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RSS FeedsRemote Sensing, Vol. 11, Pages 932: Estimation of Global and Diffuse Photosynthetic Photon Flux Density under Various Sky Conditions Using Ground-Based Whole-Sky Images (Remote Sensing)

 
 

17 april 2019 19:00:04

 
Remote Sensing, Vol. 11, Pages 932: Estimation of Global and Diffuse Photosynthetic Photon Flux Density under Various Sky Conditions Using Ground-Based Whole-Sky Images (Remote Sensing)
 


A knowledge of photosynthetic photon flux density (PPFD: μmol m−2 s−1) is crucial for understanding plant physiological processes in photosynthesis. The diffuse component of the global PPFD on a short timescale is required for the accurate modeling of photosynthesis. However, because the PPFD is difficult to determine, it is generally estimated from incident solar radiation (SR: W m−2), which is routinely observed worldwide. To estimate the PPFD from the SR, photosynthetically active radiation (PAR: W m−2) is separated from the SR using the PAR fraction (PF; PAR/SR: unitless), and the PAR is then converted into the PPFD using the quanta-to-energy ratio (Q/E: μmol J−1). In this procedure, PF and Q/E are considered constant values; however, it was reported recently that PF and Q/E vary under different sky conditions. Moreover, the diffuse ratio (DR) is needed to distinguish the diffuse component in the global PAR, and it is known that the DR varies depending on sky conditions. Ground-based whole-sky images can be used for sky-condition monitoring, instead of human-eye interpretation. This study developed a methodology for estimating the global and diffuse PPFD using whole-sky images. Sky-condition factors were derived through whole-sky image processing, and the effects of these factors on the PF, the Q/E of global and diffuse PAR, and the DR were examined. We estimated the global and diffuse PPFD with instantaneous values using the sky-condition factors under various sky conditions, based on which the detailed effects of the sky-condition factors on PF, Q/E, and DR were clarified. The results of the PPFD estimations had small bias errors of approximately +0.3% and +3.8% and relative root mean square errors of approximately 27% and 20% for the global and diffuse PPFD, respectively.


 
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Remote Sensing, Vol. 11, Pages 931: Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm (Remote Sensing)
Remote Sensing, Vol. 11, Pages 930: Aerial Image Road Extraction Based on an Improved Generative Adversarial Network (Remote Sensing)
 
 
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