1. Land and Ocean Color

3. Though we often take the plants and trees around us for granted, almost every aspect of our lives depends upon them. By carefully measuring the wavelengths and intensity of visible and near-infrared light reflected by the land surface back up into space a "Vegetation Index" may be formulated to quantify the concentrations of green leaf vegetation around the globe. Normalized Difference Vegetation Index (NDVI) Distinct colors (wavelengths) of visible and near-infrared sunlight reflected by the plants determine the density of green on a patch of land and ocean. The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 0.4 to 0.7 μm) for use in photosynthesis. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 μm). The more leaves a plant has or the more phytoplankton there is in the column, the more these wavelengths of light are affected, respectively. Measuring Vegetation (NDVI, EVI, and Ocean Color)

4. Attenuation in the Visible Wavelengths Grant Petty, 2004

5. Attenuation in the Visible Wavelengths Grant Petty, 2004

6. Daytime Visibility Distant Dark Objects Appear Brighter “Clear” Day Hazy Day

7. Nighttime Visibility Distant Bright Objects are dimmer

8. Revisiting Daytime Visibility White Sunlight Top of Atmosphere Horizon Color and Intensity Horizon always Whitened relative to Sky above Distance to the Dark Object Henceforth, we shall only consider scattering by clouds and aerosols

9. Revisiting Daytime Visibility White Sunlight Top of Atmosphere Increased contribution of white light Object appears lighter with distance Longer Distance to the Dark Object

10. Daytime Visibility Revisitied Distant Dark Objects Appear Brighter “Clear” Day Hazy Day

11. ENVI-1200 Atmospheric Physics Aerosol Hygroscopic Growth Deliquescence – Dry crystal to solution droplet Hygroscopic – Water-attracting Efflorescence – Solution droplet to crystal (requires ‘nucleation’) Hysteresis – Particle size and phase depends on humidity history

12. 500 nm RV Ron Brown Central Pacific AOT=0.08 Sea of Japan AOT=0.98 AMF Niamey, Niger AOT=2.5-3 Sky Imaging

13. NDVI NDVI = (NIR — VIS)/(NIR + VIS) Calculations of NDVI for a given pixel always result in a number that ranges from minus one (-1) to plus one (+1) --no green leaves gives a value close to zero. --zero means no vegetation --close to +1 (0.8 - 0.9) indicates the highest possible density of green leaves. NASA Earth Observatory (Illustration by Robert Simmon)

14. NDVI NDVI is calculated from the visible and near- infrared light reflected by vegetation. Healthy vegetation (left) absorbs most of the visible light that hits it, and reflects a large portion of the near-infrared light. Unhealthy or sparse vegetation (right) reflects more visible light and less near-infrared light. Real vegetation is highly variable.

15. Advanced Very High Resolution Radiometer (AVHRR) NOAA has two polar-orbiting meteorological satellites in orbit at all times, with one satellite crossing the equator in the early morning and early evening and the other crossing the equator in the afternoon and late evening. Morning-satellite data are most commonly used for land studies, while data from both satellites are used for atmosphere and ocean studies.

16. NOAA 11 AVHRR 1980200019901985201020051995 NOAA 7 AVHRR NOAA 9 AVHRR NOAA 14 AVHRR SeaWiFS SPOT MODISes NOAA-16 NPP NOAA 9 NOAA-17 Satellite NDVI data sources NOAA-18 C. Tucker

17. EVI (Enhanced Vegetation Index) In December 1999, NASA launched the Terra spacecraft, the flagship in the agency’s Earth Observing System (EOS) program. Aboard Terra flies a sensor called the Moderate-resolution Imaging Spectroradiometer, or MODIS, that greatly improves scientists’ ability to measure plant growth on a global scale. Briefly, MODIS provides much higher spatial resolution (up to 250-meter resolution), while also matching AVHRR’s almost-daily global cover and exceeding its spectral resolution.MODIS,

18. History of the NDVI & Vegetation Indices Compton Tucker NASA/UMD/CCSPO

19. Vegetation Indices from Susan Ustin C. Tucker

20. Beltsville USA winter wheat biomass C. Tucker

21. Winter wheat biomass “harvest” C. Tucker

22.  NDVI vs. total dry biomass Explained 80% of biomass accumulation C. Tucker

23. Marked contrasts between the dry and wet seasons (~300 mm/yr @ Senegal) C. Tucker

24. Average NDVI 1981-2006 NDVI = (ir- red) (ir+red) ~40,000 orbits of satellite data C. Tucker

25. Species mapping with physiological indices Meg Andrew

26. Spectral Indices: NDVI Creosote Ag NDVI = 0.922 NDVI = 0.356 Meg AndrewMore info: cstars.ucdavis.edu/classes/ECL290/docs/Andrews.ppt

27. Global Vegetation Mapping SeaWiFS Ocean Chlorophyll Land NDVI

28. 5 SeaWiFS land bands

29. Ocean Color Locates and enables monitoring of regions of high and low bio-activity. – Food (phytoplankton associated with chlorophyll) – Climate (phytoplankton possible CO 2 sink) Reveals ocean current structure and behavior. – Seasonal influences – River and Estuary influences – Boundary currents Reveals Anthropogenic influences (pollution) Remote sensing reveals large and small scale structures that are very difficult to observe from the surface.

30. Tasmanian Sea

31. This figure shows four typically observed wavelength bands and the water leaving radiance in high (dotted) and low (solid) chlorophyll waters without the atmospheric signal (lower curves) and with the atmospheric signal (upper curves). The satellite observes the water leaving radiance with the signal due to the atmosphere (upper curves). [Gordon and Wang]

32. a) The light path of the water-leaving radiance. b) Shows the attenuation of the water-leaving radiance. c) Scattering of the water-leaving radiance out of the sensor's FOV. d) Sun glint (reflection from the water surface). e) Sky glint (scattered light reflecting from the surface). f) Scattering of reflected light out of the sensor's FOV. g) Reflected light is also attenuated towards the sensor. h) Scattered light from the sun which is directed toward the sensor. i) Light which has already been scattered by the atmosphere which is then scattered toward the sensor. j) Water-leaving radiance originating out of the sensor FOV, but scattered toward the sensor. k) Surface reflection out of the sensor FOV which is then scattered toward the sensor. L w Total water-leaving radiance. L r Radiance above the sea surface due to all surface reflection effects within the IFOV. L p Atmospheric path radiance. (Gordan and Wang)

33. Atmospheric Correction Methods Develop Theoretical Atmosphere. Include: Rayleigh Scattering - (Strongest in Blue region) Ozone Aerosols - (Absorption and Scattering Characteristics) Use Data from Infrared (IR) band and assume that all of this signal comes from the atmosphere to get knowledge of aerosols. Solve Radiative Transfer Equation Geometry Location (types of aerosols possible) Other considerations: – Sun Glint. Avoid - Use wind speed to estimate surface roughness. – White Caps. Measure - Use wind speed to estimate coverage.

34. Atmospheric Aerosol Correction Procedure BlueGreenRedNear-IR Upwelling Radiance At Satellite Cloudy Cloudless-Polluted Clear H2O Biological

35. Miller, Bartholomew, Reynolds

37. A break in the clouds over the Barents Sea on August 1, 2007 revealed a large, dense phytoplankton bloom to the orbiting MODIS aboard the Terra satellite. The bright aquamarine hues suggest that this is likely a coccolithophore bloom. The visible portion of this bloom covers about 150,000 square kilometers (57,000 square miles) or roughly the area of Wisconsin.