Abstract The match-up data comparisons with field data published on the SeaBASS web site show accuracies for MODIS Terra radiances and chlor_a_2 chlorophyll-a retrievals. The data include in situ and MODIS Terra radiances and sea-surface temperature values, allowing calculation of both chlor_a_2 and chlor_a_3 MODIS data products. The purpose of this presentation is to conveniently show side-by-side comparisons of the two chlorophyll-a products with synoptic field data. Chlorophyll-a retrievals using in situ radiances compared to in situ chlorophyll-a concentrations are accurate within 55% (linear RMS difference) for chlor_a_2 and within 25% (linear RMS difference) for chlor_a_3. This establishes the inherent algorithm accuracy if all field measurements were perfect. Chlorophyll-a retrievals using MODIS radiances versus in situ chlorophyll a values were only within 62% and 53% for chlor_a_2 and chlor_a_3, respectively. While chlor_a_3 is significantly more accurate than is chlor_a_2 when accurate radiances are used, the inherently more accurate bands (443, 488, 551 nm) of MODIS Terra used for chlor_a_2 retrievals compensate for the algorithmic advantage of chlor_a_3. Improvements in sensor calibration and atmospheric correction are the primary improvements needed to permit chlorophyll a concentrations from space to reach the chlorophyll accuracy goal of 35%. It can be reached with chor_a_3 if accuracies of water- leaving radiance for all bands are less than 15%, but no amount of improvement of radiance accuracies will allow chlor_a_2 values to reach the goal of 35% accuracy for chlorophyll a. Methods Estimates of chor_a_2 and chlor_a_3 were made using both in situ and MODIS Terra radiances (Collection 041 ocean processing) along with MODIS sea-surface temperatures for the in situ, match-up sites. The latitudes and longitudes for the match-up stations used at the SeaBASS web site (Fig. 1) were used for extraction of MODIS SST values required by the chor_a_3 algorithm. Chlor_a_2 retrievals of chlorophyll a were determined using the OC-3 empirical algorithm of O’Reilly et al. (1998) along with remote-sensing reflectance or normalized water-leaving radiance data. Chlor_a_3 values were determined using the semi-analytic method of Carder et al. (1999) along with an updated nitrate-depletion-temperature (NDT) method of adjusting phytoplankton absorption for the pigment-package effect (Carder et al. 2004; Advances in Space Research). NDT values used in the chlor_a_3 algorithm for the globe are shown in Figure 2 in degrees Celsius (after Kamykowski et al. 2002). SST values are compared to NDT values to determine the appropriate chlorophyll-specific absorption coefficient. Modeling the error fields as the sum of the squares of the algorithm accuracies and each MODIS radiance accuracy for the channels used provides the following: This simple model basically assumes that the various sources of error are random and sum together based upon the inherent algorithm error and the errors for each of the bands used in the algorithm. We can use this model to simulate the retrieval accuracy expected if each of the MODIS bands were more accurate. Errors are shown in Table 1. Applications are shown in Discussion. Results Figures 3 and 4 show chlorophyll-a retrievals using in situ radiances with the chlor_a_2 and chlor_a_3 algorithms, respectively, compared to in situ chlorophyll-a values. Note that exclusion of an apparently erroneous data point significantly improves accuracies for both figures. Moving the decimal point of the field chlorophyll value by one digit places the “erroneous” point appropriately amongst a cluster of other points on each figure. Chlorophyll-a retrievals via chlor_a_2 and chlor_a_3 algorithms using MODIS Terra radiances are compared to field validation chlorophyll a values(Fig. 5 and 6). Again, one apparently erroneous data point is removed to improve accuracies for both comparisons. Discussion An algorithm can only be as accurate as is permitted by the radiance data available. Given consistently measured in situ radiance data, retrievals from chor_a_2 and chlor_a_3 algorithms are compared (Figs. 3, 4). Chlor_a_2 has a linear RMS error of 54.6% due in part to inaccurate slope (0.84), bias (-0.11), and scatter (r 2 = 86.5%) factors for this log-log plot. Chlor_a_3, however, has a much lower linear RMS error of 25.3%, due in part to more accurate slope (1.01) and bias (0.0017) terms, and less scatter (r 2 = 95.6%) factors for this log-log plot. The 35% linear accuracy goal for chlorophyll a of the ocean-color community (e.g. McClain et al. 1998; Kilpatrick et al. 2002) can be met with chlor_a_3 given accurate water-leaving radiance retrievals. The 5% accuracy goal for satellite ocean-color radiances (McClain et al. 1998; Gordon and Voss