TheOcean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400–1020 nm. The OLCI is important to the expansion of remote sensing monitoring of inland waters using water color satellite data. In this study, we developed a dual band ratio algorithm for the downwelling diffuse attenuation coefficient at 490 nm (K_d(490)) for the waters of Lake Taihu, a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with Sentinel-3A-OLCI data. The results show that: (1) Compared to the available K_d(490) estimation algorithms, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a higher estimation accuracy (N = 26, coefficient of determination (R²) = 0.81, root-mean-square error (RMSE) = 0.99 m⁻¹ and mean absolute percentage error (MAPE) = 19.55%) and validation accuracy (N = 14, R² = 0.83, RMSE = 1.06 m⁻¹ and MAPE = 27.30%), making it more suitable for turbid inland waters; (2) A comparison of the OLCI K_d(490) product and a similar Moderate Resolution Imaging Spectroradiometer (MODIS) product reveals a high consistency between the OLCI and MODIS products in terms of the spatial distribution of K_d(490). However, the OLCI product has a smoother spatial distribution and finer textural characteristics than the MODIS product and contains notably higher-quality data; (3) The K_d(490) values for Lake Taihu exhibit notable spatial and temporal variations. K_d(490) is higher in seasons with relatively high wind speeds and in open waters that are prone to wind- and wave-induced sediment resuspension. Finally, the Sentinel-3A-OLCI has a higher spatial resolution and is equipped with a relatively wide dynamic range of spectral bands suitable for inland waters. The Sentinel-3B satellite will be launched soon and, together with the Sentinel-3A satellite, will form a two-satellite network with the ability to make observations twice every three days. This satellite network will have a wider range of application and play an important role in the monitoring of inland waters with complex optical properties.

The identification of spatial and temporal dynamics of phytoplankton and macrophytes is crucial for deepening the knowledge of lake primary productivity and shifts in trophic status of inland water bodies. Earth Observation (EO) can provide sensitive information on both groups of primary producers, but their possible coexistence within the same site is often not taken into account by satellite-based analyses. Indeed, macrophyte and phytoplankton coexistence is not rare event, especially in shallow eutrophic lakes subject to cyanobacteria blooms, and common methods based on optical VNIR spectral response features for estimating water constituents often fail in distinguishing dense surface accumulation of cyanobacteria forming at peak of bloom events with floating and emergent macrophyte cover. Few authors have tackled this issue in scientific literature, the most effective approach to our knowledge being the one proposed by Oyama et al. (2015; Remote Sensing of Environment, 157: 35-47), based on the exploitation of SWIR range reflectance. On this topic, Bresciani et al. (2014; Remote Sensing of Environment, 146: 124-135) have demonstrated the potential of combined optical and SAR data in delivering accurate information on algae blooms and scum events in Curonian lagoon (Lithuania).

In this work, we take advantage of new generation EO sensors (i.e. ESA-Copernicus Sentinel-1 and -2) for investigating the capabilities of optical (broadband multi-spectral) and SAR (C-band) data integration in providing an effective method for distinguishing cyanobacteria scum and floating macrophytes in Lake Taihu (Jiangsu, China). Matchup pairs of Sentinel-2 and Sentinel-1 data acquired with less than 5 day difference have been pre-processed to derive surface reflectance and backscattering coefficient (sigma0), respectively. Statistics of spectral reflectance and Water Adjusted Vegetation Index (WAVI; Villa et al., 2014; Int. J. Appl. Earth. Obs. Geoinf., 30: 113-127) derived from Sentinel-2 data, as well as sigma0 in VV and VH polarization combinations derived from Sentinel-1 data, have been calculated and used to assess the separability of cyanobacteria scum and floating macrophyte pixels response. Finally, a rule-based framework has been designed, parametrized and applied to Sentinel-1 and -2 data to produce maps of algae scum and macrophytes on Lake Taihu in different times of the primary producers cycle, spanning from April to October.

In China, freshwater is an increasingly scarce resource. Study location Dongting Lake is China’s second largest freshwater lake in the middle reaches of Yangtze River catchment. Its wetlands deliver important ecosystem functions such as freshwater supply, water purification, flood and climate regulation to name only a few. The development of comprehensive water resource management and nature conservation strategies requires detailed mapping and monitoring of inland waters. The generation of such information requires either large human resources for conventional ground surveying or expensive data. In addition to costly methods, the monitoring of large wetlands such as the Dongting Lake study site with a water surface of up to 3.200 km² is difficult due to its inaccessibility during annual flood period.

