the modis chlorophyll product: strategy and …
TRANSCRIPT
The MODIS Chlorophyll a Product:Strategy and Recommendations
Janet W. CampbellUniversity of New Hampshire
MODIS Science Team MeetingBaltimore, MD
March 22-24, 2005
BACKGROUND
In the past, MODIS produced 3 chlorophyllproducts. Chlor_MODIS used an empirical“case 1” algorithm; Chlor_a3 was based on asemi-analytic algorithm that also solved forCDOM absorption, absorbed radiation byphytoplankton, and other optical properties;and Chlor_a2 was introduced as an analog tothe SeaWiFS chlorophyll product.
The “SeaWiFS analog” chlorophyllproduct (chlor_a2) was consideredvalid if it agreed with the SeaWiFSchlorophyll.
Collection 4 December 2000
MODIS is currently producing only the “SeaWiFS-analog” chlorophyll product.
It employs the OC3M algorithm parameterized with thesame data set used for the SeaWiFS OC4 algorithm(n = 2,804).
BACKGROUND
OC3M
Both are described in NASA TM 2000-206892, Vol. 11 (O’Reilly et al., 2000).
> 10
5.0
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Chl, mg m-3
SeaWiFSChlorophyll
Feb. 28, 2004
MODIS AquaChlorophyll
Feb. 28, 2004
Now that both data sets are being processed by the same group, werecommend: whatever is decided about the MODIS chlorophyllalgorithm, the SeaWiFS chlorophyll algorithm should beconsistent to ensure continuity.
RECOMMENDATIONS
1. The MODIS chlorophyll-a algorithm should provide continuity withthe SeaWiFS chlorophyll record (1997-present). If we arrive atanother algorithm, then SeaWiFS data should also be re-processed withsame or “MODIS-analog” algorithm.
Fortunately, reprocessing of both data sets can be achieved quickly andthis should not be an obstacle …
Congratulations to the OBPG for the recentreprocessing in record time!
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SeaBAMAMTW. Fla . ShelfChesapeakeone-to-one
RMS = 0.293 (SeaBAM 0.184; AMT 0.256; W. Fla. Shelf 0.175; Ches. 0.787)
Approach: First test algorithms with in situ data. Four in situ data sets ofreflectance and chlorophyll data shown here total 1,119 stations.
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0.01 0.1 1 10 100 1000Measured Chlorophyll
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0.01 0.1 1 10 100 1000OC4.v4 chlorophyll
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lThe point of this plot is that there are systematicdifferences between the algorithms even when appliedto the same data set (assuming 448 ~ 490, 551 ~ 555)
The MODIS chlorophyllswill be slightly less overmost of the ocean, i.e.,where Chl < 3 mg m-3.
The algorithms are not the sameeven when using the 443:550 ratio.Differences were intentional toaccount for differences in spectralresponses of the MODIS andSeaWiFS bands and also fact that488 ?‰490 and 551 ?‰555.
The SeaWiFS algorithm (OC4.v4) is:
log10(CHL) = 0.366 – 3.067R + 1.930R2 + 0.649R3 – 1.532R4
where
R = log10[max(Rrs(443), Rrs(490), Rrs(510))/ Rrs(555)]
The Algorithms: Differences are intentional
The MODIS algorithm (OC3M) is:
log10(CHL) = 0.283 – 2.753R + 1.457R2 + 0.659R3 – 1.403R4
where
R = log10[max(Rrs(443), Rrs(488))/ Rrs(551)]
Our approach has been to test candidate algorithms first usingin situ data, and then to evaluate them when applied to near-coincident, co-registered SeaWiFS and MODIS scenes.
Comparing chlorophyll products from near-coincident, co-registered SeaWiFS and MODIS scenes is pointless until theinput radiances are consistent .. at least, until the band-to-band(ratios) are consistent.
RECOMMENDATIONS
2. Use newly created in situ data sets to test candidatealgorithms. I propose to host a workshop at UNH to evaluateand compare algorithms. It will be “SeaBAM 2” …
New in situ data sets are being assembled that include not onlychlorophyll and water-leaving radiances, but also inherent and apparentoptical properties, SST, and data such as latitude, longitude, day, etc.(missing from original SeaBAM data set).
Jeremy Werdell (GSFC) is the steward of a new compilation fromSeaBASS.
UCSB bio-optical database for algorithmdevelopment and validation
Subset of SeaBASS
* Chl* Rrs(λ)* aph(λ), ad(λ), ag(λ)* bbp(λ)* some Kd(λ)
* Will become publicsoon (end of March ?)
