Momentous increase in the popularity of explainable machine learning models coupled with
the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient …

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 …

In light of the ongoing global biodiversity crisis, the urge to monitor and map terrestrial plant
biodiversity at large spatial extents has spurred research on adequate quantitative methods …

Spaceborne imaging spectroscopy is a highly promising data source for all agricultural
management and research disciplines that require spatio-temporal information on crop …

Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and
crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer …

In preparation for new-generation imaging spectrometer missions and the accompanying
unprecedented inflow of hyperspectral data, optimized models are needed to generate …

Large-scale high-resolution satellite observations of plant functional diversity patterns will
greatly benefit our ability to study ecosystem functioning. Here, we demonstrate a potentially …

Accurate and efficient estimation of crop biophysical traits, such as leaf chlorophyll
concentrations (LCC) and average leaf angle (ALA), is an important bridge between …

Mangrove forests provide vital ecosystem services. The increasing threats to mangrove
forest extent and fragmentation can be monitored from space. Accurate spatially explicit …

Grassland ecosystems play a critical role in global carbon cycling and environmental health.
Understanding the intricate link between grassland vegetation traits and underlying soil …