1. Optimized multi-satellite merger to create time series of primary production in the California Current
Mati Kahru1
M.G. Jacox2, R.M. Kudela2, Z. Lee3, and B.G. Mitchell1
1Scripps Institution of Oceanography, University of California San Diego, USA
2University of California Santa Cruz, USA
3University of Massachusetts, Boston
Thanks to CalCOFI, NASA OBP, CCE-LTER
Liège Colloquium on Ocean Hydrodynamics, 2013/05/17

2. 1/17/2019

3. The Keeling curve is an example of climate data records (CDRs)
We need CDRs to evaluate long-term changes, e.g. in response to climate change
Satellite data cover wide space and time domains but individual satellite sensors have limited life span (6 months to ~13 years).
Problem 1: Need to merge data from multiple sensors but products from different sensors are not exactly compatible
SeaWiFS
MODISA
MERIS
1/17/2019

4. Problem 2: Satellite products may be quite inaccurate
Problem 2: Satellite products may be quite inaccurate. Most NPP models use Chl-a as main input representing phytoplankton biomass. Csat (satellite-derived surface Chl-a) is the primary output of OCR and is operationally derived using band-ratio algorithms which cannot separate Chl-a from CDOM and other absorbing components  large errors where CDOM is high or not tightly coupled to Chl-a.
Other input
Rrs
Chl-algorithm
Chl
NPP
Chl data from Ben Mustafa et al., 2012

5. Real satellite Rrs are not ideal and have errors!
We need to abandon using band-ratio Chl-a estimates in NPP models, at least in the Arctic!
CDOM (and detritus) are erroneously counted as Chl-a but are not contributing to photosynthesis; they reduce light and the depth of the euphotic zone
We can use semianalytic algorithms (e.g. GSM, QAA, GIOP, etc) that separate individual components such as Chl-a or aph, adg , bbp but they are very sensitive to errors in satellite Rrs
Semianalytic models are typically built using ideal, i.e. simulated, or in situ Rrs
Real satellite Rrs are not ideal and have errors!
1/17/2019

6. Different sensors do not give the same Rrs!
MERIS vs. MODISA, 2012,
Level3, 9km pixels
Rrs412/Rrs412
Different sensors do NOT give the same Rrs!
1/17/2019

7. Ratios of Rrs412 values estimated by different sensors (median bracket points) of the satellite to satellite match-ups at daily Level-3 images over the year of 2004 for MODISA versus SeaWiFS and MERIS versus SeaWiFS and for year 2012 for MERIS versus MODISA. The heavy black line of Rrs412/Rrs412 = 1 corresponds to the ideal case of no inter-sensor bias.
1/17/2019

8. Chl Rrs NPP aph Rrs Lee et al., 2012 Chl-OC4 Chl-QAA NPP-VGPM Aph-PP
Chl-algorithm
NPP
Rrs() ~ bb() / (a() + bb())
aph
Rrs
Lee et al., 1996; Marra et al., 2003
Lee et al., 2012
Chl-OC4
Chl-QAA
NPP-VGPM
Aph-PP

9. Minimize bias with in situ data
In order to create satellite products that are compatible with in situ data and compatible between multiple sensors we have 2 tasks:
Minimize bias with in situ data
Minimize bias between the corresponding products of different sensors
Ideally (Task 2) would follow from (Task 1) but in practice the distribution of in situ match-ups is inadequate in space, time, range and is therefore not sufficient to create convergence between products of different sensors.
We have used the QAA algorithm (Lee et al. 2007) to tune the retrieval of spectral absorption and backscattering from multiple ocean color sensors (OCTS, SeaWiFS, MODISA, MERIS, MODIST) using match-ups with in situ and between overlapping satellite Rrs. If satellite Rrs are consistently biased – no problem!
1/17/2019

10. R2: 0.78->0.93, % difference: 89% -> 24%, bias: -40% -> 2%
Optimization of aph440 retrievals from MODISA match-ups using QAA standard (left) and optimized model (right):
R2: 0.78->0.93, % difference: 89% -> 24%, bias: -40% -> 2%
1/17/2019

11. Combined (SeaWiFS, MODISA, MERIS) aph440 match-ups with in situ (top) and sat/sat (bottom) before (left) and after (right) optimization
Task 1
Task 2
1/17/2019

12. 1/17/2019

13. Estimated linear trend in the merged monthly anomalies of aph440 ( ). The trend has been calculated for the ratio anomaly.
1/17/2019

14. California Cooperative Oceanic Fisheries Investigations (CalCOFI) , 1949-present, 60+ years
CalCOFI NPP measurements ( ). Typically 4 surveys annually, current total of 1857 PP casts until Feb Red markers denote stations that have match-ups with glider profiles.

