Search Results (30)

In order to ensure the protection of lakes against eutrophication, an ongoing global problem, its causes should be determined on an individual basis. In this study, we investigated Lake Niesłysz in northwestern Poland in terms of (i) the impact of nitrogen and phosphorus on primary production, (ii) the Trophic State Index (TSI), and (iii) the hydromorphological characteristics and watershed features. We determined the thermal conditions, dissolved oxygen, organic matter, and selected forms of nitrogen and phosphorus. TSI was determined using Secchi depth (SD), chlorophyll a, total phosphorus (TP), and total nitrogen (TN). Hypolimnetic anoxia was observed in summer. Surface concentrations of chlorophyll a and organic carbon, total inorganic nitrogen (TIN), and total reactive phosphorus (TRP) were 5 μg L⁻¹, 11.7 mg C L⁻¹, 0.049 mg N L⁻¹, and 0.018 mg P L⁻¹, respectively. The TN:TP ratio was >30, while TIN:TRP was <10. The TSIs for chlorophyll a, SD, and TP ranged from 42 to 59, and for TN it was >145. The total trophic state index (T-TSI) exceeded 72. In conclusion, Lake Niesłysz has an average resistance to degradation and the catchment has little influence on the release and transport of biogenic matter into the lake. The limiting nutrient for primary production was phosphorus, but the influence of nitrogen or covariates of nitrogen cannot be excluded. Based on the oxygen conditions in the hypolimnion, the lake should be classified as eutrophic. Most of the TSIs were in the mesotrophic range, while the TSIs for TN and T-TSI classified the lake as hypertonic. The results show that Lake Niesłysz is currently at a critical stage of progressive degradation, and it is advisable to develop and implement protective measures immediately. Full article

Beira Lake, located in Colombo, Sri Lanka, has suffered severe anthropogenic impacts, with previous restoration attempts failing due to a limited understanding of pollutant dynamics. Aiming to fill this gap, a comprehensive study was conducted during dry and wet seasons to assess the spatiotemporal water pollution of Beira Lake, employing key physicochemical parameters, numerical indices, and remote sensing analysis. The water pollution index (WPI) results categorize Beira Lake as highly polluted, with WPI values ranging from 2.38 ± 0.92 in the wet season to 2.53 ± 1.32 in the dry season. Comparatively higher COD levels recorded in the Beira Lake network, especially for Gangarama Lake show significant pollution levels during both the dry and wet seasons, e.g., the highest COD levels, at 306.40 mg/L, were observed during the wet season. The Trophic State Index (TSI) results indicate eutrophic and hypereutrophic conditions in Beira Lake, which are particularly pronounced during the wet season. The heavy metal pollution index (HPI) results suggest elevated heavy metal concentrations in Beira Lake, especially in the wet season. Combined with field investigation results, a remote sensing data analysis between 2016 and 2023 reveals significant improvements in water transparency, suggesting positive effects of recent management interventions. Parameters demanding attention include COD, nitrate, and total phosphate levels due to their consistent exceedance of permissible limits. The PCA results of indices correlations between wet and dry seasons offer valuable insights into the complex dynamics of Beira Lake’s water quality. The study makes recommendations for restoring Beira Lake, including stringent pollution controls, regular dredging, green infrastructure implementation, implementing new rules and regulations, and community engagement. Full article

Chlorophyll-a concentration (Chl-a) is commonly used as a proxy for phytoplankton abundance in surface waters of large lakes. Mapping spatial and temporal Chl-a distributions derived from multispectral satellite data is therefore increasingly popular for monitoring trends in trophic state of these important ecosystems. We evaluated products of eleven atmospheric correction processors (LEDAPS, LaSRC, Sen2Cor, ACOLITE, ATCOR, C2RCC, DOS 1, FLAASH, iCOR, Polymer, and QUAC) and 27 reflectance indexes (including band-ratio, three-band, and four-band algorithms) recommended for Chl-a concentration retrieval. These were applied to the western basin of Lake Ontario by pairing 236 satellite scenes from Landsat 5, 7, 8, and Sentinel-2 acquired between 2000 and 2022 to 600 near-synchronous and co-located in situ-measured Chl-a concentrations. The in situ data were categorized based on location, seasonality, and Carlson’s Trophic State Index (TSI). Linear regression Chl-a models were calibrated for each processing scheme plus data category. The models were compared using a range of performance metrics. Categorization of data based on trophic state yielded improved outcomes. Furthermore, Sentinel-2 and Landsat 8 data provided the best results, while Landsat 5 and 7 underperformed. A total of 28 Chl-a models were developed across the different data categorization schemes, with RMSEs ranging from 1.1 to 14.1 μg/L. ACOLITE-corrected images paired with the blue-to-green band ratio emerged as the generally best performing scheme. However, model performance was dependent on the data filtration practices and varied between satellites. Full article

