A novel algorithm to assess gross primary production for terrestrial ecosystems from MODIS imagery

Yang, Yuting; Shang, Songhao; Guan, Huade; Jiang, Lei

doi:10.1002/jgrg.20056

Cited by 44 publications

(50 citation statements)

References 79 publications

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“…The water stress on LUE (i.e., fw) had been characterized by air moisture condition (i.e., VPD s ) and/or soil water content (i.e., W s ), or via land water index (i.e., WI) that can reflect the vegetation water stress in different LUE models, yet there were controversies on which one was optimal (Medlyn, 2011;Hashimoto et al, 2013;Yang et al, 2013). Coincidently, both EC-LUE and VPM, the two better-performed LUE models, used WI.…”

Section: Comparisons Of the Methods To Define Water Stressmentioning

confidence: 99%

“…The higher accuracies of EC-LUE, GLO-PEM, and VPM than CASA indicated that all these three models might be robust in estimating ecosystem GPP over large areas. However, it would be better if with briefer model structure and fewer input variables without sacrificing of model accuracy (Goetz et al, 1999;Yang et al, 2013). Among those three models, the water stress expression (i.e., fw) in GLO-PEM (Eq.…”

Section: Comparisons Of the Four Lue Models In Estimating Gppmentioning

confidence: 99%

“…For instance, fw was calculated by the evaporative fraction in the EC-LUE model (Yuan et al, 2007), which was considered to be a good indicator of moisture conditions (Kurc and Small, 2004). However, the performances of those models are rarely validated with field observations across large areas (e.g., Yuan et al, 2010), and little is known about whether the better-performed LUE models can be further improved by model structural optimization (Medlyn, 2011;Hashimoto et al, 2013;Yang et al, 2013). Specifically, most previous efforts focused on only one of the LUE models and compared the model with other kinds of models at quite a few flux sites (Zhang et al, 2007;Coops et al, 2009;Wu et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of four light use efficiency models for estimating terrestrial gross primary production

Zhang

Zhou

Fan

et al. 2015

Ecological Modelling

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a b s t r a c tLight use efficiency (LUE) models that with different structures (i.e., methods to address environmental stresses on LUE) have been widely used to estimate terrestrial gross primary production (GPP) because of their theoretical soundness and practical conveniences. However, a systematic validation of those models with field observations across diverse ecosystems is still lacking and whether the model can be further improved by structural optimization remains unclear. Using GPP estimates at global 51 eddy covariance flux towers that cover a wide climate range and diverse vegetation types, we evaluated the performances of the four major LUE models (i.e., Carnegie-Ames-Stanford approach (CASA), Global Production Efficiency Model (GLO-PEM), Vegetation Photosynthesis Model (VPM), and Eddy Covariance-Light Use Efficiency (EC-LUE)) and examined the possible further improvement of the better-performed model(s) via model structural optimization. Our results showed that the GLO-PEM, VPM, and EC-LUE exhibited the similar capabilities in simulating GPP (explained around 68% of the total variations) and overall performed better than CASA (58%). Nevertheless, the EC-LUE and VPM were the optimal ones because they required less model inputs than the GLO-PEM. For the two optimal models, we found that the minimum method is better than the multiplication approach to integrate multiple environmental stresses on LUE. Moreover, we found that the VPM can be further improved by incorporating the constraint of water vapor deficit (VPD s ). We suggested that a modified VPM by using minimum method and adding VPD s may be the best model in estimating large-scale GPP if high-quality remote sensing data available, otherwise, the modified models with the water stress reflected by VPD s only is optimal.

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Section: Comparisons Of the Methods To Define Water Stressmentioning

confidence: 99%

Section: Comparisons Of the Four Lue Models In Estimating Gppmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of four light use efficiency models for estimating terrestrial gross primary production

Zhang

Zhou

Fan

et al. 2015

Ecological Modelling

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“…While VPM uses LSWI to determine the effect of water stress, some studies used other ancillary data or variables like LST, actual and potential evapotranspiration (AET and PET) to improve the LUE parameter (Maselli, Papale, Puletti, Chirici, & Corona, 2009;Moreno et al, 2012;Yang, Shang, Guan, & Jiang, 2013). The use of LSWI is convenient as the data are available from MODIS and Landsat.…”

Section: Trade-off Between Comprehensiveness and Applicability In Satmentioning

confidence: 99%

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Dong

Xiao

Wagle

et al. 2015

Remote Sensing of Environment

102

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Berrien III, "Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought" (2015). Accurate estimation of gross primary production (GPP) is critical for understanding ecosystem response to climate variability and change. Satellite-based diagnostic models, which use satellite images and/or climate data as input, are widely used to estimate GPP. Many models used the Normalized Difference Vegetation Index (NDVI) to estimate the fraction of absorbed photosynthetically active radiation (PAR) by vegetation canopy (FPAR canopy ) and GPP. Recently, the Enhanced Vegetation Index (EVI) has been increasingly used to estimate the fraction of PAR absorbed by chlorophyll (FPAR chl ) or green leaves (FPAR green ) and to provide more accurate estimates of GPP in such models as the Vegetation Photosynthesis Model (VPM), Temperature and Greenness (TG) model, Greenness and Radiation (GR) model, and Vegetation Index (VI) model. Although these EVI-based models perform well under non-drought conditions, their performances under severe droughts are unclear. In this study, we run the four EVI-based models at three AmeriFlux sites (rainfed soybean, irrigated maize, and grassland) during drought and non-drought years to examine their sensitivities to drought. As all the four models use EVI for FPAR estimate, our hypothesis is that their different sensitivities to drought are mainly attributed to the ways they handle light use efficiency (LUE), especially water stress. The predicted GPP from these four models had a good agreement with the GPP estimated from eddy flux tower in non-drought years with root mean squared errors (RMSEs) in the order of 2.17 (VPM), 2.47 (VI), 2.85 (GR) and 3.10 g C m −2 day −1 (TG). But their performances differed in drought years, the VPM model performed best, followed by the VI, GR and TG, with the RMSEs of 1.61, 2.32, 3.16 and 3.90 g C m −2 day −1 respectively. TG and GR models overestimated seasonal sum of GPP by 20% to 61% in rainfed sites in drought years and also overestimated or underestimated GPP in the irrigated site. This difference in model performance under severe drought is attributed to the fact that the VPM uses satellite-based Land Surface Water Index (LSWI) to address the effect of water stress (deficit) on LUE and GPP, while the other three models do not have such a mechanism. This study suggests that it is essential for these models to consider the effect of water stress on GPP, in addition to using EVI to estimate FPAR, if these models are applied to estimate GPP under drought conditions.

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“…Among a variety of PEMs (Cramer et al, 1999;Wu et al, 2010;Yang et al, 2013), we employed the MOD17 model (Running et al, 2004) developed by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana (UMT). The MOD17 model is used to provide GPP/NPP estimates from MODIS data at 8-day and yearly time steps.…”

Section: The Modis Gpp (Mod17) Modelmentioning

confidence: 99%

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

Xin

Broich

Suyker

et al. 2015

Agricultural and Forest Meteorology

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A novel algorithm to assess gross primary production for terrestrial ecosystems from MODIS imagery

Cited by 44 publications

References 79 publications

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

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