Work

GEOV2-AVHRR: Continuous and consistent time series of global leaf area index and fraction absorbed PAR from 1981 to 2022

Year: 2025

Type: article

Abstract: Long-term time series of global leaf area index (LAI) and fraction of absorbed photosynthetic active radiation (FAPAR) are required for characterizing vegetation dynamics in global change studies. The recently developed GEOV2-CGLS products were demonstrated to outperform other existing LAI and FAPAR products in terms of completeness, temporal smoothness, consistency across variables and accuracy. However, these GEOV2-CGLS products are derived from the SPOT/VGT and PROBA-V constellation with temporal coverage from 1999 to 2020 which limits its applicability for global change studies. We present here an adaptation of the GEOV2-CGLS algorithm to AVHRR to extend these time series and generate long-term global vegetation products from July 1981 to December 2022. The GEOV2-AVHRR algorithm was specifically designed to maximize the temporal consistency over the successive AVHRR sensors on board NOAA and MetOp-B satellites while keeping high agreement with GEOV2-CGLS products. Neural networks first transform AVHRR surface reflectance into LAI and FAPAR values at the daily time step. The daily estimates are then filtered, smoothed, gap filled and composited every 10-day, resulting in time series with only 1% of missing data. GEOV2-AVHRR agreed well with GEOV2-CGLS: ~95% of land pixels are within ±0.5 LAI and ±0.1 FAPAR. Similarly, ~90% of land pixels agree within ±0.5 LAI and ±0.1 FAPAR. Similarly, ~90% of land pixels agree within ±0.5 LAI and ±0.1 FAPAR with GIMMS (LAI4g and FAPAR3g) and GLASS (version 5) AVHRR-derived products for the common period 1982-2016. The sign of the temporal trends of GEOV2-AVHRR, GIMMS and GLASS agree in 85% of land pixels with ~80% (~50% significant at p<0.05) of greening and ~20% (~5% significant) of browning as evaluated over the period 1982-2016 with however important discrepancies in the spatial distribution of the positive and negative significant of trends. C3S (version 3) products were also used for comparison but they showed a poor ranking for all the evaluation criterions and high differences with other products as well with ground data both for LAI and FAPAR with a noticeable underestimation of large values and inconsistencies in the transition from different sensors. GEOV2-AVHRR shows better internal consistency between LAI and FAPAR variables as well as a more realistic seasonality of time series as compared to the other datasets. GEOV2-AVHRR products show the best accuracy against ground measurements: 0.96 LAI and 0.11 FAPAR.

Source: Remote Sensing of Environment

DOI: 10.1016/j.rse.2025.115029

Authors: Aleixandre Verger, Marie Weiss, Frédéric Baret

Institutions: Centre d'Investigacions sobre Desertificació, Centre for Research on Ecology and Forestry Applications, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $4,450