Sentinel-2 Crop Trait Retrieval Using Physiological and Phenological Priors from Field Phenotyping (Graf et al., 2023, RSE)

An acquisition and processing toolkit for open access phenology data.

Spatio-Temporal Vegetation Segmentation By Using Convolutional Networks

A 3D Functionnal Structural Plant Model of perennial grasses morphogenesis and phenology

Sentinel-2 Top-Of-Atmosphere Radiometric Uncertainty Propagator (Graf et al., 2023, IEEE-JSTARS)

DNN for phenological stage detection on herbarium specimens

This is a database of sightings of birds, moths, and butterfiles in support of the phenology mismatch project.

PhenoAI is a deep learning framework designed to automate the processing chain of phenological analysis of vegetation from close-range time-lapse PhenoCam data.

Evaluating anomalously early spring onsets in the 21st century

Spatiotemporal phenology research with interpretable models

Master's thesis scripts to process proximal remote sensing sensors

A graphical user interface for the TIMESAT software, providing an easy way to configure and run time-series analyses of satellite data.

A command-line interface and workflow manager for the TIMESAT package, enabling efficient satellite time-series vegetation phenology processing.

This project focuses on classifying crop types in Noord Beveland, Netherlands, using multi-temporal NDVI data and a Random Forest classifier. The study achieves 93.18% overall accuracy by optimizing hyperparameters and analyzing feature importance, highlighting NDVI's sensitivity to crop phenology. Tools: Python and geospatial libraries.

Synthetic models for phenology in mutualistic networks

Codes developed to conduct data analyses within the Spanish approach to the Marine Strategy Framework Directive

Rooted in Earth, powered by satellites.

A small set of scripts to create time-lapse videos from PhenoCam imagery

CropMapping of Partially Anotated Time Series Satellite Images with 3D Convolutional Neural Networks