A new conventional regression model to estimate hourly photosynthetic photon flux density under all sky conditions

Foyo-Moreno, I.; Alados, I.; Alados-Arboledas, L.

doi:10.1002/joc.5063

Cited by 38 publications

(44 citation statements)

References 43 publications

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“…• , with a standard deviation of 0.15 • . The ⁄ ratio had similar values to those reported by other researchers [15], ranging between 1.21 and 2.84…”

Section: Temporal Variability Of the ⁄ Ratiosupporting

confidence: 88%

“…The monthly Q p /R s average was calculated on a daily basis from experimental data [14] collected in an arid climate at between 2.02 and 2.19 µmol•J −1 and the mean daily value was 2.16 µmol•J −1 . In Spain, Foyo-Moreno et al [15] estimated a mean value of 1.95 µmol•J −1 a value close to other values from different locations [3,16]. Hu et al [17] evaluated the Q p /R s ratio at many locations within China at between 1.75 µmol•J −1 and 2.30 µmol•J −1 .…”

Section: Introductionmentioning

confidence: 79%

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Photosynthetic Active Radiation, Solar Irradiance and the CIE Standard Sky Classification

et al. 2020

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Plant growth is directly related to levels of photosynthetic photon flux density, Qp. The improvement of plant-growth models therefore requires accurate estimations of the Qp parameter that is often indirectly calculated on the basis of its relationship with solar irradiation, RS, due to the scarcity of ground measurements of photosynthetic photon flux density. In this experimental campaign in Burgos, Spain, between April 2019 and January 2020, an average value of the Qp/Rs ratio is determined on the basis of measurements at ten-minute intervals. The most influential factor in the Qp/Rs ratio, over and above any daily or seasonal pattern, is the existence of overcast sky conditions. The CIE standard sky classification can be used to establish an unequivocal characterization of the cloudiness conditions of homogeneous skies. In this study, the relation between the CIE standard sky type and Qp/Rs is investigated. Its conclusions were that the Qp/Rs values, the average of which was 1.93±0.15 μmol·J−1, presented statistically significant differences for each CIE standard sky type. The overcast sky types presented the highest values of the ratio, while the clear sky categories presented the lowest and most dispersed values. During the experimental campaign, only two exceptions were noted for covered and partial covered sky-type categories, respectively, sky types 5 and 9. Their values were closer to those of categories classified as clear sky according to the CIE standard. Both categories presented high uniformity in terms of illumination.

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“…• , with a standard deviation of 0.15 • . The ⁄ ratio had similar values to those reported by other researchers [15], ranging between 1.21 and 2.84…”

Section: Temporal Variability Of the ⁄ Ratiosupporting

confidence: 88%

Section: Introductionmentioning

confidence: 79%

Photosynthetic Active Radiation, Solar Irradiance and the CIE Standard Sky Classification

et al. 2020

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“…Combinations of sets of variables that are commonly used in the bibliography were selected as GHI, RH, T, k t , cos θ, and the product k t •cos θ. Examples of models that utilize these variables can be found in [25,26,[32][33][34][38][39][40][41][59][60][61][62][63]. The aim making different combinations of them is to produce different models so that we can compare them and choose which model is the best and which variables are more interesting for PAR modeling.…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, data collected on the Earth's surface, such as global horizontal irradiance (GHI), air temperature (T), relative humidity (RH), sky clearness, skylight brightness parameter, and clearness index (k t ), can also develop PAR models [32][33][34][35][36][37][38]. It is also possible to train artificial neural networks (ANNs) to produce PAR estimates [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

A New Index Assessing the Viability of PAR Application Projects Used to Validate PAR Models

2021

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Photosynthetically active radiation (PAR) is a useful variable to estimate the growth of biomass or microalgae. However, it is not always feasible to access PAR measurements; in this work, two sets of nine hourly PAR models were developed. These models were estimated for mainland Spain from satellite data, using multilinear regressions and artificial neural networks. The variables utilized were combinations of global horizontal irradiance, clearness index, solar zenith angle cosine, relative humidity, and air temperature. The study territory was divided into regions with similar features regarding PAR through clustering of the PAR clearness index (kPAR). This methodology allowed PAR modeling for the two main climatic regions in mainland Spain (Oceanic and Mediterranean). MODIS 3 h data were employed to train the models, and PAR data registered in seven stations across Spain were used for validation. Usual validation indices assess the extent to which the models reproduce the observed data. However, none of those indices considers the exceedance probabilities, which allow the assessment of the viability of projects based on the data to be modeled. In this work, a new validation index based on these probabilities is presented. Hence, its use, along with the other indices, provides a double and thus more complete validation.

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“…In [31], variables such as GHI, global extraterrestrial irradiance, sunshine fraction, air temperature, and relative humidity were input data to train several ANNs, producing PAR models over Athalassa, Cyprus. On the other hand, in [32], several combinations of input data such as global solar irradiance, solar azimuth cosine, and clearness index were used to develop PAR ANN models. In [33], PAR was estimated by ANNs based on different inputs: global solar irradiance, clearness index, solar azimuth angle, air relative humidity, sky brightness, air dew point temperature, sky clearness, and total precipitable water thickness.…”

Section: Kato Bandmentioning

confidence: 99%

Analysis of Spatial and Temporal Variability of the PAR/GHI Ratio and PAR Modeling Based on Two Satellite Estimates

et al. 2020

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The main objectives of this work are to address the analysis of the spatial and temporal variability of the ratio between photosynthetically active radiation (PAR) and global horizontal irradiance (GHI), as well as to develop PAR models. The analysis was carried out using data from three stations located in mainland Spain covering three climates: oceanic, standard Mediterranean, and continental Mediterranean. The results of this analysis showed a clear dependence between the PAR/GHI ratio and the location; the oceanic climate showed higher values of PAR/GHI compared with Mediterranean climates. Further, the temporal variability of PAR/GHI was conditioned by the variability of clearness index, so it was also higher in oceanic than in Mediterranean climates. On the other hand, Climate Monitoring Satellite Facility (CM-SAF) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data were used to estimate PAR as a function of GHI over the whole territory. The validation with ground measurements showed better performance of the MODIS-estimates-derived model for the oceanic climate (root-mean-square error (RMSE) around 5%), while the model obtained from CM-SAF fitted better for Mediterranean climates (RMSEs around 2%).

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A new conventional regression model to estimate hourly photosynthetic photon flux density under all sky conditions

Cited by 38 publications

References 43 publications

Photosynthetic Active Radiation, Solar Irradiance and the CIE Standard Sky Classification

Photosynthetic Active Radiation, Solar Irradiance and the CIE Standard Sky Classification

A New Index Assessing the Viability of PAR Application Projects Used to Validate PAR Models

Analysis of Spatial and Temporal Variability of the PAR/GHI Ratio and PAR Modeling Based on Two Satellite Estimates

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