First Effort at Constructing a High-Density Photosynthetically Active Radiation Dataset during 1961–2014 in China

Abstract Photosynthetically active radiation (PAR) is a key factor for vegetation growth and climate change. Different types of PAR models, including four physically based models and eight artificial intelligence (AI) models, were proposed for predicting daily PAR. Multiyear daily meteorological parameters observed at 29 Chinese Ecosystem Research Network (CERN) stations and 2474 Chinese Meteorological Administration (CMA) stations across China were used for testing, validating, and comparing the above models. The optimized back propagation (BP) neural network based on the mind evolutionary algorithm (MEA-BP) was the model with highest accuracy and strongest robustness. The correlation coefficient R, mean absolute bias error (MAE), and RMSE for MEA-BP were 0.986, 0.302 MJ m−2 day−1 and 0.393 MJ m−2 day−1, respectively. Then, a high-density PAR dataset was constructed for the first time using the MEA-BP model at 2474 CMA stations of China. A quality control process and homogenization test (using RHtestsV4) for the PAR dataset were further conducted. This high-density PAR dataset would benefit many climate and ecological studies.

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Intercepted Photosynthetically Active Radiation (PAR) and Spatial and Temporal Distribution of Transmitted PAR under High-Density and Super High-Density Olive Orchards

Agriculture ◽

10.3390/agriculture11040351 ◽

2021 ◽

Vol 11 (4) ◽

pp. 351

Author(s):

Adolfo Rosati ◽

Damiano Marchionni ◽

Dario Mantovani ◽

Luigi Ponti ◽

Franco Famiani

Keyword(s):

Spatial Variability ◽

Photosynthetically Active Radiation ◽

Temporal Distribution ◽

High Density ◽

Radiation Use Efficiency ◽

Spatial And Temporal Distribution ◽

Active Radiation ◽

Use Efficiency ◽

And Performance ◽

Radiation Use

We quantified the photosynthetically active radiation (PAR) interception in a high-density (HD) and a super high-density (SHD) or hedgerow olive system, by measuring the PAR transmitted under the canopy along transects at increasing distance from the tree rows. Transmitted PAR was measured every minute, then cumulated over the day and the season. The frequencies of the different PAR levels occurring during the day were calculated. SHD intercepted significantly but slightly less overall PAR than HD (0.57 ± 0.002 vs. 0.62 ± 0.03 of the PAR incident above the canopy) but had a much greater spatial variability of transmitted PAR (0.21 under the tree row, up to 0.59 in the alley center), compared to HD (range: 0.34–0.43). This corresponded to greater variability in the frequencies of daily PAR values, with the more shaded positions receiving greater frequencies of low PAR values. The much lower PAR level under the tree row in SHD, compared to any position in HD, implies greater self-shading in lower-canopy layers, despite similar overall interception. Therefore, knowing overall PAR interception does not allow an understanding of differences in PAR distribution on the ground and within the canopy and their possible effects on canopy radiation use efficiency (RUE) and performance, between different architectural systems.

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GIS tool to predict photosynthetically active radiation in a Dry Valley

Antarctic Science ◽

10.1017/s0954102020000218 ◽

2020 ◽

Vol 32 (5) ◽

pp. 315-328

Author(s):

Dimitri Acosta ◽

Peter T. Doran ◽

Madeline Myers

Keyword(s):

Primary Productivity ◽

Photosynthetically Active Radiation ◽

Net Primary Productivity ◽

Light Attenuation ◽

Statistical Treatment ◽

Ground Truth ◽

Research Area ◽

Research Network ◽

Ground Truth Data ◽

Active Radiation

AbstractUnderstanding primary productivity is a core research area of the National Science Foundation's Long-Term Ecological Research Network. This study presents the development of the GIS-based Topographic Solar Photosynthetically Active Radiation (T-sPAR) toolbox for Taylor Valley. It maps surface photosynthetically active radiation using four meteorological stations with ~20 years of data. T-sPAR estimates were validated with ground-truth data collected at Taylor Valley's major lakes during the 2014–15 and 2015–16 field seasons. The average daily error ranges from 0.13 mol photons m-2 day-1 (0.6%) at Lake Fryxell to 3.8 mol photons m-2 day-1 (5.8%) at Lake Hoare. We attribute error to variability in terrain and sun position. Finally, a user interface was developed in order to estimate total daily surface photosynthetically active radiation for any location and date within the basin. T-sPAR improves upon existing toolboxes and models by allowing for the inclusion of a statistical treatment of light attenuation due to cloud cover. The T-sPAR toolbox could be used to inform biological sampling sites based on radiation distribution, which could collectively improve estimates of net primary productivity, in some cases by up to 25%.

