Retrieval of leaf fuel moisture contents from hyperspectral indices developed from dehydration experiments

Figures & data

Table 1. Published spectral indices for plant water and dry matter.

Index	Formula	References
WI	R970/R900	Peñuelas et al., [1993]
SR(1300,1450)	R1300/R1450	Seelig et al., [2008]
SRWI	R860/R1240	Zarco-Tejada et al.,[2003]
NDWI	(R860-R1240)/(R860+R1240)	Gao, [1996]
NDII	(R860-R1650)/(R860+R1650)	Hardisky et al., [1983]
NDMI	(R1649−R1722)/(R1649+R1722)	Wang et al., [2011b]
ND(2305,1495)	(R2305−R1495)/(R2305+R1495)	Romero et al., [2012]
NDII/NDMI	((R860-R1650)(R1649+R1722))/((R860+R1650) (R1649−R1722))	Wang et al., [2012]
LCA	2R2205−(R2165+R2330)	Daughtry et al., [2005]
CAI	0.5(R2031 −R2211) – R2101	Nagler et al., [2000]

Figure 1. Variations in reflectance and first-derivative reflectance for green leaves of different plant species attributed to progressive dehydration within 36 hours.

Figure 2. Variations in reflectance and first-derivative reflectance for litter leaves of different plant species attributed to progressive dehydration within 36 hours.

Table 2. Published spectral indices for the estimation of FMC, EWT, and LMA based on different datasets.

Related to	Indices	Green leaf dataset(n=224)		Litter leaf dataset(n=299)		Pooled dataset(n=523)
		R^2	RMSE	R^2	RMSE	R^2	RMSE
FMC (%)	WI	0.69	30.42	–	–	–	–
	SR(1300,1450)	0.74	27.77	0.67	53.42	0.70	44.95
	SRWI	0.67	31.17	–	–	–	–
	NDWI	0.66	31.74	–	–	–	–
	NDII	0.60	34.18	–	–	–	–
	NDMI	–	–	–	–	–	–
	ND(2305,1495)	–	–	0.31	77.15	0.33	67.80
	NDII/NDMI	0.73	28.22	–	–	–	–
	LCA	0.25	47.05	0.39	72.69	0.38	65.02
	CAI	0.53	37.00	0.61	58.25	0.57	54.14
EWT (g/cm^2)	WI	0.71	0.0022	–	–	–	–
	SR(1300,1450)	0.94	0.0010	0.86	0.0018	0.89	0.0016
	SRWI	0.67	0.0024	–	–	–	–
	NDWI	0.66	0.0024	–	–	–	–
	NDII	0.82	0.0018	–	–	–	–
	NDMI	–	–	–	–	–	–
	ND(2305,1495)	–	–	–	–	–	–
	NDII/NDMI	0.67	0.0024	–	–	–	–
	LCA	–	–	0.44	0.0037	0.38	0.0038
	CAI	0.74	0.0021	0.80	0.0022	0.76	0.0023
LMA (g/cm^2)	WI	–	–	–	–	–	–
	SR(1300,1450)	–	–	–	–	–	–
	SRWI	–	–	–	–	–	–
	NDWI	–	–	–	–	–	–
	NDII	–	–	–	–	–	–
	NDMI	0.60	0.0014	–	–	0.27	0.0018
	ND(2305,1495)	0.71	0.0012	0.61	0.0011	0.61	0.0013
	NDII/NDMI	–	–	–	–	–	–
	LCA	–	–	–	–	–	–
	CAI	–	–	–	–	–	–

P<0.001; “–”:R²<0.25.

Table 3. Evaluation of four types of indices with original reflectance for FMC, EWT, and LMA.

Datasets	Index type			Green leaf dataset(n=224)		Litter leaf dataset(n=299)		Pooled dataset(n=523)
		λ1	λ2	R^2	RMSE	R^2	RMSE	R^2	RMSE
FMC (%)	R	1390		0.80	24.02	0.68	52.86	0.70	44.91
	D	420	1385	0.81	23.75	0.70	50.71	0.74	42.40
	SR	1820	1830	0.88	18.83	0.76	45.85	0.79	38.02
	ND	1630	1680	0.83	22.28	0.78	43.81	0.79	37.69
EWT (g/cm^2)	R	1390		0.84	0.0016	0.79	0.0023	0.81	0.0021
	D	420	1385	0.84	0.0017	0.81	0.0022	0.82	0.0020
	SR	1820	1830	0.88	0.0014	0.86	0.0019	0.87	0.0017
	ND	1630	1680	0.84	0.0016	0.73	0.0026	0.78	0.0023
LMA (g/cm^2)	R	1390		–	–	–	–	–	–
	D	420	1385	–	–	–	–	–	–
	SR	1820	1830	–	–	–	–	–	–
	ND	1630	1680	–	–	–	–	–	–

P<0.001; “–”: insignificant regression.

