Hyperspectral Vegetation Indices

One of the most common purposes of hyperspectral image data is to provide information on vegetation in an agricultural context. This information, such as health status, chlorophyll content and nitrogen demand, water content, structural information, etc. is hidden within the reflectance data.

In order to extract this information from the data one way is to use spectral indices, which belong to empirical-statistical methods. By this, specific wavelengths of the electromagnetic spectrum, which are known to be sensitive to the searched vegetation parameter, are used and set into relation.

A well-known example for this is the NDVI, which uses two different wavelength regions. In the visible red region chlorophyll content causes an absorption of the incoming radiation, while in the near infrared the cell structure of leaves causes a high reflectance value. The healthier a leaf is, the higher is the discrepancy between both values.

However, lots of spectral indices have been developed within the last 20 years. After conducting an intense literature study (LOCHERER, 2014), the following list gives an overview of more than 70 several indices that are used in agriculture and forestry, showing the tremendous potential of hyperspectral data.

Those indices, when applied to hyperspectral image data, make details visible that cannot be detected by normal cameras. Furthermore, they help you visualizing the spatial distribution of vegetation parameters within your field, identifying relevant spots for agricultural decision makers.

ID INDEX NAME FORMULA RANGE SENSITIVITY REFERENCE YEAR ANNOTATION
1 ARI Anthocyanin Reflectance Index \(
\frac{1}{R_{550}}-\frac{1}{R_{700}}
\)
Carotenoid content Gitelson et al. (2002) 2002
2 BGI Blue Green Pigment Index \(
BGI = \frac{R_{450}}{R_{550}}
\)
Dry Matter / Pigment Zarco-Tejada et al. (2005) 2005
3 BRI Blue Red Pigment Index \(
BGI = \frac{R_{450}}{R_{690}}
\)
Dry Matter / Pigment Zarco-Tejada et al. (2005) 2005
4 CAI Chlorophyll Absorption Integral \(
CAI = \frac{\left\{ \intop_{550mm}^{760mm} R_{550}+\left[ i \cdot \frac{R_{760}-R_{550}}{i_{760}} \right] \right\}-\intop_{550mm}^{760mm} R_{i}}{\intop_{550mm}^{760mm} R_{550}+\left[ i \cdot \frac{R_{760}-R_{550}}{i_{760}} \right]}
\)
Chlorophyll Oppelt and Mauser (2003) 2003
5
6 CRI Carotenoid Reflectance \(CRI = \frac{1}{R_{510}}-\frac{1}{R_{550}}\) Carotenoid content Gitelson et al. (2002) 2002 first term: Chl AND Car-Absorption, latter term: Chl-absorption
7 CRI2 Carotenoid Reflectance \(CRI2 = \frac{1}{R_{510}}-\frac{1}{R_{700}}\) Carotenoid content Gitelson et al. (2002) 2002 first term: Chl AND Car-Absorption, latter term: Chl-absorption
8 CSI1 Carter Stress Index \(CSI1 = \frac{R_{695}}{R_{420}}\) > 0 Chlorophyll Carter (1994) 1994
9 CSI2 Carter Stress Index \(CSI2 = \frac{R_{695}}{R_{760}}\) > 0 Chlorophyll Carter (1994) 1994
10 CUR Curvature Index \(CUR = \frac{R_{675} \cdot R_{550}}{R_{683}^2}\) Flourescence Zarco-Tejada et al. (2000) 2000
11
12
13 G Greenness Index \(G = \frac{R_{554}}{R_{677}}\) > 0 Chlorophyll Zarco-Tejada et al. (2005) 2005
14 GM1 Gitelson & Merzlyak Index \(GM1 = \frac{750}{550}\) Chlorophyll Gitelson and Merzlyak (1997) 1997