The trophic state is an important factor reflecting the health state of lake ecosystems. To accurately assess the trophic state of large lakes, an integrated framework was developed by combining remote sensing data, field monitoring data, machine learning algorithms, and optimization algorithms. First, key meteorological and environmental factors from in situ monitoring were combined with remotely sensed reflectance data and statistical analysis was used to determine the main factors influencing the trophic state. Second, a trophic state index (TSI) inversion model was constructed using a machine learning algorithm, and this was then optimized using the sparrow search algorithm (SSA) based on a backpropagation neural network (BP-NN) to establish an SSA-BP-NN model. Third, a typical lake in China (Hongze Lake) was chosen as the case study. The application results show that, when the key environmental factors (pH, temperature, average wind speed, and sediment content) and the band combination data from Sentinel-2/MSI were used as input variables, the performance of the model was improved (R² = 0.936, RMSE = 1.133, MAPE = 1.660%, MAD = 0.604). Compared with the performance prior to optimization (R² = 0.834, RMSE = 1.790, MAPE = 2.679%, MAD = 1.030), the accuracy of the model was improved by 12.2%. It is worth noting that this framework could accurately identify water bodies in different trophic states. Finally, based on this framework, we mapped the spatial distribution of TSI in Hongze Lake in different seasons from 2019 to 2020 and analyzed its variation characteristics. The framework can combine regional special feature factors influenced by a complex environment with S-2/MSI data to achieve an assessment accuracy of over 90% for TSI in sensitive waters and has strong applicability and robustness. Full article

North Tunisia’s Joumine reservoir provides water for drinking and agriculture irrigation purposes. Therefore, its water quality is crucial, especially with the recurrence of dry years in a global climate change context. This study aims to evaluate its environmental parameters, phytoplankton community structure, and trophic status. The data were newly analyzed using multivariate statistical methods and redundancy analysis (RDA) with the Trophic State Index (TSI) and Trophic State Index deviation (TSID). Monthly sampling occurred from May 2021 to June 2022 at eight stations. Water samples were collected to assess physical-chemical parameters and Chlorophyll-a, as well as to identify phytoplankton species. Three seasonal clusters of summer, autumn, and spring were identified. Water nutrient variations primarily resulted from point and non-point source contamination, along with natural processes. Carlson’s Trophic State Index (CTSI) indicates a eutrophic status for the Joumine reservoir. TSID indicated there was no algal turbidity in the reservoir. The study identified 25 phytoplankton taxa, with Chlorophyceae exhibiting high densities and diversities. RDA revealed that NO₃⁻, NH₄⁺, DO, pH, water flow, and water temperature were the most important environmental factors controlling phytoplankton structure in the Joumine reservoir. The outcomes of this study may provide helpful information to improve the management of the Joumine reservoir. Full article

