Spatiotemporal Dynamics of Chlorophyll-a in a Small Inland Reservoir Using Field Sampling and Satellite Data

Spatiotemporal Dynamics of Chlorophyll-a in a Small Inland Reservoir Using Field Sampling and Satellite Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal Dynamics of Chlorophyll-a in a Small Inland Reservoir Using Field Sampling and Satellite Data Muhammad Zahir, Yuping Su, Alia Naz, Muhammad Imran Shahzad, Rashid Pervez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7651016/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines harmful algal bloom (HAB) dynamics in Shanzai Reservoir, Fujian Province, China, through integrated in-situ and satellite remote sensing techniques. Chlorophyll-a concentrations the primary indicator of algal biomass, were measured directly using the bbe-Moldaenke FluoroProbe II, while Sentinel-2 imagery processed via Google Earth Engine (GEE) was used to map spatiotemporal bloom patterns. Monthly field sampling was conducted from March to December in 2022 and 2023, with sites aligned to satellite acquisition points. Two spectral indices, the Normalized Difference Chlorophyll Index (NDCI) and the Normalized Difference Vegetation Index (NDVI), were applied to estimate chlorophyll-a distribution. Results showed peak algal concentrations in late spring and summer, especially in May, with highest values at reservoir edges and near Qili and Banling villages. Strong correlations (R² up to 0.93) between in-situ and satellite-derived chlorophyll-a confirmed the reliability of remote sensing for HAB monitoring. Seasonal analysis indicated cyanobacteria dominance in spring and summer, and increased diatom prevalence in autumn and winter. Findings demonstrate that combining high-frequency satellite data with targeted in-situ measurements enables effective, large-scale, and near real-time HAB monitoring in small inland reservoirs. NDCI outperformed NDVI in detecting and mapping bloom severity, supporting its use for routine water quality surveillance. Additional spectral band combinations (NIR, SWIR, red edge) further improved bloom detection. This integrative approach offers a cost-effective, scalable method for HAB assessment and supports sustainable freshwater management. While perfect temporal alignment of in-situ and satellite data is often constrained by logistics and bloom variability, coordinated monitoring enhances accuracy and reliability. Harmful Algal Blooms (HABs) Chlorophyll-a In-situ monitoring Remote Sensing Freshwater Management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1 Introduction Freshwater reservoirs, crucial for irrigation, energy, and drinking water, face threats from HABs, causing environmental and health issues (Carpenter et al., 2011 ). Monitoring HAB dynamics in these reservoirs is vital. It is essential to monitor HABs events accurately (Kislik et al., 2022 ). To determine chlorophyll a mass concentration of water, routine water quality monitoring involves collecting samples, filtering, extracting, and spectrophotometer analysis. The monitoring of the water environment in large areas relies on manual sampling or the creation of automatic stations, both of which consume large amounts of manpower and materials, and also make it difficult to obtain continuous monitoring results based on spatial and temporal distributions (Ostroumov, 2017; Park, Kim, & Lee, 2020; Zahir et al., 2024 ). In situ monitoring is a very efficient process, but it is time-consuming, expensive, and technically insufficient. It lacks human resources, provides trophic status only on the day of sampling, and not in real-time (Demetillo & Taboada, 2019; Rodríguez-López, González-Rodríguez, Duran-Llacer, Cardenas, & Urrutia, 2021). Moreover, traditional field sampling approaches cannot detect temporal or spatial fluctuations in water quality, and this is essential for proper evaluation and effective management of water resources (Aranda et al., 2021; Peterson et al., 2019; Rodríguez-López et al., 2020; Rodríguez-López et al., 2021). For in situ monitoring, we utilized the bbe-Moldaenke FluoroProbe II (FP) to measure chlorophyll concentration in the Shanzai reservoir. The instrument, operating underwater, employs fluorescence measurements to assess chlorophyll-a concentrations swiftly and dependably in real-time. Additionally, it offers estimations of total chlorophyll concentration a, pigment concentrations for four phytoplankton classes, and yellow substances (Gregor et al., (2004). Sentinel-2 satellite's high-resolution imaging aids in this by detecting chlorophyll-a, a pigment present in HABs, allowing for the mapping of HAB extent and severity (Kislik et al., 2022 ). Integrating Sentinel-2 data with bbe sampling techniques enhances our ability to monitor and respond to HABs effectively, as demonstrated in our study assessing the Shanzai Reservoir in Fujian Province, China. This study aims to assess the dynamics of HABs in Shanzai Reservoir through the synergistic integration of in-situ chlorophyll-a measurements using the FluoroProbe II and satellite-based spectral indices derived from Sentinel-2 imagery. The objective is to evaluate the reliability, spatial accuracy, and seasonal sensitivity of remote sensing techniques for effective real-time HAB monitoring and freshwater management. 2 Materials & methods 2.1 Study Area The Shanzai reservoir, also known as she Shan Lake, (26°20′–26°25′N,119°16′–119°20′ E) (Fig. 1 ) is in the northeast region of Fujian Province. In August 1992 construction, water storage and power generation began in November 1994, and was identified as Fuzhou's second drinking water source in 1997. It belongs to the third level water conservancy project, which belongs to the cascade development planning of the mainstream of Aojiang River Basin in Fujian Province. And it has the comprehensive benefits of irrigation, flood prevention, power generation, water supply and so on. The whole basin of the reservoir is long and narrow. The wide water area in the center of the reservoir is the core of the reservoir (Fig. 1 ). The total area of the reservoir is 3.5 km 2 , the total storage capacity is 172.3 million m 3 , and the regulating storage capacity is 106.4 million m 3 . It is the seasonal regulating reservoir. The climate of Shanzai reservoir belongs to the subtropical monsoon climate area (Sun et al., 2017 ). The shape of Shanzi reservoir is long and irregular, with an average water depth of more than 30 meters. The total reservoir capacity is 1.723x l0 9 m 3 , and the regulating reservoir capacity is 1.06xl08m 3 . The area of Shanzai Reservoir is open, mainly mountainous, and hilly landforms, with a population of about 180000. The main resident population around the reservoir area is about 50, 000, mainly engaged in agricultural production such as planting, forestry and so on (Sun et al., 2017 ). 2.2 In situ data and remote sensing data collection & processing We measured chlorophyll, a value of Shanzai reservoir by using bbe-Moldaenke FluoroProbe II (FP) (Fig. 2 ). The bbe Algae Field Analyzer (bbe-FluoroProbe) is a German BBE Corporation based on the fluorescence reaction characteristics of chlorophyll. (Gregor et al., (2004) developed an instrument for rapid detection of chlorophyll concentration in submerged water. The BBE algae field analyzer can quickly detect chlorophyll concentration at the deepest 100 m, and the detection data can be displayed online through the serial port or stored in the device for subsequent analysis. In addition to measuring chlorophyll content, the instrument can detect the presence of algae and classify them spectrally (cyanobacteria / cyanobacteria, green algae, diatoms / dinoflagellates, cryptophytes). To measure chlorophyll a with the BBE- the instrument is first calibrated using a standard solution of chlorophyll a. The probe is then inserted into the water sample and emits a specific wavelength of light, typically in the blue or green range. The chlorophyll a in the sample absorbs this light and emits a specific fluorescence signal. The probe detects this fluorescence signal and converts it into a chlorophyll a concentration value using the calibration data. After obtaining the chlorophyll data from shanzai reservoir, we processed the images of Sentinel-2 Level 1 using GEE. This is an application programming interface for global processing of satellite imagery (Lobo et al., 2021). GEE has been found to be effective at tracking the quality of inland water in several recent studies (Maeda et al., 2019 ; Jia et al., 2019; Zong et al., 2019; Wang et al., 2020 ; Weber et al., 2020 ; Lobo et al., 2021; Vaičiūtė et al., 2021; Zahir et al., 2025 ). Images were selected from March through December 2022 and 2023 taken five days or less prior to the monthly on-site water quality survey data. We began regular monthly sampling beginning in March. To determine the days of in situ sampling, we collected all available Sentinel-2 imagery. Firstly, we created a seasonal median composite of S2 imagery in GEE. We used atmospheric correction procedure for satellite data; our dataset was masked with the QA60 band to remove dense clouds and cirrus. We utilized the Satellite Invariant Atmospheric Correction (SIAC) package for atmospheric correction, employing Bayesian statistics and the Copernicus Atmospheric Monitoring Service (Yin et al., 2019 ). This algorithm, previously applied successfully by Lobo et al. (2021) to Sentinel-2 images in GEE for NDCI derivation, exhibited a strong correlation (Pearson R 2 = 0.96). Sentinel-2 surface reflectance. Correlations between in situ Chlorophyll-a measurements and spectral index values were sourced from various studies (Kwon et al., 2018 ; Sharma et al., 2019 ; Martinez et al., 2020 ; Konik et al., 2020 ). Median pixel values for NDVI and NDCI were generated by comparing seasonal spectral index values within situ chlorophyll data from the Shanzai Reservoir, facilitating understanding of temporal bloom patterns through seasonal analysis. 