15 GM2 Gitelson & Merzlyak Index \(GM2 = \frac{R_{750}}{R_{700}}\) Chlorophyll Gitelson and Merzlyak (1997) 1997
16 gNDVI Green NDVI \(GreenNDVI = \frac{R_{750} – R_{550}}{R_{750} + R_{550}}\) Chlorophyll Datt (1998) 1998 more sensitive to Chlorophyll-concentration
17 hNDVI hyperspectral NDVI \(hNDVI = \frac{R_{827} – R_{668}}{R_{827} + R_{668}}\) Structure Oppelt (2002) 2002
18 LAI (MSAVI) Leaf Area Index \(LAI = 0.1663^{4.2731 MSAVI}\) LAI Haboudane et al. (2004) 2004
19 LAI (MTVI2) Leaf Area Index \(LAI = 0.2227^{3.6566 MTVI2}\) LAI Haboudane et al. (2004) 2004
20 LAI (RDVI) Leaf Area Index \(LAI = 0.0918^{6.0002 RDVI}\) LAI Haboudane et al. (2004) 2004
21 LAI (TVI) Leaf Area Index \(LAI = 0.1817^{4.1469 TVI}\) LAI Haboudane et al. (2004) 2004
22 LCI Leaf Chlorophyll Index \(LCI = \frac{R_{850} – R_{710}}{R_{850} + R_{680}}\) Chlorphyll content Datt (1999) 1999
23 LIC1 Lichtenthaler Index \(LCI1 = \frac{R_{800} – R_{680}}{R_{800} + R_{680}}\) Flourescence Lichtenthaler et al. (1996) 1996
24 LIC2 Lichtenthaler Index \(LCI2 = \frac{R_{440}}{R_{690}}\) Flourescence Lichtenthaler et al. (1996) 1996
25 LIC3 Lichtenthaler Index \(LCI3 = \frac{R_{440}}{R_{740}}\) Flourescence Lichtenthaler et al. (1996) 1996
26 LWVI1 Leaf Water Vegetation Index \(LWVI1 = \frac{R_{1094} – R_{983}}{R_{1094} + R_{983}}\) [-1 ] – [+1] Water Content Galvão et al. (2005) 2005
27 LWVI2 Leaf Water Vegetation Index \(LWVI2 = \frac{R_{1094} – R_{1205}}{R_{1094} + R_{1205}}\) [-1 ] – [+1] Water Content Galvão et al. (2005) 2005
28 MCARI Modified Chlorophyll Absorption in Reflectance Index \(MCARI = [(R_{700} – R_{670}) – 0.2 \cdot (R_{700} – R_{550})] \cdot (\frac{R_{700}}{R_{670}})\) Chlorophyll Daughtry et al. (2000) 2000 responsive to chlorophyll variation in the first place; great potential for LAI prediction
29 MCARI1 Modified Chlorophyll Absorption in Reflectance Index \(MCARI1 = 1.2 \cdot [2.5 \cdot (R_{800} – R_{670}) – 1.3 \cdot (R_{800} – R_{550})]\) Structure Haboudane et al. (2004) 2004
30 MCARI2 Modified Chlorophyll Absorption in Reflectance Index \(MCARI1 = \frac {1.5 \cdot [2.5 \cdot (R_{800} – R_{670}) – 1.3 \cdot (R_{800} – R_{550})]}{\sqrt{(2 \cdot R_{800} + 1)^2 – (6 \cdot R_{800} – 5 \cdot \sqrt{R_{680}}) – 0.5}}\) Structure Haboudane et al. (2004) 2004
MLO M Locherer Chlorophyll \(MLO = \frac{R_{531}}{R_{645}}\) Chlorophyll unpublished
31 MSAVI Improved SAVI with self-adjustment factor L \(MCARI1 = \frac {1}{2} \cdot [2 \cdot R_{800} + 1 – \sqrt{(2 \cdot R_{800} + 1)^2 – 8 \cdot (R_{800} – R_{670}}\) Structure Qi et al. (1994) 1994 good LAI-Estimator
32
33 MSR Modified Simple Ratio \(MSR = \frac {NIR / RED – 1}{(NIR / RED)^{0.5}+1}\) Structure Chen (1996) 1996
34 MTVI1 Modified Triangular Vegetation Index \(MTVI1 = 1.2 \cdot [1.2 \cdot (R_{800} – R_{550}) – 2.5 \cdot (R_{670} – R_{550})]\) Structure Haboudane et al. (2004) 2004
35 MTVI2 Modified Triangular Vegetation Index \(MTVI2 = \frac {1.5 \cdot [1.2 \cdot (R_{800} – R_{550}) – 2.5 \cdot (R_{670} – R_{550})]}{\sqrt{(2 \cdot R_{800} + 1)^2 – (6 \cdot R_{800} – 5 \cdot \sqrt{R_{670}}) – 0.5}}\) Structure Haboudane et al. (2004) 2004
36
37
38 NDVI Normalized Difference Vegetation Index \(NDVI = \frac{NIR – RED}{NIR + RED}\) [-1 ] – [+1] Structure Rouse et al. (1974) 1974 saturated at LAI > 3; good corr. With green biomass (LAI, leaf cover, chlorophyll); poor corr. With pigment content