Lake Tana, the largest inland water body in Ethiopia, has witnessed significant changes due to ongoing urbanization and socioeconomic activities in recent times. In this study, the two-decade recordings of moderate resolution imaging spectroradiometer (MODIS) were used to derive Forel–Ule index (FUI). The FUI, which ranges from 1 (dark-blue pristine water) to 21 (yellowish-brown polluted water), is important to fully understand the quality and trophic state of the lake in the last two decades. The analysis of FUI over a period of 22 years (2000–2021) indicates that Lake Tana is in a eutrophic state as confirmed by FUI values ranging from 11 to 17. This is in agreement with the trophic state index (TSI) estimated from MERIS diversity-II chlorophyll a (Chl$\_a$) measurements for the overlapping 2003-2011 period. The categorical skill scores show that FUI-based lake water trophic state classification relative to MERIS-based TSI has a high performance. FUI has a positive correlation with TSI, (Chl$\_a$), turbidity, and total suspended matter (TSM) and negative relations with Chl$\_a$ and TSM (at the lake shoreline) and colored dissolved organic matter. The annual, interannual and seasonal spatial distribution of FUI over the lake show a marked variation. The hydro-meteorological, land-use–land-cover (LULC) related processes are found to modulate the spatiotemporal variability of water quality within the range of lower and upper extremes of the eutrophic state as revealed from the FUI composite analysis. The FUI composites were obtained for the terciles and extreme percentiles of variables representing hydro-meteorological and LULC processes. High FUI composite (poor water quality) is associated with above-normal and extremely high (85 percentile) lake bottom layer temperature, wind speed, precipitation, surface runoff, and hydrometeorological drought as captured by high negative standardized precipitation-evapotranspiration index (SPEI). In contrast, a high FUI composite is observed during below-normal and extremely low (15 percentile) lake skin temperature and evaporation. Conversely good water quality (i.e., low FUI) was observed during times of below-normal and above-normal values of the above two sets of drivers respectively. Moreover, FUI varies in response to seasonal NDVI/EVI variabilities. The relationship between water quality and its drivers is consistent with the expected physical processes under different ranges of the drivers. High wind speed, for instance, displaces algae blooms to the shoreline whereas intense precipitation and increased runoff lead to high sediment loads. Increasing lake skin temperature increases evaporation, thereby decreasing water volume and increasing insoluble nutrients, while the increasing lake bottom layer temperature increases microbial activity, thereby enhancing the phosphorus load. Moreover, during drought events, the low inflow and high temperature allow algal bloom, Chl$\_a$, and suspended particles to increase, whereas high vegetation leads to an increase in the non-point sources of total phosphorus and nitrogen. Full article

The mouth areas of large rivers can serve as a good model of heterogeneity sites with a pronounced trophic gradient to assess the impact of the degree of eutrophication on different plankton communities. The aim of this research was to identify the possible response of the diversity indicators of phyto- and zooplankton communities to trophic gradients in the mouth area of two large tributaries of the reservoir, formed in the Middle Volga River (Russia). Both linear regression models and canonical correlation analysis (CCA) were used to assess the role of abiotic and biotic predictors in the structural organization of plankton communities and to assess the changes in the parameters of the species plankton community structure in the trophic gradient. It was found that the species diversity (Adjusted R² = 0.116) and evenness (Adjusted R² = 0.114) of phytoplankton significantly decreased with an increase in the degree of eutrophication, while the species diversity (Adjusted R² = 0.059) and evenness (Adjusted R² = 0.073) of zooplankton increased. According to the CCA models, electrical conductivity (EC) explained the largest proportion of the observed dispersion. The Trophic State Index (TSI) explained 3.0% of the total variance in the phytoplankton community species structure and 7.8% in the zooplankton one. The variation in phyto- and zooplankton dominant complexes generally corresponded to the well-known patterns of plankton species succession in the gradient of trophic conditions and can be considered as a classic manifestation of the cascade effect in the food chains of freshwater plankton communities. Our results highlight the necessity of studying the mouth river areas, as well as applying an integrated approach to investigating the response of plankton communities to eutrophication processes of continental water bodies. Full article

This study employed different indices, namely the weighted arithmetic water quality index (WQI), Carlson Trophic State Index (TSI), van Ginkel TSI, and Trophic Level Index (TLI) to determine the water quality status of a man-made dam for the needs of sustainable water resource management in Southern Africa. The selection of indices for the study was based on the impacts of anthropogenic activities on the dam. The Roodeplaat Dam exhibited the spatial variation of physicochemical characteristics, indicative of influence by point-source pollution. Although the dam was classified as being eutro-hypertrophic, it was evident that water clarity was not a limiting factor but was P-limited, which was an indication of limiting conditions on primary production. Moreover, the WQI calculated for the dam with an average of 93.94 demonstrated very poor water quality that could be used for crop irrigation purposes only. As such, continued nutrient enrichment must be mitigated to sustain fitness for irrigation, at least. However, strategic goals should involve widening fitness for use. The selected indices were found to be effective for water resource management and could be applied to dams impacted by point-source pollution in Southern Africa. Thus, this study recommends the implementation of an integrated management approach, which needs to prioritize nutrient management to retain societal resource value. Full article

