Erica Matta

Evaluation of Multi-Resolution Satellite Sensors for Assessing Water Quality and Bottom Depth of Lake Garda

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.

Assessing water quality in the northern Adriatic Sea from HICO™ data

This letter focuses on water-quality estimation in the northern Adriatic Sea using physically-based methods applied to image obtained with the Hyperspectral Imager for the Coastal Ocean (HICO™). Optical properties of atmosphere and water were synchronously measured to parameterise such methods. HICO™-derived maps of chlorophyll-a (chl-a) and suspended particulate matter (SPM) indicated low values, in the range of 0–3 mg m−3 and 0–4 g m−3, respectively, correlating significantly with field data (R2 = 0.71 for chl-a and R2 = 0.85 for SPM). The results, on analysis, identify clear waters in the open sea and moderately turbid waters near the coast due to river sediment discharge and organic matter from coastal lagoons. These findings support the use of HICO™ data to assess water-quality parameters in coastal zones and suggest the feasibility of integrating them with future-generation space-borne hyperspectral images.

Remote sensing supports the definition of the water quality status of Lake Omodeo (Italy)

Abstract Lake Omodeo is the largest artificial reservoir of Sardinia and its waters are a valuable resource for irrigation, domestic and industrial purposes. Lake Omodeo has serious problems of eutrophication. Since 2007 the local water authority has been undertaken a monitoring program designed to test an integrated methodology based on field measurements and remote sensing. This study illustrates the production of multitemporal spatialised maps of chlorophyll-a concentrations from satellite data acquired from Medium Resolution Imaging Spectrometer (MERIS). The analysis confirmed the eutrophic status of Omodeo. especially between spring and summer (mainly due to cyanobacteria bloom) assessing their dependency on weather conditions and river inputs.

Mapping Submerged Habitats and Mangroves of Lampi Island Marine National Park (Myanmar) from in Situ and Satellite Observations

In this study we produced the first thematic maps of submerged and coastal habitats of Lampi Island (Myanmar) from in situ and satellite data. To focus on key elements of bio-diversity typically existing in tropical islands the detection of corals, seagrass, and mangrove forests was addressed. Satellite data were acquired from Landsat-8; for the purpose of validation Rapid-Eye data were also used. In situ data supporting image processing were collected in a field campaign performed from 28 February to 4 March 2015 at the time of sensors overpasses. A hybrid approach based on bio-optical modeling and supervised classification techniques was applied to atmospherically-corrected Landsat-8 data. Bottom depth estimations, to be used in the classification process of shallow waters, were in good agreement with depth soundings (R2 = 0.87). Corals were classified with producer and user accuracies of 58% and 77%, while a lower accuracy (producer and user accuracies of 50%) was found for the seagrass due to the patchy distribution of meadows; accuracies more than 88% were obtained for mangrove forests. The classification indicated the presence of 18 mangroves sites with extension larger than 5 km2; for 15 of those the coexistence of corals and seagrass were also found in the fronting bays, suggesting a significant rate of biodiversity for the study area.

Multitemporal analysis of algal blooms with MERIS images in a deep meromictic lake

Abstract MERIS images (2003–2011) were used to detect algal bloom events in Lake Idro (Northern Italy) applying a semi-empirical algorithm. From the study of an intense phenomenon occurred in late summer 2010, a retrospective analysis of similar events during late summer/early autumn period was performed. High intra-and inter-annual variability was observed and three additional bloom events were identified on 2003, 2005 and 2008. Hydrological and weather parameters were examined at different temporal intervals (August-October, September-October and monthly from August to October) to investigate the regulating factors of bloom incidence. Rather low temperatures and the persistence of clouds seem to facilitate starting and maintenance of blooms.

On the synergistic use of SAR and optical imagery to monitor cyanobacteria blooms: the Curonian Lagoon case study

Abstract Multi-sensor satellite data are used to assess cyanobacteria blooms in the Curonian Lagoon. The exploitation of SAR, in combination with optical data, is investigated to take full advantage from the all-weather, night/day SAR imaging capability. A dataset of images has been analyzed to: 1) study the effect of cyanobacteria on microwave signals; 2) assess the daily evolution of cyanobacteria bloom from multi-sensors data; and 3) evaluate the dependence of dynamics of blooms on winds. The results show a significant correlation (R2 > 0.8, p<0.001) between the X-and C-band Normalized Radar Cross Section (NRCS) attenuation and the NIR-Red band ratio Index, with the latter considered as a proxy for the presence of cyanobacteria blooms. A combined use of microwave and optical observations can improve the detection of cyanobacteria blooms and their dependency on wind action.

