REMOTE SENSING OF THE OCEAN
OPEN OCEAN & COASTAL REGIONS: STUDY OF TWO DIFFERENT WATER TYPES: CASES I & II
G1 Members:
Awaluddin (HK); Clara Loureiro (UAc); Emanuele (Cancia) Supervisor:
Ana Martins (UAc)
2013 ERASMUS summer school on FORmation of Multi-disciplinary Approaches to Training in Earth Observation (FORMAT-EO) 19th July-1st August 2013, Leicester, UK
MOTIVATION
WHY STUDY THE OCEAN? Covers 2/3of Earth's surface:
IT MAKES LIFE AS WE KNOW IT POSSIBLE
&
IT SUSTAINS HUMAN SOCIETY
CLIMATE CHANGE GLOBAL WARMING ICE AGES AND PAST CLIMATESEA LEVEL RISE ECOSSYSTEM MANAGEMENT SPECIES CONTRIBUTION WATER CYCLE
OPEN OCEAN & COASTAL PROVIDE INVALUABLE BENEFITS TO HUMANS AND MARINE LIFE They are the most important sources of economic activity, providing food, energy production, commerce and recreation.
* The Open Ocean ecosystem is highly dynamic. It is the place where phenomena's combined by water and atmosphere happen that control the earth system; * The Coastal areas are complex and dynamic ecosystem highly exposed to environmental degradation, both for natural and anthropogenic causes.
RESEARCH PROBLEM
SATELLITE ALGORITHMS FOR CASE I AND, IN PARTICULAR, FOR CASE II WATER TYPES ARE STILL NOT FULLY VALIDATED
MOTIVATION
CASE I • dominated by
phytoplankton [and its
degradation products (PIC, etc)];
• very low sediment and CDOM;
• usually open ocean;
• better accuracy of satellite derived products;
CASE II • dominated by sediment and CDOM apart from phytoplankton;
• highly productive; • coastal areas;
• low accuracy of satellite derived products;
STATE OF THE ART A gyre in oceanography is any large system of rotating ocean currents, particularly those involved with large wind movements.
The South Atlantic Gyre is the subtropical gyre in the south Atlantic Ocean. The Benguela current is part of the subtropical South Atlantic Gyre.
STATE OF THE ART
Also... In the center of the Gyres, downwelling occurs, therefore…
Gyres are considered oligotrophic, or nutrient poor because they are far from terrestrial runof: “poorest biological regions of the Ocean”
STATE OF THE ART
Coastal Regions Coastal upwelling responsible for richer biological environments Nevertheless, also more exposed to harsh environments
MAIN OBJECTIVE
CHARACTERIZE TWO REGIONS IN TERMS OF VARIABILITY USING RS (OC AND SST) SATELLITE DATA OPEAN OCEAN Phytoplankton Dominated
COASTAL AREA Phytoplankton + Suspended sediments + DOM + terrigenous particles Dominated
METHODOLOGY
AREA OF INTEREST
http://eoimages.gsfc.nasa.gov/
METHODOLOGY
AOI’S
Case I. Coastal ANGOLA vs Case II. Subtropical OPEN OCEAN
METHODOLOGY AND RESULTS
AOI’S
CASE I
CASE II
METHODOLOGY
MODIS INSTRUMENT ORBIT : 705 km, 1.30 p.m,BANDWIDTH (nm) PRIMARY USE BAND descending 1node (Aqua) 620 - 670 Land/Cloud/Aerosols Boundaries 2 : 841 - 876 SPATIAL RESOLUTION 3 459 - 479 250 m (bands 1-2) 4 545 - 565 Land/Cloud/Aerosols 5 1230 - 1250 500 m (bands 3-7) Properties 6 1628 - 1652 1000 m (bands 8-36) 7 2105 - 2155 DESIGNE LIFE: 6 years 8 405 - 420 OCEAN COLOR/PHYTOPLA NKTON/BIOGEOCHE MISTRY
Atmospheric Water Vapor
9 10 11 12 13 14 15 16
438 - 448 483 - 493 526 - 536 546 - 556 662 - 672 673 - 683 743 - 753 862 - 877
17 18 19
890 - 920 931 - 941 915 - 965
METHODOLOGY
DOWNLOADING DATA SST/ CHL-a / PIC OCEAN COLOR WEBSITE (1 KM) http://seadas.gsfc.nasa.gov
GIOVANNI (4 KM) http://disc.sci.gsfc.nasa.gov/giovanni/
BATHYMETRY GEBCO http://topex.ucsd.edu/WWW_html/srtm30_plus.html
METHODOLOGY
Home Level 1 and 2 Browser
http://oceancolor.gsfc.nasa.gov/
METHODOLOGY
HOME – GIOVANNI PORTALS – OCEAN PORTALS
http://disc.sci.gsfc.nasa.gov/giovanni/
METHODOLOGY
SST
sst =sstlo + (dBT-0.5)/(0.9-0.5)*(ssthi-sstlo) http://oceancolor.gsfc.nasa.gov/DOCS/modis_sst/
