An Empirical Model-based Method for Signal Restoration of SWIR in ASD Field Spectroradiometry

Lin, Chinsu; Tsogt, Khongor; Chang, Chein‐I

doi:10.14358/pers.78.2.119

Cited by 15 publications

(21 citation statements)

References 9 publications

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“…Third, the significant water absorption valleys could be seen at the spectral regions centered at 1450 and 1910 nm. The depth and the area of the absorption valley are negatively closely related to leaf RWC (Lin et al, 2012). Finally, a water-stressed leaf reflectance peaked at around 2000 nm as the RWC was less than or equal to 16 %.…”

Section: Reflectance Spectra Of Fresh and Water-stressed Leafmentioning

confidence: 95%

“…Typical reflectance of vegetation in the visible-infrared region will increase as water deficit occurs (Knipling, 1970;Gausman and Allen, 1973;Gausman et al, 1982;Hunt and Rock, 1989;Carter, 1991 and1993;Ceccato et al, 2001;Zygielbaum et al, 2009;Lin et al, 2012). As leaves dehydrate or vegetation suffers water stress, leaf water potential becomes increasingly negative and the rate of photosynthesis is reduced (Nilsen and Orcutt, 1996;Montagu and Woo, 1999;Keenan et al, 2009;Lavoir et al, 2009) because water deficit can cause chlorophyll degradation and thus significantly decreases foliar chlorophyll concentration (Kirnak et al, 2001;Pirzad et al, 2011;Desotgiu et al, 2012;Ghorbanli et al, 2013).…”

Section: Indexmentioning

confidence: 99%

“…The target reflectance is determined as the ratio of the energy reflected off the target (target radiance) to energy incident on the reference Spectralon (reference radiance). For each measurement, the radiance was taken with spectrum averaging set to 15 and then filtered using a median filter (Hatchell, 1999;Lin et al, 2012).…”

Section: Foliar Reflectance Measurementmentioning

confidence: 99%

“…It has been applied to describe the status of leaf water stress in remote sensing (Pu et al, 2003;Lin et al, 2012) and is therefore used in this study. Fresh leaf samples were first detached, measured for fresh weight (FW), and then spectral reflectance data were immediately collected.…”

Section: Implement Of Spectral and Chlorophyll Measurement Of Fresh Amentioning

confidence: 99%

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A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

et al. 2015

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Abstract. Water deficits can cause chlorophyll degradation which decreases the total concentration of chlorophyll a and b (Chls). Few studies have investigated the effectiveness of spectral indices under water-stressed conditions. Chlorophyll meters have been extensively used for a wide variety of leaf chlorophyll and nitrogen estimations. Since a chlorophyll meter works by sensing leaves absorptance and transmittance, the reading of chlorophyll concentration will be affected by changes in transmittance as if there were a water deficit in the leaves. The overall objective of this paper was to develop a novel and reliable reflectance-based model for estimating Chls of fresh and water-stressed leaves using the reflectance at the absorption bands of chlorophyll a and b and the red edge spectrum.Three independent experiments were designed to collect data from three leaf sample sets for the construction and validation of Chls estimation models. First, a reflectance experiment was conducted to collect foliar Chls and reflectance of leaves with varying water stress using the ASD FieldSpec spectroradiometer. Second, a chlorophyll meter (SPAD-502) experiment was carried out to collect foliar Chls and meter readings. These two data sets were separately used for developing reflectance-based or absorptance-based Chls estimation models using linear and nonlinear regression analysis. Suitable models were suggested mainly based on the coefficient of determination (R 2 ). Finally, an experiment was conducted to collect the third data set for the validation of Chls models using the root mean squared error (RMSE) and the mean absolute error (MAE). In all of the experiments, the observations (real values) of the foliar Chls were extracted from acetone solution and determined by using a Hitachi U-2000 spectrophotometer.The spectral indices in the form of reflectance ratio/difference/slope derived from the Chl b absorption bands (ρ 645 and ρ 455 ) provided Chls estimates with RMSE around 0.40-0.55 mg g −1 for both fresh and water-stressed samples. We improved Chls prediction accuracy by incorporating the reflectance at red edge position (ρ REP ) in regression models. An effective chlorophyll indicator with the form of (ρ 645 -ρ 455 ) / ρ REP proved to be the most accurate and stable predictor for foliar Chls concentration. This model was derived with an R 2 of 0.90 (P < 0.01) from the training samples and evaluated with RMSE 0.35 and 0.38 mg g −1 for the validation samples of fresh and water-stressed leaves, respectively. The average prediction error was within 14 % of the mean absolute error.

