions to identify changes in relative water content and photosynthetic pigments as indicators of physiological responses during different years' seasons. The experiment was conducted in a randomized block design with three replicates. Ten treatments were performed as a factorial of 2 × 5, where the first variation factor was the soil water content—no water deficit (NDW) with 100% field capacity (FC), and water deficit (DW) corresponding to 85.4% of the FC—and the second variation factor comprised four ecotypes and one variety of L. corniculatus. A significant effect was identified on the concentration of photosynthetic pigments, mainly total chlorophyll, with chlorophyll a in the 255301 ecotype with records of 187.8, 167.5, and 194.6 mg g−1 FW in WD, corresponding to an increase of 86.0%, 172.6%, and 16.6%, respectively, in relation the lower values obtained in the ecotype 202700 under NWD. In carotenoids, higher concentrations were observed in the 255301 and 202700 ecotypes and the Estanzuela Ganador variety under WD in most seasonal periods, except summer; a similar response was found in the 202700 ecotype and the Estanzuela Ganador variety during the winter season, also in WD. The results showed that the first two principal components accounted for 71.8% of the total variation, with PC1 representing chlorophyll a, chlorophyll b, and total chlorophyll, and PC2 representing carotenoids, temperature, relative chlorophyll index, and relative water content. The observations were grouped based on soil moisture content, with the optimal moisture group exhibiting higher chlorophyll and carotenoid concentrations. The findings suggest that soil moisture content significantly affects the performance of L. corniculatus ecotypes, and the plant shows seasonal variations in response to water-deficit conditions. This research contributes to understanding the physiological responses of L. corniculatus and its potential as a water-efficient forage crop for promoting sustainable agriculture and enhancing food security. [ABSTRACT FROM AUTHOR]

A massive bloom of the raphidophyte Heterosigma akashiwo occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll a is typically <30 mg m−3, while also capturing bloom periods of >100 mg m−3 chlorophyll a. [ABSTRACT FROM AUTHOR]

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca 2 + , Cl − , and Na + . We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition. [ABSTRACT FROM AUTHOR]

Small-scale reservoirs located in river estuaries are a significant water resource supporting agricultural and industrial activities; however, they face annual challenges of eutrophication and algal bloom occurrences due to excessive nutrient accumulation and watershed characteristics. Efficient management of algal blooms necessitates a comprehensive analysis of their spatiotemporal distribution characteristics. Therefore, this study aims to develop a chlorophyll-a (Chl-a) estimation model based on high-resolution satellite remote sensing data from Sentinel-2 multispectral sensors and multiple linear regression. The multiple linear regression (MLR) models were constructed using multiple reflectance-based variables that were collected over 2 years (2021–2022) in an estuarine reservoir. A total of 21 significant input variables were selected by backward elimination from the 2–4 band algorithms as employed in previous Chl-a estimation studies, along with the Sentinel-2 B1-B8A wavelength ratio. The developed algorithm exhibited a coefficient of determination of 0.65. Spatiotemporal variations in Chl-a concentration generated by the algorithm reflected the movement of high Chl-a concentration zones within the body of water. Through this analysis, it turned out that Sentinel-2-based spectral images were applicable to a small-scale reservoir which is relatively long and narrow, and the algorithm estimated changes in concentration levels over the seasons, revealing the dynamic nature of Chl-a distributions. The model developed in this study is expected to support effective algal bloom management and water quality improvement in a small-scale reservoir or similar complex water quality water bodies. [ABSTRACT FROM AUTHOR]

In response to the rapid changes in the chlorophyll-a concentration and eutrophication issues in lakes, with Dianchi Lake as an example, a remote sensing estimation model for chlorophyll-a, total phosphorus, and total nitrogen in Dianchi Lake was constructed using the three band method and ratio band method based on the visible-light shortwave infrared (AHSI) hyperspectral satellite data from Gaofen 5 (GF-5) and the water quality data collected at Dianchi Lake. The model results were compared with the multispectral data from the Gaofen 1 (GF-1) wide field-of-view (WFV) camera. The accuracy evaluation results indicate that the overall mean absolute percentage error of the remote sensing estimation models for chlorophyll a, total phosphorus, and total nitrogen are 7.658%, 4.511%, and 4.577%, respectively, which can meet the needs of lake water quality monitoring and evaluation. According to the remote sensing simulation results, chlorophyll a is mainly distributed in the northern part of Dianchi Lake, with phosphorus and nitrogen pollution throughout Dianchi Lake and relatively more abundant in the central and southern regions. The pollution is mainly concentrated in the northern and southern regions of Dianchi Lake, which is consistent with the actual situation. Further confirming the feasibility of using GF-5 satellite AHSI data for water quality parameter retrieval can provide new technical means for relevant departments to quickly and efficiently monitor the inland lake water environment. [ABSTRACT FROM AUTHOR]

