Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System

Running, Steven W.; Thornton, P. E.; Nemani, Ramakrishna R.; Glassy, Joseph M.

doi:10.1007/978-1-4612-1224-9_4

Cited by 534 publications

(472 citation statements)

References 51 publications

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“…The CASA model (Potter et al, 1993) was used to simulate the rangeland NPP in the study area. The parameters used were adopted from previous relevant research reports (Potter et al, 1993;Zhu et al, 2005;Sellers et al, 1994;Shao et al, 2009;Cui, 2004;Lu and Yu, 2004;Gong et al, 1993;Chen et al, 2001;Yang and Qiu, 2002;Running et al, 2000). The monthly NPP of growing season (May to September) was estimated according to the maximum monthly NDVI, and then was accumulated to obtain NPP value of the year.…”

Section: Npp Estimation By Casa Modelmentioning

confidence: 99%

Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau

Wang

Xia

et al. 2017

Quaternary International

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Abstract-the "Three-River Headwaters" region (TRHR) rangeland ecosystem is extremely sensitive and fragile, and in recent years has undergone continuous degradation. The vast area and severe nature conditions inhibit data acquisition and field experiments, resulting in different understandings about the spatial characteristics and dynamics of rangeland degradation in the region. Therefore, reliable monitoring method of rangeland degradation is urgently needed for the rangeland protection and management. In this paper, a novel rangeland degradation monitoring scheme based on Local NPP Scaling (LNS) was suggested;A suitable partition program of rangeland productivity unit was set up by using Analytical Hierarchy Process (

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Section: Npp Estimation By Casa Modelmentioning

confidence: 99%

Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau

Wang

Xia

et al. 2017

Quaternary International

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“…1). One group models, such as the Global Production Efficiency Model (GloPEM) (Prince, 1995), Carnegie-Ames-Stanford Approach (CASA) model (Potter, 1999;Potter et al, 1993), and Photosynthesis (PSN) model (Running, Thornton, Nemani, & Glassy, 2000;Zhao, Heinsch, Nemani, & Running, 2005), use the FPAR at the canopy level (FPAR canopy ). These models often use the Normalized Difference Vegetation Index (NDVI) to estimate FPAR canopy .…”

Section: Introductionmentioning

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

107

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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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“…The Moderate Imaging Spectroradiometer (MODIS) sensor was designed in part for that purpose and global estimates of 8-day gross primary production (GPP) and annual net primary production (NPP) at the 1 km spatial resolution are now being produced operationally (Running, Thornton, Nemani, & Glassy, 2000). Both GPP and NPP estimates require validation with ground-based measurements.…”

Section: Introductionmentioning

confidence: 99%

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

Turner

2003

Remote Sensing of Environment

131

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The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach. In this study, the 2001 MODIS GPP product was compared with scaled GPP estimates (25 km 2 ) based on ground measurements at two forested sites. The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode. Land cover classification and maximum annual leaf area index, as derived from Landsat ETM+ imagery, were used in model initiation. The model was driven by daily meteorological observations from an eddy covariance flux tower situated at the center of each site. Model simulated GPPs were corroborated with daily GPP estimates from the flux tower. At the hardwood forest site, the MODIS GPP phenology started earlier than was indicated by the scaled GPP, and the summertime GPP from MODIS was generally lower than the scaled GPP values. The fall-off in production at the end of the growing season was similar to the validation data. At the boreal forest site, the GPP phenologies generally agreed because both responded to the strong signal associated with minimum temperature. The midsummer MODIS GPP there was generally higher than the ground-based GPP. The differences between the MODIS GPP products and the ground-based GPPs were driven by differences in the timing of FPAR and the magnitude of light use efficiency as well as by differences in other inputs to the MODIS GPP algorithm-daily incident PAR, minimum temperature, and vapor pressure deficit. Ground-based scaling of GPP has the potential to improve the parameterization of light use efficiency in satellite-based GPP monitoring algorithms. D

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Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System

Cited by 534 publications

References 51 publications

Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau

Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau

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

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

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