Evaluation of phenotypic and photosynthetic indices to detect water stress in perennial grass species using hyperspectral, multispectral and chlorophyll fluorescence imaging

Krishna B Katuwal; Haoguang Yang; Bingru Huang; Krishna B Katuwal; Haoguang Yang; Bingru Huang

doi:10.48130/GR-2023-0016

Figures (6) Tables (5)

Figure 1.
Drought stress affected turf quality, leaf relative water content and soil volumetric water content during 20 d of stress period in Kentucky bluegrass. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
Figure 2.
Vegetation indices generated by hyperspectral sensing and sensitivity of these indices in monitoring drought in Kentucky bluegrass exposed to 20 d of drought stress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
Figure 3.
Vegetation indices generated by multispectral image analysis and sensitivity of these indices in monitoring drought in Kentucky bluegrass exposed to 20 d of drought tress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points.
Figure 4.
Chlorophyll fluorescence parameters measured by pulse amplitude modulated fluorescence imaging system and detection of drought by these parameters in Kentucky bluegrass exposed to 20 d of drought stress. * indicates significant difference between control and drought stress treatments (p ≤ 0.05) at each day of measurement. Presented values represent average of five data points. NPQ, Non-photochemical quenching; F_v /F_m, Maximum photochemical efficiency of PSII; F'_v/F'_m, Photochemical efficiency of open PSII centers; Y(PSII), Actual photochemical quantum yield of PSII centers; qP, Photochemical quenching coefficient (Puddle model); qL, Photochemical quenching coefficient (Lake model); qN, Non-photochemical quenching coefficient; Rfd, Chlorophyll fluorescence decrease ratio.
Figure 5.
Maps generated by the three most drought sensitive indices and parameters [hyperspectral structure independent pigment index (SIPI), multispectral normalized difference vegetation index (NDVI_m) and chlorophyll fluorescence NPQ]. These maps clearly separated control and drought stress after 18 d of treatment when majorities of indices and parameters detected drought stress.
Figure 6.
Comparison of predicted turfgrass quality (TQ) and leaf relative water content (RWC) versus their measured values using partial least square regression model. Turfgrass quality and relative water contents were predicted using various indices generated by hyperspectral, multispectral and chlorophyll fluorescence sensing technologies. The dashed line represents the I:I line. Regression analysis was performed using all individual data points (five replications for each control and drought stress treatments).

Vegetation index	Index abbreviation and formula
Vegetation index	Hyperspectral analysis	Multispectral analysis
Structure Independent Pigment Index	SIPI = (R₈₀₀ – R₄₄₅) / (R₈₀₀ + R₆₈₀)	SIPI_m = (R_NIR840 – R_Blue444) / (R_NIR840 + R_Red668)
Simple Ratio Index	SRI = R₈₀₀ / R₆₇₅	SRI_m = R_NIR840 / R_Red668
Plant Senescence Reflectance Index	PSRI = (R₆₈₀ –R₅₀₀) / R₇₅₀	PSRI_m = (R_Red668 – R_Blue475) / R_Rededge740
Photochemical Reflectance Index	PRI = (R₅₇₀ – R₅₃₁) / (R₅₇₀ + R₅₃₁)	PRI = (R_Green560 – R_Green531) / (R_Green560 + R_Green531)
Normalized Difference Vegetation Index	NDVI = (R₈₀₀ – R₆₈₀) / (R₈₀₀ + R₆₈₀)	NDVI_m = (R_NIR840 – R_Red668) / (R_NIR840 + R_Red668)
Normalized Difference Red Edge	NDRE = (R₇₅₀ – R₇₀₅) / (R₇₅₀ + R₇₀₅)	NDRE_m = (R_Rededge717 – R_Red668) / (R_Rededge717 + R_Red668)

Table 1.

List of vegetation indices calculated using hyperspectral and multispectral image analysis for drought stress monitoring in Kentucky bluegrass. Name and number in subscript following the letter R in each formula represent the reflectance at individual light and particular wavelength.

Band name Central wavelength (nm) Band width (nm)

Blue444 444 28
Blue475 475 32
Green531 531 14
Green560 560 27
Red650 650 16
Red668 668 14
RE705 705 10
RE717 717 12
RE740 740 18
NIR840 842 57

Table 2.
Spectral band details (center wavelength and band width) for Micasense Rededge-MX dual camera system.

Chlorophyll fluorescence parameter	Formula
Maximum photochemical efficiency of PSII (F_v / F_m)	(F_m-F_o) / F_m
Photochemical efficiency of open PSII centers (F'_v / F'_m)	(F'_m – F'_o) / F'_m
Actual photochemical quantum yield of PSII centers Y(PSII)	(F'_m – F_s) / F'_m
Photochemical quenching coefficient (Puddle model; qP)	(F'_m – F_s) / (F'_m – F'_o)
Photochemical quenching coefficient (Lake model; qL)	qP × F'_o / F_s
Non-photochemical quenching coefficient (qN)	(F_m-F'_m) / F_m
Non-photochemical quenching (NPQ)	(F_m-F'_m) / F'_m
Chlorophyll fluorescence decrease ratio (Rfd)	(F_m-F_s) / F_s

Table 3.

Chlorophyll fluorescence parameters calculated from pulse amplitude modulated fluorescence imaging system.