3 Results and Discussions 3.1 Spectral and In-situ Assessment of Chlorophyll-a Dynamics In this study, seasonal variation of spectral index values was analyzed from March to May, June to August, September to November, and December to the following February. Three seasons were chosen because in three seasons, proliferation of HABs is observed in Shanzai reservoir. Spectral indices applied to Sentinel-2 satellite imagery detected algal bloom patterns in Shanzai reservoirs (Figure 3 & 4). Overall, algae levels in Shanzai reservoir were highest in the late spring and summer months, with the highest peaks noted in May. The Sentinel-2 imagery shows that NDCI and NDVI consistently estimate chlorophyll-a concentration in Shanzai (Figure 3 and 4). Both indices show that chlorophyll-a concentrations are distributed over a large area of the reservoir. In Shanzai Reservoir, the NDVI also shows higher chlorophyll-a concentration at the edges of the reservoirs. In spring and summer, the values of NDCI were higher. Positive correlation was observed between in-situ chlorophyll-a measurements and spectral indices and in spring and summer. Linear regressions analysis between in situ chlorophyll-a and spectral values in Spring 2022 of the Shanzai reservoir (R 2 = 0.65) and ( p -Value > 0.0001) (Figure 5). While in summer (R 2 = 0.63) and ( p -Value > 0.0001 (Figure 5). So, there was a significant relationship between the in-situ measurements and the index values. Both indices performed best in Shanzai Reservoir, but the NDCI performed best overall (Figure 6). The comparison of the two indices is shown in (Figure 6). Overall, NDCI shows promise as a valuable tool for HAB monitoring. We investigated both sorts of indices for our study. To identify chlorophyll-a levels in water, bands 4 and 5 were used since chlorophyll-a is reflective from 665 to 704 nm and peaks at 704 nm. In Sentinel-2, the red edge band (5) collects wavelengths at 704 nm, while the red band (4) collects wavelengths at 664 nm. According to the study, the two-band model was superior to the three-band model at estimating chlorophyll-a. The performance of two- and three-band NIR-red models at close range was evaluated by Augusto-Silva et al. (2014) and NDCI was found to be the most accurate model. By simulating reflectance in various spectral bands of several sensors, Beck et al. (2017) compared 12 bio-optical models used for the assessment of chlorophyll-a concentrations in Harsha Lake, OH, USA. According to the authors' findings, NDCI is the model with the broadest applicability and the best performance for most inland water remote sensing sensors, including Sentinel-2 and 3, MERIS, WorldView-2, and OLCI (Ogashawara et al., 2017). The Shanzai Reservoir test findings indicate that sentinel-2 imagery is a good tool for observing small inland water bodies. Shanzai reservoir is a small body of water; it is less wide than other reservoirs in Fujian like Donzhang reservoir. Strong correlations between in situ data and satellite-derived spectral indices were found at sampling site in the Shanzai reservoir (Figure 7). In the month of May 2023, we got a very positive result. Tabe 1 shows details of in situ sampling and remote sensing sampling in Shanzai reservoir during May 2023. At that time our in-situ sampling & remote sensing date was the same, and time was also almost match. A strong correlation (R 2 = 0.93) and ( p -Value > 0.0016 was monitored between in situ chlorophyll-a measurements and spectral indices values at sampling point one (P1) (Figure 7). It is imperative to understand that time and date changes between in situ sampling and remote sensing overpasses for HAB monitoring can be determined by several factors, such as the frequency of satellite passes, the availability of field teams for sampling, and the monitoring program's requirements. Satellite overpasses occur at predefined times based on the satellite's orbit and the scheduling of imaging tasks. The time and date difference between in situ sampling and remote sensing overpasses can range from several hours to several days, depending on the specific timing of sampling events and satellite passes. Ideally, efforts are made to coordinate sampling with satellite overpasses to ensure temporal alignment and maximize the synergy between in situ and remote sensing data. However, due to logistical constraints and the dynamic nature of bloom events, perfect synchronization may not always be possible. 3.2 Seasonal Dynamics of HABs in Shanzai Reservoir Seasonal maps illustrate the temporal dynamics of HABs in Shanzai reservoir. The range of "water bloom" spread to the reservoir area during the spring, summer, and autumn, as shown in (Figure 3 & 4). According to the seasonal analysis, spring and summer are the two seasons with the highest concentration of chlorophyll -a, particularly the upper part of the Shanzai reserve near Qili village, Banling village, Xiaoyang, Niujiaowan and Chema. Local studies have also noted seasonal variations in Shanzai reservoir, with cyanobacteria dominating in spring and summer and diatoms dominating in autumn and winter (Li et al., 2003). From January to December 2003, a total of 58 species of phytoplankton were identified from the sampling points of Shanzai reservoir, belonging to 6 phylums and 36 genera, including 26 species of green algae, 16 species of diatoms, 6 species of cyanobacteria, 4 species of dinoflagellate, 3 species of cryptoalgae and 3 species of euglena. The dominant species that bloom from May to November are mainly microcystic algae bloom (SU et al., 2005). Figure 8 shows the variation of NDCI value of 6 sampling points from January to December 2023 in Shanzai reservoir. One notable point is the value of NDCI is higher in December at some points like P1, P4, P5 & P6. And during March the value was also higher in some points P1, P3, P5 and P6. This analysis also shows that the concentration of chlorophyll-a was higher during the month of May. HAB monitoring in freshwater bodies can be achieved by NDCI, which offers a straightforward and effective means of assessing chlorophyll-a concentration dynamics over large spatial scales. There may be some variation in performance depending on specific environmental conditions and the availability of ancillary data for calibration and validation. Monitoring and managing HABs can be made more comprehensive by integrating NDCI with other monitoring techniques and data sources (Khan et al.,2021; Skoufias, G., 2022; Arias et al., 2025) The seasonal succession of phytoplankton communities in the section of the dam of Shanzai reservoir showed that the phylum cyanobacteria, diatoms, cryptoalgae phylum and chlorella phylum were dominant; In late spring and summer, cyanobacteria are dominant in the water, and in late spring, cyanobacteria account for more than 90% of the total cell abundance of phytoplankton, of which nitrogen-fixing Anabaena accounts for more than 70% of the cell abundance of cyanobacteria, and microcystic algae account for 20% - 30% of the cell abundance of cyanobacteria. In summer, the phylum Cyanobacteria reaches more than 95% of the total cell abundance of phytoplankton, of which the genus Microcystic Algae accounts for more than 99% of the total cell abundance of the phylum Cyanobacteria (SU et al., 2016). Shanzai reservoir and is greatly affected by summertime phytoplankton growth (Sun et al., 2017). Our study also highlighted that maximum chlorophyll-a concentration was at dam site and upper part as we discussed above P2 sampling point showing higher chlorophyll-a content shown in (Figure 8). From the perspective of the spatial distribution pattern of phytoplankton, it generally shows a decreasing trend from upstream to downstream and from import to export. The number of algae in Emperor Cave > Qili > Rixi ≈ reservoir center > front of dam. The total body trend of the comprehensive trophic state index grade comparison among different points in the reservoir area is as follows: Emperor Cave > Qili > Rixi > Reservoir Center > Front of Dam. Lower in winter and spring, higher in summer and autumn. During the monitoring period, most of the reservoir area was in the medium to light eutrophication state, and the peak period in summer and autumn was basically in the light eutrophication state. Correlation analysis showed that cyanobacteria and dominant species Microcystis aeruginosa and Anabaena were significantly correlated with the eutrophication status of the reservoir, which indicated that the risk of cyanobacteria bloom existed in Shanzai reservoir (Lin et al., 2017). The biomass of Microcystis at dam station of Shanzi reservoir varied from 10 5 to 10 7 L － 1 ．