Phytoplankton biomass is important to monitor in lakes due to its influence on water quality and lake productivity. Phytoplankton has also been identified as sensitive to environmental change, with shifts in the seasonality of blooms, or phenology, resulting from changing temperature and nutrient conditions. A satellite remote sensing approach to retrieving and mapping freshwater phytoplankton phenology is demonstrated here in application to Lake Balaton, Hungary. Chlorophyll-a (chl-a) concentration mapping using Medium Resolution Imaging Spectrometer (MERIS) allows new insights into such spatiotemporal dynamics for Lake Balaton as bloom start, peak and end timing, duration, maximum chl-a concentrations, spatial extent, rates of increase and decrease, and bloom chl-a concentration integral. TIMESAT software is used to extract and map these phenology metrics. Three approaches to time series smoothing are compared and mapped metrics are evaluated in comparison with phenology metrics of in situ chl-a. The high degree of both spatial and temporal variability is highlighted and discussed, as are methodological limitations and correlation between phenology metrics. Both the feasibility of and novel insights permitted through such phenology mapping are demonstrated, and priority topics for future research are suggested

The 10-year archive of MEdium Resolution Imaging Spectrometer (MERIS) data is an invaluable resource for studies on lake system dynamics at regional and global scales. MERIS data are no longer actively acquired but their capacity for global scale monitoring of lakes from satellites will soon be re-established through the forthcoming Sentinel-3 Ocean and Land Colour Instrument (OLCI). The development and validation of in-water algorithms for the accurate retrieval of biogeochemical parameters is thus of key importance if the potential of MERIS and OLCI data is to be fully exploited for lake monitoring. This study presents the first extensive validation of algorithms for chlorophyll-a (chl-a) retrieval by MERIS in the highly turbid and productive waters of Lake Balaton, Hungary. Six algorithms for chl-a retrieval from MERIS over optically complex Case 2 waters, including band-difference and neural network architectures, were compared using the MERIS archive for 2007–2012. The algorithms were locally-tuned and validated using in situ chl-a data (n=289) spanning the five year processed image time series and from all four lake basins. In general, both band-difference algorithms tested (Fluorescence Line Height (FLH) and Maximum Chlorophyll Index (MCI)) performed well, whereas the neural network processors were generally found to much less accurately retrieve in situ chl-a concentrations. The Level 1b FLH algorithm performed best overall in terms of chl-a retrieval (R2=0.87; RMSE=4.19mgm−3; relative RMSE=30.75%) and particularly at chl-a concentrations of ≥10mgm−3 (R2=0.85; RMSE=4.81mgm−3; relative RMSE=20.77%). However, under mesotrophic conditions (i.e., chl-a<10mgm−3) FLH was outperformed by the locally-tuned FUB/WeW processor (relative FLH RMSE<10mgm−3=57.57% versus relative FUB/WeW RMSE<10mgm−3=46.96%). An ensemble selection of in-water algorithms is demonstrated to improve chl-a retrievals

The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology.

Since the beginning of the 1960s an escalating deterioration of reed beds in parts of Europe has been often observed. Hence, the ‘reed die-back’ as it was later named, has been a phenomenon of great scientific interest and concern to conservationists worldwide and intensively studied by field ecologists. Imaging spectroscopy has frequently been employed for vegetation mapping, but this paper is the first explicit analysis of the spectral information content for reed and an assessment of the potential for detecting the areas affected by the reed die-back syndrome using hyperspectral data at the near infrared and the chlorophyll absorption spectral regions. Leaf reflectance spectra and photophysiological information were acquired using a Hand-Held ASD spectroradiometer, a portable fluorometer and a chlorophyll metre in-situ concurrently from leaf samples along a transect perpendicular to the lake shore of Central Europe's largest inland lake in terms of area, Lake Balaton in Hungary. A strong correlation between narrowband spectral indices and chlorophyll fluorescence parameters indicates the potential of hyperspectral remote sensing in assessing plant stability. Canopy hyperspectral data were collected from an airborne AISA Eagle sensor (400–1000nm). An application of the findings from the field data analysis to airborne hyperspectral imagery reveals important information about reed condition at the study area. Y(II) values, regarded as a proxy of photosystem activity, have been calculated from high R2 combination of spectral ratio 612/516 representing Fs and 699/527 representing Fm′. ETR values are estimated based on the calculated Y(II) and the spectral ratio 463/488 for Photosynthetically Active Radiation. This research underpins the development of methods for the spectral discrimination of reed patches affected by stress caused by environmental conditions, and subsequently the reed die-back syndrome. A comparison with empirical vegetation indices from the literature shows significantly higher R2 values of the proposed indices for the specific application. We recommend spectral indices at leaf level for evaluating reed ecological status based on spectroscopic data to support the identification of affected vegetation patches and present R2 maps that can aid the selection of indices tailored to specifications of remote sensors

Despite longstanding contributions to oceanography, similar use of fluorescence light detection and ranging (LiDAR) in lake settings is not routine. The potential for ship-mounted, multispectral Ultraviolet Fluorescence LiDAR (UFL) to provide rapid, high-resolution data in variably turbid and productive lake conditions are investigated here through a series of laboratory tank and field measurements carried out on Lake Balaton, Hungary. UFL data, calibrated empirically to a set of coinciding conventionally-analyzed samples, provide simultaneous estimates of three important parameters-chlorophyll a(chla), total suspended matter (TSM) and colored dissolved organic matter (CDOM). Successful UFL retrievals from both laboratory and field measurements were achieved for chla (0.01–378 mg∙m⁻³; R = 0.83–0.92), TSM (0.1–130 g∙m⁻³; R = 0.90–0.96) and CDOM (0.003–0.125 aCDOM(440); R = 0.80–0.97). Fluorescence emission at 685 nm is shown through tank measurements to display robust but distinct relationships with chla concentration for the two cultured algae species investigated (cyanobacteria, Cylindrospermopsis raciborskii, and chlorophyta, Scenedesmus armatus). The ratio between fluorescence emissions measured at 650 nm, related to the phycocyanin fluorescence maximum, to that at 685 nm is demonstrated to effectively distinguish these two species. Validation through both laboratory measurements and field measurements confirmed that site specific calibration is necessary. This study presents the first known assessment and application of ship-mounted fluorescence LiDAR in freshwater lake conditions and demonstrates the use of UFL in measuring important water quality parameters despite the more complicated hydro-optic conditions of inland waters.