Abstract

Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website.

Highlights

  • Monitoring and modeling global vegetation dynamics in the context of climate variability and change studies require long-term data sets of key biophysical variables that characterize vegetation structure and functioning [1]

  • Observational Products from an Ensemble of Satellites (CYCLOPES) Leaf Area Index (LAI) and FPAR products derived from the Système Pour l’Observation de la Terre (SPOT) VEGETATION sensor are available at 1/112° Plate-Carrée spatial resolution and 10-day temporal frequency [66]

  • The canonical correlation analysis (CCA) is designed to select those temporal features in the LAI3g, or FPAR3g, fields that are best correlated with temporal features in springtime climatic variables such as temperature and/or precipitation

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Summary

Introduction

Monitoring and modeling global vegetation dynamics in the context of climate variability and change studies require long-term data sets of key biophysical variables that characterize vegetation structure and functioning [1]. Radiation absorbed by vegetation (FPAR) are two examples of such variables. LAI is defined as the one-sided green leaf area per unit vegetated ground area in broadleaf canopies and as one-half the total needle surface area per unit vegetated ground area in coniferous canopies. It characterizes the physiologically functioning surface area with which energy, mass (e.g., water and CO2) and momentum are exchanged between the vegetated land surface and the planetary boundary layer [2]. LAI and FPAR are key state variables in many biogeochemical, ecological, hydrological and crop yield models [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]

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