No abstract

In this study we evaluate the capabilities of three satellite sensors for assessing water composition and bottom depth in Lake Garda, Italy. A consistent physics-based processing chain was applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat-8 Operational Land Imager (OLI) and RapidEye. Images gathered on 10 June 2014 were corrected for the atmospheric effects with the 6SV code. The computed remote sensing reflectance (Rrs) from MODIS and OLI were converted into water quality parameters by adopting a spectral inversion procedure based on a bio-optical model calibrated with optical properties of the lake. The same spectral inversion procedure was applied to RapidEye and to OLI data to map bottom depth. In situ measurements of Rrs and of concentrations of water quality parameters collected in five locations were used to evaluate the models. The bottom depth maps from OLI and RapidEye showed similar gradients up to 7 m (r = 0.72). The results indicate that: (1) the spatial and radiometric resolutions of OLI enabled mapping water constituents and bottom properties; (2) MODIS was appropriate for assessing water quality in the pelagic areas at a coarser spatial resolution; and (3) RapidEye had the capability to retrieve bottom depth at high spatial resolution. Future work should evaluate the performance of the three sensors in different bio-optical conditions.

No abstract

Eutrophic lakes display unpredictable patterns of phytoplankton growth, distribution, vertical and horizontal migration, likely depending on environmental conditions. Monitoring chlorophyll-a (Chl-a) concentration provides reliable information on the dynamics of primary producers if monitoring is conducted frequently. We present a practical approach that allows continuous monitoring of Chl-a concentration by using a radiometric system that measures optical spectral properties of water. We tested this method in a shallow, nutrient-rich lake in northern Italy, the Mantua Superior Lake, where the radiometric system collected data all throughout the day (i.e. every 5 min) for ~30 days. Here, specifically developed algorithms were used to convert water reflectance to Chl-a concentration. The best performing algorithm (R2 = 0.863) was applied to a larger dataset collected in September 2011. We characterised intra- and inter-daily Chl-a concentration dynamics and observed a high variability; during a single day, Chl-a concentration varied from 20 to 130 mg m–3. Values of Chl-a concentration were correlated with meteo-climatic parameters, showing that solar radiance and wind speed are key factors regulating the daily phytoplankton growth and dynamics. Such patterns are usually determined by vertical migration of different phytoplankton species within the water column, as well as by metabolic adaptations to changes in light conditions.