Improved estimations of gross primary production using satellite-derived photosynthetically active radiation

Cai, Wenju; Yuan, Wenping; Liang, Shunlin; Zhang, Xiaolin; Dong, Wenjie; Xia, Jiangzhou; Fu, Yang; Chen, Yang; Liú, Dan; Zhang, Qiang

doi:10.1002/2013jg002456

Cited by 65 publications

(41 citation statements)

References 71 publications

(104 reference statements)

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“…The MERRA PAR product has been successfully used to investigate biogeochemical aspects of various ecosystems (Yuan et al 2011;Cai et al 2014;Sindelarova et al 2014;Yue et al 2015). Additionally, we compared it with observational PAR obtained by the SeaWiFS satellite from 1998 to 2001 for all 37 polynyas.…”

Section: Methodsmentioning

confidence: 99%

The Evident Role of Clouds on Phytoplankton Abundance in Antarctic Coastal Polynyas

La¹,

Park²

2016

Terr. Atmos. Ocean. Sci.

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High-level primary production (PP) sustainability in the Antarctic coastal polynyas has not been adequately addressed. We investigated the relationship between the Antarctic coastal polynyas phytoplankton biomass (as reflected by Chl-a) and the cloud cover, which dominantly influences the light conditions. Phytoplankton biomass in Antarctic coastal polynyas showed a robust correlation with the photosynthetically active radiation (PAR) and the cloud fraction. However, a different relationship was observed between the phytoplankton biomass and the PAR. This relationship depended on both the ultraviolet (UV) radiation and the geographical location of the coastal polynyas. High intensity UV radiation is suspected to be an inhibitor of phytoplankton biomass. This indicates that the phytoplankton biomass is effectively limited by light intensity, which can be reduced by clouds, particularly mid-and high-level clouds, but it is not limited by light under high-level UV radiation exposure conditions.

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Section: Methodsmentioning

confidence: 99%

The Evident Role of Clouds on Phytoplankton Abundance in Antarctic Coastal Polynyas

La¹,

Park²

2016

Terr. Atmos. Ocean. Sci.

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“…The variation in these results may be the result of differences among model structures [6]. In addition, the different sources and spatial resolutions of the input climate data used in the different models may also underlie this variation [51].…”

Section: Reliability and Merits Of The Swh Modelmentioning

confidence: 99%

“…Uncertainties may also be incurred by the inaccurate estimate of light-use efficiency. Many efforts have been made to solve this issue, such as improving drought constraint [51], accounting for diffusive radiation [57,58] and updating the empirical values in the equations of environmental constraints (e.g., T min , T opt , T max , VPD max ) in the GPP model [31]. Therefore, improving GPP estimation would be anticipated with the outcome of these efforts, which thus will be a benefit for improving ET modeling with the SWH model.…”

Section: Uncertainties Of the Swh Modelmentioning

confidence: 99%

Modeling and Partitioning of Regional Evapotranspiration Using a Satellite-Driven Water-Carbon Coupling Model

Zhang

et al. 2017

Remote Sensing

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Abstract:The modeling and partitioning of regional evapotranspiration (ET) are key issues in global hydrological and ecological research. We incorporated a stomatal conductance model and a light-use efficiency-based gross primary productivity (GPP) model into the Shuttleworth-Wallace model to develop a simplified carbon-water coupling model, SWH, for estimating ET using meteorological and remote sensing data. To enable regional application of the SWH model, we optimized key parameters with measurements from global eddy covariance (EC) tower sites. In addition, we estimated soil water content with the principle of the bucket system. The model prediction of ET agreed well with the estimates obtained with the EC measurements, with an average R 2 of 0.77 and a root mean square error of 0.72 mm·day −1 . The model performance was generally better for woody ecosystems than herbaceous ecosystems. Finally, the spatial patterns of ET and relevant model outputs (i.e., GPP, water-use efficiency and the ratio of soil water evaporation to ET) in China with the model simulations were assessed.

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“…In comparison, the VPM model only needs calibration for the maximum LUE or it can be acquired from existing publications; it is therefore more suitable to upscale simulations at various spatial and temporal domains. Satellite-derived PAR data from high spatial resolution GLASS images have been proven effective in GPP simulations (Cai et al, 2014), and the VPM model can also use LST data to replace air temperature data, which can help to reduce the dependence of the model on meteorological measurements and make VPM an independent, satellite-driven, and more operational model.…”

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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Improved estimations of gross primary production using satellite-derived photosynthetically active radiation

Cited by 65 publications

References 71 publications

The Evident Role of Clouds on Phytoplankton Abundance in Antarctic Coastal Polynyas

The Evident Role of Clouds on Phytoplankton Abundance in Antarctic Coastal Polynyas

Modeling and Partitioning of Regional Evapotranspiration Using a Satellite-Driven Water-Carbon Coupling Model

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

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