showing 4 related works from this author
Atmospheric correction of ENVISAT/MERIS data over inland waters: Validation for European lakes
2010
Traditional methods for aerosol retrieval and atmospheric correction of remote sensing data over water surfaces are based on the assumption of zero water reflectance in the near-infrared. Another type of approach which is becoming very popular in atmospheric correction over water is based on the simultaneous retrieval of atmospheric and water parameters through the inversion of coupled atmospheric and bio-optical water models. Both types of approaches may lead to substantial errors over optically-complex water bodies, such as case II waters, in which a wide range of temporal and spatial variations in the concentration of water constituents is expected. This causes the water reflectance in t…
Optical types of inland and coastal waters
2017
Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of condi…
Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery
2021
Retrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (?R) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations in cyanobacteria biomass in specific regions, their performance has not been assessed on hyperspectral images from satellite sensors. Our work leverages a machine-learning model, Mixture Density Networks (MDNs), trained on a large (N = 939) dataset of collocated in situ chlorophyll-a concentrat…
Mapping snow density through thermal inertia observations
2023
Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale.…