Understanding the spatiotemporal distributions and variation characteristics of water quality parameters is crucial for ecosystem restoration and management of lakes, in particular, Lake Qilu (QL), a typical plateau shallow lake on the Yunnan-Guizhou Plateau, southwestern China. To identify the main causes of harmful algal blooming and continuous water quality decline, the total phosphorus (TP), total nitrogen (TN), water temperature (WT), dissolved oxygen (DO), chlorophyll-a (Chl-a), pH, and turbidity in hypereutrophic Lake Qilu from January 2017 to December 2021 were analyzed. The results showed a complex pattern in spatiotemporal distribution and variation. WT showed no significant change in the vertical profile. DO and pH value variations were caused by both physical and biochemical processes, especially at the bottom of Lake QL with an anaerobic environment. The Trophic State Index (TSI) assessment results showed that Lake QL is a eutrophic (70.14% of all samples, 50 < TSI < 70) to a hypereutrophic lake (29.86%, 70 < TSI) with poor water quality (WQI < 25). TP and WT were the main factors controlling harmful algal blooms (HABs) based on the statistical analysis of Principal Component Analysis (PCA), Random Forest Model (RFM), and Correlation Analysis (CA). In lake QL, TP loading reduction and water level increase might be the key strategies for treating HABs in the future. Based on our results, reducing TP loading may be more effective than reducing TN to prevent HABs in the highly eutrophicated Lake Qilu. Full article

Phytoplankton is one of the five biological quality elements used in the assessment of the ecological status of surface waters according to the European Water Framework Directive established in 2000. In this study, we determined the ecological status of three small and shallow lakes in the Polesie Plain, Eastern Poland, by using indices based on phytoplankton assemblages. The predominant phytoplankton of all three lakes were filamentous cyanobacteria, both heterocystous and non-heterocystous, represented by the genera Aphanizomenon, Planktothrix, Limnothrix, and Planktolyngbya. We used the Hungarian Q index, German PSI (Phyto-See-Index), and recently developed PMPL (Phytoplankton Metrics for Polish Lakes) for Polish lakes. We compared the results from the calculation of the indices to physicochemical data obtained from the lake water and Carlson’s Trophy State Index (TSI). On the basis of TSI, Gumienek and Glinki lakes were classified as advanced eutrophic, whereas Czarne Lake had a better score and was classified as slightly eutrophic. The trophic state was generally confirmed by the ecological status based on phytoplankton indices and also showed the diverse ecological situation in the lakes studied. Based on the Polish PMPL, Gumienek Lake was classified as having bad status (ecological quality ratio (EQR) = 0.05), whereas Glinki and Czarne lakes were classified within the poor status range (EQR = 0.25 and 0.35, respectively). However, based on the German PSI, the lakes were classified in a different manner: the status of Gumienek and Czarne lakes was better, but unsatisfactory, because they were still below the boundary for the good status category recommended by the European Commission. The best ecological status for the studied lakes was obtained using the Q index: Gumienek Lake with EQR = 0.42 had a moderate status, and Czarne Lake with EQR = 0.62 obtained a good status. However, Glinki Lake, with EQR = 0.40, was classified at the boundary for poor and moderate status. Based on our study, it seems that the best index for ecological status assessment based on phytoplankton that can be used for small lakes is the Polish (PMPL) index. Full article

