Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

Lunche Wang,Aiwen Lin,Ling Zou,Hongji Zhu,Wenmin Qin,Qiyong Du

doi:10.3390/rs9050418

Abstract

The latest MODIS GPP (gross primary productivity) product, MOD17A2H, has great advantages over the previous version, MOD17A2, because the resolution increased from 1000 m to 500 m. In this study, MOD17A2H GPP was assessed using the latest eddy covariance (EC) flux data (FLUXNET2015 Dataset) at eighteen sites in six ecosystems across the globe. The sensitivity of MOD17A2H GPP to the meteorology dataset and the fractional photosynthetically- active radiation (FPAR) product was explored by introducing site meteorology observations and improved Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) products. The results showed that MOD17A2H GPP underestimated flux-derived GPP at most sites. Its performance in estimating annual GPP was poor (R2 = 0.62) and even worse over eight days (R2 = 0.52). For the MOD17A2H algorithm, replacing the reanalysis meteorological datasets with the site meteorological measurements failed to improve the estimation accuracies. However, great improvements in estimating the site-based GPP were gained by replacing MODIS FPAR with GLASS FPAR. This indicated that in the existing MOD17A2H product, the errors were originated more from FPAR than the meteorological data. We further examined the potential error contributions from land cover classification and maximum light use efficiency (εmax). It was found that the current land cover classification scheme exhibited frequent misclassification errors. Moreover, the εmax value assigned in MOD17A2H was much smaller than the inferred εmax value. Therefore, the qualities of FPAR and land cover classification datasets should be upgraded, and the εmax value needs to be adjusted to provide more accurate GPP estimates using MOD17A2H for global ecosystems.

Highlights

GPP, the total photosynthetic uptake of carbon by vegetation, plays a key role in understanding the carbon balance between the atmosphere and biosphere [1,2]
We introduced the Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) product to assess the influences of fractional photosynthetically- active radiation (FPAR) on the effectiveness of MO17A2H GPP
Meteorology data and MOD15A2H FPAR were chosen as meteorology inputs and FPAR inputs, respectively (MOD_GMAO GPP); (2) using site meteorological data as meteorology inputs and MOD15A2H FPAR as FPAR inputs (MOD_Tower GPP); (3) Global Modeling and Assimilation Office (GMAO) meteorology data were used as meteorology inputs, and GLASS FPAR were introduced as FPAR inputs (GMAO_GLASS GPP); (4) using site meteorological data and GLASS FPAR to estimate

Summary

Introduction

GPP, the total photosynthetic uptake of carbon by vegetation, plays a key role in understanding the carbon balance between the atmosphere and biosphere [1,2]. GPP values can be estimated from satellite remote sensing products [7,8,9]. The first consistent, near-real-time GPP dataset allowing the estimation of global vegetation in an eight-day interval at Remote Sens. 1-km resolution is the MOD17A2 MODIS product [10,11]. There are still many potential error sources in MODIS GPP products arising from input data, the parameters describing the biophysical properties of vegetation and the algorithm itself [13,14]. The spatial resolution of meteorological reanalysis data is quite different from that of MODIS products, which introduces inaccuracies in estimating atmospheric conditions at a scale that matches heterogeneities in the land surface [15]. The land cover classification provided by MODIS is not always accurate, and the misclassifications are quite common for similar land cover classes [17]

Objectives

Methods

Results

Discussion

Conclusion