	RWC	TQ	F_V/F_m	F'_v/F'_m	Y(PSII)	NPQ	qN	qP	qL	Rfd	SIPI	SRI	PSRI	PRI	NDVI	NDRE	WBI	SIPI_m	PSRI_m	PRI_m	NDVI_m	NDRE_m
RWC	1.00
TQ	0.95^*	1.00
F_V/F_m	0.87^*	0.85^*	1.00
F'_v/F'_m	0.81^*	0.77^*	0.95^*	1.00
Y(PSII)	0.85^*	0.74^*	0.80^*	0.74^*	1.00
NPQ	0.88^*	0.89^*	0.95^*	0.84^*	0.75^*	1.00
qN	0.84^*	0.83^*	0.96^*	0.84^*	0.77^*	0.96^*	1.00
qP	0.82^*	0.70^*	0.73^*	0.66^*	0.99^*	0.69^*	0.72^*	1.00
qL	0.89^*	0.81^*	0.90^*	0.86^*	0.97^*	0.83^*	0.86^*	0.95^*	1.00
Rfd	0.84^*	0.82^*	0.89^*	0.83^*	0.77^*	0.92^*	0.86^*	0.72^*	0.83^*	1.00
SIPI	0.84^*	0.71^*	0.63^*	0.58^*	0.51^*	0.57^*	0.69^*	0.48^*	0.60^*	0.46^*	1.00
SRI	0.57^*	0.62^*	0.44^*	0.45^*	0.33	0.41^*	0.45^*	0.30	0.40	0.33	0.83^*	1.00
PSRI	−0.83^*	−0.90^*	−0.90^*	−0.86^*	−0.76^*	−0.83^*	−0.87^*	−0.71^*	−0.86^*	−0.76^*	−0.75^*	−0.57^*	1.00
PRI	0.94^*	0.82^*	0.80^*	0.76^*	0.71^*	0.79^*	0.71^*	0.66^*	0.77^*	0.78^*	0.26	0.17	−0.78^*	1.00
NDVI	0.53^*	0.65^*	0.41^*	0.43^*	0.41^*	0.42^*	0.40	0.38	0.43^*	0.42^*	0.50^*	0.42^*	−0.54^*	0.31	1.00
NDRE	0.64^*	0.73^*	0.64^*	0.63^*	0.45^*	0.54^*	0.64^*	0.40	0.56^*	0.44^*	0.92^*	0.85^*	−0.75^*	0.33	0.50^*	1.00
SIPI_m	0.52^*	0.50^*	0.56^*	0.58^*	0.47^*	0.52^*	0.49^*	0.43^*	0.52^*	0.51^*	0.33	0.28	−0.58^*	0.61^*	0.27	0.39	−0.28	1.00
PSRI_m	−0.92^*	−0.85^*	−0.85^*	−0.85^*	−0.83^*	−0.80^*	−0.77^*	−0.79^*	−0.88^*	−0.77^*	−0.40	−0.23	0.77^*	−0.82^*	−0.41	−0.40	0.32	−0.52^*	1.00
PRI_m	0.20	−0.03	0.06	−0.01	0.28	0.14	0.11	0.31	0.20	0.18	0.05	0.10	0.01	−0.04	0.00	0.09	0.06	0.09	−0.04	1.00
NDVI_m	0.75^*	0.74^*	0.77^*	0.78^*	0.67^*	0.72^*	0.68^*	0.62^*	0.73^*	0.70^*	0.43^*	0.33	−0.76^*	0.81^*	0.37	0.47^*	−0.35	0.93^*	−0.76^*	−0.05	1.00
NDRE_m	0.90^*	0.89^*	0.89^*	0.89^*	0.81^*	0.83^*	0.81^*	0.76^*	0.88^*	0.81^*	0.52^*	0.41^*	−0.87^*	0.87^*	0.45^*	0.53^*	−0.32	0.62^*	−0.87^*	−0.04	0.85^*	1.00
Details for individual abbreviations of vegetation indices and fluorescence parameters used in this table were previously mentioned in Tables 1 & 3. Some other abbreviations are: RWC, leaf relative water content; and TQ, turfgrass quality. Values followed by * indicate significant correlation at p ≤ 0.05. Correlation analysis was performed using all individual data points (five replications for each control and drought stress treatments).

Table 4.

Correlations among several physiological traits, vegetation indices and chlorophyll fluorescence parameters.

Sensing technology used for prediction	Predicted variable	No. of predictors used	Predictor variables	Root mean PRESS	Percent variation explained for cumulative Y	Cumulative Q²
Hyperspectral	TQ	4	PRI, PSRI, NDRE, SIPI	0.36	87	0.99
Hyperspectral	RWC	4	PRI, PSRI, NDRE, SIPI	0.38	89	0.99
Multispectral	TQ	3	PSRI_m, NDVI_m, NDRE_m	0.44	85	0.97
Multispectral	RWC	3	PSRI_m, NDVI_m, NDRE_m	0.40	86	0.97
Chlorophyll fluorescence	TQ	4	F_v/F_m, NPQ, qN, qL	0.46	83	0.95
Chlorophyll fluorescence	RWC	3	F_v/F_m, NPQ, qL	0.59	84	0.93

Table 5.

Summary of partial least square model showing predictability of individual models using specific numbers of predictor variables (identified by leave one out cross validation) generated by different sensing technologies. Details of individual abbreviations are mentioned in previous tables. Partial least square was performed using all individual data points (five replications for each control and drought stress treatments).

Band name	Central wavelength (nm)	Band width (nm)
Blue444	444	28
Blue475	475	32
Green531	531	14
Green560	560	27
Red650	650	16
Red668	668	14
RE705	705	10
RE717	717	12
RE740	740	18
NIR840	842	57