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Section: Reflectance Spectra Of Fresh and Water-stressed Leafmentioning

confidence: 95%

Section: Indexmentioning

confidence: 99%

Section: Foliar Reflectance Measurementmentioning

confidence: 99%

Section: Implement Of Spectral and Chlorophyll Measurement Of Fresh Amentioning

confidence: 99%

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A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

et al. 2015

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“…This problem is particularly pronounced in 1350-1410 nm and 1700-1900 nm because of the broad absorption of atmospheric water vapor [15], thus these spectra are rarely used in practical sensing [16][17][18][19][20]. Even though the low signal-to-noise ratio (SNR) is considered as troublesome, the absorption feature centered at 1450 nm and 1940 nm has been demonstrated as quite valuable for estimating the water content of plants [21][22][23][24] and nitrogen content [25][26][27]. In general, a degraded hyperspectral is represented as a combination of a true signal and additive noise and a denoising method is designed to estimate the original signal by penalized least squares optimization [28].…”

Section: Introductionmentioning

confidence: 99%

A Generalized Logistic-Gaussian-Complex Signal Model for the Restoration of Canopy SWIR Hyperspectral Reflectance

Lin

2018

Remote Sensing

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The continuum of the SWIR (short-wave infrared) signals from 1320 to 1650 nm contains valuable information for effectively diagnosing water, chlorophyll, and nitrogen content. The SWIR spectra of in situ spectroradiometric data and airborne spectrometric images are frequently contaminated by significant noise. Based on a Logistic-Gaussian complex signal model (LGCM), the noise-free signals at 1330-1349 and 1411-1430 nm wavelengths can provide critical bases for restoring the 1350-1410 nm wavelength signals for a single point of data. This paper proposes a generalized LGCM (GLGCM) technique to expand the ability of LGCM to process large data with variant reflectance values. A 12-year-old red cypress plantation located in a central Taiwan temperate forest was selected for this study. Hundreds of reflectance spectra of tree crowns were obtained using an ASD FR Spectroradiometer. The in-laboratory blank test showed that the GLGCM technique was able to achieve sufficient performance with an RMSE (root mean square error) of 0.0015 ± 0.0005 and 0.0011 ± 0.0005 for the front-edge and end-edge signal bases respectively, and 0.0014 ± 0.0006 in between the two signal bases. A significant level of noise between −0.2 and 0.4 was successfully removed from the in situ contaminated reflectance in the 1350-1410 nm wavelengths. The estimation bias for the signals of front-edge and end-edge bases was low, averaging 0.0031 ± 0.0003 and 0.0032 ± 0.0012. The consistency between the blank test and the in situ experimental results indicates that the GLGCM technique has potential in using batch processing to fix the problem of the noisy SWIR spectra in spectroradiometeric data and also airborne spectrometric images.

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Diagnosing pristine pine forest development through pansharpened-surface-reflectance Landsat image derived aboveground biomass productivity

Lin

Lee

Lumbres

et al. 2021

Forest Ecology and Management

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An Empirical Model-based Method for Signal Restoration of SWIR in ASD Field Spectroradiometry

Cited by 15 publications

References 9 publications

A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

A novel reflectance-based model for evaluating chlorophyll concentrations of fresh and water-stressed leaves

A Generalized Logistic-Gaussian-Complex Signal Model for the Restoration of Canopy SWIR Hyperspectral Reflectance

Diagnosing pristine pine forest development through pansharpened-surface-reflectance Landsat image derived aboveground biomass productivity

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