The maximum biomass of Microcystis occurred in summer which was 10 7 L － 1 ，meanwhile the percentage of Microcystis among the phytoplankton reached up over 95% (SU et al., 2016). The nutrition status of Shanzai Reservoir was from mesotrophic to eutrophic. The high contaminated period of microcystins was in summer. The concentration of MC was related to the habitat and nutritional status of Shanzai Reservoir (Zhu, 2013). From the monthly scale mapping of (Figure 9) chlorophyll concentration in Shanzai Reservoir showed obvious fluctuations between different months. In 2022, the concentration of chlorophyll-a in Shanzai reservoir was lower than that in 2023, and the monthly average concentration fluctuated greatly. Figure 10 shows the comparison of monthly average concentration of in-situ and remote sensing chlorophyll-a data. The dataset showing a similar trend during the month of May 2023 chlorophyll -a content was higher than other months. Based on the monitoring data from 2009 to 2011, the eutrophication of Shanzai reservoir was evaluated by fuzzy mathematics. The results showed that the water quality of Shanzi reservoir showed different states with the change of seasons, but the water quality was basically stable from mesotrophic to eutrophic level (Huang, 2012). From 2015 to May 2016, the dominant species of bloom in the reservoir area in summer and autumn was aeruginosa Microcystis, while the dominant species of water spray in the reservoir area in late spring was planktonic Anabaena (Zhou et al., 2017). The monitoring of water quality and algae in Shanzi Reservoir for many years showed that the concentration of nutrients decreased year by year, and the prevention and control of eutrophication pollution was effective, but the phenomenon of algal bloom was not eliminated, and the biomass of algae was still large. The goal of eutrophication prevention and control can only be realized after a long period of comprehensive treatment with scientific ecological restoration measures based on effective control of nutrient pollution load (Zhuang et al., 2013). According to the Phytoplankton cell density indicator method, the nutrient status of the water body in the Shanzai reservoir area was between oligotrophic and oligotrophic from June to December 2012. In 2013 and 2014, it was between extremely poor nutrition and moderate nutrition, and in January-May 2015, it was between poor nutrition and poor nutrition. From the annual variation of phytoplankton, diatoms are the main in winter and spring, and cyanobacteria are the main in summer and autumn. Especially during algae blooms Cyanobacteria are usually dominant species, occasionally dominated by Cyanophyta. (Zahir et al., 2025) examined the impact of land use and climate change on HABs in Shanzai Reservoir (1996–2023) using Landsat imagery. Forest cover declined from 94.4% to 91.4%, while cropland expanded. HAB events increased over time, peaking in 2018 with 40% of the reservoir affected. Findings highlight the role of LULCC and warming in HAB growth, emphasizing the need for remote sensing-based monitoring and adaptive management. Table 1 Details of in-situ sampling and remote sensing sampling of the Shanzai reservoir in May 2023 are provided. Sampling & Sensing Date RS time In-situ sampling time NDCI Of the whole Reservoir NDCI of P1 (26.3781°N) (1193116°E) In-situ Chl-a (µg/L) WST/Weather 3-May-23 2:35:51 1:30 PM 0.241 0.147 202.59 23.9 ℃ /Sunny, East Wind (Force 3) 8-May-23 2:35:51 1:30 PM 0.007 -0.006 15.12 22.6 ℃ Cloudy, East Wind (Force 4) 13-May-23 2:35:51 1:30 PM 0.162 0.019 22.46 23.5 ℃ Sunny, east wind (force 3) 18-May-23 2:50:55 1:30 PM 0.252 0.108 147.75 25.397 °C 23-May-23 2:35:51 1:30 PM 0.056 0.025 45.63 24.71 °C, Overcast, NE wind (force 3), 28-May-23 2:35:51 1:30 PM 0.265 0.107 202.59 28.277 °C, Sunny, southwest wind (force 2), 23-30 3.3 Proliferation of HABs in response to water surface temperature and water movement During May 2023 our in-situ sampling & remote sensing date was the same, and time was also almost matched as we discussed in above section. Tabe 1 shows details of in situ sampling, remote sensing sampling and weather conditions like water surface temperature and wind direction of the Shanzai reservoir. Water surface temperature and water movement can impact habitat growth differently depending on the species of habitat. Generally, warmer water temperatures can accelerate the growth of certain types of habitats, such as algae and some aquatic plants. These warmer conditions can provide optimal conditions for photosynthesis and metabolic processes, leading to increased growth rates. Strong water movement, such as currents and waves, can have both positive and negative effects on habitat growth. On one hand, it can help distribute nutrients and oxygen, which are essential for habitat growth. On the other hand, excessive water movement can dislodge habitats from their substrate or cause physical damage, inhibiting growth. Calm or low water movement conditions can allow habitats to establish and grow undisturbed. However, stagnant water may also lead to the accumulation of sediment or pollutants, which can negatively impact habitat growth and health (Brooks et al., 201; Burford et al., 2018). To find out the effect of water surface temperature and water movement we monitor both parameters in May 2023 in Shanzai reservoir. Sentinel 2 provides 6 images (values) in a month and (Figure 11) showing the chlorophyll-a content of the Shanzai reservoir during the month of May 2023. During this time the water surface temperature of the Shanzai reservoir was above 20 °C (Figure 11). The growth of Cyanobacteria increases significantly when surface water temperatures exceed 20 °C, indicating that climate change will provide more growth time for Cyanobacteria (Bhatti et al., 2024). The chlorophyll-a content on 08 May 2023 was lower than other days, so we discovered the Shanzai reservoir management opened the gates so that water could move freely (Figure 11). As a result of stagnant water becoming thermal stratified, warm water may float over cooler water, promoting algal growth (Hasan et al., 2014). Several causes contribute to reduced water flow, including drought, extraction of water for irrigation or drinking, and man-made structures that alter natural waterways, such as dams and canals (Newson, 2008). Overall, the relationship between water surface temperature, water movement, and habitat growth is complex and species-specific. Understanding these factors is crucial for managing and conserving aquatic habitats effectively. 3.4 Extraction of HABs from some other bands combination We processed Sentinel 2 images using a combination of different bands (NIR, SWIR1, and red-edge) to identify relevant high-value clusters, integrate findings across bands, and assess the severity of algal blooms. Red-edge, NIR, and SWIR1 bands were selected for mapping HABs in the Shanzai Reservoir, as this combination hadn't been utilized there previously for HAB extraction. Turbid water significantly influences HAB mapping using visible range spectral bands, while shortwave infrared (SWIR2) cannot distinguish HABs from open water. HABs exhibit higher reflectance in red-edge, NIR, and SWIR1 bands compared to turbid and open water (Xu et al., 2021; Zhen, 2017). In the Erhai Lake watershed during autumn, significant correlations were found between Sentinel-2 satellite data and chlorophyll-a mass concentration, with bands B6, B7/B6, and (B6 + B8)/ (B7 + B8a) showing the highest correlation coefficients among single-band, single-band ratio, respectively (Junen, 2022). Below are some results from sentinel-2 MSI images. (a) Extractions from band 4,5,6,7,8,8a, and 11 (Figure 12 & 13). Details of spectral band information of Sentinel-2A are given in Table 2. Table 2 Spectral band information of Sentinel-2A . 