Introduction to Satellite Oceanography Dr. Anindya Wirasatriya, ST, MSi, MSc.

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1 Introduction to Satellite Oceanography Dr. Anindya Wirasatriya, ST, MSi, MSc

2 Academic Background S1: Marine Science, Diponegoro University (1995-2000) S2 : Coastal Resources Management, Diponegoro University (2002-2005) S2 : Satellite Oceanography, Tohoku University (2009-2011) S3 : Satellite Oceanography, Tohoku University (2012-2015)

3 Oceanography from satellite – the words themselves sound incongruous and, to a generation scientists accustomed to Nansen bottles and reversing termometers, the idea may seem absurd (Ewing, 1965)

4 Monthly distribution of ocean observation by ships and buoys and received by the NOAA Pacific Marine Environmental Group in Monterey, California (Stewart, 1985)

5 Basic Question Why do we need Satellite Oceanography? 1.To obtain the global picture of the ocean, in order to study basin-wide phenomena 2.To observe regions which are not easily observed by ships. For example Southern Ocean around Antartica in winter 3.To make a measurements that are either impossible or difficult by ordinary means, such as observing oceanic rainfall, or the distribution of small waves on the sea surface. (Stewart, 1985)

6 Gifford Ewing noted that for over a century, oceanographers had been forced to consider ‘the class of problems that derive from the vertical distribution of properties at station widely separated in space and time’. → traditional oceanography With the introduction of satellite remote sensing in 1970s, traditional oceanographers were provided a new tool to collect synoptic observations of conditions at or near the surface of the global ocean. Since that time, progress has been dramatic. Satellites are revolutionizing oceanography!!! (Stewart, 1985) Traditional oceanography and remote sensing oceanography

7 Infrared SST and Microwave SST http://www2.hawaii.edu/~jmaurer/sst/

8 Oceanic phenomena captured by AHIGHERS- SST data

9 Rainfall captured by TRMM https://feww.wordpress.com/tag/australia-rainfall-anomalies-trmm/

10 Irene Hurricane captured by TRMM http://www.sciencenewsblog.com/blog/825114

11 The different basic concept of remote sensing for land and ocean (1) Montly Disribution of Clorophyl –a derived from Sea-WiFS (Kiyofuji etal, 2006) (Kusuma et al 2008)

12 The different basic concept of remote sensing for land and ocean (2) NecessityLand RSOcean RS Spatial resolution High (< 100 m)Low (> 1 km) Temporal resolution Low (> monthly)High (< daily) Land → static → many information can be gained in 1 snapshot → spectral analysis is needed Ocean → Dynamic → need to consider hydrodynamics and/or thermodynamic concept to interpret the phenomena captured by satellite → signal processing is needed →need programming skill to process

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14 Spatial TSSI map in Berau Delta 1979 (landsat 20/2/79)Spatial TSSI map in Berau Delta 1994 (landsat 10/7/94) Spatial TSSI map in Berau Delta 2002 (landsat 8/7/02)Spatial TSSI map in Berau Delta 2006 (Spot 9/6/06) What happened in the reality???

15 Then, can we conclude that the high resolution ocean parameters (SST, wave, oil slick, surface wind etc) derived from satellite are useless????? Those kind of data can be used for modeling verification

16 Example : Study on surface wave In Sendai bay using PALSAR onboard ALOS Wei (2010) An example of a PALSAR image in Sendai Bay, captured at 01:06 UTC on 24 Sept 2006

17 Theoritical wavelength map in Sendai Bay derived by the dispersion relation of linear wave theory combined with the wave period from a wave gauge and JODC topography data Wavelength and direction map in Sendai Bay retreived from the PALSAR image Wavelength difference map between SAR-retieved and the theoritical wavelength maps The model looks smooth compared with PALSAR snapshot The difference should be corrected by the other theories

18 Merged satellite SST VS reanalysis SST data (model)

19 What kind of remote sensing parameter in coastal area that can be treated as land remote sensing? Mangrove? Coral reef? Chlorophyll? SST? Coastal Line? TSS?

20 Can the signal received from satellite directly be used? 1.Geometric Correction 2.Algorithm tuning and or validation 3.Removing noises

21 Geometric Correction Geometric distortion Ground Control Points (GCPs) are used for the accurate geometric correction The original AVHRR image has geometric distortion due to the earth shape and the earth rotation the geometric correction is needed.

22 Removing Noises

23 Algorithm tuning and validation Daytime Nighttime http://rst.gsfc.nasa.gov/Intro/Part2_4.html Comparison between in-situ SST and Brightness Temperature ch 4 of NOAA-19 satellite. Wirasatriya, et al (2009)

24 Daytime Nighttime Daytime data Nighttime Data Validation Bias ( 0 C)0.0054439890.008397944 RMSD ( 0 C)0.6136170.546453 Correlation0.9906270.991979 Comparison statistics of the match-ups for validation Wirasatriya, et al (2009)

25 Fortunately many ocean parameter products have already passed those steps and ready for use!!! And most of them are FREE!!!! What we need are just fast internet to download the data and programming skill to process the data!!!

26 1)2)3)4) (4) DSST map case 4 Wirasatriya (2011)

27 Important thing to remember! Remote sensing can effectively capture phenomena happened in the ocean (true evident) Remote sensing is not only a method how to draw the satellite data but also how to do more advanced analysis to get better conclusion and understand the mechanism of phenomena Remote sensing is just one of methods to investigate ocean phenomena, the other methods, basic theory etc are still needed to give better understanding about the phenomena

28 Why do you let the foreigners understand our ocean better than us? Use the advantages of satellite oceanography!! Let’s begin to observe our ocean!!!!

29 The traditional MCSST equation using the split-window brightness temperatures of AVHRR is : MCSST = a + b T4 + c (T4-T5) + d (T4-T5)(sec φ-1) T4 and T5 denote the brightness temperatures of channel 4 and 5, respectively, Φ the satellite zenith angle (o) and a, b, c and d are constants Applying multiple regression analysis on match ups (buoy SST and brightness temperature from satellite) for daytime: SST = -0.82029 + 1.073049 T4 + 1.391844 (T4-T5) + 0.959019(T4-T5)(sec φ-1), for nighttime: SST = -0.2197929 + 1.08664 T4 + 1.694175 (T4-T5) + 0.796074(T4-T5)(sec φ-1). MCSST equation for NOAA-19

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