Table 4. Evaluation of four types of indices with first derivative of reflectance for FMC, EWT, and LMA.

Datasets	Index type			Green leaf dataset(n=224)		Litter leaf dataset(n=299)		Pooled dataset(n=523)
		λ1	λ2	R^2	RMSE	R^2	RMSE	R^2	RMSE
FMC (%)	dR	1915		0.72	28.45	0.62	56.83	0.64	49.56
	dD	1550	1985	0.70	29.64	0.68	52.19	0.71	44.68
	dSR	1655	1910	0.68	30.62	0.82	39.34	0.81	36.17
	dND	1900	2095	0.87	19.33	0.83	38.19	0.85	31.53
EWT (g/cm^2)	dR	1915		0.72	0.0022	0.75	0.0025	0.75	0.0024
	dD	1550	1985	0.85	0.0016	0.80	0.0022	0.82	0.0020
	dSR	1655	1910	0.68	0.0023	0.61	0.0031	0.64	0.0029
	dND	1900	2095	0.83	0.0017	0.73	0.0026	0.76	0.0023
LMA (g/cm^2)	dR	1915		–	–	–	–	–	–
	dD	1550	1985	–	–	–	–	–	–
	dSR	1655	1910	–	–	–	–	–	–
	dND	1900	2095	–	–	–	–	–	–

P<0.001; “–”: insignificant regression.

Figure 3. R² and RMSE of FMC estimation with dND type indices (pooled dataset). The positions with black circles in the figures are the selected dND (1900, 2095).

Figure 4. Variations in first derivative spectra at 1900nm (d1900) and 2095 nm (d2095) with dehydration time.

Figure 5. Regressions of SR (1300, 1450) and EWT, ND (2305, 1495) and LMA, and SR (1300, 1450)/ND (2305, 1495) and FMC.

Figure 6. Regressions of dND (1900, 2095) and FMC, and (dND-NDVI)/(dND+NDVI) and FMC.

Peñuelas, J., Filella, I., Biel, C., Serrano, L., Save, R. (1993) - The reflectance at the 950–970 nm region as an indicator of plant water status. International journal of remote sensing, 14(10): 1887–1905. doi: 10.1080/01431169308954010.

 Google Scholar

Seelig, H.D., Hoehn, A., Stodieck, L., Klaus, D., Adams I. ii, Emery, W. (2008) - The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared. International Journal of Remote Sensing, 29(13): 3701–3713. doi: 10.1080/01431160701772500.

 Google Scholar

Zarco-Tejada, P.J., Rueda, C., Ustin, S. (2003) - Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1): 109–124. doi: 10.1016/S0034-4257(02)00197-9.

 Google Scholar

Gao, B.C. (1996) - NDWI—anormalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 58(3): 257–266. doi: 10.1016/S0034-4257(96)00067-3.

 Google Scholar

Hardisky M., Klemas V., Smart M. (1983) - The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing 48, 77–84.

 Google Scholar

Wang, L., Qu, J.J., Hao, X., Hunt Jr, E.R. (2011b) - Estimating dry matter content from spectral reflectance for green leaves of different species. International Journal of Remote Sensing, 32(22): 7097–7109. doi: 10.1080/01431161.2010.494641.

 Google Scholar

Romero, A., Aguado, I., Yebra, M. (2012) - Estimation of dry matter content in leaves using normalized indexes and PROSPECT model inversion. International Journal of Remote Sensing, 33(2): 396–414. doi: 10.1080/01431161.2010.532819.

 Google Scholar

Wang, Q., Li, P. (2012) - Identification of robust hyperspectral indices on forest leaf water content using PROSPECT simulated dataset and field reflectance measurements. Hydrological Processes, 26(8): 1230–1241. doi: 10.1002/hyp.8221.

 Google Scholar

Daughtry, C., Hunt, E., Doraiswamy, P., McMurtrey, J. (2005) - Remote sensing the spatial distribution of crop residues. Agronomy Journal, 97(3): 864–871. doi: 10.2134/agronj2003.0291.

 Google Scholar

Nagler, P., Daughtry, C., Goward, S. (2000) - Plant litter and soil reflectance. Remote Sensing of Environment, 71(2): 207–215. doi: 10.1016/S0034-4257(99)00082-6.

 Google Scholar