4 Conclusion The measurement of algal blooms in small reservoirs can be improved by analyzing Sentinel-2 imagery despite their limitations. Sentinel-2 imagery can be used to improve algal bloom monitoring, and GEE time series information and seasonal maps can be used to identify specific locations that require additional sampling or mitigation measures to control algal blooms. The upper part of Shanzai Reservoir was found to have the highest chlorophyll-a concentration, especially near Qili Village, Banling Village, Xiaoyang, Niujiaowan and Chema. The use of satellite imagery could also enable more frequent monitoring of algal blooms to warn the public of potential health risks. We strongly recommend using Sentinel-2 imagery to monitor algal blooms in the Shanzai Reservoir more frequently. The use of open-access satellite imagery can help communities prepare for and respond to such toxic events by increasing reservoir monitoring. As a result, our study provides information on algal biomass estimation and provides baseline data that leaders can use in preparing ecological restoration projects for the Shanzai Reservoir. As funding and time for additional in situ sampling is limited, supplementing sampling with satellite imagery analysis has clear advantages over simply increasing sampling in the field. Spectral indices exhibit a strong correlation within situ data, enabling managers to utilize satellite-derived products such as seasonal maps of the Shanzai reservoir. By comprehending the spatial and temporal dynamics of algal bloom systems, we gain deeper insights into their dynamics. NDCI has proven effective in interpreting cyanobacteria dynamics and patterns in small reservoirs and lakes, informing localized management and mitigation strategies. Satellite imagery facilitates the study of algal bloom dynamics across both space and time, offering a more comprehensive understanding of aquatic system variability compared to monthly in situ observations alone. Declarations Funding This research was supported by the Fujian Province Ecological Environment Department, Environmental Protection Science and Technology projects: The research and the application of the spatial integration water body eutrophication three-dimensional prevention and control technique (Ref: 2025R010 ). One slice three project main reservoir phytoplankton community evolution rule and algal bloom warning research (Ref: 2024R016 ). Risk assessment and early warning of 24K lake and reservoir eutrophication (ID: Y07204062417B05 ). Statements & Declarations All the authors have agreed to submit this manuscript to this journal. All the authors declare that they have no conflicts of interest. Authorship contribution statement: Muhammad Zahir (Writing - Original Draft, Methodology, Data curation, Formal analysis); Yuping Su (Conceptualization, Supervision, Investigation, Project administration, Writing - Review & Editing, Funding acquisition); Alia Naz , Muhammad Imran Shahzad and Rashid Pervez ( Formal analysis, Writing - Review & Editing). Data availability statement The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References Arias, F., Zambrano, M., Galagarza, E. and Broce, K., 2025. Mapping Harmful Algae Blooms: The Potential of Hyperspectral Imaging Technologies. Remote Sensing , 17 (4), p.608. https://doi.org/10.3390/rs17040608 Beck, Richard, Min Xu, Shengan Zhan, Hongxing Liu, Richard A. Johansen, Susanna Tong, Bo Yang et al. 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International Journal of Environmental Science and Technology, 16 , 1789-1806. https://doi.org/10.1007/s13762-018-2108-x Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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11:52:24","extension":"xml","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109310,"visible":true,"origin":"","legend":"","description":"","filename":"f20d6bab32454016a0e1d66c0ccf52921structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/dcb65fe9f828a02165b65835.xml"},{"id":94661637,"identity":"d7a447ec-a0da-435a-bf54-a01fe3160c19","added_by":"auto","created_at":"2025-10-29 11:52:24","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119374,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/04318632a0c6c463c9a4432b.html"},{"id":94661599,"identity":"0dd77c3d-0d8e-42c8-82a4-e6989fdbda50","added_by":"auto","created_at":"2025-10-29 11:52:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191977,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing Shanzai reservoir, Fujian, China, with in-situ sampling points indicated by black dots surrounded by 30-meter buffers representing Sentinel-2 satellite data acquisition sites.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/bb4467bc09f9c0150501a6b9.png"},{"id":94661602,"identity":"7f817495-4c10-4063-adb4-19fc6c2436cc","added_by":"auto","created_at":"2025-10-29 11:52:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":656194,"visible":true,"origin":"","legend":"\u003cp\u003eMeasuring chlorophyll, a content in Shanzai Reservoir (May 2023) by using bbe-Moldaenke FluoroProbe.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/b4154987ddb2f49671da1316.png"},{"id":94672524,"identity":"b3c849cc-3b20-40fe-9892-1b1902c7993a","added_by":"auto","created_at":"2025-10-29 13:40:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84871,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation of NDVI (median pixel values of each season Spring, Summer, and Autumn) in the Shanzai Reservoir in 2022.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/14ddb80996ade7e2cff730b0.png"},{"id":94661600,"identity":"489c248f-f771-47ea-8c63-40e9ed0deed7","added_by":"auto","created_at":"2025-10-29 11:52:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94855,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variation of NDCI (median pixel values of each season Spring, Summer, and Autumn) in the Shanzai Reservoir in 2022.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/2fb7b7452d52651be797df57.png"},{"id":94661605,"identity":"168b3ad1-594c-4b75-8d68-f662561ea49c","added_by":"auto","created_at":"2025-10-29 11:52:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":75141,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regressions analysis between in situ data and spectral indices values in spring and summer 2022 of the Shanzai Reservoir\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/c9955a085ca363e305de33a4.png"},{"id":94672452,"identity":"6116d79d-a86a-4af7-b237-4b654091386c","added_by":"auto","created_at":"2025-10-29 13:40:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":491759,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of NDCI \u0026amp; NDVI values from January to December 2022, 2023, in Shanzai Reservoir.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/d6e02e346b800921a1328c2d.png"},{"id":94672373,"identity":"1c998489-7857-498b-af0e-05d3d88ae21e","added_by":"auto","created_at":"2025-10-29 13:40:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":184672,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regressions analysis between in situ chlorophyll-a data and remote sensing values of sampling point one (P1) in May 2023, of the Shanzai Reservoir.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/c743b0cffed843236581a718.png"},{"id":94672574,"identity":"3cd6cbec-6229-4b4e-9779-190d28204221","added_by":"auto","created_at":"2025-10-29 13:40:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":767059,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in NDCI values 6 sampling points from January to December 2023 in Shanzai reservoir.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/782247c0480d3523e9a42732.png"},{"id":94661609,"identity":"9acbe422-8462-4455-9c0d-cc3cc66e92d9","added_by":"auto","created_at":"2025-10-29 11:52:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":121462,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly mapping of chlorophyll a concentration of Shanzai Reservoir in 2023.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/c2ee7a668b23fb5d4ee4e06b.png"},{"id":94672780,"identity":"3f431a84-e5b8-4e56-bd43-85f87ff7a12b","added_by":"auto","created_at":"2025-10-29 13:40:57","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":75330,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of in-situ chlorophyll a content and remote sensing value in Shanzai reservoir in 2023 (Monthly average value of both datasets).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/6525759515e225486f292784.png"},{"id":94673445,"identity":"dda1ebe7-9a32-448c-a11c-3cbe7381d75b","added_by":"auto","created_at":"2025-10-29 13:41:24","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":44093,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between chlorophyll content (NDCI) and water surface temperature in Shanzai reservoir in 2023.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/308ba8137138374003c543fd.png"},{"id":94673056,"identity":"1734d12d-1cee-4207-9241-3a452fb294be","added_by":"auto","created_at":"2025-10-29 13:41:11","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":163520,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Median pixel of May 2022 by using B11, B8 and B4 in GEE (B) 28 May 2022 and (C) median pixel of June 2022 extracted from B11.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/5ad931e4c42624701e3b89f6.png"},{"id":94661615,"identity":"9f51c18c-4984-435a-89f6-9e6039180857","added_by":"auto","created_at":"2025-10-29 11:52:24","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":201317,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Median pixel of Spring 2022, extracted by using B11in GEE (B) Median pixel of Spring 2022 by using B4 and (C) median pixel of Summer 2022 extracted by B11.