The accurate estimation of the spatial and temporal distribution of chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is crucial for understanding marine ecosystem dynamics and water quality assessment. However, the challenge of missing values in satellite-derived Chl-a data has hindered obtaining complete spatiotemporal information. Traditional methods for deriving Chl-a are based on the modeling of measured sensor data and in situ measurements. Spatiotemporal imputation of Chl-a is difficult due to the inaccessibility of the measured Chl-a. In this study, we introduce an innovative approach that incorporates an ocean dynamics dataset and utilizes the random forest algorithm for predicting the Chl-a concentration in the SCS. The method combines the spatiotemporal feature pattern of Chl-a and the main influencing factors, and it introduces ocean dynamics data, which has a high correlation with the spatiotemporal distribution of Chl-a, as the input data through feature engineering. Also, we compared Random Forest (RF) with other Machine Learning (ML) methods. The results show that (1) ocean dynamics datasets can provide important data support for Chl-a imputation by capturing the impact of dynamical processes on ecological roles in the South China Sea. (2) The RF method is the superior imputation method for the reconstruction of Chl-a in the South China Sea, with better model performance and smaller errors. This study provides valuable insight for researchers and practitioners in choosing suitable machine learning methods for the imputation of the Chl-a concentration in the SCS, facilitating a better understanding of the region's marine ecosystems and supporting effective environmental management. [ABSTRACT FROM AUTHOR]

The polarization crossfire (PCF) suite carried onboard the Chinese GaoFen-5B satellite is composed of a Particulate Observing Scanning Polarimeter (POSP) and a Directional Polarimetric Camera (DPC), which can provide multi-angle, multi-spectral, and polarization data. In this paper, the influence of polarization and the directionality of reflectance in open oceans on the inversion of chlorophyll a (Chla) concentrations are investigated, from 410 nm to 670 nm. First, we exploit a vector radiative transfer model to simulate the absolute and relative magnitudes of the water-leaving radiance signal (I) and the parallel polarization radiance (PPR) to the top-of-atmosphere (TOA) radiation field. The simulation results show that the PPR can enhance the relative contribution of the water-leaving signal, especially in sunglint observation geometry. The water-leaving signal for PPR exhibits significant directional and spectral variations relative to the observation geometries, and the maximum value of the water-leaving signal for PPR occurs in the backscattering direction. In addition, the sensitivity of the PPR to the Chla concentration is sufficient. The synthetic datasets are utilized to develop retrieval algorithms for the Chla concentrations based on the back-propagation neural network (BPNN). The inversion results show that the PCF strategy improves the accuracy of Chla retrieval, with an RMSE of 0.014 and an RRMSE of 6.57%. Thus, it is an effective method for retrieving the Chla concentration in open oceans, by utilizing both the directionality and polarization of the reflectance. [ABSTRACT FROM AUTHOR]

Water deficit is one of the most important stress factors affecting yield and production quality. Breeders are focusing on breeding wheat cultivars and crop lines that are more resistant to water deficit, so there is a possibility that plants with changes in their ear morphologies, such as long chaff and multi-rowed varieties, will be more resistant to water deficit. Therefore, our research focused on the study of changes in the physiological parameters of wheat cultivar 'Bohemia' (normal cob) with an altered morphotype (genotypes '284-17' (long chaff) and genotype '29-17' (multirow cob)), in relation to the duration of the water deficit. The experiment was set up as a container experiment under partially controlled greenhouse conditions. The experimental design included four treatments. The control (C) variant was irrigated regularly. The other treatments were stressed by water deficit, which was induced through the method of gradually drying the substrate: treatment D1 involved 10 days without irrigation, 4 days of watering, 10 days with a re-induced water deficit and 4 days of watering; treatment D2 involved 10 days of watering, and then stress was induced via water deficit until the end of the experiment; treatment D3 involved 10 days of stress and then irrigation until the end of the experiment. The pigment content, gas exchange rate, chlorophyll fluorescence and water potential were monitored in the juvenile wheat plants. The obtained results showed that the contents of photosynthetically active pigments (chlorophyll a and b and carotenoids) were influenced by the gene type. The chlorophyll and carotenoid content were higher in genotype '29-17' (0.080 and 1.925 nM cm−2, respectively) and lowest in cultivar 'Bohemia' (0.080 and 0.080 nM cm−2, respectively). The chlorophyll content decreased due to water deficit most significantly in the D2 variant (0.071 nM cm−2), compared to the control (0.138 nM cm−2). The carotenoid content significantly decreased due to water deficiency in the cultivar 'Bohemia', D2 (0.061 nM cm−2) and the genotype '284-17' (0.075 nM cm−2) and non-significantly decreased in '29-17' (1.785 nM cm−2). In the control plants, the carotenoid content decreased in the following order: genotype '29-17' (1.853 nM cm−2) > genotype '284-17' (0.088 nM cm−2) > cv. 'Bohemia' (0.087 nM cm−2). Wheat plants had a decreased photosynthetic rate due to the closure of stomata and reduction in substomatal CO2 levels, which were caused by water deficit. The above effect was observed in genotype '29-17' and cultivar 'Bohemia'. The transpiration rate increased by 0.099 mM m−2 s−1 (5.69%) in the variety 'Bohemia', due to water deficit. On the other hand, the transpiration rate of genotype '29-17' and genotype '284-17' decreased by 0.261 mM m−2 s−1 (88.19%) and 0.325 mM m−2 s−1 (81.67%), respectively, compared to the control. Among the genotypes studied, genotype '29-17' showed higher photosynthesis and transpiration rates, compared to genotype '284-17' and the variety 'Bohemia'. The effect of genotype and water deficit on chlorophyll fluorescence parameters was also shown. In all genotypes studied, there was a significant decrease in water potential due to water deficit, most significantly in the Bohemia variety, then in the genotype '284-17', and the least significant decrease in water potential was seen in the genotype '29-17'. Genotype '29-17' appears promising with respect to drought tolerance and photosynthetic rate, despite increased transpiration and reduced water potential; it also appears promising for better water management, with respect to reduced water potential in aboveground organs. On the other hand, the variety Bohemia appears to be less suitable for dry areas, since, despite its relative plasticity, it shows not only high water potential values in the water deficit region but also the most significant decrease in water potential. [ABSTRACT FROM AUTHOR]