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Global radiation, photosynthetically active radiation, and the diffuse component dataset of China, 1981–2010

Earth System Science Data ◽

10.5194/essd-10-1217-2018 ◽

2018 ◽

Vol 10 (3) ◽

pp. 1217-1226 ◽

Cited By ~ 8

Author(s):

Xiaoli Ren ◽

Honglin He ◽

Li Zhang ◽

Guirui Yu

Keyword(s):

Solar Radiation ◽

Photosynthetically Active Radiation ◽

Global Radiation ◽

Terrestrial Ecosystem ◽

Diffuse Radiation ◽

Research Network ◽

Diffuse Component ◽

Ecosystem Productivity ◽

Ecosystem Models ◽

Active Radiation

Abstract. Solar radiation, especially photosynthetically active radiation (PAR), is the main energy source of plant photosynthesis, and the diffuse component can enhance canopy light use efficiency, thus increasing ecosystem productivity. In order to predict the terrestrial ecosystem productivity precisely, we not only need global radiation and PAR as driving variables, but also need to treat diffuse radiation and diffuse PAR explicitly in ecosystem models. Therefore, we generated a series of radiation datasets, including global radiation, diffuse radiation, PAR, and diffuse PAR of China from 1981 to 2010, based on the observations of the China Meteorology Administration (CMA) and the Chinese Ecosystem Research Network (CERN). The dataset should be useful for the analysis of the spatiotemporal variations of solar radiation in China and the impact of diffuse radiation on terrestrial ecosystem productivity based on ecosystem models. The dataset is freely available from Zenodo on the following website: https://zenodo.org/record/1198894#.Wx6–C_MwWo (https://doi.org/10.11922/sciencedb.555, Ren et al., 2018).

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Global radiation, photosynthetically active radiation, and the diffuse components dataset of China, 1981–2010

10.5194/essd-2018-4 ◽

2018 ◽

Author(s):

Xiaoli Ren ◽

Honglin He ◽

Li Zhang ◽

Guirui Yu

Keyword(s):

Solar Radiation ◽

Photosynthetically Active Radiation ◽

Global Radiation ◽

Terrestrial Ecosystem ◽

Diffuse Radiation ◽

Research Network ◽

Ecosystem Productivity ◽

Ecosystem Models ◽

Active Radiation ◽

The Impact

Abstract. Solar radiation, especially photosynthetically active radiation (PAR), is the main energy source of plant photosynthesis; and the diffuse component can enhance canopy light use efficiency, thus increasing ecosystem productivity. In order to predict the terrestrial ecosystem productivity precisely, we not only need global radiation and PAR as driving variables, but also need to treat diffuse radiation and diffuse PAR explicitly in ecosystem models. Therefore, we generated a series of radiation datasets, including global radiation, diffuse radiation, PAR, and diffuse PAR of China from 1981 to 2010, based on the observations of China Meteorology Administration (CMA) and Chinese Ecosystem Research Network (CERN). The dataset should be useful for the analysis of the spatio-temporal variations of solar radiation in China and the impact of diffuse radiation on terrestrial ecosystem productivity based on ecosystem models. The dataset is freely available from the Zenodo at the website of https://zenodo.org/record/1198894 (DOI: 10.11922/sciencedb.555).

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Comparison of Artificial Intelligence and Physical Models for Forecasting Photosynthetically-Active Radiation

Remote Sensing ◽

10.3390/rs10111855 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1855 ◽

Cited By ~ 8

Author(s):

Lan Feng ◽

Wenmin Qin ◽

Lunche Wang ◽

Aiwen Lin ◽

Ming Zhang

Keyword(s):

