Generation of global vegetation products from EUMETSAT AVHRR/METOP satellites

TL;DR

This paper presents a novel joint retrieval method for global vegetation parameters from EUMETSAT AVHRR/METOP satellites using multi-output Gaussian Processes Regression, improving the accuracy and consistency of vegetation products.

Contribution

The paper introduces a new joint retrieval approach for LAI, FAPAR, and FVC using multi-output GPR trained on PROSAIL simulations, enhancing existing satellite vegetation product methods.

Findings

Successful implementation of joint retrieval method

Preliminary quality assessment shows promising results

Intercomparison with MODIS and PROBA-V indicates competitive performance

Abstract

We describe the methodology applied for the retrieval of global LAI, FAPAR and FVC from Advanced Very High Resolution Radiometer (AVHRR) onboard the Meteorological-Operational (MetOp) polar orbiting satellites also known as EUMETSAT Polar System (EPS). A novel approach has been developed for the joint retrieval of three parameters (LAI, FVC, and FAPAR) instead of training one model per parameter. The method relies on multi-output Gaussian Processes Regression (GPR) trained over PROSAIL EPS simulations. A sensitivity analysis is performed to assess several sources of uncertainties in retrievals and maximize the positive impact of modeling the noise in training simulations. We describe the main features of the operational processing chain along with the current status of the global EPS vegetation products, including details about its overall quality and preliminary assessment of the products based on intercomparison with equivalent (MODIS, PROBA-V) satellite vegetation products.