Mapping Posidonia meadow from high spatial resolution images in the Gulf of Oristano (Italy)

This work deals with the retrieval of distribution of seabed vegetation cover (essentially Posidonia Oceanica) in the coastal waters (1-10m) of the Gulf of Oristano. A physical approach was used to derive bottom cover map from airborne and satellite imagery (MIVIS, KOMPSAT-2 and RapidEye) acquired in summer 2001, 2011 and 2013. A bathymetric map was also derived from the RapidEye image of 2003. A field campaign was held on 1-8 August 2013 to parameterize the physical model and to validate the 2013 results. Accuracy of bottom cover map and regression between measured and modelled water depths was good providing an overall accuracy of 0.88% and a correlation coefficient of 0.84, respectively. The retrospective analysis revealed that P. oceanica meadow seems to be stable over the 12 years showing only very small variations (-0.2km2). Sea bottom morphology and vegetation cover were also related.

Use of Satellite and In Situ Reflectance Data for Lake Water Color Characterization in the Everest Himalayan Region

This study applied remote sensing techniques to the study of water color in Himalayan glacial lakes as a proxy of suspended solid load. In situ measurements gathered in 5 lakes in October 2014 during satellite data acquisition enabled the characterization of water reflectance and clarity and supported image processing. Field data analysis led to a distinction between 3 water colors and a consequent lake water color classification on a regional scale from Landsat-8 data previously corrected for atmospheric and adjacency effects. Several morphometric parameters (lake size and shape, distance between lake and glacier) were also computed for the lakes thus classified. The results showed spatial and temporal variations in lake water color, suggestive of relationships between glacier shrinkage and the presence of brighter and more turbid water. A finer-scale analysis of the spatial variability of water reflectance on Chola Lake (based on GeoEye-1 data captured on 18 October 2014) showed the contribution of water component absorption from the inflow. Overall, the findings support further research to monitor Himalayan lakes using both Landsat-8 and Sentinel-2 (with its improved resolutions).

Imaging Spectrometry of Inland Water Quality in Italy Using MIVIS: An Overview

Airborne imaging spectrometry is a powerful tool to investigate key biophysical parameters in inland waters. High spectral resolution data forms a contiguous spectrum that enables the detection and identification of a variety of key water quality indicators (e.g. cyanobacteria pigments). High spatial resolution imagery is suitable for fine-scale observation (e.g. the patchy spatial distribution of phytoplankton in productive waters). Airborne observations ensure flexible flight paths that allow observations of unexpected events to be acquired promptly. In this chapter, we present an overview of remote sensing techniques, by focusing on imaging spectrometry, for assessing water quality parameters in inland waters such as lakes, streams, rivers, reservoirs and ponds (defined ‘Case-2 waters’ according to a traditional remote sensing terminology). Then, we present examples of applications by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) images of Italian inland waters acquired at a spatial resolution varying from 3 to 5 m. Those examples include the retrieval of water quality parameters (i.e. chlorophyll-a, suspended particulate matter and coloured dissolved organic matter), the detection and monitoring of submerged vegetation, the observation of a cyanobacteria bloom in productive lakes and the investigation of the signal reflected by floating materials of terrestrial origin (i.e. pollens and oil).

Hyperspectral observations of optical properties in lakes in perspective of future satellite sensors — A case study in ITALY

HICO, Hyperion and MERIS have already demonstrated the capabilities of hyperspectral sensors to gather information on optical properties in inland waters. With the advent of Sentinel-2, Sentinel-3, EnMap, PRISMA and HyspIRI we expect that application of remote sensing techniques for inland water quality assessment will progress even more. Some clear advantages rely on: 1) improved spatial resolution (so that even medium/small lakes are imaged), 2) increased frequency of overpasses (so that environmental processes are detected in time) and 3) enhanced sensor characteristics (which allow, for instance, the secondary phytoplankton pigments to be retrieved). In this study we present how these improvements will permit to deliver improved products for inland water quality addressing better the end-user demands.