Global parameterization and validation of a two‐leaf light use efficiency model for predicting gross primary production across FLUXNET sites

Abstract

Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP).However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (ε msh ) was 2.63 to 4.59 times that of sunlit leaves (ε msu ). Generally, the relationships of ε msh and ε msu with ε max ZHOU ET AL. were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR.

Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau

“…Validations in parameters of LUE model in most recent studies revealed that the realistic ε max values were undervalued in the MODIS default GPP algorithms [12,25,27,34] with a few exceptions [26], which emphasized the urgent need to reconcile the optimized ε max for more extensive biomes [17,29,35,38,69]. The global look-up table of ε max in the MOD17A2 GPP algorithm is hard to satisfy all vegetation properties due to various biomes with complex climatic, soil types, and associated stand structures and ages [25,28].…”

“…One is the parameter inputs in MOD17A2 algorithm [34]. First, many relevant researches proposed that the VPD only represents part of the atmospheric evaporative demand but is not entirely representative indicator of water availability condition thus it does not adequately reflect the observational GPP [25,26,35]. Therefore, they advised that soil moisture [17], remote water index [4,15], or precipitation [25] should be added as a stress factor in the MOD17 algorithm to improve GPP simulation [26].…”

Satellite-Based Models Need Improvements to Simulating Annual Gross Primary Productivity: A Comparison of Six Models for Regional Modeling of Deciduous Broadleaf Forests

“…The VPM model achieved an R 2 of 0.92 at a 10-day time scale when assessed using flux tower data [46]. A recent study that used the FLUXNET dataset for model evaluation reported that the MOD17 algorithm achieved an R 2 of 0.88 and an RMSE of 12.61 gC/m 2 /8 day (or 1.58 gC/m 2 /day equivalently) and the TL-LUE model achieved an R 2 of 0.90 and an RMSE of 11.21 gC/m 2 /8 day (or 1.40 gC/m 2 /day equivalently) [50]. Comparative studies on global vegetation products using the FLUXNET dataset found that the BESS product achieved an R 2 of 0.72 with an RMSE of 2.59 gC/m 2 /day and the MOD17 product achieved an R 2 of 0.68 with an RMSE of 2.97 gC/m 2 /day for deciduous broadleaf forests [18].…”

“…Following previous studies [45,50,51], key parameters for the biome of deciduous broadleaf forest in TL-LUE are set as follows:…”

Analysis and Applications of GlobeLand30: A Review

“…These include quantifying land use for each watershed in Panamanian drainage basins [55]; identifying types of sampling sites based on the hydrology and land-use characteristics to monitor contaminants in river sediments [46][47][48][49][50][51][52][53][54][55][56][57], discriminating fire types from MODIS active fire products, such as forest fire, grassland fire, agricultural burning and so on [58]; assessing flooded arable land of a major flood in Myanmar [46]; selecting eddy-covariance flux towers with relatively homogenous land cover in the light use efficiency models to simulate GPP [59]; analyzing habitat of bats in Lao PDR and Cambodia [60][61][62]; and providing validation sources to evaluate the classification performance of the water body extraction from MODIS eight-day products [62]. In particular, GlobeLand30 data has been used to derive useful information about the status and change of land cover, to examine their causes and consequence analysis, and to explore future development scenarios.…”