Artificial Intelligence ◽

Photosynthetically Active Radiation ◽

Genetic Model ◽

Mainland China ◽

Temporal Variations ◽

Physical Models ◽

The Other ◽

Bias Error ◽

Active Radiation ◽

Artificial Intelligence Models

Different kinds of radiative transfer models, including a relative sunshine-based model (BBM), a physical-based model for tropical environment (PBM), an efficient physical-based model (EPP), a look-up-table-based model (LUT), and six artificial intelligence models (AI) were introduced for modeling the daily photosynthetically-active radiation (PAR, solar radiation at 400–700 nm), using ground observations at twenty-nine stations, in different climatic zones and terrain features, over mainland China. The climate and terrain effects on the PAR estimates from the different PAR models have been quantitatively analyzed. The results showed that the Genetic model had overwhelmingly higher accuracy than the other models, with the lowest root mean square error (RMSE = 0.5 MJ m−2day−1), lowest mean absolute bias error (MAE = 0.326 MJ m−2day−1), and highest correlation coefficient (R = 0.972), respectively. The spatial–temporal variations of the annual mean PAR (APAR), in the different climate zones and terrains over mainland China, were further investigated, using the Genetic model; the PAR values in China were generally higher in summer than those in the other seasons. The Qinghai Tibetan Plateau had always been the area with the highest APAR (8.668 MJ m−2day−1), and the Sichuan Basin had always been the area with lowest APAR (4.733 MJ m−2day−1). The PAR datasets generated by the Genetic model, in this study, could be used in numerous PAR applications, with high accuracy.

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Estimation of Daily Mean Photosynthetically Active Radiation under All-Sky Conditions Based on Relative Sunshine Data

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-10-05018.1 ◽

2012 ◽

Vol 51 (1) ◽

pp. 150-160 ◽

Cited By ~ 22

Author(s):

Jun Qin ◽

Kun Yang ◽

Shunlin Liang ◽

Wenjun Tang

Keyword(s):

Photosynthetically Active Radiation ◽

Sunshine Duration ◽

Surface Radiation ◽

Radiation Budget ◽

Mean Bias Error ◽

Bias Error ◽

Active Radiation ◽

Clear Sky ◽

Surface Radiation Budget ◽

Sky Conditions

AbstractPhotosynthetically active radiation (PAR) is absorbed by plants to carry out photosynthesis. Its estimation is important for many applications such as ecological modeling. In this study, a broadband transmittance scheme for solar radiation at the PAR band is developed to estimate clear-sky PAR values. The influence of clouds is subsequently taken into account through sunshine-duration data. This scheme is examined without local calibration against the observed PAR values under both clear- and cloudy-sky conditions at seven widely distributed Surface Radiation Budget Network (SURFRAD) stations. The results indicate that the scheme can estimate the daily mean PAR at these seven stations under all-sky conditions with root-mean-square error and mean bias error values ranging from 6.03 to 6.83 W m−2 and from −2.86 to 1.03 W m−2, respectively. Further analyses indicate that the scheme can estimate PAR values well with globally available aerosol and ozone datasets. This suggests that the scheme can be applied to regions for which observed aerosol and ozone data are not available.

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Faculty Opinions recommendation of Far-red photons have equivalent efficiency to traditional photosynthetic photons: Implications for redefining photosynthetically active radiation.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.737290967.793571659 ◽

2020 ◽

Author(s):

Donald Ort ◽

Rebecca Slattery

Keyword(s):

Photosynthetically Active Radiation ◽

Active Radiation

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Relationship between Fraction of Photosynthetically Active Radiation and Vegetation Indices, Leaf Area Index of Corn and Soybean

ACTA AGRONOMICA SINICA ◽

10.3724/sp.j.1006.2008.02046 ◽

2009 ◽

Vol 34 (11) ◽

pp. 2046-2052

Author(s):

Fei YANG

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Photosynthetically Active Radiation ◽

Vegetation Indices ◽

Area Index ◽

Active Radiation

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Analysis of the information management system in the manufacturing process of cigarette enterprises using fuzzy AHP

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189648 ◽

2021 ◽

pp. 1-11

Author(s):

Song Gang ◽

Wang Xiaoming ◽

Wu Junfeng ◽

Li Shufang ◽

Liu Zhuowen ◽

...

Keyword(s):

Quality Control ◽

Production Process ◽

Information Management ◽

Manufacturing Process ◽

Management System ◽

Fuzzy Ahp ◽

Control Process ◽

Information Management System ◽

Manufacturing Execution System ◽

Quality Control Process

In view of the production quality management of filter rods in the manufacturing and execution process of cigarette enterprises, this paper analyzes the necessity of implementing the manufacturing execution system (MES) in the production process of filter rods. In this paper, the filter rod quality system of cigarette enterprise based on MES is fully studied, and the constructive information management system demand analysis, cigarette quality control process, system function module design, implementation and test effect are given. This paper utilizes the Fuzzy analytic hierarchy process to find the optimal system for processing the manufacturing of cigarette. The implementation of MSE based filter rod quality information management system for a cigarette enterprise ensures the quality control in the cigarette production process. Through visualization, real-time and dynamic way, the information management of cigarette production is completed, which greatly improves the quality of cigarette enterprise manufacturing process.

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