An optical tool for quantitative assessment of phycocyanin pigment concentration in cyanobacterial blooms within inland and marine environments

Quantitative assessment of the pigment phycocyanin (PC) in cyanobacterial blooms is essential to assess their abundance and distribution and consequently aid their management in many recreational waters within inland and coastal environments. In contrast to the open-ocean waters, these water bodies are very complex with a pronounced heterogeneity of their optical properties, and hence accurate retrieval of the water-leaving radiances and PC concentration from satellite observations is notoriously difficult with existing algorithms. In the present study, a new inversion algorithm is developed as a rapid cyanobacteria bloom assessment method and its retrievals of PC are compared with in-situ and satellite observations and those from a previously reported inversion algorithm. The new algorithm estimates PC concentration on the basis of the unique absorption feature of phycocyanin at 620 nm which is isolated from the total pigment absorption by taking advantage of the well-recognized absorption and reflectance features in the red and near-infrared (NIR) wavelengths (less impacted by the influences of the overlapping absorption signatures of the mixture constituents and pigment packaging). The by-products of this work include chl-a concentration and predictions from reflectance data to monitor the cyanobacterial component and non-cyanobacterial component of the phytoplankton assemblage and to evaluate PC:Chl-a pigment weight ratios for specific water types. Initial validation of the algorithm was performed using in-situ field data in turbid productive waters dominated by phycocyanin and other pigments, yielding coefficients of determination and slope close to unity and mean errors less than a few percent. These results suggest that the algorithm could be used as a rapid assessment tool for the remote-sensing assessment of the spatial distribution and relative abundance of cyanobacterial blooms in many regional water bodies.     

Sensitivity of remotely sensed pigment concentration via Mixture Density Networks (MDNs) to uncertainties from atmospheric correction

Lake Erie, the shallowest of the five North American Laurentian Great Lakes, exhibits degraded water quality associated with recurrent phytoplankton blooms. Optical remote sensing of these optically complex inland waters is challenging due to the uncertainties stemming from atmospheric correction (AC) procedures. In this study, the accuracy of remote sensing reflectance (R_rs) derived from three different AC algorithms applied to Ocean and Land Colour Instrument (OLCI) observations of western Lake Erie (WLE) is evaluated through comparison to a regional radiometric dataset. The effects of uncertainties in R_rs products on the retrieval of near-surface concentration of pigments, including chlorophyll-a (Chla) and phycocyanin (PC), from Mixture Density Networks (MDNs) are subsequently investigated. Results show that iCOR contained the fewest number of processed (unflagged) days per pixel, compared to ACOLITE and POLYMER, for parts of the lake. Limiting results to the matchup dataset in common between the three AC algorithms shows that iCOR and ACOLITE performed closely at 665 nm, while outperforming POLYMER, with the Median Symmetric Accuracy (MdSA) of ∼30 %, 28 %, and 53 %, respectively. MDN applied to iCOR- and ACOLITE-corrected data (MdSA < 37 %) outperformed MDN applied to POLYMER-corrected data in estimating Chla. Large uncertainties in satellite-derived R_rs propagated to uncertainties ∼100 % in PC estimates, although the model was able to recover concentrations along the 1:1 line. Despite the need for improvements in its cloud-masking scheme, we conclude that iCOR combined with MDNs produces adequate OLCI pigment products for studying and monitoring Chla across WLE.     

Mapping cyanobacterial blooms in Lake Champlain's Missisquoi Bay using QuickBird and MERIS satellite data

C-phycocyanin (C-PC) and chlorophyll-a (Chl-a) concentrations for the eutrophic waters of Missisquoi Bay, Lake Champlain (VT–QC) were retrieved from Envisat's MERIS radiance data (300 m spatial resolution) and validated against coincident georeferenced transect observations. Pigment concentrations were also predicted from empirically calibrated QuickBird data (2.4 m spatial resolution) using selected band ratios and principal components analysis. The QuickBird NIR/Red band ratio accounted for approximately 80% of the variability in observed Chl-a concentration, allowing for detailed mapping of phytoplankton spatial distributions. C-PC concentrations, in contrast, were somewhat poorly modeled (R² = 0.68). Use of these data for monitoring purposes, however, is also limited by the need for coincident field observations. Chl-a concentrations were also accurately retrieved from the MERIS data (Mean Relative Error = -0.6%) despite high concentrations of suspended particles and dissolved organic matter in the bay waters. C-PC concentrations were underestimated on average by 2.1%, but by 10–20% at high C-PC concentrations (≥ 80 μg/L) and as the proportion of cyanobacteria in the phytoplankton community decreased. The relatively high overall accuracies observed, however, attest to the robustness of the MERIS semi-analytical retrieval algorithms used to quantify potentially toxic cyanobacteria cell densities without the need for coincident field data. Our analyses over a 17 day period captured the peak and collapse of a late summer cyanobacterial bloom, illustrating the value of remote sensing to provide synoptic and timely information on the abundance and distribution of cyanobacterial populations that, in turn, can facilitate public health risk assessment.     