Given the great importance of lakes in Earth’s environment and human life, continuous water quality (WQ) monitoring within the frame of the Water Framework Directive (WFD) is the most crucial aspect for lake management. In this study, Earth Observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM⁺) and Landsat 8 Operational Land Imager (OLI) sensors have been combined with co-orbital in situ measurements from 50 lakes located in Greece with the main objective of delivering robust WQ assessment models. Correlation analysis among in situ co-orbital WQ data (Chlorophylla, Secchi depths, Total phosphorus-TP-) contributed to distinguishing their inter-relationships and improving the WQ models’ accuracy. Subsequently, stepwise multiple regression analysis (MLR) of the available TP and Secchi depth datasets was implemented to explore the potential to establish optimal quantitative models regardless of lake characteristics. Then, further MLR analysis concerning whether the lakes are natural or artificial was conducted with the basic aim of generating different remote sensing derived models for different types of lakes, while their combination was further utilized to assess their trophic status. Correlation matrix results showed a high and positive relationship between TP and Chlorophyll-a (0.85), whereas high negative relationships were found between Secchi depth with TP (−0.84) and Chlorophyll-a (−0.83). MLRs among Landsat data and Secchi depths resulted in 3 optimal models concerning the assessment of Secchi depth of all lakes (Secchi_general; R = 0.78; RMSE = 0.24 m), natural (Secchi_natural; R = 0.95; RMSE = 0.14 m) and artificial (Secchi_artificial; R = 0.62; RMSE = 0.1 m), with reliable accuracy. Study findings showed that TP-related MLR analyses failed to deliver a statistically acceptable model for the reservoirs; nevertheless, they delivered a robust TP_general (R = 0.71; RMSE = 1.41 mg/L) and TP_natural model (R = 0.93; RMSE = 1.43 mg/L). Subsequently, trophic status classification was conducted herein, calculating Carlson’s Trophic State Index (TSI) initially throughout all lakes and then oriented toward natural-only and artificial-only lakes. Those three types of TSI (general, natural, artificial) were calculated based on previously published satellite-derived Chlorophyll-a (Chl-a) assessment models and the hereby specially designed WQ models (Secchi depth, TP). The higher deviation of satellite-derived TSI values in relation to in situ ones was detected in reservoirs and shallower lakes (mean depth < 5 m), indicating noticeable divergences among natural and artificial lakes. All in all, the study findings provide important support toward the perpetual WQ monitoring and trophic status prediction of Greek lakes and, by extension, their sustainable management, particularly in cases when ground truth data is limited. Full article

Water quality degradation is one of the most pressing environmental challenges in reservoirs around the world and makes the trophic status assessment of reservoirs essential for their restoration and sustainable use. The main aims of this study were to determine the spatial variations in water quality and trophic state of 204 South Korean reservoirs at different altitude levels. The results demonstrated mean total phosphorus (TP), chlorophyll-a (CHL-a), total suspended solids (TSS), organic matter indicators (chemical oxygen demand: COD; total organic carbon: TOC), water temperature (WT), and electrical conductivity (EC) remain consistently higher in the very lowland reservoirs (VLLR) than those in other altitudes, due to sedimentary or alluvial watersheds. The average TP and CHL-a levels in VLLR crossed the limit of the eutrophic water, symptomizing a moderate risk of cyanobacterial blooms. Empirical models were developed to identify critical variables controlling algal biomass and water clarity in reservoirs. The empirical analyses of all reservoir categories illustrated TP as a better predictor of CHL-a (R² = 0.44, p < 0.01) than TN (R² = 0.02, p < 0.05) as well as showed strong P-limitation based on TN:TP ratios. The algal productivity of VLLR (R² = 0.61, p < 0.01) was limited by phosphorus, while highland reservoirs (HLR) were phosphorus (R² = 0.23, p < 0.03) and light-limited (R² = 0.31, p < 0.01). However, TSS showed a highly significant influence on water clarity compared to TP and algal CHL-a in all reservoirs. TP and TSS explained 47% and 34% of the variance in non-algal turbidity (NAT) in HLR. In contrast, the TP and TSS variances were 18% and 29% in midland reservoirs (MLR) and 32% and 20% in LLR. The trophic state index (TSI) of selected reservoirs varied between mesotrophic to eutrophic states as per TSI (TP), TSI (CHL-a), and TSI (SD). Mean TSI (CHL-a) indicated all reservoirs as eutrophic. Trophic state index deviation (TSID) assessment also complemented the phosphorus limitation characterized by the blue-green algae (BGA) domination in all reservoirs. Overall, reservoirs at varying altitudes reflect the multiplying impacts of anthropogenic factors on water quality, which can provide valuable insights into reservoir water quality management. Full article

The Yangtze River Delta (YRD) is one of the regions with the most intensive human activities. The eutrophication of lakes in this area is becoming increasingly serious with consequent negative impacts on the water supply of the surrounding cities. But the spatial-temporal characteristics and driving factors of the trophic state of the lake in this region are still not clearly addressed. In this study, a semi-analytical algorithm for estimating the trophic index (TSI) using particle absorption at 645 nm based on MODIS images is proposed to monitor and evaluate the trophic state of 41 large lakes (larger than 10 km²) in the YRD from 2002 to 2020. The performance of the proposed algorithm is evaluated using an independent dataset. Results showed that the root-mean-square error (RMSE) of the algorithm is less than 6 and the mean absolute percentage error (MAPE) does not exceed 8%, indicating that it can be applied for remotely deriving the TSI in the YRD. The spatial-temporal patterns revealed that there were significantly more lakes with moderate eutrophication in the Lower Yangtze River (LYR) than in the Lower Huaihe River (LHR). The overall average value of the TSI reaches a maximum in summer and a minimum in winter. The TSI value in the YRD over the period 2002–2020 showed a downward trend, especially after 2013. Individually, 33 lakes showed a downward trend and 8 lakes showed an upward trend. Furthermore, marked seasonal and interannual temporal variations can be clearly observed in the LYR and LHR and the sum of the variance contributions of seasonal and interannual components is more than 50%. Multiple linear regression analysis showed that human activities can explain 65% of the variation in the lake TSI in the YRD. Full article