Offshore wind farms (OWFs), built extensively in recent years, induce changes in the surrounding water environment. The changes in the suspended sediment concentration (SSC) and chlorophyll-a concentration (Chl-aC) induced by an OWF in the Yangtze River Estuary were analyzed based on Chinese Gaofen (GF) satellite data. The results show the following: (1) The flow near the wind turbines makes the bottom water surge, driving the sediment to "re-suspend" and be lost, deepening the scour pit around the bottom of the wind turbines, which is known as "self-digging". The interaction between the pillar of a wind turbine and tidal currents makes hydrodynamic factors more complicated. Blocking by wind turbines promoting the scour of the bottom seabed of the OWF results in speeding up the circulation rate of sediment loss and "re-suspension", which contributes to the change in the SSC and Chl-aC. This kind of change in sediment transport in estuarine areas due to human construction affects the balance of the ecological environment. Long-term sediment loss around wind turbines also influences the safety of wind turbines. (2) The SSC and Chl-aC are mainly in the range of 200–600 mg/L and 3–7 μg/L, respectively, in the OWF area, higher than the values obtained in surrounding waters. The SSC and Chl-aC downstream of the OWF are higher than those upstream, with differences of 100–300 mg/L and 0.5–2 μg/L. High SSC and Chl-aC "tails" appear downstream of wind turbines, consistent with the direction of local tidal currents, with lengths in the range of 2–4 km. In addition, the water environment in the vicinity of a wind turbine array, with a roughly 2–5 km scope (within 4 km during flooding and around 2.5 km during ebbing approximately) downstream of the wind turbine array, is impacted by the OWF. (3) In order to solve the problem of "self-digging" induced by OWFs, it is suggested that the distance between two wind turbines should be controlled within 2–3.5 km in the main flow direction, promising that the second row of wind turbines will be placed on the suspended sediment deposition belt induced by the first row. In this way, the problems of ecosystem imbalance and tidal current structure change caused by sediment loss because of local scouring can be reduced. Furthermore, mutual compensation between wind turbines can solve the "self-digging" problem to a certain extent and ensure the safety of OWFs. [ABSTRACT FROM AUTHOR]

Drought stress is an important factor that reduces plant biomass production and quality. As one of the most important economic forage grasses, orchardgrass (Dactylis glomerata) has high drought tolerance. Auxin/indole-3-acetic acid (Aux/IAA) is one of the early responsive gene families of auxin and plays a key role in the response to drought stress. However, the characteristics of the Aux/IAA gene family in orchardgrass and their potential function in responding to drought stress remain unclear. Here, 30 Aux/IAA members were identified in orchardgrass. Segmental duplication may be an important driving force in the evolution of the Aux/IAA gene family in orchardgrass. Some Aux/IAA genes were induced by IAA, drought, salt, and temperature stresses, implying that these genes may play important roles in responding to abiotic stresses. Heterologous expression in yeast revealed that DgIAA21 can reduce drought tolerance. Similarly, the overexpression of DgIAA21 also reduced drought tolerance in transgenic Arabidopsis, which was supported by lower total chlorophyll content and relative water content as well as higher relative electrolyte leakage and malondialdehyde content (MDA) than Col-0 plants under drought conditions. The results of this study provided valuable insight into the function of DgIAAs in response to drought stress, which can be further used to improve forage grass breeding programs. [ABSTRACT FROM AUTHOR]

The water of high Andean lakes is strongly affected by anthropic activities. However, due to its complexity this ecosystem is poorly researched. This study analyzes water quality using Sentinel-2 (S2) images in high Andean lakes with apparent different eutrophication states. Spatial and temporal patterns are assessed for biophysical water variables from automatic products as obtained from versions of C2RCC (Case 2 Regional Coast Color) processor (i.e., C2RCC, C2X, and C2X-COMPLEX) to observe water characteristics and eutrophication states in detail. These results were validated using in situ water sampling. C2X-COMPLEX appeared to be an appropriate option to study bodies of water with a complex dynamic of water composition. C2RCC was adequate for lakes with high transparency, typical for lakes of highlands with excellent water quality. The Yambo lake, with chlorophyll-a concentration (CHL) values of 79.6 ± 5 mg/m3, was in the eutrophic to hyper-eutrophic state. The Colta lake, with variable values of CHL, was between the oligotrophic to mesotrophic state, and the Atillo lakes, with values of 0.16 ± 0.1 mg/m3, were oligotrophic and even ultra-oligotrophic, which remained stable in the last few years. Automatic S2 water products give information about water quality, which in turn makes it possible to analyze its causes. [ABSTRACT FROM AUTHOR]

