Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Autor: Masoumeh Aghababaei, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi, Adrián Pérez-Suay, Miguel Morata, Jose Luis Garcia, Juan Pablo Rivera Caicedo, Jochem Verrelst

Publikováno v: Remote Sensing, Vol 14, Iss 18, p 4452 (2022)

Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and r

Externí odkaz: https://doaj.org/article/3ccc01f7b08040b9b35a550ecf57909d

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

Autor: Katja Berger, Juan Pablo Rivera Caicedo, Luca Martino, Matthias Wocher, Tobias Hank, Jochem Verrelst

Publikováno v: Remote Sensing, Vol 13, Iss 2, p 287 (2021)

The current exponential increase of spatiotemporally explicit data streams from satellite-based Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics p

Externí odkaz: https://doaj.org/article/49789638de30419c8c0aef8e2d7eb8fc

SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra

Autor: Jochem Verrelst, Juan Pablo Rivera Caicedo, Jordi Muñoz-Marí, Gustau Camps-Valls, José Moreno

Publikováno v: Remote Sensing, Vol 9, Iss 9, p 927 (2017)

Progress in advanced radiative transfer models (RTMs) led to an improved understanding of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and canopy. Among advanced canopy RTMs that have been recently modif

Externí odkaz: https://doaj.org/article/ae85a6d39409400c9c28534ce6c6b0b6

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Autor: José Estévez, Matías Salinero-Delgado, Katja Berger, Luca Pipia, Juan Pablo Rivera-Caicedo, Matthias Wocher, Pablo Reyes-Muñoz, Giulia Tagliabue, Mirco Boschetti, Jochem Verrelst

Publikováno v: José Estévez, Matías Salinero-Delgado, Katja Berger, Luca Pipia, Juan Pablo Rivera-Caicedo, Matthias Wocher, Pablo Reyes-Muñoz, Giulia Tagliabue, Mirco Boschetti, Jochem Verrelst (2022). Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data. Remote Sensing of Environment, 273, 112958.
Remote Sensing of Environment

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval mod

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::824150b7fb5e755b4f72a1c0da194ded
https://pubmed.ncbi.nlm.nih.gov/36081832

Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring

Autor: Eatidal Amin, Luca Pipia, Antonio Ruiz-Verdú, Juan Pablo Rivera-Caicedo, Jose Moreno, Jochem Verrelst

Publikováno v: Remote Sensing of Environment

For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ec407b79d52774639c415ca61bcda10
https://pubmed.ncbi.nlm.nih.gov/36060228