Light penetration within a clear water body. E z = E 0 e -kz

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1 THE BLUE PLANET 1

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3 Light penetration within a clear water body E z = E 0 e -kz 3

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7 Pure Seawater Phytoplankton b w 10-2 m -1 b w 10-2 m -1 b w, Morel (1974) a w, Pope and Fry (1997) b chl,loisel and Morel (1998) a chl, Sathyendranath et al. (2001) 7

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11 Photosynthesis Ocean Color 11

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14 SENSOR AGENCY SATELLITE CZCS CMODIS COCTS CZI GLI MERIS MOS OCI OCM OCTS OSMI POLDER POLDER-2 POLDER-3 SeaWiFS NASA (USA) CNSA (China) CNSA (China) NASDA (Japan) ESA (Europe) DLR (Germany) NEC (Japan) ISRO (India) NASDA (Japan) KARI (Korea) CNES (France) CNES (France) CNES (France) NASA (USA) Nimbus-7 (USA) SZ-3 (China) HY-1A (China) ADEOS-II (Japan) ENVISAT (Europe) IRS P3 (India) ROCSAT-1 (Taiwan) IRS-P4 (India) ADEOS (Japan) KOMPSAT-1 /Arirang- 1(Korea) ADEOS (Japan) ADEOS-II (Japan) Parasol OrbView-2 (USA) OPERATING DATES 24/10/78-22/6/86 SWATH (km) SPATIAL RESOLUTION (m) # OF BANDS SPECTRAL COVERAGE(n m) ORBIT Polar 25/3/02-15/9/ ,500 Polar 15/5/02-1/4/04 14/12/02-24/10/ FROM: Updated 02/28/ , Polar / ,500 Polar 1/3/02-9/5/ / Polar 21/3/96-31/5/ Polar 27/01/99-16/6/ ,500 Polar 26/5/99-8/8/ / Polar 17/8/96-29/6/ ,500 Polar 20/12/99-31/1/ Polar 17/8/96-29/6/ km Polar 14/12/02-24/10/03 Dec Dec /08/97-14/02/ Polar Polar Polar 14

15 SENSOR AGENCY SATELLITE LAUNCH DATE SWATH (km) SPATIAL RESOLUTION (m) BANDS SPECTRAL COVERAGE (nm) ORBIT COCTS CZI CNSA (China) HY-1B (China) 11 April , Polar GOCI KARI/KIOST (South Korea) COMS 26 June Geostationary HICO ONR, DOD and NASA JEM-EF Int. Space Stn. 18 Sept km Selected coastal scenes o, 15.8 orbits p/d MERSI CNSA (China) FY-3A (China) 27 May / Polar MERSI CNSA (China) FY-3B (China) 5 November / Polar MERSI CNSA (China) FY-3C (China) 23 September / Polar MODIS-Aqua MODIS-Terra OCM-2 VIIRS NASA (USA) NASA (USA) ISRO (India) NOAA (USA) Aqua (EOS-PM1) Terra (EOS-AM1) Oceansat-2 (India) 4 May /500/ ,385 Polar 18 Dec /500/ ,385 Polar 23 Sept / Polar Suomi NPP 28 Oct / ,800 Polar FROM: Updated 03/04/2014 SENSOR AGENCY SATELLITE OLCI COCTS CZI SGLI SCHEDULED LAUNCH SWATH (km) SPATIAL RESOLUTION (m) # OF BANDS SPECTRAL COVERAGE (nm) ORBIT ESA/ EUMETSAT Sentinel 3A June / Polar CNSA (China) JAXA (Japan) HY-1C/D (China) , GCOM-C / ,500 Polar HSI DLR (Germany) EnMAP Polar Polar VIIRS OLCI COCTS CZI NOAA /NASA (USA) ESA/ EUMETSAT CNSA (China) JPSS / ,800 Polar Sentinel-3B Polar HY-1E/F (China) , Polar Multi-spectral Optical Camera INPE / CONAE SABIA-MAR / / ,800 Polar GOCI-II KARI/KIOST 1200 x GeoKompsat 2B / (South Korea) TBD TBD Geostationary OCI NASA PACE 2018 * * * * Polar OES NASA ACE >2020 TBD Polar Coastal Ocean Color Imaging Spec (Name TBD) NASA GEO-CAPE >2022 TBD TBD Geostationary VSWIR and TIR Instruments NASA HYSPIRI > nm contiguous bands LEO, Sun Sync. FROM: Updated 02/28/

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21 PROCESSING ALGORITHMS Based on Gordon et al. (1980) and Gordon et al. (1983) The algorithm used for estimating the pigments content of the ocean from CZCS measurements involves the use of radiance ratios. The general form of the equation is Where log(c) = a + b*log[lw(1)/lw(2)] C is the pigment concentration (mg/m^3) a,b are regression coefficients Lw(1),Lw(2) are the atmospherically corrected radiances for a pair of CZCS channels For CZCS pigments processing, these channel pairs are (443, 550 nm), for C < 1.5 mg/m^3 (520, 550 nm), for C > 1.5 mg/m^3 21

22 Monthly Composite of CZCS During September

23 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) CZCS BANDS Band Wavelength (nm) Phytoplankton Chl-a 23

