Index: Optimized Soil Adjusted Vegetation Index

Optimized Soil Adjusted Vegetation Index

Basic information

Name	Optimized Soil Adjusted Vegetation Index
Abbreviation	OSAVI
Formula	$(1 + Y) \frac{800nm - 670nm}{800nm + 670nm + Y}$
Variables	Y=0.16
Expl. of Variables	mit Y = 0.16
Wavelengths	670,800
Source	Original formula
Description

Visualisation of required spectral range

Bands
zoom Bands

Sensors

Nr.	Name	Spectral Range	Bands	Specific Formula	Calculated
1	AHS	428.5-13140	80	$(1 + 0.16) \frac{13 - 9}{13 + 9 + 0.16}$	Automatic
2	AISA	400-2506.7	498	$(1 + 0.16) \frac{174 - 118}{174 + 118 + 0.16}$	Automatic
3	ALI	433-2350	10	$(1 + 0.16) \frac{MS___4 - MS___3}{MS___4 + MS___3 + 0.16}$	Automatic
4	ALOS	0-890	6	$(1 + 0.16) \frac{5 - 4}{5 + 4 + 0.16}$	Automatic
5	ARIES-1	400-2587	105	$(1 + 0.16) \frac{22 - 15}{22 + 15 + 0.16}$	Automatic
6	ASAS	400-1020	62	$(1 + 0.16) \frac{40 - 27}{40 + 27 + 0.16}$	Automatic
7	ASTER	520-11650	15	$(1 + 0.16) \frac{VNIR_Band3N - VNIR_Band2}{VNIR_Band3N + VNIR_Band2 + 0.16}$	Automatic
8	AVHRR	580-12500	6	$(1 + 0.16) \frac{2 - 1}{2 + 1 + 0.16}$	Automatic
9	AVIRIS	355.15-2515	224	$(1 + 0.16) \frac{48 - 34}{48 + 34 + 0.16}$	Automatic
10	AVIS	550-994	74	$(1 + 0.16) \frac{42 - 20}{42 + 20 + 0.16}$	Automatic
11	Azimuth Systems AZ-16	0-13000	12	$(1 + 0.16) \frac{7 - 5}{7 + 5 + 0.16}$	Automatic
12	CASI spectral	391-905.5	96	$(1 + 0.16) \frac{77 - 53}{77 + 53 + 0.16}$	Automatic
13	CBERS-1/CBERS-2	450-890	8	$(1 + 0.16) \frac{infrared - red}{infrared + red + 0.16}$	Automatic
14	ChrisProbaM1	406-1003	62	$(1 + 0.16) \frac{A44 - A25}{A44 + A25 + 0.16}$	Automatic
15	ChrisProbaM5	438-1036	37	$(1 + 0.16) \frac{H24 - H8}{H24 + H8 + 0.16}$	Automatic
16	CZCS	433-12500	6	$(1 + 0.16) \frac{5 - 4}{5 + 4 + 0.16}$	Automatic
17	Daedalus	0-13000	12	$(1 + 0.16) \frac{7 - 5}{7 + 5 + 0.16}$	Automatic
18	DAIS-7915	400-12600	79	$(1 + 0.16) \frac{22 - 15}{22 + 15 + 0.16}$	Automatic
19	Deimos-2	420-890	5	$(1 + 0.16) \frac{MS4 - MS3}{MS4 + MS3 + 0.16}$	Automatic
20	DubaiSat-1	420-890	5	$(1 + 0.16) \frac{MS4_Near_Infrared - MS3_Red}{MS4_Near_Infrared + MS3_Red + 0.16}$	Automatic
21	Early Bird	450-890	4	$(1 + 0.16) \frac{4 - 3}{4 + 3 + 0.16}$	Automatic
22	EnMap	420-2450	244	$(1 + 0.16) \frac{59 - 39}{59 + 39 + 0.16}$	Automatic
23	Formosat-2	450-900	5	$(1 + 0.16) \frac{B4 - B3}{B4 + B3 + 0.16}$	Automatic
24	GeoEye-1	450-920	5	$(1 + 0.16) \frac{NIR1 - Red}{NIR1 + Red + 0.16}$	Automatic
25	GLI	375-12500	36	$(1 + 0.16) \frac{23 - 11}{23 + 11 + 0.16}$	Automatic
26	HYDICE	400-2500	210	$(1 + 0.16) \frac{40 - 27}{40 + 27 + 0.16}$	Automatic
27	HyMap	429-2493.8	126	$(1 + 0.16) \frac{25 - 16}{25 + 16 + 0.16}$	Automatic
28	Hyperion	349.896-2582.28	242	$(1 + 0.16) \frac{B45 - B32}{B45 + B32 + 0.16}$	Automatic
29	IKONOS-2	445-900	5	$(1 + 0.16) \frac{nahe_Infrarot - Rot}{nahe_Infrarot + Rot + 0.16}$	Automatic
30	INSAT-2E	550-12500	6	$(1 + 0.16) \frac{CCD_Near_Infrared - CCD_Visible}{CCD_Near_Infrared + CCD_Visible + 0.16}$	Automatic