39 NDVI (Aparicio) Normalized Difference Vegetation Index \(NDVI_{Aparicio} = \frac{R_{900} – R_{680}}{R_{900} + R_{680}}\) [-1 ] – [+1] Structure Aparicio et al. (2002) 2002
40 NDVI (Datt) Normalized Difference Vegetation Index \(NDVI_{Datt} = \frac{R_{800} – R_{680}}{R_{800} + R_{680}}\) [-1 ] – [+1] Structure Datt (1998) 1998
41 NDVI (Haboudane) Normalized Difference Vegetation Index \(NDVI_{Haboudane} = \frac{R_{800} – R_{670}}{R_{800} + R_{670}}\) [-1 ] – [+1] Structure Haboudane (2004) 2004
42 NDVI (Zarco-Tejada) Normalized Difference Vegetation Index \(NDVI_{Zarco-Tejada} = \frac{R_{774} – R_{677}}{R_{774} + R_{677}}\) [-1 ] – [+1] Structure Zarco-Tejada (1999) 1999
43 NDWI Normalized Difference Water Index \(NDWI = \frac{R_{860} – R_{1240}}{R_{860} + R_{1240}}\) [-1 ] – [+1] Water Content Gao (1996) 1996
44 NPCI Normalized Pigment Chlorophyll Index \(NPCI = \frac{R_{680} – R_{430}}{R_{680} + R_{430}}\) [-1 ] – [+1] Dry Matter / Pigment Penuelas et al. (1994) 1994
45 NPCI Normalized total Pigment to Chlorphyll Index \(NPCI = \frac{R_{680} – R_{430}}{R_{680} + R_{430}}\) Senescence Penuelas (1994) 1994
46 NPQI Normalized Phaeophytinization Index \(NPQI = \frac{R_{415} – R_{435}}{R_{415} + R_{435}}\) [-1 ] – [+1] Chlorophyll Barnes (1992) 1992 estimated parameter: chlorophyll degradation
47 OSAVI Optimized Soil Adjusted Vegetation Index \(OSAVI = \frac{(1 + 0.16) \cdot (R_{800} – R_{670})}{R_{800} + R_{670} + 0.16}\) Structure Rondeaux et al. (1996) 1996
48 PRI Photochemical Reflectance Index \(PRI = \frac{R_{528} – R_{567}}{R_{528} + R_{567}}\) Chlorophyll Gamon et al. (1992) 1992 more sensitive to Chl than Car; influenced by leaf age; problems when rlated to water stress, correlated with wheat yield
49 PSSRa Pigment Specific Simple Ratio for Chl a \(PSSRa = \frac{R_{800}}{R_{675}}\) Chlorophyll a Blackburn (1998) 1998 strong correlation with Chl a concentration
50 PSSRb Pigment Specific Simple Ratio for Chl b \(PSSRb = \frac{R_{800}}{R_{650}}\) Chlorophyll b Blackburn (1998) 1998 strong correlation with Chl b concentration
51 PSSRc Pigment Specific Simple Ratio for Carotenoids \(PSSRc = \frac{R_{800}}{R_{500}}\) Carotenoid content Blackburn (1998) 1998 strong correlation with Cars concentration
52 PWI Plant Water Index \(PWI = \frac{R_{970}}{R_{900}}\) > 0 Water Content Penuelas et al. (1997) 1997
53 RDVI Renormalized Difference Vegetation Index \(RDVI = \frac{R_{800}-R_{670}} {\sqrt{R_{800}+R_{670}}}\) Structure Rougean and Breon (1995) 1995
54 REIP1 Red Edge Inflection Point \(REIP1 = (700 + \frac{740}{700}) \cdot \frac{(R_{i}-R_{780})} {({R_{740}+R_{701})}}; R_{i} = \frac{R_{780}}{R_{670}}\) Chlorophyll Guyot et al. (1988) 1988
55 REP Red Edge Point \(REIP1 = 700 + 40 \cdot \frac{R_{rededge} – R_{700}}{R_{740} – R_{700}}; R_{rededge} = \frac{R_{670}+R_{780}} {2}\) Chlorophyll Dawson and Curran (1998) 1998 good indicator for chlorophyll content (leaf level, but also canopy level); no relation with Carotenoids
56 RGI Red-Green Pigment Index \(RGI = \frac{R_{690}}{R_{550}}\) Dry Matter / Pigment Zarco-Tejada et al. (2005) 2005
57 SAVI Soil Adjusted Vegetation Index \(SAVI = (1 + L) \cdot \frac{R_{800}-R_{670}}{R_{800} + R_{670} + L} [L\in(0.1)]\) Structure Huete (1988) 1988