24 SeaWiFS ALGORITHMS 24

25 GLOBAL ESTIMATION OF PHYTOPLANKTON CHLOROPHYLL-A USING SEAWIFS DATA 25

26 Launched on December 18, 1999 Launched on May 4,

27 MODIS Technical Specifications Orbit: Scan Rate: 705 km, 10:30 a.m. descending node (Terra) or 1:30 p.m. ascending node (Aqua), sun-synchronous, near-polar, circular 20.3 rpm, cross track Swath Dimensions: Telescope: 2330 km (cross track) by 10 km (along track at nadir) cm diam. off-axis, afocal (collimated), with intermediate field stop Size: 1.0 x 1.6 x 1.0 m Weight: kg Power: W (single orbit average) Data Rate: 10.6 Mbps (peak daytime); 6.1 Mbps (orbital average) Quantization: 12 bits Spatial Resolution: Design Life: 250 m (bands 1-2) 500 m (bands 3-7) 1000 m (bands 8-36) 6 years MODIS BANDS Primary Use Band Bandwidth 1 Spectral Radiance 2 Required SNR 3 Land/Cloud/Aerosols Boundaries Land/Cloud/Aerosols Properties Ocean Color/ Phytoplankton/ Biogeochemistry Atmospheric Water Vapor

28 MODIS BANDS Primary Use Band Bandwidth 1 Spectral Radiance 2 Required NE[delta]T(K) 4 Surface/Cloud (300K) 0.05 Temperature (335K) (300K) (300K) 0.07 Atmospheric Temperature Cirrus Clouds Water Vapor (250K) (275K) (SNR) (240K) (250K) 0.25 Cloud Properties (300K) 0.05 Ozone (250K) 0.25 Surface/Cloud (300K) 0.05 Temperature (300K) 0.05 Cloud Top Altitude (260K) (250K) (240K) (220K) 0.35 Sea Surface Temperature (Celsius Degree) Phytoplankton Chlorophyll-a (mg m^3) 28

29 Weekly MODIS Chlorophyll March 6-13, 2001 Weekly Ocean Net Primary Productivity 29

30 Global problems for ocean color remote sensing are also present in the Caribbean Better understanding of the temporal and spatial variability of inherent and apparent optical properties is needed. Site-specific bio-optical algorithms are required to better estimates the concentration of Chlorophyll-a and Suspended Sediments. CDOM and suspended sediments are seasonally produced by rivers discharge and their correlation controls the bio-optical variability. Photosynthetic picoplankton, like cyanobacteria, are competing with large phytoplankton for the quality and quantity of light. Current satellite sensors do not provide accurate estimates of water quality parameters in coastal areas due to all the above problems. But, three unique challenges for remote sensing are also found in Caribbean coastal waters 1. Size of the coastal regions-requires sensors with very high spatial resolution. 2. Low concentration of the parameters-requires sensors with very high S/N ratio. 3. Short-term effects of dramatic seasonal events, like hurricanes, on land-sea interactions-requires sensors with high temporal resolution. 30

31 PHYTOPLANKTON DYNAMICS AFFECTED BY LARGE REGIONAL RIVERS AS DETECTED BY SEAWIFS But, SeaWiFS images fail in coastal waters with local rivers 31

32 Low Chl for developing bio-optical algorithms (also the number of data points are limited) Reflectance ratio (R443/R550) y = x R 2 = Chlorophyll-a (ug/l) Low reflectance signal and no fluorescence peak 32

33 PHYTOPLANKTON DYNAMICS AFFECTED BY HURRICANES September 19 September 25 October 15 Opportunities for Ocean Color in Caribbean Coastal Waters 33

34 Easy access to coastal waters Mayaguez Bay at Western P.R. Deep and Clear Waters Añasco River Sewage Outfall Yaguez River Guanajibo River Shallow and Clear Waters with Coral Reefs It is an accessible natural laboratory with large spatial and temporal variations. It is affected by rivers discharge and anthropogenic effects. Past and current research has provided excellent background information. Its is an ideal place to develop and test remote sensing techniques for coastal waters. Good sampling equipment for sensors validation and algorithms development 34

35 New algorithms for MODIS [Chlorophyll-a] = Empirical algorithm 500 m resolution [Chl-a]= *(B3/B4) [Chlorophyll-a] = OC3 MODIS algorithm 1 km resolution SATELLITE DATA COLLECTION BY THE UPRM-TCESS SPACE INFORMATION LABORATORY 35

36 L-BAND ANTENNA Orbview 2 NOAA 14/16 X-BAND ANTENNA RADARSAT LANDSAT-7 AQUA TERRA 36

37 UPRM Station Viewing Area PHYTOPLANKTON DYNAMICS AFFECTED BY COASTAL UPWELLING AVHRR Sea Surface Temperature SeaWiFS Chlorophyll-a 37

38 Airborne Sensors AOCI 90 s ATLAS 2004 AVIRIS 2004 Empirical Algorithm to estimate Suspended Sediments in Mayaguez Bay using AVIRIS SS (mg/l) = (R777) Where R777 = AVIRIS Reflectance at 777 nm 38

39 Sensors with high spatial resolution 39

40 Read Chapter 19 and answer the review questions 1, 4, and 9 (at the end of the chapter). 40