Drought stress imposes severe constraints on crop growth and yield. The NAC transcription factors (TF) play a pivotal role in regulating plant stress responses. However, the biological functions and regulatory mechanisms of many cotton NACs have not been explored. In this study, we report the cloning and characterization of GhNAC2-A06, a gene encoding a typical cotton NAC TF. The expression of GhNAC2-A06 was induced by PEG treatment, drought stress, and ABA treatment. Furthermore, we investigated its function using the virus-induced gene silencing (VIGS) method. GhNAC2-A06 silenced plants exhibited a poorer growth status under drought stress conditions compared to the controls. The GhNAC2-A06 silenced cotton plants had a lower leaf relative water and chlorophyll content and a higher MDA content compared to the controls under the drought treatment. The levels of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) enzyme activity in the GhNAC2-A06 silenced plants were found to be lower compared to the controls when exposed to drought stress. Additionally, the downregulation of the drought stress-related genes, GhSAP12-D07, GhNCED1-A01, GhLEA14-A11, GhZAT10-D02, GhPROT2-A05, GhABF3-A03, GhABF2-D05, GhSAP3-D07, and GhCPK1-D04, was observed in the GhNAC2-A06 silenced cotton. Together, our research reveals that GhNAC2-A06 plays a role in the reaction of cotton to drought stress by affecting the expression of genes related to drought stress. The data obtained from this study lay the theoretical foundation for further in-depth research on the biological function and regulatory mechanisms of GhNAC2-A06. [ABSTRACT FROM AUTHOR]

Realizing real-time and rapid monitoring of crop growth is crucial for providing an objective basis for agricultural production. To enhance the accuracy and comprehensiveness of monitoring winter wheat growth, comprehensive growth indicators are constructed using measurements of above-ground biomass, leaf chlorophyll content and water content of winter wheat taken on the ground. This construction is achieved through the utilization of the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE) model. Additionally, a correlation analysis is performed with the selected vegetation indexes (VIs). Then, using unmanned aerial vehicle (UAV) multispectral orthophotos to construct VIs and extract texture features (TFs), the aim is to explore the potential of combining the two as input variables to improve the accuracy of estimating the comprehensive growth indicators of winter wheat. Finally, we develop comprehensive growth indicator inversion models based on four machine learning algorithms: random forest (RF); partial least squares (PLS); extreme learning machine (ELM); and particle swarm optimization extreme learning machine (PSO-ELM), and the optimal model is selected by comparing the accuracy evaluation indexes of the model. The results show that: (1) The correlation among the comprehensive growth indicators (CGIs) constructed by EWM (CGIewm) and FCE (CGIfce) and VIs are all improved to different degrees compared with the single indicators, among which the correlation between CGIfce and most of the VIs is larger. (2) The inclusion of TFs has a positive impact on the performance of the comprehensive growth indicator inversion model. Specifically, the inversion model based on ELM exhibits the most significant improvement in accuracy. The coefficient of determination (R2) values of ELM-CGIewm and ELM- CGIfce increased by 20.83% and 20.37%, respectively. (3) The CGIfce inversion model constructed by VIs and TFs as input variables and based on the ELM algorithm is the best inversion model (ELM-CGIfce), with R2 reaching 0.65. Particle swarm optimization (PSO) is used to optimize the ELM-CGIfce (PSO-ELM-CGIfce), and the precision is significantly improved compared with that before optimization, with R2 reaching 0.84. The results of the study can provide a favorable reference for regional winter wheat growth monitoring. [ABSTRACT FROM AUTHOR]

The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, "water sampling—bio-optical models", and this link must have certain intermediate characteristics. The most crucial of them are the high-precision measurements of the main water quality parameters, such as the concentration of chlorophyll a (Chl a), colored dissolved organic matter (CDOM), and total suspended sediments (TSS) in the upper water layer, together with a high operational rate and the ability to cover a large water area in a short time, which corresponds to a satellite overpass. A possible solution is to utilize laser-induced fluorescence (LIF) of water constituents measured by a marine LiDAR in situ with a high sampling rate from a high-speed vessel. This allows obtaining a large ground-truth dataset of the main water quality parameters simultaneously with the satellite overpass within the time interval determined by NASA protocols. This method was successfully applied to the oligotrophic Issyk-Kul Lake in Kyrgyzstan, where we obtained more than 4000 and 1000 matchups for the Chl a and TSS, respectively. New preliminary regional bio-optical models were developed on the basis of a one-day survey and tested for archive Sentinel-2A data for 2022. This approach can be applied for regular monitoring and further correction in accordance with seasonal variability. The obtained results, together with previously published similar studies for eutrophic coastal and productive inland waters, emphasize the applicability of the presented method for the development or adjustment of regional bio-optical models for water bodies of a wide trophic range. [ABSTRACT FROM AUTHOR]