58 SAVI2 Soil Adjusted Vegetation Index \(SAVI 2 = \frac{R_{800}}{R_{670} + \frac{a^2}{b^2} }\) Structure Major et al. (1990) 1990
59 SIPI Structural Independent Pigment Index \(SIPI = \frac{R_{445} – R_{800}}{R_{670} – R_{800}}\) Carotenoids: chlorophyll a ratio Penuelas and Filella (1998) 1998 very good semi-empirical estimation of CAR/Chla -Ratio
60 SPVI Spectral Polygon Vegetation Index \(SPVI = 0.4 \cdot [3.7 \cdot (R_{800}- R_{670}) – 1.2(R_{530} – R_{670})]\) Structure Vincini et al. (2006) 2006
61 SR Simple Ratio Index \(SR = \frac{NIR}{RED}\) > 0 Structure Jordan (1969); Rouse et al. (1979) 1969 generally: green biomass
62 SR ( Chl a) Simple Ratio \(SR_{Chla} = \frac{R_{675}}{R_{700}}\) Chlorophyll (Chl a) Datt (1998) 1998 good correlation to Chl a
63 SR (Chl b) Simple Ratio \(SR_{Chlb} = \frac{R_{675}}{R_{650} \cdot R_{700}}\) Chlorophyll (Chl b) Datt (1998) 1998 good correlation to Chl b
64 SR (Chl b2) Simple Ratio \(SR_{Chlb2} = \frac{R_{672}}{R_{708}}\) Chlorophyll (Chl b) Datt (1998) 1998 best correlation with Chl b
65 SR (Chl tot) Simple Ratio \(SR_{Chltot} = \frac{R_{760}}{R_{500}}\) Chlorophyll (total) Datt (1998) 1998 good correlation to Chl tot; good correlation to Cars concentration (Gitelson 2002)
66 SR (Chl) Simple Ratio \(SR_{Chl} = \frac{R_{672}}{R_{550} \cdot R_{700}}\) Chlorophyll Datt (1998) 1998 best correlation with Chl a, b and total Cars
67 SR705 Simple Ratio at 705 Index \(SR 705 = \frac{R_{750}}{R_{705}}\) > 0 Chlorophyll Sims and Gamon (2002) 2002
68 SRPI Simple Ratio Pigment Index \(SRPI = \frac{R_{430}}{R_{680}}\) > 0 Dry Matter / Pigment Penuelas et al. (1995) 1995
69
70
71 TCARI Transformed Chlorophyll Absorption in Reflectance Index \(TCARI = 3 \cdot [(R_{700} – R_{670}) – 0.2 \cdot (R_{700} – R_{550})] \cdot (\frac{R_{700}}{R_{670}})\) Chlorophyll Haboudane et al. (2002) 2002
72 TCARI over OSAVI Ratio \(Ratio = \frac{TCARI}{OSAVI}\) Chlorophyll
73 tmNDVI Thematic Mapper NDVI \(TM – NDVI = \frac{\frac{1}{i_{900nm}}\sum_{i=760nm}^{900nm}R_{i}-\frac{1}{i_{690nm}}\sum_{i=630nm}^{690nm}R_{i}}{\frac{1}{i_{}900nm}\sum_{i=760nm}^{900nm} R_{i}+\frac{1}{i_{690nm}}\sum_{i=630nm}^{690nm} R_{i}}\) [-1 ] – [+1] Structure Rouse et al. (1974) 1974
74 TSAVI Transformed Soil Adjusted Vegetation Index \(TSAVI = a \cdot \frac{(R_{800} – aR_{670} – b)}{(R_{670} + aR_{850} – ab)}\) Structure Baret et al. (1989) 1989
75 TVI Triangular Vegetation Index \(TVI = 0.5 \cdot [120 \cdot (R_{750} – R_{550}) – 200 \cdot (R_{670} – R_{550})]\) Chlorophyll Broge and Leblanc (2000) 2000 characterizes radiant energy absorbed by leaf pigments; idea on fact: total area of triangle (G, R, NIR) will increase through Chl-Absorption and leaf abundance
76 VOG1 Vogelmann Index \(VOG1 =\frac{R_{740} }{R_{720}}\) Chlorophyll Vogelmann (1993) 1993
77 VOG2 Vogelmann Index \(VOG2 =\frac{R_{734} – R_{747}}{R_{715} – R_{720}}\) [-1 ] – [+1] Chlorophyll Vogelmann (1993) 1993
78 VOG2 Vogelmann Index \(VOG3 =\frac{R_{734} – R_{747}}{R_{715} – R_{726}}\) [-1 ] – [+1] Chlorophyll Vogelmann (1993) 1993
80 ZTM Zarco-Tejada & Miller \(ZTM =\frac{R_{750}}{R_{710}}\) > 0 Chlorophyll Zarco-Tejada et al. (2001) 2001

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