Abstract
Detecting sun-induced chlorophyll fluorescence (SIF) offers a new approach for remote sensing photosynthesis. However, to analyse the response characteristics of SIF under different stress states, a long-term time-series comparative observation of vegetation under different stress states must be carried out at the canopy scale, such that the similarities and differences in SIF change law can be summarized under different time scales. A continuous comparative observation system for vegetation canopy SIF is designed in this study. The system, which is based on a high-resolution spectrometer and an optical multiplexer, can achieve comparative observation of multiple targets. To simultaneously measure the commonly used vegetation index and SIF in the O2-A and O2-B atmospheric absorption bands, the following parameters are used: a spectral range of 475.9 to 862.2 nm, a spectral resolution of approximately 0.9 nm, a spectral sampling interval of approximately 0.4 nm, and the signal-to-noise ratio (SNR) can be as high as 1000:1. To obtain data for both the upward radiance of the vegetation canopy and downward irradiance data with a high SNR in relatively short time intervals, the single-step integration time optimization algorithm is proposed. To optimize the extraction accuracy of SIF, the FluorMOD model is used to simulate sets of data according to the spectral resolution, spectral sampling interval and SNR of the spectrometer in this continuous observation system. These data sets are used to determine the best parameters of Fraunhofer Line Depth (FLD), Three FLD (3FLD) and the spectral fitting method (SFM), and 3FLD and SFM are confirmed to be suitable for extracting SIF from the spectral measurements. This system has been used to observe the SIF values in O2-A and O2-B absorption bands and some commonly used vegetation index from sweet potato and bare land, the result of which shows: (1) the daily variation trend of SIF value of sweet potato leaves is basically same as that of photosynthetically active radiation (PAR); and (2) the bare land is a non-fluorescent emitter, the SIF of which is significantly smaller than that of sweet potato; and (3) analysis result based on the measured data is basically same as that based on simulated data. The above results verified the reliability of the SIF extracted from the measured data and the feasibility of comparatively observing the SIF value and the commonly used vegetation index of multiple vegetation canopy with this continuous observation system. This approach is beneficial for comprehensively analysing the stress response characteristics of vegetation canopies.
Highlights
As the fluorescence emission of chlorophyll in vegetation is closely related to the photosynthesis occurring in the leaves [1], information about photosynthesis can be obtained by detecting chlorophyll fluorescence
To optimize the extraction accuracy of sun-induced chlorophyll fluorescence (SIF), the FluorMOD model is used to simulate sets of data according to the spectral resolution, spectral sampling interval and signal-to-noise ratio (SNR) of the spectrometer in this continuous observation system
Through repeated trials of different parameters of each algorithm and referencing the method of Meroni et al [16], the settings for each parameter in the O2 -A and O2 -B absorption bands are summarized in Tables 5 and 6
Summary
As the fluorescence emission of chlorophyll in vegetation is closely related to the photosynthesis occurring in the leaves [1], information about photosynthesis can be obtained by detecting chlorophyll fluorescence. Sun-induced chlorophyll fluorescence (SIF) may provide an effective means for detecting stress in vegetation. A water-stress experiment by Zarco-Tejada et al showed that the chlorophyll fluorescence computed in a hyperspectral image is correlated with the stomatal conductance and water potential of orange leaves [2]. Lee et al extracted SIF data of the Amazon Basin from GOSAT satellite data and showed that SIF and ground water are both reduced at noon during the dry season [3]. The canopy water content is highly correlated with the canopy SIF at the time scale of a month
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