Mapping of coastal surface chlorophyll-a concentration by multispectral reflectance measurement from unmanned aerial vehicles

In subtropical coastal waters, the explosive growth of phytoplankton under favorable conditions can lead to water discolouration and massive fish kills. Manual field sampling and laboratory analysis of chlorophyll-a concentration (Chl-a) as an indicator to algal biomass, is resources intensive and time consuming, delaying responses to disastrous harmful algal blooms. Cloudy weather often precludes the use of satellite images for water quality and algal bloom monitoring. This study aims at developing an estimator algorithm for quantitative mapping of surface Chl-a for coastal waters, based on surface reflectance measurement from an Unmanned Aerial Vehicle (UAV) with a five-band multispectral camera. The surface reflectance is obtained from calibrated multispectral images which are radiometric-corrected against incoming solar radiation. It is found that Chl-a has an inverse correlation with the Normalized Green-Red Difference Index (NGRDI). A regression estimator model for Chl-a from NGRDI is developed, showing excellent performance for fish farms in coastal waters with different characteristics. The technology is demonstrated for mapping the spatial and temporal variation of Chl-a during an algal bloom, offering a useful complement to traditional field monitoring for fisheries management and emergency response.     

Effects of temperature and nutrients on phytoplankton biomass during bloom seasons in Taihu Lake

Long-term variations of phytoplankton chlorophyll-a (Chl-a), nutrients,and suspended solids (SS) in Taihu Lake, a large shallow freshwater lake in China, during algal bloom seasons from May to August were analyzed using the monthly investigated data from 1999 to 2007. The effective accumulated water temperature (EAWT) in months from March to June was calculated with daily monitoring data from the Taihu Laboratory for Lake Ecosystem Research (TLLER).The concentrations of Chl-a and nutrients significantly decreased from Meiliang Bay to Central Lake. Annual averages of the total nitrogen (TN), total phosphorus (TP), and Chl-a concentrations, and EAWT generally increased in the nine years. In Meiliang Bay, the concentration of Chl-a was significantly correlated with EAWT, ammonia nitrogen (NH4+-N ), TN, the soluble reactive phosphorus (SRP),TP, and SS. In Central Lake, however, the concentration of Chl-a was only correlated with EAWT, TP, and SS. Multiple stepwise linear regression revealed that EAWT, dissolved total phosphorus (DTP), and TP explained 99.2% of the variation of Chl-a in Meiliang Bay, and that EAWT, NH4+-N, and TP explained 98.7% of the variation of Chl-a in Central Lake. Thus EAWT is an important factor influencing the annual change of phytoplankton biomass. Extreme climate change, such as extremely hot springs or cold springs, could cause very different bloom intensities in different years. It is also suggested that both nutrients and EAWT played important roles in the growth of phytoplankton in Taihu Lake. The climate factors and nutrients dually controlled the risk of harmful algal blooms in Taihu Lake. Cutting down phosphorus and nitrogen loadings from catchments should be a fundamental strategy to reduce the risk of blooms in Taihu Lake.     

基于MODIS遥感数据源的内陆水体叶绿素a浓度反演算法综述

由于MODIS遥感具有较高的空间、光谱以及时间分辨率,遥感反演方法已经逐渐成为内陆水体叶绿素浓度信息提取研究的重要技术之一.本文回顾了国内外MODIS遥感反演算法研究进展,总结了目前若干反演方法的基本原理与典型算法,将反演方法归纳为经验模型法、半经验模型法以及分析模型法,对比分析各方法的特点,并提出区域性与普适性问题是所有目前算法模型的通病,未来以高光谱数据源反演是趋势,算法要以解决区域局限性与水色复杂性为主.     