31	IRS-1A	450-860	4	$(1 + 0.16) \frac{4 - 3}{4 + 3 + 0.16}$	Automatic
32	IRS-1B	450-860	4	$(1 + 0.16) \frac{4 - 3}{4 + 3 + 0.16}$	Automatic
33	IRS-1C	500-1700	5	$(1 + 0.16) \frac{NIR - Red}{NIR + Red + 0.16}$	Automatic
34	IRS-1D	500-1700	5	$(1 + 0.16) \frac{NIR - Red}{NIR + Red + 0.16}$	Automatic
35	IRS-P2	450-860	4	$(1 + 0.16) \frac{4 - 3}{4 + 3 + 0.16}$	Automatic
36	Kompsat-2	0-900	5	$(1 + 0.16) \frac{5 - 4}{5 + 4 + 0.16}$	Automatic
37	LandsatETM+ (Landsat7)	450-12500	8	$(1 + 0.16) \frac{NIR - Red}{NIR + Red + 0.16}$	Automatic
38	LandsatMSS (Landsat1-3)	500-1100	4	$(1 + 0.16) \frac{3 - 2}{3 + 2 + 0.16}$	Automatic
39	LandsatTM (Landsat4,5)	450-12500	7	$(1 + 0.16) \frac{NIR - Red}{NIR + Red + 0.16}$	Automatic
40	MMR	450-2300	7	$(1 + 0.16) \frac{4 - 3}{4 + 3 + 0.16}$	Automatic
41	Monitor-E	510-900	4	$(1 + 0.16) \frac{4 - 3}{4 + 3 + 0.16}$	Automatic
42	MSU-MR	500-12500	6	$(1 + 0.16) \frac{2 - 1}{2 + 1 + 0.16}$	Automatic
43	Orbview-1	450-900	5	$(1 + 0.16) \frac{5 - 4}{5 + 4 + 0.16}$	Automatic
44	Orbview-4	450-2450	200	$(1 + 0.16) \frac{35 - 22}{35 + 22 + 0.16}$	Automatic
45	PHI	400-1000.35	244	$(1 + 0.16) \frac{163 - 110}{163 + 110 + 0.16}$	Automatic
46	Pleiades-1	430-950	5	$(1 + 0.16) \frac{NIR1 - Red}{NIR1 + Red + 0.16}$	Automatic
47	PROBA-V	440-1635	4	$(1 + 0.16) \frac{NIR - RED}{NIR + RED + 0.16}$	Automatic
48	QuickBird	450-900	5	$(1 + 0.16) \frac{near_infrared - red}{near_infrared + red + 0.16}$	Automatic
49	RapidEye	440-850	5	$(1 + 0.16) \frac{Near_Infrared - Red}{Near_Infrared + Red + 0.16}$	Automatic
50	Resource21	450-1650	6	$(1 + 0.16) \frac{4 - 3}{4 + 3 + 0.16}$	Automatic
51	Resurs-DK1	500-800	4	$(1 + 0.16) \frac{4 - 3}{4 + 3 + 0.16}$	Automatic
52	SCIAMACHY	240-2380	15	$(1 + 0.16) \frac{PMD4_NIR - PMD3_VIS}{PMD4_NIR + PMD3_VIS + 0.16}$	Automatic
53	SeaWiFS	412-905	8	$(1 + 0.16) \frac{7 - 6}{7 + 6 + 0.16}$	Automatic
54	Sentinel-2A	433-2280	13	$(1 + 0.16) \frac{8 - 4}{8 + 4 + 0.16}$	Automatic
55	SEVIRI	560-14400	12	$(1 + 0.16) \frac{VIS_0.8_- VIS_0.6}{VIS_0.8_+ VIS_0.6 + 0.16}$	Automatic
56	SIS	450-900	5	$(1 + 0.16) \frac{5 - 4}{5 + 4 + 0.16}$	Automatic
57	SPOT 1-3	500-890	4	$(1 + 0.16) \frac{near_infrared - red}{near_infrared + red + 0.16}$	Automatic
58	SPOT 4	500-1750	5	$(1 + 0.16) \frac{near_infrared - red}{near_infrared + red + 0.16}$	Automatic
59	SPOT 5	480-1750	5	$(1 + 0.16) \frac{near_infrared - red}{near_infrared + red + 0.16}$	Automatic
60	SPOT 6	450-890	5	$(1 + 0.16) \frac{NIR - RED}{NIR + RED + 0.16}$	Automatic
61	SPOT 7	450-890	5	$(1 + 0.16) \frac{NIR - RED}{NIR + RED + 0.16}$	Automatic
62	UK-DMC 2	520-900	3	$(1 + 0.16) \frac{NIR - Red}{NIR + Red + 0.16}$	Automatic
63	VIIRS	412-13350	22	$(1 + 0.16) \frac{DNB - I1}{DNB + I1 + 0.16}$	Automatic
64	WorldView-2	400-1040	9	$(1 + 0.16) \frac{NIR1 - Red}{NIR1 + Red + 0.16}$	Automatic
65	WorldView-3	400-2365	29	$(1 + 0.16) \frac{Near_IR1 - Red}{Near_IR1 + Red + 0.16}$	Automatic

