Using High-Frequency PAR Measurements to Assess the Quality of the SIF Derived from Continuous Field Observations

Abstract

Fluctuations in illumination are one of the major sources for SIF retrieval errors during temporal continuous field measurements. In this study, we propose a method for evaluating the quality of SIF based on simultaneous measurements of photosynthetically active radiation (PAR), which are acquired using a quantum sensor at a sampling frequency higher than that obtained using spectral measurements. The proposed method is based on the coefficient of variation (known as relative standard deviation) of the high-frequency PAR during a SIF measurement to determine the quality of the SIF value. To evaluate the method, spectral and PAR data of a healthy maize canopy were collected under various illumination conditions, including clear, cloudy, and rapidly fluctuating illumination. The SIF values were retrieved by 3FLD, SFM, and SVD. The results showed that SFM and 3FLD were sensitive to illumination stability. The determination coefficients (R2) between PAR and SIF extracted by SFM and 3FLD were higher than 0.8 on clear and cloudy days and only approximately 0.48 on the day with rapidly fluctuating illumination. By removing the unqualified data using the proposed method, the R2 values of SFM and 3FLD on the day of rapidly fluctuating illumination significantly increased to 0.72. SVD was insensitive to illumination stability. The R2 values of SVD on days with clear, cloudy, and rapidly fluctuating illumination were 0.73, 0.76, and 0.61, respectively. By removing the unqualified data, the R2 values of SVD were increased to 0.66 on the day with rapidly fluctuating illumination. The results indicated that the quality assessment method based on high-frequency PAR data can eliminate the incorrect SIFs due to unstable illumination. The method can be used to extract more accurate and reliable SIF datasets from long-term field observations for the study of the relationship between SIF and vegetation photosynthesis.

Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence

“…PAR data can eliminate the effect of SIF due to light instability. This method can extract a more accurate and reliable SIF data set from long-term field observations for studying the relationship between SIF and vegetation photosynthesis [73]. SIF can better estimate yield after PAR normalization [74,75].…”

Roles of Physiological and Nonphysiological Information in Sun-Induced Chlorophyll Fluorescence Variations for Detecting Cotton Verticillium Wilt

“…Under unstable weather conditions, rapid changes of the illumination occurred during the measurement, leading to notable differences between the real E when measuring L (EL) and the measured EM. It will result in a significant error in the calculation of canopy reflectance SIF derived from E and L, introducing uncertainty into the subsequent data analysis [38]. Therefore, before data processing, we filtered and removed abnormal data caused by abrupt changes in light intensity.…”