Remote sensing offers a mature and comprehensive tool to solve this task with large area coverage at very low costs. Until today, in addition to satellite data with lower temporal resolution such as Envisat ASAR or Landsat, daily MODIS satellite data are a frequently selected source for capturing lake dynamics with medium spatial resolution of 250 m up to 1 km. Satellite data from the new European Sentinel fleet has been available since 2014 and provides high-resolution information free of costs. In this study we present the application of Sentinel-1 time series data for spatio-temporal high-resolution wetland mapping. New is the level of detail that can be achieved with Sentinel-1 data. Potential and limitations are analyzed and mid-term results presented.

In recent years, the climate change and human activities have a great influence on lake environments and ecosystem^[1]. As the second largest freshwater lake of Yunnan Province, Erhai Lake, is the indispensable drinking water source in Dali. However, due to the increasing human activities, Erhai lake have suffered a great deal of environmental stressors, such as eutrophication, heavy metal pollution, etc^[2]. Water quality deterioration and eutrophication led to the occurrence of algal blooms and affected the normal ecological function of lakes^[3]. Thus, the primary task for protecting Erhai Lake is monitoring and risk pre-warning of blooms. When algal bloom occurs, the content of chlorophyll a in the water increases, resulting in significant differences in the spectral characteristics between the bloom and non-bloom waters, so that cyanobacterial bloom can be detected by remote sensing.

The launch of MultiSpectral Instrument (MSI) orbited on Sentinel-2A (S2) and Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) on board ESA Sentinel-3A (S3) opened a new world in water environmental remote sensing^[4]. Lacking of in situ data and complexity of air and water conditions are the challenges of validating and evaluating S2 and S3 products. It provides multispectral data at high, medium and low resolution to meet different purposes by combinating S2 and S3 missions.

The aims of the study are: 1) to find out how Senitnel 2 MSI contributes to the long-term monitoring of algal blooms in inland lakes, 2) to get distribution of algal bloom and water surface temperature (WST) in spatial and temporal scales, 3) to figure out how Sentinel 3 plays a role in inland water monitoring and the driving factors of algal bloom, with the combination of high resolution MSI images.

In this study, a total of 22 Sentinel-2A MSI images with 50% cloud cover combining with Landsat OLI images (a total of 13 images) were used to monitoring algal blooms in Erhai Lake during November 2016 and December 2017. The VB-FAH (Virtual-Baseline Floating microalgae Height) index was used to identify and extract water bloom by using Sentinel-2A MSI and Landsat OLI sensors. Besides, 84 images of Sentinel-3 SLSTR level-1 products (from October 2016 to December 2017) and MODIS Terra Global 1Km Grid (short as MOD11A1, from January 2003 to December 2016) were used to analyze the relationship between WST and algal bloom. The SLSTR WST Products were processed by using split-window algorithm^[5].

Accompany with Landsat OLI and Sentinel-2A algal bloom maps, the processes of algal bloom development during October 2016 to December 2017 was presented. It’s apparently indicated that algal bloom was first observed in October 2016, located in the central of lake and then move to north Erhai Lake from October 2016 to the late spring of 2017. In order to describe the continuous process of algal bloom, the occurrence and duration of the algal bloom are set as follows: if the algal bloom is observed in two scenes within 3-5 days, and during this time the meteorological conditions are stable, then these two observation can be treat as one observation of bloom, and the time of first scene is recorded as the start of this algal bloom. Following the rules that mentioned above, with the aid of Sentinel 2 MSI monitoring on 22 and 24 November ,the three observations of algal bloom in November 2016 (11-20, 11-23, 11-27, detected by Landsat ) should be synthesized as one large scale bloom, which means Sentinel 2 MSI has a greater contribution to the long-term monitoring of algal blooms in Erhai lake^[6,7].

To demonstrate the spatial variability of the SLSTR maps, WST in Erhai Lake present a typical seasonal changes with the lowest temperature apparent in December 2016 and the highest temperature apparent in April 2017. A south to north difference was observed in every climatological monthly mean WST maps. Water temperature in south lake is 0.03K lower than what in north lake for the total of 70 images. The year 2016 had a relatively cool October according to SLSTR L1B result, which is consistent with the metrological air temperature record by Chinese metrological centre.

The monthly average WST in 2016 and 2017 were calculated from the MOD11A and Sentinel 3. Comparing with the 14years average monthly WST from MOD11A during 2003-2017. The monthly average WST in Erhai in 2016 is 0.9-2.5°C higher than 14 years average value. In autumn and early winter of 2016, there was a wide range of algal bloom, indicating that the higher WST was a favorable factor for the propagation and eruption of algal bloom. Otherwise, in 2017, the monthly mean WST of SLSTR is 0.5-3.5°C higher than 14 years average WST of MOD11A (2003-2017), higher WST was found in January and February when algal bloom occurred.