* Data policy (accessand usage) will beidentical to SeaBASS
* Web-based
* Queries by:•Variable•Date•Wavelength•Experiment
UCSB bio-optical database for algorithmdevelopment and validation
ACE_ASIA 43AMLR 86BBOP 299BIOCOMPLEXITY 39CALCOFI 293EcoHAB 191INDOEX 48JGOFS 30Kieber_Photochemistry_02 110LMER-TIES 242NOAA_Gulf_of_Maine 52Oceania 47Okeechobee 4ONR-MAB 85Plumes_and_Blooms 497Scotia_Prince_ferry 259Sea_of_Japan 37TOTO 74
Experiment Number ofStations
Total 2,436
UCSB database: Stations with Chl and Rrs measurements
experiment CountOfIOP_IDACE_ASIA 46AMLR 91Arc00 20BBOP 82BIOCOMPLEXITY 35CALCOFI 188EcoHAB 191GLOBEC 30INDOEX 53IOFFE 99Kieber_Photochemistry_02 47Lab96 17LMER-TIES 93NASA_Gulf_of_Maine 53NOAA_Gulf_of_Maine 125Okeechobee 4ONR-MAB 38Plumes_and_Blooms 497Scotia_Prince_ferry 222Sea_of_Japan 35TOTO 84
Experiment Number ofStations
Total 2,050
UCSB database: Stations with Chl and absorption (aph, ad and ag)
experiment CountOfRrs_IDACE_ASIA 38AMLR 81BBOP 49BIOCOMPLEXITY 27CALCOFI 116EcoHAB 193INDOEX 46Kieber_Photochemistry_02 119LMER-TIES 108NOAA_Gulf_of_Maine 104Okeechobee 6ONR-MAB 38Plumes_and_Blooms 612Scotia_Prince_ferry 230Sea_of_Japan 35TOTO 93
Experiment Number ofMatchups
Total 1,895
UCSB database: Stations with Chl, Rrs and absorption (aph, ad and ag)
Goodnews:A lot ofdata areavailable
Bad news:*Most ofthem arecoastal
*Not manynew dataenterSeaBASS
UCSB database: Stations location
RECOMMENDATIONS
3. As a minimum, we should aim to develop an algorithm thatcan solve for CDOM and Chl-a simulaneously. The Carderalgorithm (Chlor-a3) did this, but it requires SST which isn’tavailable with SeaWiFS. Can we use AVHRR Pathfinder datawith SeaWiFS to accommodate algorithms that require SST? Orcan we use an algorithm such as the Siegel, Maritorena, Nelsonalgorithm?
Colored Dissolved Organic Material
First view of global CDOM distribution from SeaWiFSCDOM regulates light absorption [especially in UV]CDOM is the precursor for many photochemical rxn’sCDOM may work as natural water mass tracer
Siegel, Maritorena & Nelson [UCSB]
FINAL QUESTION:
Should MODIS produce and distribute more thanone chlorophyll product?
A complete re-processingof CZCS and SeaWiFSwas undertaken in orderto make the data sets asconsistent as possible.
It would be worthwhiledoing a similarreprocessing of MODISAqua data to extend thistime series, but theresulting MODIS “CZCS-type” chlorophyll datawill not be the bestchlorophyll.
So question is: will thechlorophyll data in thelong-term time series besame as our bestcontemporary product?
RECOMMENDATIONS
1. The MODIS chlorophyll-a algorithm should providecontinuity with the SeaWiFS chlorophyll record (1997-present). If we arrive at another algorithm, then SeaWiFS datashould also be re-processed with same or “MODIS-analog”algorithm.
2. Use newly created in situ data sets to test candidatealgorithms. I propose to host another “SeaBAM” workshop atUNH to evaluate and compare algorithms.
3. As a minimum, we should aim to develop an algorithm thatcan solve for CDOM and Chl-a simulaneously. The Carderalgorithm (Chlor-a3) did this, but it requires SST which isn’tavailable with SeaWiFS. Can we use AVHRR Pathfinder datawith SeaWiFS to accommodate algorithms that require SST? Orcan we use an algorithm such as the Siegel, Maritorena, Nelsonalgorithm?
• Chl-a: CAMPBELL, Trees, Maritorena, Clark, O’Reilly, Carder• IOPs: LEE, Carder, Gould, Stamnes• AOPs: MCCLAIN, VOSS, HOOKER, CLARK, WANG, Mueller, Gordon,
Carder, Evans, Kearns, Gould, Stumpf (includes L-2 processing ofsome 250m and 500m bands)
• FLH: LETELIER, Behrenfeld• Kd(490): CLARK, Mueller, Trees (Provide recommendation for K(PAR)• PP: BEHRENFELD (Provide recommendation on SeaWiFS Chl/K490
product, i.e., do we need to continue generating it?)• POC: CLARK, Stramski• PIC: BALCH, Gordon• SST: MINNETT, Evans• PAR: GREGG• CDOM: SIEGEL, Nelson• DOC: Hoge
MODIS OCEAN ALGORITHM WORKING GROUPS
I believe that we should not separate Chl-a algorithm from IOPs, CDOM, POC, PIC…because we should seek an algorithm that solves for all simulaneously.
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