0000000000424948

AUTHOR

Gabriele Candiani

0000-0001-5270-071x

showing 4 related works from this author

Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery

2022

The recently launched and upcoming hyperspectral satellite missions, featuring contiguous visible-to-shortwave infrared spectral information, are opening unprecedented opportunities for the retrieval of a broad set of vegetation traits with enhanced accuracy through novel retrieval schemes. In this framework, we exploited hyperspectral data cubes collected by the new-generation PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency to develop and test a hybrid retrieval workflow for crop trait mapping. Crop traits were mapped over an agricultural area in north-east Italy (Jolanda di Savoia, FE) using PRISMA images collected during the 2020 and 202…

Machine learning regressionWater contentEarth ObservationComputers in Earth SciencesNitrogen contentRemote sensingEngineering (miscellaneous)Chlorophyll contentArticleAtomic and Molecular Physics and OpticsComputer Science ApplicationsISPRS Journal of Photogrammetry and Remote Sensing
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A Simple Fusion Method for Image Time Series Based on the Estimation of Image Temporal Validity

2015

High-spatial-resolution satellites usually have the constraint of a low temporal frequency, which leads to long periods without information in cloudy areas. Furthermore, low-spatial-resolution satellites have higher revisit cycles. Combining information from high- and low- spatial-resolution satellites is thought a key factor for studies that require dense time series of high-resolution images, e.g., crop monitoring. There are several fusion methods in the bibliography, but they are time-consuming and complicated to implement. Moreover, the local evaluation of the fused images is rarely analyzed. In this paper, we present a simple and fast fusion method based on a weighted average of two in…

TeledeteccióComputer scienceforêt tropicalehttp://aims.fao.org/aos/agrovoc/c_714remote sensingSimple (abstract algebra)K01 - Foresterie - Considérations généralesBiomassehttp://aims.fao.org/aos/agrovoc/c_6498validationUtilisation des terresEucalyptusFusionQhttp://aims.fao.org/aos/agrovoc/c_14093http://aims.fao.org/aos/agrovoc/c_9000094Plantation forestièreséquestration du carbonehttp://aims.fao.org/aos/agrovoc/c_926http://aims.fao.org/aos/agrovoc/c_1070http://aims.fao.org/aos/agrovoc/c_25409http://aims.fao.org/aos/agrovoc/c_4182P01 - Conservation de la nature et ressources foncièresSpectrométriePhénologiehttp://aims.fao.org/aos/agrovoc/c_2683TélédétectionScienceImage (mathematics)Cartographie de l'occupation du solhttp://aims.fao.org/aos/agrovoc/c_24904TermodinàmicaCouverture végétalehttp://aims.fao.org/aos/agrovoc/c_7283http://aims.fao.org/aos/agrovoc/c_1666http://aims.fao.org/aos/agrovoc/c_8176http://aims.fao.org/aos/agrovoc/c_3048MODIS; Landsat; validation; remote sensingRemote sensingChangement climatiqueSeries (mathematics)business.industryCiències de la terraPattern recognitionVégétationhttp://aims.fao.org/aos/agrovoc/c_331583Constraint (information theory)http://aims.fao.org/aos/agrovoc/c_5774SpectroradiometerMODISSatelliteGeneral Earth and Planetary SciencesArtificial intelligenceU30 - Méthodes de recherchebusinessLandsatRemote Sensing; Volume 7; Issue 1; Pages: 704-724
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Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission

2022

In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This mission will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the “agriculture and food security” domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the…

chlorophyll contentmachine learning regression algorithmactive learningGeneral Earth and Planetary Sciencesspaceborne imaging spectroscopyradiative transfer modelingGaussian process regressionnitrogen contentRemote Sensing
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Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

2022

Atmospheric Scienceprecision farmingradiative transfer modelsApplied Mathematicsplant nitrogen uptake estimationComputers in Earth Sciencesmachine learning regression algorithmsGeneral Environmental ScienceEuropean Journal of Remote Sensing
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