15. Using high-resolution Level-2 Rrs from multiple sensors:
We use the same optimization approach to create optimized NPP products for multiple sensors: by minimizing differences with in situ NPP (Task 1) and between sensors (Task 2).
Using high-resolution Level-2 Rrs from multiple sensors:
OCTS,
SeaWiFS,
MODISA, 2002-present
MERIS,
MODIST, 2000-present
VIIRS – to be added
1/17/2019

16. Work in progress…. For each satellite match-up extract Rrs(), …
MERIS, MODIST, MODISA, SeaWiFS
QAA-CalFit
Rrs
a(), bb(), aph(), adg()
Lee et al., 2002, 2007;
Adapted in Kahru et al., 2013
Merged from MODIST, MODIA, SeaWiFS
NPP
PAR(0)
a490, aph440, bbp490
Work in progress….
Lee et al. (2011)
1/17/2019

17. SeaWiFS L2 match-ups with NPP before (left) and after (right) optimization
1/17/2019

18. MERIS L2 match-ups with NPP before (left) and after (right) optimization
1/17/2019

19. SeaWiFS + MERIS + MODISA L2 match-ups with NPP before (left) and after (right) optimization
1/17/2019

20. How does this compare to other NPP methods? Not much better!
From Kahru et al (2009), smaller dataset, using only SeaWiFS
Algorithm r2
RMSD
RMSDcp
Slope
Intercept
ESQRT
0.553
0.242
0.220
0.461
1.553
VGPM
0.662
0.269
0.188
1.091
VGPM-KI
0.620
0.248
0.210
0.439
1.616
Marra
0.636
0.216
0.207
0.789
0.503
CbPM
0.389
0.262
0.260
0.537
1.286
VGPM-CAL
0.661
1.0
0.0
RMSD = total root mean square difference (log10)
RMSDcp = centered pattern RMSD, unbiased RMSD
VGPM-CAL = B & F model fitted to CalCOFI NPP data
1/17/2019

21. Jacox et al. (2013): Using vertical profiles of Chl and light improves model skill by much more than is possible from improved satellite surface estimates
Improvement in predicting NPP:
Sat Chl, SST
in situ surface Chl0 and SST (i.e. ideal satellite input)
Chl vertical profile
Chl and light profiles - potentially measurable from gliders
1/17/2019

22. Conclusions
We created optimized, multi-satellite algorithms for Chl-a, IOPs and NPP for the CC area that are (1) empirically tuned to in situ match-ups and (2) are consistent between multiple sensors
Vertical structure in Chl-a and light profiles is poorly estimated from surface ocean color measurements and limits the skill of satellite NPP models
This 16-year time series is too short to separate interannual and multidecadal cycles from climate trends but the observed trends of lower productivity in the North Pacific gyre and higher productivity in the central California upwelling zone are consistent with the effects of climate change (16 years < ~ 40 years, S. Henson et al.)
Thank you!

23. 1/17/2019

24. aph440 trend (1996-2012) Chl-a trend (1996-2011)
from Kahru et al, 2012
We can estimate the trend for every pixel and evaluate the significance of the trend. Here pixels with a significant (95%) increasing trend are shown in red and those with significant (95%) decreasing trend are shown in blue. Decrease in North Pacific gyre and off Southern Baja, increasing in the central California upwelling areas.

25. Different sensors do not give the same Rrs!
Rrs443/Rrs443
Rrs490/Rrs488
Different sensors do not give the same Rrs!
MERIS vs MODISA, 2012
Rrs670/Rrs667
Rrs560/Rrs547
Different sensors do NOT give the same Rrs!
1/17/2019

26. Refined Csat/Cins match-ups. Dotted lines: 1:1, 2x, 3x and 5x.
The other sensors processed by NASA have similar features – all show underestimation at higher Chl. For MERIS we are using Algal-1 as Algal-2 was heavily biased and not useful. MERIS is slighly overestiating at low Chl but has a few very high matchups close to the one-to-one line.
Refined Csat/Cins match-ups. Dotted lines: 1:1, 2x, 3x and 5x.

27. Standard band-ratio Chl-algorithms are inaccurate even in typical Case1 waters, such as California Current: underestimation at high Chl
median “brackets” of Chl versus sat MBR.
Blue curves = OC3/OC4
Red curves = best fit
Kahru et al., DSR, 2012,
So, what is the problem? The main problem was that we increased the Chl values at high levels for all the sensors but the fits were weakly constrained at high Chl. The values increased and diverged more and the statistics became worse.

28. Optimization of aph440 and adg440 retrievals from OCTS match-ups using standard QAA (left) and optimized model (right)
No radiometric closure between satellite Rrs(λ) and in situ IOPs  satellite retrievals are forced to in situ measurements by optimization of the model coefficients so that have no mean bias in the log-log space.
Optimal coefficients are found by the Trust-Region method, a variant of the Levenberg-Marquardt method.
1/17/2019

29. Combined (OCTS, SeaWiFS, MODISA, MERIS) adg440 match-ups with in situ (top) and sat/sat (bottom) before (left) and after (right) optimization
1/17/2019

30. bbp490 and aph440 retrievals from MERIS match-ups using standard QAA (left) and optimized model (right)
1/17/2019

31. bbp490 and aph440 retrievals from MODISA match-ups using standard QAA (left) and optimized model (right)
1/17/2019

32. We can estimate the trend for every pixel and evaluate the significance of the trend. Here pixels with a significant (95%) increasing trend are shown in red and those with significant (95%) decreasing trend are shown in blue. Decrease in North Pacific gyre and off Southern Baja, increasing in the central California upwelling areas.
Estimated trend in the multi-sensor (OCTS, SeaWiFS, MODISA, MERIS) merged Chl-a ( ).
Kahru M. et al. Trends in the surface chlorophyll of the California Current: Merging data from multiple ocean color satellites, Deep-Sea Research II, 2012

33. Trends in SST fronts FFsst (29 years, 1981-2009) and Chl fronts FFchl (14 years, 1996-2010)