SLSTR can help finding relationship between algal bloom and water surface temperature. Higher water surface temperature is incentive to the eruption of algal bloom.

Wetlands are an important natural resource that requires monitoring. Water area is an important factor to the wetland monitoring. Methods of water extraction from optical images were very mature, such as NDWI, MNDWI and etc. But in rainy and cloudy area, there are always no enough optical images could be used while the underlying surface keeps in a highly dynamic changing speed. Synthetic Aperture Radar(SAR) data are helpful in these conditions and they could be used to map and monitor changes in surface water extent and flooded vegetation areas. These two factors are very helpful for the wetland management to understand the wetland vegetation distributions and changes. We reviewed a few techniques to extract water from optical and SAR images, including NDWI for the optical images and grey-level thresholding for SAR images and compared their differences in different seasons. Since the penetration character in SAR images, We compared the difference water extraction results from optical images and SAR images. And then, We used the polarimetric decompositions to map flooded vegetation to distinguish it from the surface water area. We used H/alpha composition method to detect the flooded vegetation area and found that it is useful to improve the accuracy of water area extraction from SAR images. We recommend that SAR data are very important to acquire the water area in the delta of wetland and differences from Optical and SAR images are very helpful to the wetland management to obtain the accurate water area data.

Global warming has increased the frequency of algal blooms in internal water bodies. The algal blooms are an unpleasant sight and hinder various recreational and economic. The increase in the anthropogenic load of nutrients (eutrophication) has led to an increase in the presence of toxic algae, the blue-green algae in the coastal and internal water bodies. A mature flowering of blue-green algae often emerges on top like a layer of foam containing high concentrations of toxins. Contact with these toxins poses a direct health risk for both humans and animals. Therefore, monitoring the concentration of algae and the occurrence of scum in lakes has become a topic of interest for management and science.

Optical remote sensing is a validated tool for sensing, monitoring and developing better understanding of the state of lakes. However, it is highly hindered by clouds. For regions with frequent cloud cover, this means loss of data, which derails the purpose of sensing. This makes difficult to spatially and temporally characterize scum area for a comprehensive ecological analysis. Combining data obtained using different types of sensor can be an option worth investigating, and a good candidate for this purpose is the synthetic aperture radar (SAR), due partly to its capacity to collect data independent of cloudy cover.

We use a synergistic approach involving optical and SAR images together with meteorological parameters to monitor algal cyanobacteria blooms over Tai Hu and Chaohu lakes and Curonian Lagoon. The satellite images are provided by the Sentinel 1, 2 and 3 satellites. Meteorological parameters come from in situ stations or from the European Centre for Medium-Range Weather Forecasts (ECMWF) database. With respect to optical data, the scum index was developed using ratio of TOA reflectance in NIR and RED bands exploiting the high difference in backscattering and absorption between water with and without scum. For S1 imagery, a polarimetric index is defined and results able to identify anomalies on the lakes surface. The use of Google Earth Engine helped with the images selection and the time series analysis of the indexes. A preliminary study suggests that this index combined with the knowledge of wheatear variables, such as wind speed and the 2 meters air temperature, can reliably detect the occurrences of algal blooms.

Biodiversity stakes within Yangtze watershed are very important at national level by also international ones. These very rich ecosystems, being key wintering areas for many waterfowl of SE Asia, are suffering from rapidly changing environments due to human activities. It’s crucial to understand what the key factors and their effects are. As data within large spatial and temporal scales are difficult to get, remote sensing and spatial analysis technology turns out to be a useful tool to access information. Works are on progress over Poyang Lake, in regards to vegetation recognition and dynamic particularly within the core of the Poyang Lake natural Reserve, but also over smaller and less known sensitive areas such as Wuchang and Shengjin lakes (Anhui Province).

Since its launch in 2015, the Sentinel-2 mission is capturing the world with an unprecedented combination of spatial and temporal resolutions, going from 10 to 60 m of spatial resolution and 5 days of revisit time at the equator, all in free access. The interval of spectral bands monitored goes from coastal blue at 443 nm to Short-Wave InfraRed (SWIR) at 2190 nm, divided in 13 bands. The mission is composed of 2 identical satellites, Sentinel-2A sent on June 2015, and Sentinel-2B sent on March 2017. Sentinel-2 allows a precise and systematic follow-up of hydrological systems regarding water surface area or vegetation coverage.

