0000000000246162

AUTHOR

Enrico Tomelleri

0000-0001-6546-6459

showing 3 related works from this author

Sun-induced chlorophyll fluorescence II: Review of passive measurement setups, protocols, and their application at the leaf to canopy level

2019

Imaging and non-imaging spectroscopy employed in the field and from aircraft is frequently used to assess biochemical, structural, and functional plant traits, as well as their dynamics in an environmental matrix. With the increasing availability of high-resolution spectroradiometers, it has become feasible to measure fine spectral features, such as those needed to estimate sun-induced chlorophyll fluorescence (F), which is a signal related to the photosynthetic process of plants. The measurement of F requires highly accurate and precise radiance measurements in combination with very sophisticated measurement protocols. Additionally, because F has a highly dynamic nature (compared with othe…

VegetationUFSP13-8 Global Change and BiodiversityFIS/06 - FISICA PER IL SISTEMA TERRA E PER IL MEZZO CIRCUMTERRESTREScienceQ1900 General Earth and Planetary SciencesGEO/12 - OCEANOGRAFIA E FISICA DELL'ATMOSFERASun-induced fluorescence; Spectroradiometer; Spectrometer; Vegetation; Radiance; Reflectance; Remote sensing; FLEXReflectanceRadianceRemote sensingSpectrometerGEO/11 - GEOFISICA APPLICATAFLEX10122 Institute of GeographyGEO/10 - GEOFISICA DELLA TERRA SOLIDASun-induced fluorescenceSpectroradiometerGeneral Earth and Planetary Sciencesddc:620910 Geography & travel
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Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data

2015

Abstract The accurate quantification of carbon fluxes at continental spatial scale is important for future policy decisions in the context of global climate change. However, many elements contribute to the uncertainty of such estimate. In this study, the uncertainties of eight days gross primary production (GPP) predicted by Random Forest (RF) machine learning models were analysed at the site, ecosystem and European spatial scales. At the site level, the uncertainties caused by the missing of key drivers were evaluated. The most accurate predictions of eight days GPP were obtained when all available drivers were used (Pearson's correlation coefficient, ρ ~ 0.84; Root Mean Square Error (RMSE…

Correlation coefficientEddy covarianceSpatial ecologySoil ScienceEnvironmental sciencePrimary productionGeologyContext (language use)Land coverComputers in Earth SciencesUncertainty analysisRandom forestRemote sensingRemote Sensing of Environment
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Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

2022

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative result…

Precision agriculturemultispectralbiotic and abiotic stresatelliteSoil Sciencesolar induced fluorescenceGeologymulti-modalPrecision agriculture multi-modal solar-induced fluorescence satellite hyperspectral multispectral biotic and abiotic stressUNESCO::CIENCIAS TECNOLÓGICASITC-HYBRIDhyperspectralITC-ISI-JOURNAL-ARTICLEddc:550Computers in Earth Sciences
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