The prairie waterbodies face a future of warming temperatures and growing water demands. There are increasing concerns about how water quality will be affected. Water quality models are an effective tool for examining scenarios of future conditions that cannot be measured directly. This study combined WASP and CE-QUAL-W2 to investigate the potential impacts of changing flow management and climate change in the Canadian Prairies. The two models were loosely coupled to simulate a strategically managed river-reservoir network. Climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model ensemble were used to create future climate scenarios. Interbasin water transfers were then simulated through the coupled models to determine if any negative impacts from climate change on water quality could be offset through flow management. Climate change impacts in the river stretch were minimised due to the rapid flow travel time along the channel. The interbasin water transfers had a greater influence on water quality concentrations in the river. This result was limited by the uncertain hydro-climatic future of the contributing watershed. Climate change impacts in the downstream reservoir were far more apparent. Evaporative losses increased approximately 150% from the base model by the 2080–2100 period. Chlorophyll-a concentrations increased an average of 53% in this same period based on monthly mean percentage change. Reservoir water quality was improved after adding the interbasin water transfers. Results indicated that flow management would have a positive impact on water quality in the reservoir in the face of future climate change. [ABSTRACT FROM AUTHOR]

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)2), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality. [ABSTRACT FROM AUTHOR]

WRKY proteins are a superfamily of transcription factors (TFs) that play multiple roles in plants' growth, development, and environmental stress response. In this study, a novel WRKY gene called GsWRKY23 that is specifically upregulated in salt-tolerant Glycine soja accession BB52 seedlings was identified by transcriptomic analysis under salt stress. How the physiological functions and mechanisms of the GsWRKY23 gene affect salt tolerance was investigated using transformations of soybean hairy roots and Arabidopsis, including wild-type (WT) and atwrky23-mutant plants. The results showed that GsWRKY23 in the roots, stems, and leaves of BB52, along with its promoter in the cotyledons and root tips of GsWRKY23pro::GUS Arabidopsis seedlings, displayed enhanced induction under salt stress. GsWRKY23 localises to the nucleus and shows transcriptional activation ability in yeast cells. Compared to GsWRKY23-RNAi wild soybean hairy-root composite plants under salt stress, obvious improvements, such as superior growth appearance, plant height and fresh weight (FW), and leaf chlorophyll and relative water content (RWC), were displayed by GsWRKY23-overexpressing (OE) composite plants. Moreover, their relative electrolytic leakage (REL) values and malondialdehyde (MDA) contents in the roots and leaves declined significantly. Most of the contents of Na+ and Cl− in the roots, stems, and leaves of GsWRKY23-OE plants decreased significantly, while the content of K+ in the roots increased, and the content of NO3− displayed no obvious change. Ultimately, the Na+/K+ ratios of roots, stems, and leaves, along with the Cl−/NO3− ratios of roots and stems, decreased significantly. In the transgenic WT-GsWRKY23 and atwrky23-GsWRKY23 Arabidopsis seedlings, the salt-induced reduction in seed germination rate and seedling growth was markedly ameliorated; plant FW, leaf chlorophyll content, and RWC increased, and the REL value and MDA content in shoots decreased significantly. In addition, the accumulation of Na+ and Cl− decreased, and the K+ and NO3− levels increased markedly to maintain lower Na+/K+ and Cl−/NO3− ratios in the roots and shoots. Taken together, these results highlight the role of GsWRKY23 in regulating ionic homeostasis in NaCl-stressed overexpressed soybean composite plants and Arabidopsis seedlings to maintain lower Na+/K+ and Cl−/NO3− ratios in the roots and shoots, thus conferring improved salt tolerance. [ABSTRACT FROM AUTHOR]

The purpose of this study was to elucidate the patterns and mechanisms driving seasonal and interannual variations of carbon and nitrogen stable isotopes in the zooplankton crustacean community of Lake Maggiore (Italy), during the period 2009–2020. Different zooplankton taxa and groups showed different ranges of δ13C signatures, giving an insight into food sources and niche partition. In particular, cyclopoids had a restricted range with more negative δ13C‰ values and an increase in δ13C fractionation with the establishment of water thermal vertical stratification, highlighting the importance of vertical distribution as a key factor for taxa coexistence in a vertically heterogenous environment. The δ13C values of the zooplankton community and of Daphnia were positively related to water temperature (R2 = 0.58 p < 0.0001 and R2 = 0.68 p < 0.0001, respectively), and the δ13C Daphnia signature was positively related to chlorophyll a (R2 = 0.32, p < 0.0001). Decomposition of the time-series data for zooplankton carbon and nitrogen signatures and environmental parameters identified increasing trends in water temperature, chlorophyll a and water conductivity and a decrease in nitrate that matched changes in carbon isotopic signature trends in some zooplankton taxa (Bosmina, Daphnia and Diaptomids). Overall, the observed patterns in zooplankton isotopic signatures were interpreted as integrations of the effects of climate warming in Lake Maggiore, affecting both the availability of food sources and environmental conditions. [ABSTRACT FROM AUTHOR]

Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake's eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R2 of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality. [ABSTRACT FROM AUTHOR]

