Jochem Verrelst-rekin lankidetzan egindako argitalpenak (23)

2024

  1. In-season forecasting of within-field grain yield from Sentinel-2 time series data

    International Journal of Applied Earth Observation and Geoinformation, Vol. 126

  2. Inferring global terrestrial carbon fluxes from the synergy of Sentinel 3 & 5P with Gaussian process hybrid models

    Remote Sensing of Environment, Vol. 305

  3. Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

    Biogeosciences, Vol. 21, Núm. 2, pp. 473-511

  4. Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring

    Multitemporal Earth Observation Image Analysis: Remote Sensing Image Sequences (wiley), pp. 151-183

2023

  1. In-situ crop phenology dataset from sites in Bulgaria and France

    Zenodo

  2. In-situ crop phenology dataset from sites in Bulgaria and France

    Zenodo

  3. In-situ crop phenology dataset from sites in Bulgaria and France

    Zenodo

  4. In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level

    Data in Brief

  5. Satellite remote sensing dataset of Sentinel-2 for phenology metrics extraction from sites in Bulgaria and France

    Zenodo

  6. Satellite remote sensing dataset of Sentinel-2 for phenology metrics extraction from sites in Bulgaria and France

    Zenodo

2022

  1. Monitoring cropland phenology on google earth engine using gaussian process regression

    Remote Sensing, Vol. 14, Núm. 1

  2. Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

    Remote Sensing, Vol. 14, Núm. 8

  3. Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

    Remote Sensing, Vol. 14, Núm. 6

  4. Trends in Satellite Sensors and Image Time Series Processing Methods for Crop Phenology Monitoring

    Springer Optimization and Its Applications (Springer), pp. 199-231

2021

  1. CROP PHENOLOGY RETRIEVAL THROUGH GAUSSIAN PROCESS REGRESSION

    International Geoscience and Remote Sensing Symposium (IGARSS)

  2. Green lai mapping and cloud gap-filling using gaussian process regression in google earth engine

    Remote Sensing, Vol. 13, Núm. 3

  3. MAPPING ESSENTIAL VEGETATION VARIABLES OVER EUROPE USING GAUSSIAN PROCESS REGRESSION AND SENTINEL-3 DATA IN GOOGLE EARTH ENGINE

    International Geoscience and Remote Sensing Symposium (IGARSS)

  4. PROTOTYPING VEGETATION TRAITS MODELS IN THE CONTEXT OF THE HYPERSPECTRAL CHIME MISSION PREPARATION

    International Geoscience and Remote Sensing Symposium (IGARSS)

2020

  1. DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection

    Environmental Modelling and Software, Vol. 127

  2. Optimizing Gaussian process regression for image time series gap-filling and crop monitoring

    Agronomy, Vol. 10, Núm. 5