被动微波遥感土壤水分反演研究进展

准确获取区域土壤水分时空分布和变化对于水文过程模拟、洪旱灾害监测等方面具有重要意义.近几十年,快速发展的遥感技术为大区域地表土壤水分连续观测提供了机遇.尤其,被动微波遥感具有诸多优势,被认为是进行大区域表层土壤水分连续监测的最有潜力的手段.国内外学者对被动微波遥感土壤水分反演开展了大量的研究.为了更好了解国内外研究前沿...     

Prediction of chlorophyll a concentration using HJ-1 satellite imagery for Xiangxi Bay in Three Gorges Reservoir简

Since the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/（B2＋B3） and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management.     

The impact of phytoplankton community composition on optical properties and satellite observations of the 2017 western Lake Erie algal bloom

Since the early 2000s Lake Erie has seen a dramatic increase in phytoplankton biomass, manifested in particular by the rise in the severity of cyanobacteria blooms and the prevalence of potentially toxic taxa such as Microcystis. Satellite remote sensing has provided a unique capacity for the synoptic detection of these blooms, enabling spatial and temporal trends in their extent and severity to be documented. Algorithms for satellite detection of Lake Erie algal blooms often rely on a single consistent relationship between algal or cyanobacterial biomass and spectral indices such as the Maximum Chlorophyll Index (MCI) or Cyanobacteria Index (CI). Blooms, however, are known to vary significantly in community composition over space and time. A suite of phytoplankton and optical property measurements during the western Lake Erie algal bloom of 2017 showed highly diverse bloom composition with variable absorption and backscatter properties. Elevated backscattering coefficients were observed in the Maumee Bay, likely due to phytoplankton cell morphology and buoyancy regulating gas vacuoles, compared with typically Planktothrix dominated blooms in Sandusky Bay. MCI and CI calibrated to historical chlorophyll observations and applied to Sentinel 3's OLCI sensor accurately captured the 2017 bloom in Maumee Bay but underestimated the Sandusky Bay bloom by nearly 80%. The phycoerythrin-rich picocyanobacteria Aphanothece and Synechococcus were found in abundance throughout the western and central basins, resulting in substantial biomass underestimations using blue to green ratio-based algorithms. Potential misrepresentation of bloom severity resulting from phytoplankton optical properties should be considered in assessments of bloom conditions on Lake Erie.     

Cyanobacterial blooms in the central basin of Lake Erie: Potentials for cyanotoxins and environmental drivers

Lake Erie western basin (WB) cyanobacterial blooms are a yearly summer occurrence; however, blooms have also been reported in the offshore waters of the central basin (CB), and very little is known about what drives these blooms or their potential for cyanobacterial toxins. Cyanobacteria Index was quantified using MODIS and MERIS data for the CB between 2003 and 2017, and water samples were collected between 2013 and 2017. The goals were to 1) quantify cyanobacteria, 2) determine environmental drivers of CB blooms, and 3) determine the potential for cyanobacterial toxins in the CB. Dolichospermum (Anabaena) occurred in the CB during July before the onset of the WB bloom, and then in August and September, the cyanobacteria community shifted towards Microcystis. The largest Dolichospermum blooms (2003, 2012, 2013, and 2015) were associated with reduced water clarity (Secchi disk depth?<?4?m), whereas large CB Microcystis blooms (2011 and 2015) were associated with large WB blooms. Dolichospermum blooms occurred in high nitrate concentrations (>20?μmol/L) and high nitrogen-to?phosphorus ratios (>100), which indicate nutrient concentrations or ratios did not select for Dolichospermum. Additionally, the sxtA gene, but not mcyE or microcystins, were detected in the CB during July 2016 and 2017. The mcyE gene and microcystins were detected in the CB during August 2016 and 2017. The results indicate the CB's potential for cyanotoxins shifts from saxitoxins to microcystins throughout the summer. Continued monitoring of cyanobacteria and multiple cyanobacterial toxins is recommended to ensure safe drinking water for CB coastal communities.     

Remote Estimation of Nutrients for a Drinking Water Source Through Adaptive Modeling