Fenugreek (Trigonella foenum-graecum) is an annual plant belonging to the family Fabaceae and has fodder, medicinal and spice uses, and is also used as an organic fertilizer. A total of 18 treatments including the combination of two light environments (with and without supplementary blue light), three concentrations of potassium iodate (0, 2 and 4 mg L−1) and four concentrations of sodium selenate (0, 2 and 4 mg L−1) were organized in a three-way factorial experiment to evaluate the growth characteristics, pigments, trigonelline and seed yield of fenugreek in a greenhouse. The application of 4 mg L−1 of Se resulted in the highest carotenoid, anthocyanin, plant length, fresh weight, chlorophyll and relative water content. The fresh and dry weight of the shoot and the anthocyanin increased with the 2 h supplementation of sunlight with a blue spectrum; however, the fresh root decreased. The interaction of blue light with 0 mg L−1 of Se significantly reduced the plant length. The content of trigonelline was significantly improved with the application of blue light supplementation without negatively affecting the seed yield. In general, 2 h supplementing of sunlight with blue light and feeding with 4 mg L−1 of selenium and iodine are recommended to improve various traits, including trigonelline content. [ABSTRACT FROM AUTHOR]

The Pomeranian lakes in Northern Poland and the nearby coastal waters of the Baltic Sea belong to optically complex water bodies characterised by high eutrophication levels. These water types require a local approach when developing bio-optical algorithms that combine the inherent and the apparent properties of seawater. Well-established local algorithms are of great value for understanding and addressing rapid changes in water quality related mostly to human activities in coastal and near-shore zones, as well as in optically similar lakes. Our research analyses the possibility of using the multi-parameter algorithms of absorption a(λ), backscattering bb(λ) and remote sensing reflectance Rrs(λ), originally developed for the coastal waters of the Southern Baltic Sea, for three selected Pomeranian lakes. Our multi-parameter algorithms are based on the input concentrations of the biogeochemical components measured in the lake waters, i.e., chlorophyll a (Chl a), suspended particulate matter (SPM), inorganic suspended particulate matter (SPMinorg), the sum of the surface concentrations of accessory pigments (ΣC) and coloured dissolved organic matter with a wavelength of 400 nm (aCDOM(400)). Rrs(λ) and a(λ) output values were compared with independent measurements of these parameters conducted in the lake waters at 20 sampling stations. Our algorithm output values of bb(λ) were compared to the values obtained based on the algorithm provided by Ficek, previously developed and validated for Pomeranian lakes, at the same stations. The statistical analyses conducted afterwards showed that the multi-parameter algorithms of Rrs(λ) and a(λ) for the Southern Baltic Sea are sufficient to be used for the stations investigated in the aforementioned three lakes. Specifically, the correlations between the bb(λ) values obtained based on the Ficek algorithm and the bb(λ) values obtained using our multi-parameter algorithm reveal a statistical error rate of less than 20%. [ABSTRACT FROM AUTHOR]

The accurate forecast of algal blooms can provide helpful information for water resource management. However, the complex relationship between environmental variables and blooms makes the forecast challenging. In this study, we build a pipeline incorporating four commonly used machine learning models, Support Vector Regression (SVR), Random Forest Regression (RFR), Wavelet Analysis (WA)-Back Propagation Neural Network (BPNN) and WA-Long Short-Term Memory (LSTM), to predict chlorophyll-a in coastal waters. Two areas with distinct environmental features, the Neuse River Estuary, NC, USA—where machine learning models are applied for short-term algal bloom forecast at single stations for the first time—and the Scripps Pier, CA, USA, are selected. Applying the pipeline, we can easily switch from the NRE forecast to the Scripps Pier forecast with minimum model tuning. The pipeline successfully predicts the occurrence of algal blooms in both regions, with more robustness using WA-LSTM and WA-BPNN than SVR and RFR. The pipeline allows us to find the best results by trying different numbers of neuron hidden layers. The pipeline is easily adaptable to other coastal areas. Experience with the two study regions demonstrated that enrichment of the dataset by including dominant physical processes is necessary to improve chlorophyll prediction when applying it to other aquatic systems. [ABSTRACT FROM AUTHOR]

The Sahara desert is a major global source of dust that is mostly transported southwest over the ocean off West Africa. The presence of this dust impacts the remote sensing of ocean surface properties. These aerosols have absorbing properties that are poorly accounted for in the standard ocean color data processing algorithm. This can result in an overestimation of the atmospheric contribution to the ocean color signal and consequently an underestimation of the oceanic contribution. A two-step algorithm initially applied to the Sea-viewing Wide field-of-view Sensor (SeaWiFS) data was adapted to the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) sensor in the Northwest African region. The Northwest African region is a very productive region, where pelagic resources are an important socio-economic sector. Improving atmospheric correction of ocean color products is, thus, of particular interest for this oceanic region. The two-step approach of classifying the top-of-atmosphere radiance spectra for a better estimate of aerosol type on the one hand, and using an optimization method to fit the parameters of these aerosols and chlorophyll-a concentration (Chla) on the other hand, allows for a better representation of the optical thickness, a correction of the marine reflectance spectrum, and an increase in the spatio-temporal coverage of the area. To the extent that the properties of the water color signal are improved by this data processing, the Chla estimates should also be improved by this approach. However, it is difficult to conclude on this point from the available in situ observations. [ABSTRACT FROM AUTHOR]