Chlor-a
chlor_a = 10^(a0 + a1*X + a2*X^2 + a3*X^3 + a4*X^4) http://oceancolor.gsfc.nasa.gov/REPROCESSING/R2009/ocv6/
PIC
3-band PIC algorithm (Gordon et al., 2001)
METHODOLOGY
Free tool; Multi-mission; Display tools; Analysis tools; Processing; Open source
http://seadas.gsfc.nasa.gov
RESULTS
Case II, Chl-a in April 2012
RESULTS
All parameters April averages lower in subtropical gyre area WARM SEASON
RESULTS
July monthly Chla and PIC averages are higher in Angola but SST is lower than in the subtropical gyre ((due to coastal upwelling)
COLD SEASON
RESULTS
TIMES SERIES SST - Chl-a time series: case I – Open Ocean 30 29
Chl-a
0.14
28
0.1
26
0.08
25 24
0.06
23
Chl-a (mg/m3)
0.12
27
0.04
22
0.02
21
0 May/12
Feb/12
Nov/11
Aug/11
May/11
Feb/11
Nov/10
Aug/10
May/10
Feb/10
Nov/09
Aug/09
Feb/09
May/09
Time (months)
Nov/08
Aug/08
May/08
Feb/08
Nov/07
Aug/07
May/07
Feb/07
Nov/06
Aug/06
May/06
Feb/06
Nov/05
Aug/05
May/05
Feb/05
Nov/04
Aug/04
May/04
Feb/04
20 Nov/03
SST ( o C)
SST
0.16
SST –seasonal variability is evident in monthly averages. Values between 23 (Oct, cold period) and 27 (April, warm period) oC approximately. OC (Chla) – some periodicity but less evident as SST. Concentrations range from 0.04 mg m-3 (warm period) to 0.14 mg m-3 (cold season).
RESULTS
TIMES SERIES 30
35 30
24.086
25
25
Temperature (ºC)
20
20
15 15
Chl-a SST
10
10
5
5
May/12
Feb/12
Nov/11
Aug/11
May/11
Feb/11
Nov/10
Aug/10
May/10
Feb/10
Nov/09
Aug/09
May/09
Feb/09
Nov/08
Aug/08
May/08
Feb/08
Nov/07
Aug/07
May/07
Feb/07
Nov/06
Aug/06
May/06
Feb/06
Nov/05
Aug/05
May/05
Feb/05
Nov/04
Aug/04
May/04
Feb/04
0 Nov/03
0
Time (months)
SST –seasonal variability is evident in monthly averages. Values between 20 (Aug, cold period) and 29 (Feb/Mar, warm period) oC approximately. OC (Chla) – seasonal variability but less evident as SST. Concentrations range from 0.50 mg m-3 (warm period) to 24 mg m-3 (cold season).
RESULTS
SST & OC (CHLA CORRELATION SST vs Chl-a: case2
30 28 26 24 22 20
Warm… R² = 0.665
0.02
0.04
0.06
SST (oC)
T (o C)
SST vs Chl-a: case1
30 28 26 24 22 20
Warm… R² = 0.0405 0
0.08
SST vs Chl-a: case1
0.02
0.07 Chl-a
0.12 (mg/m3)
0.17
SST (oC)
T (o C)
Cold…
21
3
4
(mg/m3)
SST vs Chl-a: case2
R² = 0.2085
26
2
Chl-a
Chl-a (mg/m3)
31
1
R² = 0.2858 cold…
30 28 26 24 22 20 0
10
Chl-a
20
30
(mg/m3)
SST and OC – vary inversely (exception case II warm period)
RESULTS
SEASONAL ANOMALIES
Warm seasons – less variability in Chl-a
RESULTS
SEASONAL ANOMALIES
Warm seasons – less variability
SST anomalies in warm and cold season vary almost similarly
RESULTS
SEASONAL ANOMALIES
Warm seasons – less variability
PIC anomalies in warm and cold season vary similarly
RESULTS
INTERANNUAL ANOMALIES CASE I: Open Ocean 2003 (SST) – most negative Other years, not significant changes from the mean
CASE II: Coastal Both parameters show significant anomalies variability
MAIN CONCLUSIONS Physical and biological parameters in the ocean, especially in the coastal ocean, exhibit significant spatial and temporal variability.
Long time-series are required to unravel the mechanisms for this variability, to monitor changes, to distinguish long-term trends, and to document rare and/or catastrophic events that may play a critical role on those systems. RS provide an excellent two-dimensional, relatively high spatial resolution, low-frequency synoptic time-series over long periods of time with automatic area-averaging, suitable for ocean studies.
-
Nevertheless, for these to be efficient it is necessary:
In situ data validation Improvement of regional algorithms Development of satellite data merging techniques Beta testers of world recognized databases.
THANK YOU
