F. Near infrared (NIR) bands
12
855
20
Biophysical quantities and yield
: leaf area index, wet and dry biomass, plant height,
grain yield, crop type, crop discrimination, total chlorophyll
13
910
5
Moisture, biomass, and protein
: peak NIR reflectance. Useful for computing crop
moisture sensitivity index.
G. Near infrared (NIR) bands
14
970
10
Water, Moisture and biomass:
Center of moisture sensitive “trough”; water band index,
leaf water, biomass
H. Far near infrared (FNIR) bands
15
1075
5
Biophysical and biochemical quantities
: leaf area index, wet and dry biomass,
plant height, grain yield, crop type, crop discrimination, total chlorophyll, anthocyanin,
carotenoids
16
1180
5
Water absorption band
17
1245
5
Water sensitivity
: water band index, leaf water, biomass. Reflectance peak in 1050-1300
nm.
I. Early short-wave infrared (ESWIR) bands
18
1450
5
Vegetation classification and discrimination
: ecotype classification; plant moisture
sensitivity. Moisture absorption trough inearly short wave infrared (ESWIR)
19
1518
5
Moisture and biomass
: A point of most rapid rise in spectra with unit change in
wavelength in SWIR. Sensitive to plant moisture.
20
1650
5
Heavy metal stress, Moisture sensitivity
: Heavy metal stress due to reduction in
Chlorophyll. Sensitivity to plant moisture fluctuations in ESWIR. Use as an index with
1548 or 1620 or 1690 nm..
21
1725
5
Lignin, biomass, starch, moisture
: sensitive to lignin, biomass, starch. Discrimiating
crops and vegetation.
J. Far short-wave infrared (FSWIR) bands
22
1950
5
Water absorption band
: highest moisture absorption trough in FSWIR. Use as an index
with any one of 2025 nm, 2133 nm, and 2213 am. Affected by noise at times.
23
2025
5
Litter (plant litter), lignin, cellulose: litter-soil differentiation
: moderate to low
moisture absorption trough in FSWIR. Use as an index with any one of 2025 nm, 2133 nm,
and 2213 am.
24
2133
5
Litter (plant litter), lignin, cellulose:
typically highest refectivity in FSWIR for
vegetation. Litter-soil differentiation
25
2205
5
Litter, lignin, cellulose, sugar, startch, protein; Heavy metal stress:
typically,
second highest reflectivity in FSWIR for vegetation. Heavy metal stress due to reduction
in Chlorophyll
26
2260
5
Moisture and biomass
: moisture absorption trough in far short-wave infrared (FSWIR).
A point of most rapid change in slope of spectra based on land cover, vegetation type, and
vigor.
27
2295
5
Stress
: sensitive to soil background and plant stress
28
2359
5
Cellulose, protein, nitrogen
: sensitive to crop stress, lignin, and starch
Note:
1 = most hyperspectral narrowbands (HNBs)_ that adjoin one another are highly correlated for a given application. Hence
from a large number of HNBs, these non-redundant (optimal) bands are selected
2 = these optimal HNBs are for studying vegetation and agricultural crops. When we use these wavebands, we can attain
highest possible classification accuracies in classifying vegetation categories or crop types
3 = wavebands selected here are based on careful evaluation of large number of studies. These studies are widely discussed
and referenced in Thenkabail et al. 2011, Thenkabail et al., 2012, Thenkabail et al., 2013, Marshall and Thenkabail, 2014,
Thenkabail et al., 2004a, Thenkabail et al., 2004b, Thenkabail et al., 2002, and Thenkabail et al., 2000.
4 = for detals on physical relevance of these wavebands please refer to Thenkabail et al. 2011, Thenkabail et al., 2012
5 = the hyperspectral vegetation indices (HVIs) recommended in Table 2 are derived using these HNBs
6 = this Table is derived, modified, and revised based on recent work discussed in Thenkabail et al., 2013, Marshall and
Thenkabail, 2014, Thenkabail et al., 2004a, Thenkabail et al., 2004b, Thenkabail et al., 2002, and Thenkabail et al., 2000.
702
August 2014
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