Morse Reservoir, a major water supply for the Indianapolis metropolitan area, IN, USA, experiences nuisance cyanobacterial blooms due to agricultural and point source nutrient loadings. Hyperspectral remote sensing data from both in situ and airborne AISA measurements were applied to an adaptive model based on Genetic Algorithms-Partial Least Squares (GA-PLS) by relating the spectral signal with total nitrogen (TN) and phosphorus (TP) concentrations. Results indicate that GA-PLS relating in situ spectral reflectance to the nutrients yielded high coefficients of determination (TN: R ²?=?0.88; TP: R ²?=?0.91) between measured and estimated TN (RMSE?=?0.07 mg/L; Range: 0.6–1.88 mg/L), and TP (RMSE?=?0.017; Range: 0.023–0.314 mg/L). The GA-PLS model also yielded high performance with AISA imaging data, showing close correlation between measured and estimated values (TN: RMSE?=?0.11 mg/L; TP: RMSE?=?0.02 mg/L). An analysis of in situ data indicated that TN and TP were highly correlated with chlorophyll-a and suspended matter in the water column, setting a basis for remotely sensed estimates of TN and TP. Spatial correlation of TN, TP with chlorophyll-a and suspended matters further confirmed that remote quantification of nutrients for inland waters is based on the strong association of optically active constituents with nutrients. Based on these results, in situ and airborne hyperspectral remote sensors can provide both quantitative and qualitative information on the distribution and concentration of nutrients in Morse Reservoir. Our modeling approach combined with hyperspectral remote sensing is applicable to other productive waters, where algal blooms are triggered by nutrients.     

Using 3D modeling and remote sensing capabilities for a better understanding of spatio-temporal heterogeneities of phytoplankton abundance in large lakes

Lake biological parameters show important spatio-temporal heterogeneities. This is why explaining the spatial patchiness of phytoplankton abundance has been a recurrent ecological issue and is an essential prerequisite for objectively assessing, protecting and restoring freshwater ecosystems. The drivers of these heterogeneities can be identified by modeling their dynamics. This approach is useful for theoretical and applied limnology. In this study, a 3D hydrodynamic model of Lake Geneva (France/Switzerland) was created. It is based on the Delft3D suite software and includes the main tributary (Rhône River) and two-dimensional high-resolution meteorological forcing. It provides 3D maps of water temperature and current velocities with a 1?h time step on a 1?km horizontal grid size and with a vertical resolution of 1?m near the surface to 7?m at the bottom of the lake. The dynamics and the drivers of phytoplankton heterogeneities were assessed by combining the outputs of the model and chlorophyll-a concentration (Chl-a) data from MERIS satellite images between 2008 and 2012. Results highlight physical mechanisms responsible for the occurrence of seasonal hot-spots in phytoplankton abundance in the lake. At the beginning of spring, Chl-a heterogeneities are usually caused by an earlier onset of phytoplankton growth in the shallowest and more sheltered areas; spatial differences in the timing of phytoplankton growth can be explained by spatial variability in thermal stratification dynamics. In summer, transient and locally higher phytoplankton abundances are observed in relation to the impact of basin-scale upwelling.     

Algal bloom transport in Lake Erie using remote sensing and hydrodynamic modelling: Sensitivity to buoyancy velocity and initial vertical distribution

In this study, we simulate three-dimensional transport of algal blooms in Lake Erie using a combination of remote sensing and hydrodynamic modelling. The remote sensing algorithms use data from the Sentinel-3 OLCI satellite sensor to derive chlorophyll-a concentration from cyanobacteria blooms in Lake Erie. The derived chlorophyll-a concentration initializes an algal bloom transport model driven by the lake component of the Water Cycle Prediction System for the Great Lakes, a system of coupled atmosphere-lake-hydrological models operated out of Environment and Climate Change Canada. The bloom is modelled as Microcystis aeruginosa, a buoyant species that is often dominant in harmful algal blooms in western Lake Erie. Short-term (a few days) predictions of algal bloom transport from July 27 to October 8, 2017 are modelled in both Eulerian and Lagrangian frameworks. The Eulerian framework is used to evaluate the sensitivity of model results to the initial vertical distribution of the bloom. In this work, the Lagrangian framework is limited to two-dimensional surface confined particles. We use several error metrics to evaluate model predictions. We find that results are sensitive to the buoyancy velocity for cases where the bloom was initially distributed over a large portion of the water column. An initial vertical distribution selected from modelled chlorophyll-a half depth shows the highest accuracy for the entire range of buoyancy velocities tested. We also find that the Pierce skill score is difficult to interpret, particularly in cases where bloom intensity is greatly overpredicted by the model.     

