Pablo Reyes Muñoz

• MS
• Researcher at University of Valencia

Recent efforts in upscaling terrestrial carbon fluxes (TCFs) from eddy covariance (EC) flux towers have gained momentum with machine learning, capturing complex relationships between TCFs and their driving variables. We applied Gaussian process regression (GPR) models to upscale TCF products from tower-to-global scale and studied the predictive cap...

Due to their importance in monitoring and modelling Earth’s climate, the Global Climate Observing System (GCOS) designates leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) as essential climate variables (ECVs). The Simplified Level 2 Biophysical Processor (SL2P) has proven particularly popular for decam...

Advances in Earth observation capabilities mean that there is now a multitude of spatially resolved data sets available that can support the quantification of water and carbon pools and fluxes at the land surface. However, such quantification ideally requires efficient synergistic exploitation of those data, which in turn requires carbon and water...

Operational Earth observation missions, like the Sentinel-3 (S3) satellites, aim to provide imagery for long-term environmental assessment to monitor and analyze vegetation changes and dynamics. However, the S3 archive is limited in temporal availability to the year 2016. Although S3 provides continuity of previous missions, key vegetation products...

Advances in Earth Observation capabilities mean that there is now a multitude of spatially resolved data sets available that can support the quantification of water and carbon pools and fluxes at the land surface. However, such quantification ideally requires efficient synergistic exploitation of those data, which in turn requires carbon and water...

The ongoing monitoring of terrestrial carbon fluxes (TCF) goes hand in hand with progress in technical capacities, such as the next-generation Earth observation missions of the Copernicus initiative and advanced machine learning algorithms. Proceeding along this line, we present a physically-based data-driven workflow for quantifying gross primary...

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data a...

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global...

Global mapping of essential vegetation traits (EVTs) through data acquired by Earth-observing satellites provides a spatially explicit way to analyze the current vegetation states and dynamics of our planet. Although significant efforts have been made, there is still a lack of global and consistently derived multi-temporal trait maps that are cloud...

Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radarsurface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical dom...

- Retrieved gap-free FAPAR, FVC ,LAI ,LCC with S3-OLCI TOA data and hybrid models - Validated over ten sites (FAPAR,FVC,LAI) LCC compared to OLCI Terrestrial Chlorophyll Index - Long-term temporal reconstruction 2002-2022 - Used MODIS data as predictor variables to reconstruct past S3 OLCI based variables

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction o...

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 models are of interest to run in these platforms as they combine the advantages of physically- based ra...

Satellite imaging spectroscopy for terrestrial applications is reaching maturity with recently launched and up-coming science-driven missions, e.g. PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), respectively. Moreover, the high-priority mission candidate Copernicus Hyperspectral...

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is in preparation to carry a unique visible to shortwave infrared spectrometer. CHIME will globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. The mission shall provid...

Mediante información basada en teledetección hemos cuantificado cambios porcentuales en el índice de vegetación EVI (Enahnced Vegetation Index) como indicador del estado de conservación de los alcornocales (Quercus suber) próximos al estrecho de Gibraltar para un intervalo de tiempo entre 2000-2018. Los cambios analizados nos permiten localizar pro...