Aquaponics is a new type of composite farming system, which combines aquaculture and hydroponics through ecological design to achieve scientific synergism. However, the effects of aquaponics on the welfare status and stress parameters of fish are unclear. In this study, 150 crucian carp with an average initial body weight of 7.06 ± 0.32 g were selected. Nine fish were randomly selected as the control group (NC group and hypoxia group) for stress, antioxidant and gene expression parameters after acclimation and hypoxia stress, respectively. The remaining crucian carps were randomly divided into three experimental groups with 20 crucian carps in each group (T0, T1 and T2, respectively), with three replicates. The fresh weight of the lettuce root in the T2 group had no significant effect (p > 0.05). Compared with the control group (T0 and T1), there were significant differences in the specific growth rate, the weight gain rate, the fresh leaf weight, the chlorophyll content and the water quality parameters in group T2 (p < 0.05). Regarding the biochemical parameters, superoxide dismutase and catalase showed significant differences between the T2 and T1 groups (p < 0.05). At the same time, the HSP70 and Prdx3 genes were upregulated in the liver of the T2 group compared to the NC group and the hypoxia group. The research suggests that aquaponics may reduce the hypoxia stress of crucian carp without affecting the growth of crucian carp and lettuce. [ABSTRACT FROM AUTHOR]

A variety of environmental issues are affecting crops all across the world, but rising temperatures are posing the greatest threat. High temperature has been found to drastically inhibit seedling emergence and cause leaf necrosis at the seedling stage, which results in poor plant stand and significantly decreased yields. Polyamines (PAs) are positively charged, low-molecular-weight aliphatic nitrogenous bases present in all living organisms and are involved in various biological processes in plant growth and development, including senescence and response to different abiotic stresses. Putrescine (Put) functions as a master growth regulator that promotes optimal plant development and greater stress tolerance. Here, the current study aimed to elucidate how Put (1 mM) functions in reducing the negative impacts of high temperature on four varieties of Brassica juncea (RH-1707, RH-1708, RH-1566 and RH-1999-42). Exposure of plants to high temperature resulted in decrease in growth parameters, chlorophyll content and relative water content. Simultaneously, increases were found in antioxidant enzymes, electrolyte leakage, lipid peroxidation, hydrogen peroxide content and stomatal density. High temperature more significantly affected varieties RH-1707 and RH-1708, while RH-1566 and RH-1999-42 showed lesser effects. Exogenous application of Put mitigated the negative impacts of high temperature by enhancing growth, chlorophyll content, relative water content and antioxidant enzyme activities and, simultaneously, it reduces oxidative damage and stomatal density. This study specifies that varieties RH-1707 and RH-1708 are sensitive whereas RH-1566 and RH-1999-42 are tolerant of high temperature and provides an insight into the effectiveness of Put in mitigating the effects of high temperature to a significant extent in B. juncea seedlings. [ABSTRACT FROM AUTHOR]

Deficit irrigation is applied to several agricultural crops as a water-saving irrigation strategy. The tomato plant is sensitive to water stress; however, integration with biostimulant applications, based on seaweed extracts, could be a strategy for plants adapting to this abiotic condition. The objective of this study was to evaluate agronomic and quality aspects of tomato cultivated under deficit irrigation combined with Ascophyllum nodosum extract (ANE) application. The experiment was conducted using a completely randomized design with two water replacement levels, 70 and 100% of crop evapotranspiration (ETc), and five doses of ANE (0, 0.1, 0.2, 0.3 and 0.4%) applied via soil drench. The interaction between ANE and ETc was significant (p < 0.05) in terms of plant growth, physiological parameters, fruit yield, yield components and fruit quality. Results indicated that when the tomato plant is under deficit irrigation, a higher ANE dose is required to achieve better development when compared to the 100% ETc condition, where the dose is lower. Under deficit irrigation, the largest fruit yield was obtained with 0.3 and 0.4% ANE, and with 100% ETc, the largest fruit yield was obtained with 0.2% ANE. ANE applications were also effective in increasing plant height, stem diameter, plant biomass, leaf area, chlorophyll and relative water content. In addition, tomato quality was also favored under deficit irrigation and seaweed extract application. We conclude that ANE applications attenuate water deficit effects in tomato plants and provide a strategy to ameliorate tomato yield, tomato quality and water use in agriculture. [ABSTRACT FROM AUTHOR]

Studies on the morpho-physiology of cryo-derived pineapple plants after acclimatization have been quite limited. Therefore, in the present study, the morpho-anatomical and physiological characteristics of cryo-derived Ananas comosus var. comosus 'MD-2' plants after acclimatization were investigated. Plants obtained from cryopreserved and non-cryopreserved shoot tips, as well as in vitro stock cultures (control), showed similar morphological development (viz. plant height, number of leaves, D leaf length, D leaf width, D leaf area, diameter of stem base, number of roots, plant fresh weight and plant dry weight) to conventionally micropropagated and non-cryopreserved plants. The pineapple plantlets developed efficient anatomical leaf structures that allowed them to adapt to the transition process from in vitro to ex vitro. In all groups of plants, the content of water and chlorophylls (a, a + b, a/b) decreased during the first 15 days of acclimatization and then remained constant until the end of the evaluation. The mesophilic succulence index increased to its maximum value after 15 days, then decreased and remained constant up to 45 days. Although physiological indicators fluctuated during the 45 days of acclimatization, no differences were observed in any of the indicators evaluated when plantlets obtained from cryopreserved shoot tips were compared with controls. The results of the plants from cryopreserved shoot tips show that they switched from C3 to Crassulacean acid metabolism, which denoted metabolic stability during acclimatization. [ABSTRACT FROM AUTHOR]