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/bc39e9768bdd28c205a9a19f.png"},{"id":103001849,"identity":"1e1c8e34-0975-43df-92e4-54c1528ec2bb","added_by":"auto","created_at":"2026-02-19 13:54:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3780243,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7651016/v1/0cba500c-e0ff-494d-93c8-dd0c3b6646c1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Dynamics of Chlorophyll-a in a Small Inland Reservoir Using Field Sampling and Satellite Data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFreshwater reservoirs, crucial for irrigation, energy, and drinking water, face threats from HABs, causing environmental and health issues (Carpenter et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Monitoring HAB dynamics in these reservoirs is vital. It is essential to monitor HABs events accurately (Kislik et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To determine chlorophyll a mass concentration of water, routine water quality monitoring involves collecting samples, filtering, extracting, and spectrophotometer analysis. The monitoring of the water environment in large areas relies on manual sampling or the creation of automatic stations, both of which consume large amounts of manpower and materials, and also make it difficult to obtain continuous monitoring results based on spatial and temporal distributions (Ostroumov, 2017; Park, Kim, \u0026amp; Lee, 2020; Zahir et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In situ monitoring is a very efficient process, but it is time-consuming, expensive, and technically insufficient. It lacks human resources, provides trophic status only on the day of sampling, and not in real-time (Demetillo \u0026amp; Taboada, 2019; Rodr\u0026iacute;guez-L\u0026oacute;pez, Gonz\u0026aacute;lez-Rodr\u0026iacute;guez, Duran-Llacer, Cardenas, \u0026amp; Urrutia, 2021). Moreover, traditional field sampling approaches cannot detect temporal or spatial fluctuations in water quality, and this is essential for proper evaluation and effective management of water resources (Aranda et al., 2021; Peterson et al., 2019; Rodr\u0026iacute;guez-L\u0026oacute;pez et al., 2020; Rodr\u0026iacute;guez-L\u0026oacute;pez et al., 2021). For in situ monitoring, we utilized the bbe-Moldaenke FluoroProbe II (FP) to measure chlorophyll concentration in the Shanzai reservoir. The instrument, operating underwater, employs fluorescence measurements to assess chlorophyll-a concentrations swiftly and dependably in real-time. Additionally, it offers estimations of total chlorophyll concentration a, pigment concentrations for four phytoplankton classes, and yellow substances (Gregor et al., (2004).\u003c/p\u003e\u003cp\u003eSentinel-2 satellite's high-resolution imaging aids in this by detecting chlorophyll-a, a pigment present in HABs, allowing for the mapping of HAB extent and severity (Kislik et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Integrating Sentinel-2 data with bbe sampling techniques enhances our ability to monitor and respond to HABs effectively, as demonstrated in our study assessing the Shanzai Reservoir in Fujian Province, China. This study aims to assess the dynamics of HABs in Shanzai Reservoir through the synergistic integration of in-situ chlorophyll-a measurements using the FluoroProbe II and satellite-based spectral indices derived from Sentinel-2 imagery. The objective is to evaluate the reliability, spatial accuracy, and seasonal sensitivity of remote sensing techniques for effective real-time HAB monitoring and freshwater management.\u003c/p\u003e"},{"header":"2 Materials \u0026 methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eThe Shanzai reservoir, also known as she Shan Lake, (26\u0026deg;20\u0026prime;\u0026ndash;26\u0026deg;25\u0026prime;N,119\u0026deg;16\u0026prime;\u0026ndash;119\u0026deg;20\u0026prime; E) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is in the northeast region of Fujian Province. In August 1992 construction, water storage and power generation began in November 1994, and was identified as Fuzhou's second drinking water source in 1997. It belongs to the third level water conservancy project, which belongs to the cascade development planning of the mainstream of Aojiang River Basin in Fujian Province. And it has the comprehensive benefits of irrigation, flood prevention, power generation, water supply and so on. The whole basin of the reservoir is long and narrow. The wide water area in the center of the reservoir is the core of the reservoir (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The total area of the reservoir is 3.5 km\u003csup\u003e2\u003c/sup\u003e, the total storage capacity is 172.3\u0026nbsp;million m\u003csup\u003e3\u003c/sup\u003e, and the regulating storage capacity is 106.4\u0026nbsp;million m\u003csup\u003e3\u003c/sup\u003e. It is the seasonal regulating reservoir.\u003c/p\u003e\u003cp\u003eThe climate of Shanzai reservoir belongs to the subtropical monsoon climate area (Sun et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The shape of Shanzi reservoir is long and irregular, with an average water depth of more than 30 meters. The total reservoir capacity is 1.723x l0\u003csup\u003e9\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003e, and the regulating reservoir capacity is 1.06xl08m\u003csup\u003e3\u003c/sup\u003e. The area of Shanzai Reservoir is open, mainly mountainous, and hilly landforms, with a population of about 180000. The main resident population around the reservoir area is about 50, 000, mainly engaged in agricultural production such as planting, forestry and so on (Sun et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 In situ data and remote sensing data collection \u0026amp; processing\u003c/h2\u003e\u003cp\u003eWe measured chlorophyll, a value of Shanzai reservoir by using bbe-Moldaenke FluoroProbe II (FP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The bbe Algae Field Analyzer (bbe-FluoroProbe) is a German BBE Corporation based on the fluorescence reaction characteristics of chlorophyll. (Gregor et al., (2004) developed an instrument for rapid detection of chlorophyll concentration in submerged water. The BBE algae field analyzer can quickly detect chlorophyll concentration at the deepest 100 m, and the detection data can be displayed online through the serial port or stored in the device for subsequent analysis. In addition to measuring chlorophyll content, the instrument can detect the presence of algae and classify them spectrally (cyanobacteria / cyanobacteria, green algae, diatoms / dinoflagellates, cryptophytes).\u003c/p\u003e\u003cp\u003eTo measure chlorophyll a with the BBE- the instrument is first calibrated using a standard solution of chlorophyll a. The probe is then inserted into the water sample and emits a specific wavelength of light, typically in the blue or green range. The chlorophyll a in the sample absorbs this light and emits a specific fluorescence signal. The probe detects this fluorescence signal and converts it into a chlorophyll a concentration value using the calibration data.\u003c/p\u003e\u003cp\u003eAfter obtaining the chlorophyll data from shanzai reservoir, we processed the images of Sentinel-2 Level 1 using GEE. This is an application programming interface for global processing of satellite imagery (Lobo et al., 2021). GEE has been found to be effective at tracking the quality of inland water in several recent studies (Maeda et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jia et al., 2019; Zong et al., 2019; Wang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Weber et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lobo et al., 2021; Vaičiūtė et al., 2021; Zahir et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Images were selected from March through December 2022 and 2023 taken five days or less prior to the monthly on-site water quality survey data. We began regular monthly sampling beginning in March. To determine the days of in situ sampling, we collected all available Sentinel-2 imagery. Firstly, we created a seasonal median composite of S2 imagery in GEE.\u003c/p\u003e\u003cp\u003eWe used atmospheric correction procedure for satellite data; our dataset was masked with the QA60 band to remove dense clouds and cirrus. We utilized the Satellite Invariant Atmospheric Correction (SIAC) package for atmospheric correction, employing Bayesian statistics and the Copernicus Atmospheric Monitoring Service (Yin et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This algorithm, previously applied successfully by Lobo et al. (2021) to Sentinel-2 images in GEE for NDCI derivation, exhibited a strong correlation (Pearson R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.96). Sentinel-2 surface reflectance. Correlations between in situ Chlorophyll-a measurements and spectral index values were sourced from various studies (Kwon et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Martinez et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Konik et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Median pixel values for NDVI and NDCI were generated by comparing seasonal spectral index values within situ chlorophyll data from the Shanzai Reservoir, facilitating understanding of temporal bloom patterns through seasonal analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results and Discussions","content":"\u003cp\u003e\u003cstrong\u003e3.