Assessing the suitability of multi-spectral satellite data for the development of data-driven models of phytoplankton dynamics in lakes and reservoirs
Abstract Phytoplankton blooms threaten aquatic ecosystems worldwide, with implications going beyond their apparent ecological aspects. Management solutions are needed to control the appearance of phytoplankton blooms and alleviate their impacts. Such solutions are supported by scientific results, many of which derive from modeling approaches. Data-driven…
The impact of assimilating Earth Observation and in situ data on seasonal hydrological predictions in a snow-dominated river system
Abstract Earth Observations (EO) have become popular in hydrology because they provide valuable information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EO’s into hydrological models to improve the estimation of initial…
Application of New Hyperspectral Sensors in the Remote Sensing of Aquatic Ecosystem Health: Exploiting PRISMA and DESIS for Four Italian Lakes
Abstract The monitoring of water bio-physical parameters and the management of aquatic ecosystems are crucial to cope with the current state of inland water degradation. Not only does water quality monitoring support management decision making, it also provides vital insights to better understand changing structural…
Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery
Abstract This study presents a first assessment of the Top-Of-Atmosphere (TOA) radiances measured in the visible and near-infrared (VNIR) wavelengths from PRISMA (PRecursore IperSpettrale della Missione Applicativa), the new hyperspectral satellite sensor of the Italian Space Agency in orbit since March 2019. In particular, the…
What is the impact of earth observation and in-situ data assimilation on seasonal hydrological predictions?
Abstract Earth Observations (EO) have become popular in hydrology because they provide information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EOs into hydrological models to improve the estimation of initial states…
A data-driven approach to predict algal blooms using satellite-derived water quality and hydrometeorological drivers
Abstract The present work leverages simulated hydrometeorological factors and satellite-derived chlorophyll-a to predict phytoplankton dynamics for Mulargia reservoir (Sardinia, Italy). A Random Forest (RF) model was (a) calibrated to minimize out-of-bag errors of chlorophyll-a predictions for a 5-year-long period (2015-2019), and (b) benchmarked against a…
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