Applications

Nr.	Name	Comment
1	Vegetation

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	Daughtry, C. S. T.; Walthall, C. L.; Kim, M. S.; de Colstoun, E. Brown; McMurtrey Iii, J. E. - Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance	2000
3	Eitel, J. U. H.; Long, D. S.; Gessler, P. E.; Smith, A. M. S. - Using insitu measurements to evaluate the new RapidEyeâ„¢ satellite series for prediction of wheat nitrogen status	2007
4	Gandia, S.; FernÃ¡ndez, G.; GarcÃa, J. C.; & Moreno, J. - Retrieval of vegetation biophysical variables from CHRIS/PROBA data in the SPARC campaing	2004
5	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
6	Heiskanen, J. - Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data	2006
7	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
8	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
9	Oppelt, N.; Mauser, W. - Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data	2004
10	Rondeaux, GeneviÃ¨ve; Steven, Michael; Baret, FrÃ©dÃ©ric - Optimization of soil-adjusted vegetation indices	1996
11	Wu, Chaoyang; Niu, Zheng; Tang, Quan; Huang, Wenjiang - Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation	2008
12	Zarco-Tejada P.J.; Sepulcre-CantÃ³, G. - REMOTE SENSING OF VEGETATION BIOPHYSICAL PARAMETERS FOR DETECTING STRESS CONDITION AND LAND COVER CHANGES	2007