Index: TCARI/OSAVI

TCARI/OSAVI

Basic information

Name	TCARI/OSAVI
Abbreviation	TCARI/OSAVI
Formula	$\frac{3 (700nm - 670nm) - 0.2 (700nm - 550nm) \frac{700nm}{670nm}}{(1 + 0.16) \frac{800nm - 670nm}{800nm + 670nm + 0.16}}$
Variables
Expl. of Variables
Wavelengths	550,670,700,800
Source	Original formula
Description	Haboudane 2002

Visualisation of required spectral range

Bands
zoom Bands

Sensors

Nr.	Name	Spectral Range	Bands	Specific Formula	Calculated
1	AHS	428.5-13140	80	$\frac{3 (10 - 9) - 0.2 (10 - 5) \frac{10}{9}}{(1 + 0.16) \frac{13 - 9}{13 + 9 + 0.16}}$	Automatic
2	AISA	400-2506.7	498	$\frac{3 (131 - 118) - 0.2 (131 - 66) \frac{131}{118}}{(1 + 0.16) \frac{174 - 118}{174 + 118 + 0.16}}$	Automatic
3	ARIES-1	400-2587	105	$\frac{3 (16 - 15) - 0.2 (16 - 8) \frac{16}{15}}{(1 + 0.16) \frac{22 - 15}{22 + 15 + 0.16}}$	Automatic
4	ASAS	400-1020	62	$\frac{3 (30 - 27) - 0.2 (30 - 15) \frac{30}{27}}{(1 + 0.16) \frac{40 - 27}{40 + 27 + 0.16}}$	Automatic
5	AVIRIS	355.15-2515	224	$\frac{3 (37 - 34) - 0.2 (37 - 21) \frac{37}{34}}{(1 + 0.16) \frac{48 - 34}{48 + 34 + 0.16}}$	Automatic
6	AVIS	550-994	74	$\frac{3 (25 - 20) - 0.2 (25 - 1) \frac{25}{20}}{(1 + 0.16) \frac{42 - 20}{42 + 20 + 0.16}}$	Automatic
7	Azimuth Systems AZ-16	0-13000	12	$\frac{3 (6 - 5) - 0.2 (6 - 3) \frac{6}{5}}{(1 + 0.16) \frac{7 - 5}{7 + 5 + 0.16}}$	Automatic
8	CASI spectral	391-905.5	96	$\frac{3 (58 - 53) - 0.2 (58 - 30) \frac{58}{53}}{(1 + 0.16) \frac{77 - 53}{77 + 53 + 0.16}}$	Automatic
9	ChrisProbaM1	406-1003	62	$\frac{3 (A29 - A25) - 0.2 (A29 - A13) \frac{A29}{A25}}{(1 + 0.16) \frac{A44 - A25}{A44 + A25 + 0.16}}$	Automatic
10	ChrisProbaM5	438-1036	37	$\frac{3 (H10 - H8) - 0.2 (H10 - H4) \frac{H10}{H8}}{(1 + 0.16) \frac{H24 - H8}{H24 + H8 + 0.16}}$	Automatic
11	Daedalus	0-13000	12	$\frac{3 (6 - 5) - 0.2 (6 - 3) \frac{6}{5}}{(1 + 0.16) \frac{7 - 5}{7 + 5 + 0.16}}$	Automatic
12	DAIS-7915	400-12600	79	$\frac{3 (16 - 15) - 0.2 (16 - 8) \frac{16}{15}}{(1 + 0.16) \frac{22 - 15}{22 + 15 + 0.16}}$	Automatic
13	EnMap	420-2450	244	$\frac{3 (44 - 39) - 0.2 (44 - 20) \frac{44}{39}}{(1 + 0.16) \frac{59 - 39}{59 + 39 + 0.16}}$	Automatic
14	HYDICE	400-2500	210	$\frac{3 (30 - 27) - 0.2 (30 - 15) \frac{30}{27}}{(1 + 0.16) \frac{40 - 27}{40 + 27 + 0.16}}$	Automatic
15	HyMap	429-2493.8	126	$\frac{3 (18 - 16) - 0.2 (18 - 9) \frac{18}{16}}{(1 + 0.16) \frac{25 - 16}{25 + 16 + 0.16}}$	Automatic
16	Hyperion	349.896-2582.28	242	$\frac{3 (B35 - B32) - 0.2 (B35 - B20) \frac{B35}{B32}}{(1 + 0.16) \frac{B45 - B32}{B45 + B32 + 0.16}}$	Automatic
17	LandsatMSS (Landsat1-3)	500-1100	4	$\frac{3 (3 - 2) - 0.2 (3 - 1) \frac{3}{2}}{(1 + 0.16) \frac{4 - 2}{4 + 2 + 0.16}}$	Automatic
18	Orbview-4	450-2450	200	$\frac{3 (25 - 22) - 0.2 (25 - 10) \frac{25}{22}}{(1 + 0.16) \frac{35 - 22}{35 + 22 + 0.16}}$	Automatic
19	PHI	400-1000.35	244	$\frac{3 (122 - 110) - 0.2 (122 - 61) \frac{122}{110}}{(1 + 0.16) \frac{163 - 110}{163 + 110 + 0.16}}$	Automatic
20	RapidEye	440-850	5	$\frac{3 (Red_edge - Red) - 0.2 (Red_edge - Green) \frac{Red_edge}{Red}}{(1 + 0.16) \frac{Near_Infrared - Red}{Near_Infrared + Red + 0.16}}$	Automatic
21	SCIAMACHY	240-2380	15	$\frac{3 (VIS2 - PMD3_VIS) - 0.2 (VIS2 - VIS1) \frac{VIS2}{PMD3_VIS}}{(1 + 0.16) \frac{PMD4_NIR - PMD3_VIS}{PMD4_NIR + PMD3_VIS + 0.16}}$	Automatic
22	Sentinel-2A	433-2280	13	$\frac{3 (5 - 4) - 0.2 (5 - 3) \frac{5}{4}}{(1 + 0.16) \frac{8 - 4}{8 + 4 + 0.16}}$	Automatic
23	WorldView-2	400-1040	9	$\frac{3 (Pan - Red) - 0.2 (Pan - Green) \frac{Pan}{Red}}{(1 + 0.16) \frac{NIR1 - Red}{NIR1 + Red + 0.16}}$	Automatic