The rapid growth of urbanization, industrialization and poor wastewater management practices have led to an intense water quality impediment in Lake Hawassa Watershed. This study has intended to engage the different water quality indices to categorize the suitability of the water quality of Lake Hawassa Watershed for anthropogenic uses and identify the trophic state of Lake Hawassa. Analysis of physicochemical water quality parameters at selected sites and periods was conducted throughout May 2020 to January 2021 to assess the present status of the Lake Watershed. In total, 19 monitoring sites and 21 physicochemical parameters were selected and analyzed in a laboratory. The Canadian council of ministries of the environment (CCME WQI) and weighted arithmetic (WA WQI) water quality indices have been used to cluster the water quality of Lake Hawassa Watershed and the Carlson trophic state index (TSI) has been employed to identify the trophic state of Lake Hawassa. The water quality is generally categorized as unsuitable for drinking, aquatic life and recreational purposes and it is excellent to unsuitable for irrigation depending on the sampling location and the applied indices. Specifically, in WA WQI, rivers were excellent for agricultural uses and Lake Hawassa was good for agricultural uses. However, the CCME WQI findings showed rivers were good for irrigation but lake Hawassa was marginal for agricultural use. Point sources were impaired for all envisioned purposes. The overall category of Lake Hawassa falls under a eutrophic state since the average TSI was 65.4 and the lake is phosphorous-deficient, having TN:TP of 31.1. The monitored point sources indicate that the city of Hawassa and its numerous industrial discharges are key polluters, requiring a fast and consequent set-up of an efficient wastewater infrastructure, accompanied by a rigorous monitoring of large point sources (e.g., industry, hospitals and hotels). In spite of the various efforts, the recovery of Lake Hawassa may take a long time as it is hydrologically closed. Therefore, to ensure safe drinking water supply, a central supply system according to World Health organization (WHO) standards also for the fringe inhabitants still using lake water is imperative. Introducing riparian buffer zones of vegetation and grasses can support the direct pollution alleviation measures and is helpful to reduce the dispersed pollution coming from the population using latrines. Additionally, integrating aeration systems like pumping atmospheric air into the bottom of the lake using solar energy panels or diffusers are effective mitigation measures that will improve the water quality of the lake. In parallel, the implementation and efficiency control of measures requires coordinated environmental monitoring with dedicated development targets. Full article

Due to increasing algae bloom occurrence and water degradation on a global scale, there is a demand for water quality monitoring systems based on remote sensing imagery. This paper describes the scientific, theoretical, and methodological background for creating a cloud-computing interface on Google Earth Engine (GEE) which allows end-users to access algae bloom related products with high spatial (30 m) and temporal (~5 day) resolution. The proposed methodology uses Sentinel-2 images corrected for atmospheric and sun-glint effects to generate an image collection of the Normalized Difference Chlorophyll-a Index (NDCI) for the entire time-series. NDCI is used to estimate both Chl-a concentration, based on a non-linear fitting model, and Trophic State Index (TSI), based on a tree-decision model classification into five classes. Once the Chl-a and TSI algorithms had been calibrated and validated they were implemented in GEE as an Earth Engine App, entitled Algae Bloom Monitoring Application (AlgaeMAp). AlgaeMAp is the first online platform built within the GEE platform that offers high spatial resolution of water quality parameters. The App benefits from the huge processing capability of GEE that allows any user with internet access to easily extract detailed spatial (30 m) and long temporal Chl-a and TSI information (from August 2015 and with images every 5 days) throughout the most important reservoirs in the State of São Paulo/Brazil. The application will be adapted to extend to other relevant areas in Latin America. Full article