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

Abstract

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 (R 2 = 0.62) and even worse over eight days (R 2 = 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 (ε max ). It was found that the current land cover classification scheme exhibited frequent misclassification errors. Moreover, the ε max value assigned in MOD17A2H was much smaller than the inferred ε max value. Therefore, the qualities of FPAR and land cover classification datasets should be upgraded, and the ε max value needs to be adjusted to provide more accurate GPP estimates using MOD17A2H for global ecosystems.

Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach

“…When compared to the site level of flux tower-based GPP (Figure 6), the slope values in most sites of the study regions were also less than 1.0, except for some desert ecosystem sites. This showed that the MOD17 product underestimated GPP in most high productivity sites of cropland, grassland, and forest ecosystems in the arid region, but overestimated GPP at some low productivity sites of desert ecosystems compared with tower-based GPP, consistent with the results of Reference [12] and Reference [27].…”

“…To date, MODIS has issued multiple versions of GPP [14,16]. Currently, the MOD17 product has been updated to Collection 6 (C6), which has improved the algorithm parameters and forcing data of previous collections [15,17], as the spatial resolution has increased from 1000 m to 500 m. A large number of studies have validated the capacity of MODIS GPP products with eddy covariance (EC) measurements across multiple biomes, such as forests [18,19], shrublands [20], grasslands [21,22], savanna [23], croplands [24], and across biomes [12,[25][26][27]. However, most of these studies validated previous versions of MODIS GPP products (i.e., Collection 4 and 5).…”

“…MODIS GPP may underestimate at some sites, such as at cropland sites [24], overestimate at some low productivity sites [25,28], or agree well [26] with tower-based GPP. Meanwhile, the MODIS GPP Collection 6 products (i.e., MOD17A2H) also tend to overestimate GPP in alpine meadows of the Tibetan Plateau [22] and underestimate flux-derived GPP at most sites across the globe [27]. However, because of inadequate observations in arid regions compared with other regions, it remains uncertain whether these biases also exist in other ecosystems in arid regions for the improved Collection 6 GPP products.…”

“…The overall uncertainty of carbon flux modelling includes uncertainty of input variables, model structure, and model parameters [29], which can significantly impact carbon flux at regional scales. Several attempts have been made to address the uncertainties of the PEM algorithm [26,27,30,31]. For the MOD17 products, inaccuracies in the parameterization of model parameters (such as maximum light-use efficiency (ε max or LUE max )) were found to be one of the most important factors attributed to the bias of MODIS GPP [12,20].…”

Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing

“…The relationships between the LSWI-GPP and LSWI-NPP were stronger because the strong correlation between the EVI and LSWI supported this relationship. In addition, previous studies confirmed that the potential of this method for estimating terrestrial GPP is similar to that when using the MOD17 GPP and NPP datasets [54,55]. No measured data were available in the fire study area, so this study could not easily conclude that the EVI had the potential to estimate terrestrial GPP during vegetation recovery.…”