New Spectral Fitting Method for Full-Spectrum Solar-Induced Chlorophyll Fluorescence Retrieval Based on Principal Components Analysis

Abstract

Abstract:The full-spectrum Solar-Induced chlorophyll Fluorescence (SIF) within the 650800 nm spectral region can provide important information regarding physiological and biochemical activities in vegetation. This paper proposes a new Full-spectrum Spectral Fitting Method (F-SFM) for the retrieval of SIF spectra based on Principal Components Analysis (PCA). Using F-SFM, both the full-spectrum reflectance and SIF within the 650800 nm region were modeled by PCA based on a training dataset simulated by the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model, and the weighting coefficients of the principal components were estimated by the least-squares fitting method. An iterative process was employed to improve the accuracy of the estimation of the reflectance. In each iteration, the SIF spectra retrieved from the last run were removed from the total upwelling radiance to minimize the small contribution of the SIF to the apparent reflectance outside the absorption bands. Then, the F-SFM algorithm was tested using both simulated and field-measured data with different Spectral Resolutions (SRs) and Signal-to-Noise Ratios (SNRs). For data with an SR of 0.3 nm and without noise, the Relative Root Mean Square Error (RRMSE) was less than 14% within the spectral region that was studied, and the peak-value ratio (SIF735/SIF685) was accurately estimated with an RRMSE of 3.56%. In addition, the F-SFM algorithm proved less sensitive to the SR than the three-band Fraunhofer Line Discrimination (3 FLD) and improved FLD (iFLD) methods. In OPEN ACCESSRemote Sens. 2015, 7 10627 the case of the field spectral data with SRs of 3 nm and 0.3 nm, the double-peak shape and the diurnal variation trend of the SIF spectra could be reasonably reconstructed by F-SFM, and the retrieved SIF values at the O2-A and O2-B bands were consistent with those retrieved by 3FLD from data with a high SR (0.3 nm) and SNR (1000). Therefore, the F-SFM method can provide full-spectrum SIF information with high accuracy even at relatively low SRs and SNRs, and shows promise for use in applications involving the SIF shape information.

Comparison of Sun-Induced Chlorophyll Fluorescence Estimates Obtained from Four Portable Field Spectroradiometers

“…The Ocean Optics devices have an ultrafine resolution (FWHM ď 1 nm) and a corresponding percentage depth of the O 2 -A band greater than 75%, a value that, in our experiment, we should consider as a reasonable threshold to obtain reliable far-red SIF data. On the contrary, only a few studies report the absolute values of the red TOC SIF [9,24,[41][42][43][44]. In all cases, the red SIF never exceeded the value of 3 mW¨m´2¨sr´1¨nm´1.…”

Effects of the Temporal Aggregation and Meteorological Conditions on the Parameter Robustness of OCO-2 SIF-Based and LUE-Based GPP Models for Croplands

“…where f esc ≈ 1 can be set when far-red SIF data are used from cropland and grassland [54] because these land types have a lower leaf absorptance in the far-red waveband, a higher LAI, and a simpler canopy structure compared with other ecosystems [19,60]. The TL-LUE model divides GPP into two components, sunlit and shaded leaves, by combining the algorithm of the SL-LUE model and the new generation of the dynamic land model (DLM) [32]:…”

“…Wand et al [49] suggests that the SIF can be accurately extracted in the O 2 -A band near 760 nm rather than the O 2 -B band near 687 nm when the spectral resolution is 3.3 nm. Compared with the FLD [50] and the improved FLD (iFLD) algorithm [51], 3FLD can get more accurate SIF values [52] and is more robust than the iFLD, which appears to be highly sensitive to noise [53,54]. In this approach, three spectral channels are required for each absorption line, and the changes of fluorescence and reflectance in and out of the absorption line within a very narrow band are hypothesized to be linear.…”

A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing

“…PCA has been used to significantly accelerate hyperspectral RT computations [7][8][9]. Following a determination of representative components from a training dataset of trace-gas and solar-induced chlorophyll fluorescence, the hyperspectral inversions of these quantities are sped up by retrieving PC weights [10][11][12]. In another retrieval application, Multi-angle Imaging SpectroRadiometer (MISR) operational aerosol retrieval over land uses spatial contrasts to derive PCs of the surface related contribution to the top-of-the-atmosphere (TOA) radiances [13,14].…”