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSpectral and In-situ Assessment of Chlorophyll-a Dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, seasonal variation of spectral index values was analyzed from March to May, June to August, September to November, and December to the following February. Three seasons were chosen because in three seasons, proliferation of HABs is observed in Shanzai reservoir. Spectral indices applied to Sentinel-2 satellite imagery detected algal bloom patterns in Shanzai reservoirs (Figure 3 \u0026amp; 4). Overall, algae levels in Shanzai reservoir were highest in the late spring and summer months, with the highest peaks noted in May. The Sentinel-2 imagery shows that NDCI and NDVI consistently estimate chlorophyll-a concentration in Shanzai (Figure 3 and 4). Both indices show that chlorophyll-a concentrations are distributed over a large area of the reservoir. In Shanzai Reservoir, the NDVI also shows higher chlorophyll-a concentration at the edges of the reservoirs. In spring and summer, the values of NDCI were higher. Positive correlation was observed between in-situ chlorophyll-a measurements and spectral indices and in spring and summer. Linear regressions\u0026nbsp;analysis between in situ chlorophyll-a and spectral values in Spring 2022 of the Shanzai reservoir (R\u003csup\u003e2\u003c/sup\u003e = 0.65) and (\u003cem\u003ep\u003c/em\u003e-Value \u0026gt; 0.0001) (Figure 5). While in summer (R\u003csup\u003e2\u003c/sup\u003e = 0.63) and (\u003cem\u003ep\u003c/em\u003e-Value \u0026gt; 0.0001 (Figure 5). So, there was a significant relationship between the in-situ measurements and the index values. Both indices performed best in Shanzai Reservoir, but the NDCI performed best overall (Figure 6). The comparison of the two indices is shown in (Figure 6). Overall, NDCI shows promise as a valuable tool for HAB monitoring. We investigated both sorts of indices for our study. To identify chlorophyll-a levels in water, bands 4 and 5 were used since chlorophyll-a is reflective from 665 to 704 nm and peaks at 704 nm.\u003c/p\u003e\n\u003cp\u003eIn Sentinel-2, the red edge band (5) collects wavelengths at 704 nm, while the red band (4) collects wavelengths at 664 nm. According to the study, the two-band model was superior to the three-band model at estimating chlorophyll-a. The performance of two- and three-band NIR-red models at close range was evaluated by Augusto-Silva et al. (2014) and NDCI was found to be the most accurate model. By simulating reflectance in various spectral bands of several sensors, Beck et al. (2017) compared 12 bio-optical models used for the assessment of chlorophyll-a concentrations in Harsha Lake, OH, USA. According to the authors\u0026apos; findings, NDCI is the model with the broadest applicability and the best performance for most inland water remote sensing sensors, including Sentinel-2 and 3, MERIS, WorldView-2, and OLCI (Ogashawara et al., 2017).\u003c/p\u003e\n\u003cp\u003eThe Shanzai Reservoir test findings indicate that sentinel-2 imagery is a good tool for observing small inland water bodies. Shanzai reservoir is a small body of water; it is less wide than other reservoirs in Fujian like Donzhang reservoir. Strong correlations between in situ data and satellite-derived spectral indices were found at sampling site in the Shanzai reservoir (Figure 7). In the month of May 2023, we got a very positive result. Tabe 1 shows details of in situ sampling and remote sensing sampling in Shanzai reservoir during May 2023. At that time our in-situ sampling \u0026amp; remote sensing date\u0026nbsp;\u003cbr\u003ewas the same, and time was also almost match.\u003c/p\u003e\n\u003cp id=\"_Toc168138598\"\u003eA strong correlation (R\u003csup\u003e2\u003c/sup\u003e = 0.93) and (\u003cem\u003ep\u003c/em\u003e-Value \u0026gt; 0.0016 was monitored between in situ chlorophyll-a measurements and spectral indices values at sampling point one (P1) (Figure 7). \u0026nbsp;It is imperative to understand that time and date changes between in situ sampling and remote sensing overpasses for HAB monitoring can be determined by several factors, such as the frequency of satellite passes, the availability of field teams for sampling, and the monitoring program\u0026apos;s requirements. Satellite overpasses occur at predefined times based on the satellite\u0026apos;s orbit and the scheduling of imaging tasks. The time and date difference between in situ sampling and remote sensing overpasses can range from several hours to several days, depending on the specific timing of sampling events and satellite passes. Ideally, efforts are made to coordinate sampling with satellite overpasses to ensure temporal alignment and maximize the synergy between in situ and remote sensing data. However, due to logistical constraints and the dynamic nature of bloom events, perfect synchronization may not always be possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Seasonal Dynamics of HABs in Shanzai Reservoir\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeasonal maps illustrate the temporal dynamics of HABs in Shanzai reservoir. The range of \u0026quot;water bloom\u0026quot; spread to the reservoir area during the spring, summer, and autumn, as shown in (Figure 3 \u0026amp; 4). According to the seasonal analysis, spring and summer are the two seasons with the highest concentration of chlorophyll -a, particularly the upper part of the Shanzai reserve near Qili village, Banling village, Xiaoyang, Niujiaowan and Chema. Local studies have also noted seasonal variations in Shanzai reservoir, with cyanobacteria dominating in spring and summer and diatoms dominating in autumn and winter (Li et al., 2003). From January to December 2003, a total of 58 species of phytoplankton were identified from the sampling points of Shanzai reservoir, belonging to 6 phylums and 36 genera, including 26 species of green algae, 16 species of diatoms, 6 species of cyanobacteria, 4 species of dinoflagellate, 3 species of cryptoalgae and 3 species of euglena. The dominant species that bloom from May to November are mainly microcystic algae bloom (SU et al., 2005). Figure 8 shows the variation of NDCI value of 6 sampling points from January to December 2023 in Shanzai reservoir.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne notable point is the value of NDCI is higher in December at some points like P1, P4, P5 \u0026amp; P6. And during March the value was also higher in some points P1, P3, P5 and P6. This analysis also shows that the concentration of chlorophyll-a was higher during the month of May. HAB monitoring in freshwater bodies can be achieved by NDCI, which offers a straightforward and effective means of assessing chlorophyll-a concentration dynamics over large spatial scales. There may be some variation in performance depending on specific environmental conditions and the availability of ancillary data for calibration and validation.\u0026nbsp;Monitoring and managing HABs can be made more comprehensive by integrating NDCI with other monitoring techniques and data sources (Khan et al.,2021; Skoufias, G., 2022;\u0026nbsp;Arias et al., 2025)\u003c/p\u003e\n\u003cp\u003eThe seasonal succession of phytoplankton communities in the section of the dam of Shanzai reservoir showed that the phylum cyanobacteria, diatoms, \u003cem\u003ecryptoalgae\u003c/em\u003e phylum and chlorella phylum were dominant; In late spring and summer, cyanobacteria are dominant in the water, and in late spring, cyanobacteria account for more than 90% of the total cell abundance of phytoplankton, of which nitrogen-fixing Anabaena accounts for more than 70% of the cell abundance of cyanobacteria, and microcystic algae account for 20% - 30% of the cell abundance of cyanobacteria. In summer, the phylum Cyanobacteria reaches more than 95% of the total cell abundance of phytoplankton, of which the genus Microcystic Algae accounts for more than 99% of the total cell abundance of the phylum Cyanobacteria (SU et al., 2016). Shanzai reservoir and is greatly affected by summertime phytoplankton growth (Sun et al., 2017). Our study also highlighted that maximum chlorophyll-a concentration was at dam site and upper part as we discussed above P2 sampling point showing higher chlorophyll-a content shown in (Figure 8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom the perspective of the spatial distribution pattern of phytoplankton, it generally shows a decreasing trend from upstream to downstream and from import to export. The number of algae in Emperor Cave \u0026gt; Qili \u0026gt; Rixi \u0026asymp; reservoir center \u0026gt; front of dam. The total body trend of the comprehensive trophic state index grade comparison among different points in the reservoir area is as follows: Emperor Cave \u0026gt; Qili \u0026gt; Rixi \u0026gt; Reservoir Center \u0026gt; Front of Dam. Lower in winter and spring, higher in summer and autumn. During the monitoring period, most of the reservoir area was in the medium to light eutrophication state, and the peak period in summer and autumn was basically in the light eutrophication state. Correlation analysis showed that cyanobacteria and dominant species Microcystis \u003cem\u003eaeruginosa and Anabaena\u003c/em\u003e were significantly correlated with the eutrophication status of the reservoir, which indicated that the risk of cyanobacteria bloom existed in Shanzai reservoir (Lin et al., 2017). The biomass of Microcystis at dam station of Shanzi reservoir varied from 10\u003csup\u003e5\u003c/sup\u003e to 10\u003csup\u003e7\u0026nbsp;\u003c/sup\u003eL\u003csup\u003e－\u003c/sup\u003e\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e．