Assessing the application of SeaWiFS ocean color algorithms to Lake Erie

The feasibility of satellite-based monitoring of phytoplankton chlorophyll a concentrations in Lake Erie is assessed by applying globally calibrated, ocean-derived color algorithms to spatially and temporally collocated measurements of SeaWiFS remote sensing reflectance. Satellite-based chlorophyll a retrievals were compared with fluorescence-based measurements of chlorophyll a from 68 field samples collected across the lake between 1998 and 2002. Twelve ocean-derived color algorithms, one regional algorithm derived for the Baltic Sea's Case 2 waters, and a set of regional algorithms developed for the western, central and eastern basins of Lake Erie were considered. While none of the ocean-derived algorithms performed adequately, the outlook for the success of regionally calibrated and validated algorithms, with forms similar to the ocean-derived algorithms, is promising over the eastern basin and possibly the central basin of the lake. In the western basin, each of the regional algorithms considered performed poorly, indicating that alternative approaches to algorithm development, or to satellite data screening and analysis procedures will be needed.     

Interannual and intra-annual cycles of satellite-derived chlorophyll-a concentrations in the Caspian Sea

Variability of phytoplankton in the Caspian Sea (CS) is related to the complex orography, the variety of physical and chemical processes, and climatic indices. Here, interannual and intra-annual variability of satellite-derived chlorophyll-a concentrations (Chl-a) were studied using wavelet analysis during 2002–2019 in different regions of the CS. Self-Organizing Maps (SOM) analysis performed to classify the CS into the areas of similar variability of satellite-derived Chl-a. Wavelet spectral analysis showed that the Chl-a variability regulated by four primary periodic cycles: 0.5-year, 1-year, 2 to 2.5-year, and 3 to 5-year. The 0.5 and 1-year wavelet cycles mostly depicted the intensity of seasonality patterns. The 2–2.5-year and 3–5-year cycles of Chl-a showed non-stationary coherence with corresponding low-frequency cycles of NAO and ENSO. The 3–5-year wavelet amplitudes of Chl-a strongly correlated with NAO and ENSO in the southern CS. Weak correlations of 2–2.5-year cycle wavelet amplitudes of Chl-a with NAO and ENSO suggested that variations do not always directly translate to a biological response. A negative anomaly in the Chl-a autumn peaks observed during 2011–2016 in the middle and southern CS, when NAO phases were persistently positive. The interannual variations of summer peaks in the northern CS, and autumn peaks in the middle and southern CS were broadly related to the precipitation. SST and wind stress. Moreover, it was shown that the Volga discharge has a significant influence on Chl-a variability in the northern CS.     

HJ-1B和Landsat卫星蓝藻水华监测能力评估——以洱海为例

蓝藻水华是富营养化湖泊共同面临的问题。遥感技术为快速、大范围水华监测提供了可能,选取遥感数据应首先明确不同卫星的水华监测能力。以洱海为例,对比分析HJ-1B和Landsat卫星在内陆中小湖泊水华监测的时间和空间监测能力,评价两者在水华监测中的适用性及优势。结果表明:两者均能有效识别水华,提取水华分布细节信息,相比MODIS更适合用于中小湖泊水华监测;进一步分析表明,综合两者数据监测蓝藻水华,可以更加客观统计水华时间特征,描述水华空间分布发展规律,对于其它中小湖泊利用遥感手段辅助水华监测具有参考意义。     

Determining remote sensing spatial resolution requirements for the monitoring of harmful algal blooms in the Great Lakes

Harmful algal blooms (HABs) have become a major health and environmental concern in the Great Lakes. In 2014, severe HABs prompted the State of Ohio to request NASA Glenn Research Center (GRC) to assist with monitoring algal blooms in Lake Erie. The most notable species of HAB is Microcystis aeruginosa, a hepatotoxin producing cyanobacteria that is responsible for liver complications for humans and other fauna that come in contact with these blooms. NASA GRC conducts semiweekly flights in order to gather up-to-date imagery regarding the blooms' spatial extents and concentrations. Airborne hyperspectral imagery is collected using two hyperspectral imagers, HSI-2 and HSI-3. Hyperspectral imagery is necessary in order to conduct experiments on differentiation of algal bloom types based on their spectral reflectance. In this analysis, imagery from September 19, 2016 was utilized to study the subpixel variability within the footprint of arbitrary sized pixels using several analysis techniques. This particular data set is utilized because it represents a worst case scenario where there is significant potential for public health concern due to high concentrations of microcystin toxin found in the water on this day and the concurrent observational challenges to accurately measure the algal bloom concentration variability with a remote sensing system due to the blooms high spatial variability. It has been determined that the optimal spatial resolution to monitor algal blooms in the Great Lakes is at most 50 m, and for much lower error 25 m, thus allowing for greater ease in identifying high concentration blooms near the surface. This resolution provides the best sensitivity to high concentration areas that are of significant importance in regard to human health and ecological damage.     