Applications

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References

Nr.	Author/Title	Year
1	Apan, Armando; Held, Alex; Phinn, Stuart; Markley, John - Formulation and assessment of narrow-band vegetation indices from EO-1 hyperion imagery for discriminating sugarcane disease	2003
2	Gandia, S.; FernÃ¡ndez, G.; GarcÃa, J. C.; & Moreno, J. - Retrieval of vegetation biophysical variables from CHRIS/PROBA data in the SPARC campaing	2004
3	Haboudane, Driss; Miller, John R.; Tremblay, Nicolas; Zarco-Tejada, Pablo J.; Dextraze, Louise - Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture	2002
4	Herrmann, I.; Karnieli, A.; Bonfil, D. J.; Cohen, Y.; Alchanatis, V. - SWIR-based spectral indices for assessing nitrogen content in potato fields	2010
5	Hunt Jr., E. Raymond; Daughtry, C. S. T.; Eitel, Jan U. H.; Long, D. S. - Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index	2011
6	Main, Russell; Cho, Moses Azong; Mathieu, Renaud; Oâ€™Kennedy, Martha M.; Ramoelo, Abel; Koch, Susan - An investigation into robust spectral indices for leaf chlorophyll estimation	2011
7	Rondeaux, GeneviÃ¨ve; Steven, Michael; Baret, FrÃ©dÃ©ric - Optimization of soil-adjusted vegetation indices	1996
8	Wu, Chaoyang; Niu, Zheng; Tang, Quan; Huang, Wenjiang - Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation	2008
9	Zarco-Tejada P.J.; Sepulcre-CantÃ³, G. - REMOTE SENSING OF VEGETATION BIOPHYSICAL PARAMETERS FOR DETECTING STRESS CONDITION AND LAND COVER CHANGES	2007