Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest

Xiao, Xiangming; Zhang, Qingyuan; Saleska, S. R.; Hutyra, Lucy R.; Camargo, Pedro Henrique Cury de; Wofsy, Steven C.; Frolking, S. E.; Boles, Stephen; Keller, Michael; Moore, Berrien

doi:10.1016/j.rse.2004.08.015

Cited by 250 publications

(213 citation statements)

References 47 publications

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“…The modified VPM may be the best LUE model in simulating large-scale terrestrial GPP provided with high-quality remote sensing data for the calculation of WI. However, there may be data acquisition problem when estimating the GPP before 1998, since the data of VGT-S10 (10-day composite Images from the VEGETATION sensor) and MODIS surface reflectance (MOD09A1) were not available for estimating the WI of the VPM in the time (Xiao et al, 2004a(Xiao et al, , 2005b. In this case, it was more appropriate to adopt the improved VPM with water stress being defined as VPD s only.…”

Section: Comparisons Of the Methods To Define Water Stressmentioning

confidence: 99%

“…The VPM has been successfully used to simulate GPP in different ecosystems, including tropical evergreen forest (Xiao et al, 2005b), temperate deciduous forest (Xiao et al, 2004a), evergreen needle leaf forest (Xiao et al, 2004b), cropland (Kalfas et al, 2011), and grassland (Liu et al, 2011). The ε g in the model is estimated as follows:…”

Section: The Vpm Modelmentioning

confidence: 99%

“…(2b)), and fw is reflected by land water index (WI) that was calculated by satellite-derived land surface water index (LSWI) (Xiao et al, 2002;Boles et al, 2004) with consideration of the phenology effect on ε max (P s ) (Xiao et al, 2005b). The P s in the model is calculated as follow:…”

Section: The Vpm Modelmentioning

confidence: 99%

“…Specifically, the EC-LUE performed best (compared to the GLO-PEM and VPM) in the nonforest sites (e.g., savanna and grassland that usually have semi-arid climates), since the fw used by it (evaporative fraction, EF) related closely to the surface soil water content, which was documented as the major water stress on plant growth in those kinds of ecosystems (Sala et al, 1988;Kurc and Small, 2004). By contrast, the GLO-PEM and VPM performed better in the forest sites (characterized by the high annual precipitation and the deep roots system for accessing deep soil water in dry seasons), since the fw utilized by them (soil water condition (W s ) and atmosphere moisture condition (VPD s ), and the land surface water index (LSWI), respectively) can reflect both the leaf and canopy water content, which were believed to mainly limit the light use efficiency of plants in those ecosystems (Nepstad et al, 2002;Xiao et al, 2005b). Moreover, the VPM had better performance than GLO-PEM in DBF because the fw in it included the phenology scalar (P s ), which can account for the effects of leaf age on photosynthesis (Xiao et al, 2004b).…”

Section: Comparisons Of the Four Lue Models In Estimating Gppmentioning

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: The Vpm Modelmentioning

confidence: 99%

Section: The Vpm Modelmentioning

confidence: 99%

Section: Comparisons Of the Four Lue Models In Estimating Gppmentioning

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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“…Within forests, PAR varies according to the rainfall cycle. According to Xiao et al (2005), rainforests such as the Amazon Forest show a higher intensity of PAR during the dry season and a lower intensity during the rainy season.…”

Section: Introductionmentioning

confidence: 99%

Modeling the reflection of Photosynthetically active radiation in a monodominant floodable forest in the Pantanal of Mato Grosso State using multivariate statistics and neural networks

Curado

Musis

Cunha

et al. 2016

An. Acad. Bras. Ciênc.

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The study of radiation entrance and exit dynamics and energy consumption in a system is important for understanding the environmental processes that rule the biosphere-atmosphere interactions of all ecosystems. This study provides an analysis of the interaction of energy in the form of photosynthetically active radiation (PAR) in the Pantanal, a Brazilian wetland forest, by studying the variation of PAR reflectance and its interaction with local rainfall. The study site is located in Private Reserve of Natural Heritage, Mato Grosso State, Brazil, where the vegetation is a monodominant forest of Vochysia divergens Phol. The results showed a high correlation between the reflection of visible radiation and rainfall; however, the behavior was not the same at the three heights studied. An analysis of the hourly variation of the reflected waves also showed the seasonality of these phenomena in relation to the dry and rainy seasons. A predictive model for PAR was developed with a neural network that has a hidden layer, and it showed a determination coefficient of 0.938. This model showed that the Julian day and time of measurements had an inverse association with the wind profile and a direct association with the relative humidity profile.

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Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency

Madani

Kimball

Affleck

et al. 2014

J. Geophys. Res. Biogeosci.

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A common assumption of remote sensing-based light use efficiency (LUE) models for estimating vegetation gross primary productivity (GPP) is that plants in a biome matrix operate at their photosynthetic capacity under optimal climatic conditions. A prescribed constant biome maximum light use efficiency parameter (LUE max ) defines the maximum photosynthetic carbon conversion rate under these conditions and is a large source of model uncertainty. Here we used tower eddy covariance measurement-based carbon (CO 2 ) fluxes for spatial estimation of optimal LUE (LUE opt ) across North America. LUE opt was estimated at 62 Flux Network sites using tower daily carbon fluxes and meteorology, and satellite observed fractional photosynthetically active radiation from the Moderate Resolution Imaging Spectroradiometer. A geostatistical model was fitted to 45 flux tower-derived LUE opt data points using independent geospatial environmental variables, including global plant traits, soil moisture, terrain aspect, land cover type, and percent tree cover, and validated at 17 independent tower sites. Estimated LUE opt shows large spatial variability within and among different land cover classes indicated from the sparse tower network. Leaf nitrogen content and soil moisture regime are major factors explaining LUE opt patterns. GPP derived from estimated LUE opt shows significant correlation improvement against tower GPP records (R 2 = 76.9%; mean root-mean-square error (RMSE) = 257 g C m À2 yr À1 ), relative to alternative GPP estimates derived using biome-specific LUE max constants (R 2 = 34.0%; RMSE = 439 g C m À2 yr À1 ). GPP determined from the LUE opt map also explains a 49.4% greater proportion of tower GPP variability at the independent validation sites and shows promise for improving understanding of LUE patterns and environmental controls and enhancing regional GPP monitoring from satellite remote sensing.

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Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest

Cited by 250 publications

References 47 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

Modeling the reflection of Photosynthetically active radiation in a monodominant floodable forest in the Pantanal of Mato Grosso State using multivariate statistics and neural networks

Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency

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