Spatial-temporal variability of in situ cyanobacteria vertical structure in Western Lake Erie: Implications for remote sensing observations

Remote sensing has provided expanded temporal and spatial range to the study of harmful algal blooms (cyanoHABs) in western Lake Erie, allowing for a greater understanding of bloom dynamics than is possible through in situ sampling. However, satellites are limited in their ability to specifically target cyanobacteria and can only observe the water within the first optical depth. This limits the ability of remote sensing to make conclusions about full water column cyanoHAB biomass if cyanobacteria are vertically stratified. FluoroProbe data were collected at nine stations across western Lake Erie in 2015 and 2016 and analyzed to characterize spatio-temporal variability in cyanobacteria vertical structure. Cyanobacteria were generally homogenously distributed during the growing season except under certain conditions. As water depth increased and high surface layer concentrations were observed, cyanobacteria were found to be more vertically stratified and the assumption of homogeneity was less supported. Cyanobacteria vertical distribution was related to wind speed and wave height, with increased stratification at low wind speeds (<4.9 m/s) and wave heights (<0.27 m). Once wind speed and wave height exceeded these thresholds the assumption of vertically uniform cyanobacteria populations was justified. These findings suggest that remote sensing can provide adequate estimates of water column cyanoHAB biomass in most conditions; however, the incorporation of bathymetry and environmental conditions could lead to improved biomass estimates. Additionally, cyanobacteria contributions to total chlorophyll-a were shown to change throughout the season and across depth, suggesting the need for remote sensing algorithms to specifically identify cyanobacteria.     

Light-absorbing components in the Great Lakes

Features of light absorption are critical in regulating the optical signal available for remote sensing. The magnitudes, spectral characteristics, spatial patterns, and, to a lesser extent, dynamics of light-absorbing components are documented for the Laurentian Great Lakes. This includes the open waters of each of the five lakes, and selected rivers, embayments and near-shore areas. The absorption coefficient, a(m^? 1), is partitioned according to the additive components (a_x) of colored dissolved organic matter (a_CDOM), non-algal particles (a_NAP), phytoplankton (a_φ), and water itself (a_w; known). Dependencies of a_x on various metrics of optically active constituents (OACs), cross-sections, are evaluated. A wide range of magnitudes of a_x and a, and contributions of a_x to a are documented. For example, the magnitude of a at a wavelength of 440 nm was nearly 10-fold greater in the western basin of Lake Erie than in the open waters of Lake Huron. Rivers, embayments, and near-shore areas generally had higher levels than the open waters. The largest a_x throughout the system was a_CDOM, originating mostly from terrestrial sources. Most of a_NAP was associated with clay mineral particles. The distribution of a_φ was highly correlated to chlorophyll concentration. The collected data set is appropriate to support initiatives to develop and preliminarily test mechanistic retrieval algorithms for OACs in the Great Lakes.     

Application of Remote Sensing in Water Resource Management: The Case Study of Lake Trasimeno, Italy

Satellite multi-sensor data were used to investigate the evolution in time and space of Lake Trasimeno, a shallow and turbid lake in central Italy. Large-swath MERIS and MODIS sensors were proposed for regular broad scale monitoring of water quality, having compared the retrieved chlorophyll-a (Chl-a) concentration, Secchi disk (SD) depth and surface water temperature with the 2005–2008 time-series of the in situ data. Although, in a shorter time span, also the MERIS-derived total suspended matter (TSM) matched the in situ data. MERIS-derived water quality products confirmed the meso-eutrophic conditions of Lake Trasimeno (average Chl-a = 8.5 mg/m³) and the low levels of transparency (average SD = 1 m). A negative correlation found between water levels and Chl-a suggest the importance of maintaining water levels as close as possible to the hydrometric zero. A spatial analysis of TSM also reveals how small tributaries may affect the load of suspended solids in the southern part of the lake. Higher spatial resolution satellite images were exploited both to describe land use/cover transformation from 1978 to 2008 and to assess the recent changes in macrophyte colonisation patterns. Land cover change detection analysis results showed a decrease in cultivated areas starting from the early Nineties and the subsequent increase in unproductive terrain (bare land and pastures) and natural woods as well as the changing fragmentation of agricultural areas through time. A reduction in macrophyte beds from 2003 to 2008 was also observed. We expect the results of this study to support local water authorities in redrawing the management plan of Lake Trasimeno.