Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data

Hirofumi Hashimoto,Ramakrishna R Nemani,Cristina Milesi,Minoru Gamo,Ranga B Myneni,Ryuichi Hirata,Michael A White,Weile Wang,Sangram Ganguly

doi:10.3390/rs4010303

Abstract

Algorithms that use remotely-sensed vegetation indices to estimate gross primary production (GPP), a key component of the global carbon cycle, have gained a lot of popularity in the past decade. Yet despite the amount of research on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of different vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) in capturing the seasonal and the annual variability of GPP estimates from an optimal network of 21 FLUXNET forest towers sites. The tested indices include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation absorbed by plant canopies (FPAR). Our results indicated that single vegetation indices captured 50–80% of the variability of tower-estimated GPP, but no one index performed universally well in all situations. In particular, EVI outperformed the other MODIS products in tracking seasonal variations in tower-estimated GPP, but annual mean MODIS LAI was the best estimator of the spatial distribution of annual flux-tower GPP (GPP = 615 × LAI − 376, where GPP is in g C/m2/year). This simple algorithm rehabilitated earlier approaches linking ground measurements of LAI to flux-tower estimates of GPP and produced annual GPP estimates comparable to the MODIS 17 GPP product. As such, remote sensing-based estimates of GPP continue to offer a useful alternative to estimates from biophysical models, and the choice of the most appropriate approach depends on whether the estimates are required at annual or sub-annual temporal resolution.

Highlights

Understanding the mechanisms of global climate change and improving our predictions of possible future climates requires accurate quantifications of carbon uptake by terrestrial vegetation
We examined the relationship between flux tower gross primary production (GPP) and Moderate Resolution Imaging Spectroradiometer (MODIS) products with reduced major axis regression analysis, which minimizes the sum of the product of deviations from the model along both the x and y axes [66], both at the short-term and at the annual time scale
We analyzed the correlation of MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products with short-term and annual GPP measured at eddy covariance flux towers

Summary

Introduction

Understanding the mechanisms of global climate change and improving our predictions of possible future climates requires accurate quantifications of carbon uptake by terrestrial vegetation. Following the recognition that drought conditions suppress summer photosynthesis [7], process-oriented models were developed to estimate GPP with a combination of meteorological data and satellite-observed biophysical parameters, such as FPAR [8] and LAI [9]. These process-oriented models, while allowing simulations of ecosystems under future climate scenarios, present large uncertainty when used for current estimations of GPP because the required input parameters are not available with adequate accuracy at the global scale. The possibility of estimating GPP based on meteorological data only (e.g., Kato et al [10]), combinations of satellite-observed biophysical parameters (e.g., Wu et al [11]; Xiao et al [12]), or combinations of meteorological data and simple biophysical processes (e.g., temperature × (precipitation − evapotranspiration) [13]), has been explored

Results

Discussion

Conclusion