The maximum biomass of Microcystis occurred in summer\u0026nbsp;which was 10\u003csup\u003e7\u0026nbsp;\u003c/sup\u003eL \u003csup\u003e－\u003c/sup\u003e\u003csup\u003e1\u003c/sup\u003e ，meanwhile the percentage of Microcystis among the phytoplankton reached up over 95% (SU et al., 2016). The nutrition status of Shanzai Reservoir was from mesotrophic to eutrophic. The high contaminated period of \u003cem\u003emicrocystins\u003c/em\u003e was in summer. The concentration of MC was related to the habitat and nutritional status of Shanzai Reservoir (Zhu, 2013).\u003c/p\u003e\n\u003cp\u003eFrom the monthly scale mapping of (Figure 9) chlorophyll concentration in Shanzai Reservoir showed obvious fluctuations between different months. In 2022, the concentration of chlorophyll-a in Shanzai reservoir was lower than that in 2023, and the monthly average concentration fluctuated greatly. Figure 10 shows the comparison of monthly average concentration of in-situ and remote sensing chlorophyll-a data. The dataset showing a similar trend during the month of May 2023 chlorophyll -a content was higher than other months. Based on the monitoring data from 2009 to 2011, the eutrophication of Shanzai reservoir was evaluated by fuzzy \u0026nbsp;mathematics.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc168138602\"\u003eThe results showed that the water quality of Shanzi reservoir showed different states with the change of seasons, but the water quality was basically stable from mesotrophic to eutrophic level (Huang, 2012). From 2015 to May 2016, the dominant species of bloom in the reservoir area in summer and autumn was aeruginosa Microcystis, while the dominant species of water spray in the reservoir area in late spring was \u003cem\u003eplanktonic Anabaena\u003c/em\u003e (Zhou et al., 2017). The monitoring of water quality and algae in Shanzi Reservoir for many years showed that the concentration of nutrients decreased year by year, and the prevention and control of eutrophication pollution was effective, but the phenomenon of algal bloom was not eliminated, and the biomass of algae was still large. The goal of eutrophication prevention and control can only be realized after a long period of comprehensive treatment with scientific ecological restoration measures based on effective control of nutrient pollution load (Zhuang et al., 2013). According to the Phytoplankton cell density indicator method, the nutrient status of the water body in the Shanzai reservoir area was between oligotrophic and oligotrophic from June to December 2012. In 2013 and 2014, it was between extremely poor nutrition and moderate nutrition, and in January-May 2015, it was between poor nutrition and poor nutrition. From the annual variation of phytoplankton, diatoms are the main in winter and spring, and cyanobacteria are the main in summer and autumn. Especially during algae blooms Cyanobacteria are usually dominant species, occasionally dominated by Cyanophyta. (Zahir et al., 2025) examined the impact of land use and climate change on HABs in Shanzai Reservoir (1996\u0026ndash;2023) using Landsat imagery. Forest cover declined from 94.4% to 91.4%, while cropland expanded. HAB events increased over time, peaking in 2018 with 40% of the reservoir affected. Findings highlight the role of LULCC and warming in HAB growth, emphasizing the need for remote sensing-based monitoring and adaptive management.\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc168138692\"\u003eTable 1 Details of in-situ sampling and remote sensing sampling of the Shanzai reservoir in May 2023 are provided.\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSampling \u0026amp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSensing Date\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRS time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-situ sampling time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDCI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOf the whole Reservoir\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDCI of P1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(26.3781\u0026deg;N)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(1193116\u0026deg;E)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-situ\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eChl-a (\u0026micro;g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWST/Weather\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3-May-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2:35:51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1:30 PM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e202.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e23.9 ℃ /Sunny, East Wind (Force 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e8-May-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2:35:51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1:30 PM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e15.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e22.6 ℃ Cloudy, East Wind (Force 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e13-May-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2:35:51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1:30 PM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e22.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e23.5 ℃ Sunny, east wind (force 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e18-May-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2:50:55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1:30 PM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e147.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e25.397 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e23-May-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2:35:51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1:30 PM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e45.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e24.71 \u0026deg;C, Overcast, NE wind (force 3),\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e28-May-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2:35:51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1:30 PM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e202.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e28.277 \u0026deg;C, Sunny, southwest wind (force 2), 23-30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Proliferation of HABs in response to water surface temperature and water movement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring May 2023 our in-situ sampling \u0026amp; remote sensing date was the same, and time was also almost matched as we discussed in above section. Tabe 1 shows details of in situ sampling, remote sensing sampling and weather conditions like water surface temperature and wind direction of the Shanzai reservoir. \u0026nbsp; Water surface temperature and water movement can impact habitat growth differently depending on the species of habitat. Generally, warmer water temperatures can accelerate the growth of certain types of habitats, such as algae and some aquatic plants. These warmer conditions can provide optimal conditions for photosynthesis and metabolic processes, leading to increased growth rates. Strong water movement, such as currents and waves, can have both positive and negative effects on habitat growth. On one hand, it can help distribute nutrients and oxygen, which are essential for habitat growth. On the other hand, excessive water movement can dislodge habitats from their substrate or cause physical damage, inhibiting growth.\u003c/p\u003e\n\u003cp\u003eCalm or low water movement conditions can allow habitats to establish and grow undisturbed. However, stagnant water may also lead to the accumulation of sediment or pollutants, which can negatively impact habitat growth and health (Brooks et al., 201; Burford et al., 2018). To find out the effect of water surface temperature and water movement we monitor both parameters in May 2023 in Shanzai reservoir. Sentinel 2 provides 6 images (values) in a month and (Figure 11) showing the chlorophyll-a content of the Shanzai reservoir during the month of May 2023. During this time the water surface temperature of the Shanzai reservoir was above 20 \u0026deg;C (Figure 11). The growth of Cyanobacteria increases significantly when surface water temperatures exceed 20 \u0026deg;C, indicating that climate change will provide more growth time for Cyanobacteria (Bhatti et al., 2024). The chlorophyll-a content on 08 May 2023 was lower than other days, so we discovered the Shanzai reservoir management opened the gates so that water could move freely (Figure 11). As a result of stagnant water becoming thermal stratified, warm water may float over cooler water, promoting algal growth (Hasan et al., 2014). Several causes contribute to reduced water flow, including drought, extraction of water for irrigation or drinking, and man-made structures that alter natural waterways, such as dams and canals (Newson, 2008). Overall, the relationship between water surface temperature, water movement, and habitat growth is complex and species-specific. Understanding these factors is crucial for managing and conserving aquatic habitats effectively.