The water extents of Wuchang and Shengjin lakes have been extracted from Sentinel-2 time series, using all exploitable images since the beginning of the acquisitions in 2015. Being an optical sensor not all images are usable due to potential high cloud coverage. A total of 32 dates have been used and 10 Landsat 8 (Libra) have been added to try to reduce the temporal gaps in the Sentinel-2 acquisitions caused by cloudy conditions. The final time series have an average of 1 image every 22 days, going from the 20^th October of 2015 to the 7^th of April 2018. The number of available images is higher since March 2017, thanks to the launch of the Sentinel-2B satellite. Extractions were done using a SERTIT-ICube automatized routine based on a supervised Maximum Likelihood Classification, trained with Pekel water occurrence product. These extractions allow to recreate the dynamic of the two lakes and show the drought and wet periods. During the 3 years interval, the surface peaks on July 2016 for both lakes. The lowest level appears at two different dates for each lake; on January 2018 for Wuchang, on February 2017 for Shengjin. Wuchang Lake surface area appears to be more variable than Shengjin Lake, with many local maximum and minimum between the end of 2017 and April 2018.

In addition to Sentinel-2 and Landsat 8, SPOT images have been downloaded from the Theia-world website through the SPOT World Heritage program. The latter gives access to archive data from satellites SPOT 1-2-3-4-5 and extents the study duration span. A total of 19 images were either completely or partially available over Shengjin and Wuchang lakes; 2 SPOT-1, 2 SPOT-3, 13 SPOT-4 and 2 SPOT-5, from December 1987 to April 2009. The time between two images during this period is too large to capture the lakes dynamic but can be used to calculate a total water occurrence product.

In the case of Wuchang Lake, floating vegetation is a problem for automatic water surface extraction. The lake is covered by vegetation during long periods of time and the water below can’t be detected by automatic radiometric means. Nevertheless, Sentinel-2 stays a pertinent and powerful tool for hydrological monitoring of lakes confirming the expectation from the remote sensing wetland community before launch. The presence of IR and SWIR bands induces a strong discrimination between water and other classes, and the systematic acquisitions create dense time series, making analysis more consistent. It makes possible to sensor events occurring over short periods of time. These midterm results illustrate the pertinence and power of multi-source optical satellite data for environmental analysis and confirm the expectations in the Sentinel- 2 constellation.

Colored dissolved organic matter (CDOM), the key component in aquatic environment, plays an important role in biogeochemical processing. The optical characteristics of CDOM potentially permit remote sensing of CDOM. However, retrieval of CDOM for inland turbid water is challenging because of CDOM absorption at blue spectral range overlapped by the absorption caused by chlorophyll a and amounts of total suspended matter contained in turbid water. CDOM inversion algorithms developed and applied to specific regional locations may not be directly applicable for other water environment. Moreover, various CDOM sources present distinct CDOM absorption characteristics spatially and spectrally. In this study, in situ reflectance and water samples were used to develop models for estimating CDOM absorption in a complex freshwater environment in Poyang Lake, China. Poyang Lake is the largest fresh water lake in China. It is a complex flood-path lake with significant annual water level variations caused by hydrological conditions and monsoon climate. The lake also exerts an irreplaceable role for drinking water supply, flood control, waterway shipping and conservation of biological diversity. However, in recent years, the water environment of Poyang Lake has been affected by anthropogenic impacts such as sand mining and major hydrologic engineering. The in situ water reflectance spectra, CDOM absorption spectra and other water-color parameters from 92 samples collected in four representative study areas between 2015 and 2016 were analyzed. Band ratio models were established to estimate CDOM absorption coefficient at 355 nm [ag(355)] based on the correlation analysis between reflectance and ag(355). The results indicated that the band ratio models performed well for estimating ag(355) when the 92 samples were divided into two datasets with the threshold of concentration of total suspended matter (TSM) as 10 mg/L. The band ratios of R(689) / R(497) and R(767) / R(826) were selected to establish model for retrieval of ag(355) in clean water (TSM < 10 mg/L) and turbid water (TSM ≥ 10 mg/L), respectively. The determination coefficients (r2) of calibration models for clean and turbid water were 0.70 and 0.73, respectively. The percentage root-mean-square errors (%RSME) of validation models for clean and turbid water were 13.2% and 11.6%, respectively. The simulated Sentinel-2 and Landsat-8 bands based on reflectance spectra were used to examine potential capability for retrieving CDOM using these sensors. The results indicated that Sentinel-2 would perform better than Landsat-8 for estimating ag(355). The Sentinel-2 band ratio of B4 / B2 or B5 / B2 and B7 / B8 or B7 / B8A would be useful for retrieval CDOM in clean and in turbid water of Poyang Lake, respectively. The performance of the models for Sentinel-2 image acquired on July 26, 2016 was stable both in clean and turbid water. Further evaluate performance for extended temporal application is required in Poyang Lake. The findings from this study provide algorithms foundation for monitoring spatial and temporal dynamic of CDOM in Poyang Lake using remote sensing.