0000000000391732

AUTHOR

Francesco Nutini

0000-0001-5430-5991

showing 3 related works from this author

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

2017

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and funct…

Atmospheric Sciencefood industryMonitoring010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesInformation Dissemination02 engineering and technology01 natural sciencesElectronic mailData modelingRemote SensingERMESremote sensingFood IndustryComputers in Earth Sciences021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingDownstream (petroleum industry)agriculture2. Zero hungerData collectionEnd userbusiness.industryEnvironmental resource managementModelingAgriculturemodeling15. Life on landmonitoringAgriculturebusiness
researchProduct

Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring

2016

Abstract This paper presents an operational chain for high-resolution leaf area index (LAI) retrieval from multiresolution satellite data specifically developed for Mediterranean rice areas. The proposed methodology is based on the inversion of the PROSAIL radiative transfer model through the state-of-the-art nonlinear Gaussian process regression (GPR) method. Landsat and SPOT5 data were used for multitemporal LAI retrievals at high-resolution. LAI estimates were validated using time series of in situ LAI measurements collected during the rice season in Spain and Italy. Ground LAI data were collected with smartphones using PocketLAI, a specific phone application for LAI estimation. Temporal…

2. Zero hunger010504 meteorology & atmospheric sciencesMean squared error0211 other engineering and technologiesSoil ScienceGeologyInversion (meteorology)02 engineering and technologyCrop monitoring; Rice; Leaf area index (LAI) retrieval; PROSAIL; Smartphone; Gaussian process regression (GPR); Landsat; SPOT5 Take501 natural sciencesAtmospheric radiative transfer codesKrigingSatellite dataGround-penetrating radarEnvironmental scienceComputers in Earth SciencesLeaf area indexRice crop021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
researchProduct

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
researchProduct