\u003c/p\u003e\n\u003cp id=\"_Toc168137158\"\u003e\u003cstrong\u003e3.4 Extraction of HABs from some other bands combination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe processed Sentinel 2 images using a combination of different bands (NIR, SWIR1, and red-edge) to identify relevant high-value clusters, integrate findings across bands, and assess the severity of algal blooms. Red-edge, NIR, and SWIR1 bands were selected for mapping HABs in the Shanzai Reservoir, as this combination hadn\u0026apos;t been utilized there previously for HAB extraction. Turbid water significantly influences HAB mapping using visible range spectral bands, while shortwave infrared (SWIR2) cannot distinguish HABs from open water. HABs exhibit higher reflectance in red-edge, NIR, and SWIR1 bands compared to turbid and open water (Xu et al., 2021; Zhen, 2017). In the Erhai Lake watershed during autumn, significant correlations were found between Sentinel-2 satellite data and chlorophyll-a mass concentration, with bands B6, B7/B6, and (B6 + B8)/ (B7 + B8a) showing the highest correlation coefficients among single-band, single-band ratio, respectively (Junen, 2022). Below are some results from sentinel-2 MSI images. (a) Extractions from band 4,5,6,7,8,8a, and 11 (Figure 12 \u0026amp; 13). Details of spectral band information of Sentinel-2A are given in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2\u003cspan id=\"_Toc161449937\"\u003e\u0026nbsp;Spectral band information of Sentinel-2A\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1761738091.png\" width=\"742\" height=\"272\"\u003e\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThe measurement of algal blooms in small reservoirs can be improved by analyzing Sentinel-2 imagery despite their limitations. Sentinel-2 imagery can be used to improve algal bloom monitoring, and GEE time series information and seasonal maps can be used to identify specific locations that require additional sampling or mitigation measures to control algal blooms. The upper part of Shanzai Reservoir was found to have the highest chlorophyll-a concentration, especially near Qili Village, Banling Village, Xiaoyang, Niujiaowan and Chema. The use of satellite imagery could also enable more frequent monitoring of algal blooms to warn the public of potential health risks. We strongly recommend using Sentinel-2 imagery to monitor algal blooms in the Shanzai Reservoir more frequently. The use of open-access satellite imagery can help communities prepare for and respond to such toxic events by increasing reservoir monitoring. As a result, our study provides information on algal biomass estimation and provides baseline data that leaders can use in preparing ecological restoration projects for the Shanzai Reservoir. As funding and time for additional in situ sampling is limited, supplementing sampling with satellite imagery analysis has clear advantages over simply increasing sampling in the field.\u003c/p\u003e\u003cp\u003eSpectral indices exhibit a strong correlation within situ data, enabling managers to utilize satellite-derived products such as seasonal maps of the Shanzai reservoir. By comprehending the spatial and temporal dynamics of algal bloom systems, we gain deeper insights into their dynamics. NDCI has proven effective in interpreting cyanobacteria dynamics and patterns in small reservoirs and lakes, informing localized management and mitigation strategies. Satellite imagery facilitates the study of algal bloom dynamics across both space and time, offering a more comprehensive understanding of aquatic system variability compared to monthly in situ observations alone.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research was supported by the Fujian Province Ecological Environment Department, Environmental Protection Science and Technology projects:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cem\u003eThe research and the application of the spatial integration water body eutrophication three-dimensional prevention and control technique\u003c/em\u003e (Ref: \u003cstrong\u003e2025R010\u003c/strong\u003e).\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eOne slice three project main reservoir phytoplankton community evolution rule and algal bloom warning research\u003c/em\u003e (Ref: \u003cstrong\u003e2024R016\u003c/strong\u003e).\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eRisk assessment and early warning of 24K lake and reservoir eutrophication\u003c/em\u003e (ID: \u003cstrong\u003eY07204062417B05\u003c/strong\u003e).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eStatements \u0026amp; Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors have agreed to submit this manuscript to this journal. All the authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement:\u0026nbsp;\u003c/strong\u003e Muhammad Zahir (Writing - Original Draft, Methodology, Data curation, Formal analysis); Yuping Su (Conceptualization, Supervision, Investigation, Project administration, Writing - Review \u0026amp; Editing, Funding acquisition); Alia Naz\u003csup\u003e,\u003c/sup\u003e Muhammad Imran Shahzad\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand Rashid Pervez\u0026nbsp;\u003csup\u003e(\u003c/sup\u003eFormal analysis, Writing - Review \u0026amp; Editing).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArias, F., Zambrano, M., Galagarza, E. and Broce, K., 2025. 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Harmful algal blooms (red tide): a review of causes, impacts and approaches to monitoring and prediction. \u003cem\u003eInternational Journal of Environmental Science and Technology,\u003c/em\u003e 16\u003cstrong\u003e,\u003c/strong\u003e 1789-1806. https://doi.org/10.1007/s13762-018-2108-x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Harmful Algal Blooms (HABs), Chlorophyll-a, In-situ monitoring, Remote Sensing, Freshwater Management","lastPublishedDoi":"10.21203/rs.3.rs-7651016/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7651016/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines harmful algal bloom (HAB) dynamics in Shanzai Reservoir, Fujian Province, China, through integrated in-situ and satellite remote sensing techniques. Chlorophyll-a concentrations the primary indicator of algal biomass, were measured directly using the bbe-Moldaenke FluoroProbe II, while Sentinel-2 imagery processed via Google Earth Engine (GEE) was used to map spatiotemporal bloom patterns. Monthly field sampling was conducted from March to December in 2022 and 2023, with sites aligned to satellite acquisition points.\u003c/p\u003e\u003cp\u003eTwo spectral indices, the Normalized Difference Chlorophyll Index (NDCI) and the Normalized Difference Vegetation Index (NDVI), were applied to estimate chlorophyll-a distribution. Results showed peak algal concentrations in late spring and summer, especially in May, with highest values at reservoir edges and near Qili and Banling villages. Strong correlations (R\u0026sup2; up to 0.93) between in-situ and satellite-derived chlorophyll-a confirmed the reliability of remote sensing for HAB monitoring. Seasonal analysis indicated cyanobacteria dominance in spring and summer, and increased diatom prevalence in autumn and winter. Findings demonstrate that combining high-frequency satellite data with targeted in-situ measurements enables effective, large-scale, and near real-time HAB monitoring in small inland reservoirs. NDCI outperformed NDVI in detecting and mapping bloom severity, supporting its use for routine water quality surveillance. Additional spectral band combinations (NIR, SWIR, red edge) further improved bloom detection.\u003c/p\u003e\u003cp\u003eThis integrative approach offers a cost-effective, scalable method for HAB assessment and supports sustainable freshwater management. While perfect temporal alignment of in-situ and satellite data is often constrained by logistics and bloom variability, coordinated monitoring enhances accuracy and reliability.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal Dynamics of Chlorophyll-a in a Small Inland Reservoir Using Field Sampling and Satellite Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 11:52:19","doi":"10.21203/rs.3.rs-7651016/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3349f221-36d4-487c-8702-50d41dc74b2c","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T13:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 11:52:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7651016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7651016","identity":"rs-7651016","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}