Ocean Color Remote Sensing

Transcription

1 Ocean Color Remote Sensing

2 Outline 1. What do we mean by Ocean Color? 2. How are the measurements made? 3. What parameters can be derived? 4. Where are the data?

3 Components of a remote sensing system source sensor signal raw data in situ calibration processing / dissemination

4 Geostationary Orbit Polar Orbit 36,000km altitude (for wide view) 850km altitude Stays over same location Travels over poles Can document evolving systems Sees whole globe High temporal resolution Low temporal resolution Lower spatial resolution Higher spatial resolution No current color sensors (Several) current color sensors

5 Ocean color (chlorophyll) Passive measurement - energy source is the Sun In contrast to altimetry, SST etc, looks at subsurface, not skin Measures light emitted from the ocean distinguish between emission and reflection Actual parameter measured (raw data) is normalized water leaving radiance, often denoted nl w Most of the signal (>90%) at the satellite is NOT ocean color - atmospheric interference Also interference from other colored material in the ocean sediments, colored dissolved organic matter (CDOM)

6 Why measure chlorophyll? Because we can Phytoplankton, the base of the food chain, contain chlorophyll Chlorophyll, by virtue of its color, is easy to observe But chl is not a perfect indicator of biomass or productivity Chlorophyll per cell changes (species, light, nutrients) Carbon/particles might better represent biomass/cells Chlorophyll? productivity

7 The ocean color measurement and how it s used Main signals: Atmosphere, reflection and ocean color L w E d nl w (R rs ) = L w E d R = log 10 R rs (443nm,490nm,510nm) R rs (555nm) Chl = 10 ( R R R 3 )

8 The chlorophyll algorithm n=2849 R = log 10 R s (490nm) Rs(555nm) Chl =10 ( R R R 3 )

9 Measuring chlorophyll from space The Sea-viewing Wide Field of view Sensor: SeaWiFS The instrument The satellite

10 What SeaWiFS sees in one day The gap here is caused by the satellite tilting as it passes over the equator

11 Global picture of ocean and land pigments

12 A problem with visible remote sensing: Clouds 1 day

13 3 days

14 8 days

15 MODIS Terra L2 1 km resolution scene from October 3rd 2001 Sea Surface Temperature ( C) (mg m-3) Chl a Chl Fluorescence Line Height -2-1 (W m mm sr-1) From OSU-COAS EOS DB Station

16 The sensors and data Coastal Zone Color Scanner (CZCS): Proof of concept mission, SeaWiFS: Launched 1997, very successful, well-calibrated. Outperformed all expectations, died MODIS Aqua: Launched 2002, has fluorescence channel that SeaWiFS lacked. Some recent issues re sensor deterioration. MERIS: European sensor, some useful extra channels and high spatial resolution (300m) Data available at spatial resolutions from ~1km to 9km Data available at daily resolution with the caveats previously discussed Data gateway depends on user: NASA HDF: Giovanni:

17 NPZ modelling: This afternoon s lab" The model is described by a series of equations in the Matlab script. We can write the equations out in words like this:" change in P "= +nutrient uptake mortality (of P) grazing" change in Z "= +growth efficiency x grazing mortality (of Z)" change in N "= -nutrient uptake + (1-growth efficiency) x grazing + mortality (of P) + mortality (of Z)" " dp dt = V mnp K s + N mp ZR m(1 e ΛP ) dz dt = γzr (1 m e ΛP ) kz dn dt = V mnp K s + N + (1 γ)zr m(1 e ΛP )+ mp + kz

18 N uptake" dp dt = V maxnp K N + N cf light curves for photosynthesis

19 Goals of the lab" The scenario we are simulating here is much like the idealized spring bloom described in the lecture:" "mid-to-high latitude ocean" "around the beginning of spring" "water column has just stratified" "nutrients are at moderate to high levels" "phytoplankton and zooplankton biomass are low to begin." " The exercise is in several parts: Run the basic model, then tweak the parameters three different ways and examine the output." " Lab will also cover NASA s Giovanni, for the course project."