Calanus finmarchicus is a keystone zooplankton species that is commercially harvested and is critical in sustaining many important fisheries in the North Atlantic. However, due to their patchy population distributions, they are notoriously difficult to map using traditional ship-based techniques. This study involves the use of a combined approach of standardized ocean colour imagery and radiative transfer modelling to identify reflectance anomalies potentially caused by surface swarms of C. finmarchicus in the northern Norwegian Sea. Here, we have standardized satellite eRGB imagery that depicts a distinct 'red' patch, which coincides with in situ measurements of high surface concentrations of C. finmarchicus. Anomaly mapping using a novel colour matching technique shows a high degree of anomaly within this patch compared to the surrounding waters, indicating the presence of something other than the standard bio-optical model constituents influencing the optics of the water column. Optical closure between modelled and satellite-derived reflectance signals is achieved (and the anomaly is significantly reduced) through the addition of C. finmarchicus absorption into the model. Estimations of the surface concentrations of C. finmarchicus suggest between 80,000 and 150,000 individuals m−3 within the extent of the identified red patch. Furthermore, analysis of the impact of C. finmarchicus absorption on the OC3M algorithm performance points to the potential for the algorithm to over-estimate chlorophyll concentrations if C. finmarchicus populations are present in the surface waters. [ABSTRACT FROM AUTHOR]

Ensuring food security with severe shortages of freshwater and drastic changes in climatic conditions in arid countries requires the urgent development of feasible and user-friendly strategies. Relatively little is known regarding the impacts of the co-application (Co-A) of salicylic acid (SA), macronutrients (Mac), and micronutrients (Mic) through foliar (F) and soil (S) application strategies on field crops under arid and semiarid climatic conditions. A two-year field experiment was designed to compare the impacts of seven (Co-A) treatments of this strategy, including a control, FSA + Mic, FSA + Mac, SSA + FMic, SSA + FSA + Mic, SSA + Mic + FSA, and SSA + Mic + FMac + Mic on the agronomic performance, physiological attributes, and water productivity (WP) of wheat under normal (NI) and limited (LMI) irrigation conditions. The results reveal that the LMI treatment caused a significant reduction in various traits related to the growth (plant height, tiller and green leaf numbers, leaf area index, and shoot dry weight), physiology (relative water content and chlorophyll pigments), and yield components (spike length, grain weight and grain numbers per spike, thousand-grain weight, and harvest index) of wheat by 11.4–47.8%, 21.8–39.8%, and 16.4–42.3%, respectively, while WP increased by 13.3% compared to the NI treatment. The different Co-A treatments have shown a 0.2–23.7%, 3.6–26.7%, 2.3–21.6%, and 12.2–25.0% increase in various traits related to growth, physiology, yield, and WP, respectively, in comparison to the control treatment. The SSA+ FSA + Mic was determined as the best treatment that achieved the best results for all studied traits under both irrigation conditions, followed by FSA + Mic and SSA + Mic + FSA under LMI in addition to FSA + Mac under NI conditions. It can be concluded that the Co-A of essential plant nutrients along with SA accomplished a feasible, profitable, and easy-to-use strategy to attenuate the negative impacts of deficit irrigation stress, along with the further improvement in the growth and production of wheat under NI conditions. [ABSTRACT FROM AUTHOR]

The phytoplankton distribution in estuaries is influenced by multiple spatially variable growth and loss processes. As phytoplankton are transported by tidal and net flows, they are exposed to changing conditions of turbidity, depth, temperature, stratification, and grazing. Understanding the factors influencing the observed phytoplankton distribution patterns will allow better-informed restoration and water management efforts. We developed a Lagrangian approach driven by three-dimensional hydrodynamic model results and a simple representation of the production and losses of phytoplankton, allowing a highly efficient closed-form solution for phytoplankton biomass. Our analysis used continuous observations of chlorophyll concentration at four stations and a near-synoptic chlorophyll dataset collected underway from a boat in the channels of Suisun Marsh in the San Francisco Estuary. We divided the study region into four compartments defined by the water depth and location. For each observation location, hydrodynamic model simulations calculated the time that water parcels spent in each of these compartments and the mean depth encountered by water parcels in those compartments. Then, using that information and continuous monitoring data, we inferred compartment-specific grazing rates and two additional ecological parameters. The underway chlorophyll dataset was used for model validation. The model predicted patterns of observed spatial and tidal variability in chlorophyll in Suisun Marsh. The modeling indicated that the chlorophyll concentration at a point in space in time depends largely on the relative exposure to shallow areas, with positive net productivity and deep areas having negative net productivity. [ABSTRACT FROM AUTHOR]

The world's water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31-year time series (1989 to 2020) of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll-a, three models showed better performance (XGBoost, LightGBM, and AdaBoost). The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root-mean-square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources. [ABSTRACT FROM AUTHOR]