scholarly journals Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 329
Author(s):  
Jun Zhang ◽  
Xufeng Wang ◽  
Jun Ren
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Gross Primary Productivity ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Light Use Efficiency ◽  
Multiple Model ◽  
Use Efficiency

Gross primary productivity (GPP) is the most basic variable in a carbon cycle study that determines the carbon that enters the ecosystem. The remote sensing-based light use efficiency (LUE) model is one of the primary tools that is currently used to estimate the GPP at the regional scale. Many remote sensing-based GPP models have been developed in the last several decades, and these models have been well evaluated at some sites. However, an accurate estimation of the GPP remains challenging work using LUE models because of uncertainties in the model caused by model parameters, model forcing, and vegetation spatial heterogeneity. In this study, five widely used LUE models, Glo-PEM, VPM, EC-LUE, the MODIS GPP algorithm, and C-fix, were selected to simulate the GPP of the Heihe River Basin forced using in situ measurements. A multiple-model averaging method, Bayesian model averaging (BMA), was used to combine the five models to obtain a more reliable GPP estimation. The BMA was trained using carbon flux data from five eddy covariance towers located at dominant vegetation types in the study area. Generally, the BMA method performed better than any single LUE model. From the case study in the study area, it is indicated that the trained BMA is an efficient method to combine multiple LUE models and can improve the GPP simulation accuracy.

scholarly journals Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna

2017 ◽  
Vol 14 (1) ◽  
pp. 111-129 ◽  
Author(s):  
Caitlin E. Moore ◽  
Jason Beringer ◽  
Bradley Evans ◽  
Lindsay B. Hutley ◽  
Nigel J. Tapper
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Seasonal Pattern ◽  
Gross Primary Productivity ◽  
Perennial Grasses ◽  
Light Use Efficiency ◽  
Tropical Savanna ◽  
Flux Tower ◽  
Use Efficiency

Abstract. The coexistence of trees and grasses in savanna ecosystems results in marked phenological dynamics that vary spatially and temporally with climate. Australian savannas comprise a complex variety of life forms and phenologies, from evergreen trees to annual/perennial grasses, producing a boom–bust seasonal pattern of productivity that follows the wet–dry seasonal rainfall cycle. As the climate changes into the 21st century, modification to rainfall and temperature regimes in savannas is highly likely. There is a need to link phenology cycles of different species with productivity to understand how the tree–grass relationship may shift in response to climate change. This study investigated the relationship between productivity and phenology for trees and grasses in an Australian tropical savanna. Productivity, estimated from overstory (tree) and understory (grass) eddy covariance flux tower estimates of gross primary productivity (GPP), was compared against 2 years of repeat time-lapse digital photography (phenocams). We explored the phenology–productivity relationship at the ecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and flux tower GPP. These data were obtained from the Howard Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Two greenness indices were calculated from the phenocam images: the green chromatic coordinate (GCC) and excess green index (ExG). These indices captured the temporal dynamics of the understory (grass) and overstory (trees) phenology and were correlated well with tower GPP for understory (r2 =  0.65 to 0.72) but less so for the overstory (r2 =  0.14 to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPP at the ecosystem scale (r2 =  0.70). Lastly, we used GCC and EVI to parameterise a light use efficiency (LUE) model and found it to improve the estimates of GPP for the overstory, understory and ecosystem. We conclude that phenology is an important parameter to consider in estimating GPP from LUE models in savannas and that phenocams can provide important insights into the phenological variability of trees and grasses.

scholarly journals Measured and modeled interactive effects of potassium deficiency and water deficit on gross primary productivity and light‐use efficiency in E ucalyptus grandis plantations

2015 ◽  
Vol 21 (5) ◽  
pp. 2022-2039 ◽  
Author(s):  
Mathias Christina ◽  
Guerric Le Maire ◽  
Patricia Battie‐Laclau ◽  
Yann Nouvellon ◽  
Jean‐Pierre Bouillet ◽  
...  
Keyword(s):  
Water Deficit ◽  
Primary Productivity ◽  
Potassium Deficiency ◽  
Gross Primary Productivity ◽  
Interactive Effects ◽  
Light Use Efficiency ◽  
Use Efficiency

scholarly journals LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING

2020 ◽  
Vol V-3-2020 ◽  
pp. 571-578
Author(s):  
S. Wang ◽  
Z. Li ◽  
Y. Zhang ◽  
D. Yang ◽  
C. Ni
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Seasonal Changes ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Light Use Efficiency ◽  
Use Efficiency

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.

scholarly journals Satellite-based Sun-Induced Chlorophyll Fluorescence in the Greater Alpine Space: Spatial Patterns and Relationship to Gross Primary Productivity

2020 ◽  
Author(s):  
Ulisse Gomarasca ◽  
Gregory Duveiller ◽  
Alessandro Cescatti ◽  
Georg Wohlfahrt
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Land Cover ◽  
Spatial Patterns ◽  
Primary Productivity ◽  
Climate Models ◽  
Gross Primary Productivity ◽  
Accurate Estimation ◽  
Small Scale ◽  
Alpine Space

<p>Accurate estimation of terrestrial gross primary productivity is essential for the development of credible future carbon cycle and climate simulations. Current remote sensing techniques allow retrieval of sun-induced chlorophyll fluorescence (SIF) as a valid proxy for GPP, but low resolution, sparse coverage, or resolution mismatches between the different satellite sensors hinder our ability to effectively link SIF to many environmental variables at fine scales. In order to better characterize heterogeneous landscapes, several attempts to downscale SIF products to higher resolutions have been made. We investigate the ability of the downscaled GOME-2 product developed by Duveiller et al. (2019), to capture the differences in spatiotemporal dynamics over the Greater Alpine Space. We analyse SIF in connection to land cover and elevation, and calculate land phenology metrics based on seasonal SIF time series. Ground-based GPP validation suggests biome-specific SIF-GPP relationships, but the comparison was hindered by the resolution mismatch of the data. Moreover, missing data are present at high elevations, diminishing the suitability of current SIF products to analyse cloud-prone mountainous areas. Important insights into spatial patterns and seasonal trends could be inferred at forest and other large-area land cover types, typical of mid elevations in the Alps, but many anthropogenic habitats at low elevations, as well as high elevation grasslands and other small-scale heterogeneous environments could not be thoroughly investigated and are likely to be underrepresented or prone to biases. Similar downscaling procedures might be applied at finer scales to e.g. TROPOMI products, or alternative advanced remote sensing SIF techniques and instruments might be needed in order to enable detailed and systematic evaluations of the Alpine region or similar highly heterogenous landscapes, before a process-oriented monitoring and unbiased implementation into climate models may be performed.</p>

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