Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality

2007 ◽  
Vol 13 (1) ◽  
pp. 67-77 ◽  
Author(s):  
KAZUHITO ICHII ◽  
HIROFUMI HASHIMOTO ◽  
MICHAEL A. WHITE ◽  
CHRISTOPHER POTTER ◽  
LUCY R. HUTYRA ◽  
...  
Keyword(s):  
Primary Production ◽  
Satellite Data ◽  
Gross Primary Production ◽  
Ecosystem Modeling ◽  
Tropical Rainforests

scholarly journals Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1346 ◽  
Author(s):  
Joanna Joiner ◽  
Yasuko Yoshida ◽  
Yao Zhang ◽  
Gregory Duveiller ◽  
Martin Jung ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Satellite Data ◽  
Gross Primary Production ◽  
Satellite Observation ◽  
Data Driven ◽  
Spatial And Temporal Variability ◽  
Light Use Efficiency ◽  
High Productivity ◽  
Use Efficiency

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.

scholarly journals P-model v1.0: An optimality-based light use efficiency model for simulating ecosystem gross primary production

2019 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Primary Production ◽  
Satellite Data ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel).

scholarly journals Evaluation of satellite based indices for primary production estimates in a sparse savanna in the Sudan

2008 ◽  
Vol 5 (4) ◽  
pp. 2985-3011 ◽  
Author(s):  
M. Sjöström ◽  
J. Ardö ◽  
L. Eklundh ◽  
B. A. El-Tahir ◽  
H. A. M. El-Khidir ◽  
...  
Keyword(s):  
Water Stress ◽  
Primary Production ◽  
Satellite Data ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Arid Ecosystems ◽  
Stress Index ◽  
Accurate Information ◽  
Water Stress Index ◽  
Production Modelling

Abstract. One of the more frequently applied methods for integrating controls on primary production through satellite data is the Light Use Efficiency (LUE) approach. Satellite indices such as the Enhanced Vegetation Index (EVI) and the Shortwave Infrared Water Stress Index (SIWSI) have previously shown promise as predictors of primary production in several different environments. In this study, we evaluate EVI and SIWSI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor against in-situ measurements from central Sudan in order to asses their applicability in LUE-based primary production modelling within a water limited environment. Results show a strong correlation between EVI against gross primary production (GPP), demonstrating the significance of EVI for deriving information on primary production with relatively high accuracy at similar areas. Evaluation of SIWSI however, reveal that the fraction of vegetation apparently is to low for the index to provide accurate information on canopy water content, indicating that the use of SIWSI as a predictor of water stress in satellite data-driven primary production modelling in similar semi-arid ecosystems is limited.

scholarly journals ESTIMATION OF GROSS PRIMARY PRODUCTION USING SATELLITE DATA AND GIS IN URBAN AREA, DENPASAR

2011 ◽  
Vol 7 (1) ◽  
Author(s):  
A.R. As-syakur ◽  
T. Osawa ◽  
IW.S. Adnyana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Primary Production ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Gross Primary Production ◽  
Remote Sensing Data ◽  
Sensing Data

Remote sensing data with high spatial resolution is very useful to provideinformation about Gross Primary Production (GPP) especially over spatial coverage in theurban area. Most models of ecosystem carbon exchange based on remote sensing data usedlight use efficiency (LUE) model. The aim of this research was to analyze the distributionof annual GPP urban area of Denpasar. Two main satellite data used in this study wereALOS/AVNIR-2 and Aster satellite data. Result showed that annual value of GPP usingALOS/AVNIR-2 varied from 0.130 gC m-2 yr-1 to 2586.181 gC m-2 yr-1. Meanwhile, usingAster the value varied from 0.144 gC m-2 yr-1 to 2595.264 gC m-2 yr-1. The annual value ofGPP ALOS was lower than the value of Aster, because ALOS have high spatial resolutionand smaller interval of spectral resolution compared to Aster. Different land use couldeffect the value of GPP, because the different land use has different vegetation type,distribution, and different photosynthetic pathway type. The high spatial resolution of theremote sensing data is crucial to discriminate different land cover types in urban region.With heterogeneous land cover surface, maximum value of GPP using ALOS/AVNIR-2was smaller than that of Aster, however, the annual mean of GPP value usingALOS/AVNIR-2 was higher than that of Aster.

scholarly journals Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data

2012 ◽  
Vol 4 (1) ◽  
pp. 303-326 ◽  
Author(s):  
Hirofumi Hashimoto ◽  
Weile Wang ◽  
Cristina Milesi ◽  
Michael A. White ◽  
Sangram Ganguly ◽  
...  
Keyword(s):  
Primary Production ◽  
Satellite Data ◽  
Gross Primary Production ◽  
Forested Areas

Phosphorus limit to the CO2 fertilization effect in tropical rainforests as informed from a coupled biogeochemical model

2021 ◽  
Author(s):  
Zhuonan Wang ◽  
Hanqin Tian ◽  
Shufen Pan ◽  
Hao Shi ◽  
Jia Yang ◽  
...  
Keyword(s):  
Primary Production ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Land Use Changes ◽  
Anthropogenic Factors ◽  
Tropical Rainforests ◽  
Biogeochemical Model ◽  
P Limitation ◽  
C Cycle ◽  
Fertilization Effect

<p>Tropical rainforests play an important role in sequestering carbon (C) and mitigating climate warming. Many terrestrial biosphere models (TBMs) estimate productivity increase in tropical rainforests due to the CO<sub>2</sub> fertilization effect. However, most TBMs neglect phosphorus (P) limitation on tropical rainforest productivity. Here, we used a process-based Dynamic Land Ecosystem Model with coupled C-N-P dynamics (DLEM-CNP) with varied V<sub>cmax­25 </sub>to examine how P limitation has affected C fluxes of tropical rainforests to environmental and anthropogenic factors, including N deposition, land-use changes, climate variability, and atmospheric CO<sub>2</sub>, during 1860-2018. The model results showed that consideration of the P cycle reduced the response of tropical rainforests gross primary production (GPP) by 25% and 39%, net primary production (NPP) by 25% and 43%, and net ecosystem production (NEP) by 21% and 41% to the CO<sub>2</sub> fertilization effect relative to CN-only and C-only models. The DLEM-CNP estimated that the tropical rainforests had a GPP of 41.1 + 0.5 Pg C year<sup>-1</sup>, NPP of 19.7 + 0.3 Pg C year<sup>-1 </sup>and NEP of 0.44 + 0.34 Pg C year<sup>-1</sup> under 1860-2018 environmental conditions. Factorial experiments with DLEM-CNP suggested that deforestation has stronger impacts on GPP and NPP reduction compared to the enhanced GPP and NPP benefiting from the CO<sub>2</sub> fertilization effect. Additionally, tropical rainforests NEP showed a continuously increasing trend owing to the CO<sub>2</sub> fertilization effect. Our study highlights the importance of P limitation on the C cycle and the weakened CO<sub>2</sub> fertilization effect due to nutrients limitation in the tropical rainforests.</p>

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