ATBD 1999) is probably the best that can be achieved for SeaWiFS even with 3x3 pixel smoothing due to digitization errors (Hu et al. 2001). Digitization noise will not limit the accuracy of MODIS in the same way because it uses 12-bit rather than 10-bit digitization, but calibration and atmospheric correction accuracies can be limiting. Even with accurate field radiances, however, chlor_a_2 does not achieve the 35% goal using the global database and algorithm parameterization presently available. The radiances from Terra MODIS have been compared to ship-borne radiance measurements as shown at the SeaBASS web site, with the highest RMS errors found for the shorter blue wavebands (e.g %, 25.49%, 22.24%, and 15.44% for 412, 443, 488, and 551 nm, respectively). Rather than repeating that exercise here, we have evaluated the effects of the scatter inherent in these data on chlorophyll a retrievals. Since only chlor_a_3 uses the 412 nm MODIS channel, and it has the highest RMS difference relative to field radiances, one would expect this inaccuracy of MODIS radiances to more deleteriously affect chlor_a_3 than chlor_a_2. Figures 5 and 6 and Table 1 illustrate this effect, as linear RMS error for chlor_a_3 has doubled from 25.3% to 48.8%, while RMS error for chlor_a_2 has only increased from 54.6% to 57.2%. Note that Fig. 5 has less bias than Fig. 3 with more scatter, accounting for the similar error values. Using the error model (1) and RMSs of radiance, the 57.2% and 48.8% errors for chlor_a_2 and chlor_a_3 retrievals can be simulated for MODIS Terra data, where [ ] 0.5 = 62.2% and [ ] 0.5 = 52.6% (Table 1), within 5% and 4%, respectively. If all of the MODIS radiances were only 15% in error, then the chlor_a_3 retrievals are expected to be accurate within about 36%. If all radiances were only 10% in error, then chlor_a_3 retrievals would have about 30% error. On the other hand, if MODIS Terra radiance retrievals were perfect (e.g. matched field radiances), chlor_a_2 retrievals would not be more accurate than 55%. Since the goal of the ocean color community has been to provide MODIS and SeaWiFS radiance accuracies of about 5% except perhaps for the 412 nm band (e.g. Gordon et al., ATBD 1999), the objective of providing a chlor_a_3 algorithm capable of delivering accuracies better than 35% appears to be achievable using chlor_a_3 with satellite retrievals of water-leaving radiance of better than 15%. However, no amount of improvement of radiance accuracies will allow chlor_a_2 values to reach the goal of 35% accuracy for chlorophyll a. Table 1. MODIS Terra errors for normalized water-leaving radiance and the inherent (field) errors for chlorophyll-a algorithms chlor_a_2 and Chlor_a_3. Algorithm and total error values are linearized from figures as RMS linear = 0.5*[(10 RMS1 - 1) + (1 – 10 -RMS1 )]. Bandn[Lw] Algorithm Model Total Model Model Error­ Error Error Error Error 15% Error 10% In situ chlor_a_3; 4. chlor_a_2 RMS error model 2. In situ chlor_a_2; 5. chlor_a_3 measured error (MODIS) 3. chlor_a_3 RMS error model 6. chlor_a_2 measured error (MODIS) 7. Chlor_a_3 performance with more accurate n[Lw]s References Carder, K.L., F.R. Chen, Z.P. Lee, S.K. Hawes, and D. Kamykowski, Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophyll-a and absorption with bio-optical domains based on nitrate-depletion temperatures, JGR 104, Carder, K.L., F.R. Chen, J.P. Cannizzaro, J.W. Campbell, and B.G. Mtichell, Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a, Advances in Space Research, in press, Hu, C., K.L. Carder and F.E. Muller Karger, How precise are SeaWiFS ocean color estimates? Implications of digitization-noise errors, Remote Seansing of Environment 76 (2), Gordon, H., and K. Voss, MODIS Normalized Water-leaving Radiances ATBD vol. 4, Kamykowski, D., S. Zentara, J. M. Morrison, and A. C. Switzer, Dynamic global patterns of nitrate, phosphate, silicate, and iron availability and phytoplankton community composition from remote sensing data, Global Biogeochemical Cycles, vol. 16, No. 4, Kilpatrick K., E. Kearns, K. Voss, R. Evans, W. Esaias, and V. Salomonson, Status of the MODIS Ocean Color Calibration, McClain, C.R., M.L. Cleave, G.C. Feldman, W.W. Gregg, S.B. Hooker, and N. Kuring, Science quality SeaWiFS data for global biosphere research, Sea Technology 39, O’Reilly, J.E., S. Maritorena, B. G. Mitchell, D.A. Siegal, K.L. Carder, S.A. Garver, M. Kahru, and C.R. McClain, Ocean color algorithms for SeaWiFS, J.G.R 103, 24,937-24,953. Fig. 1 SeaBASS validation data points (23 Jan., 2004). Fig. 2 Nitrate-depletion temperature map based upon Kamykowski et al. (2002). Fig. 3 Chlor_a_2 retrieval accuracy for in situ radiances. Fig. 4 Chlor_a_3 retrieval accuracy for in situ radiances. Fig. 5 Chlor_a_2 retrieval accuracy for MODIS Terra radiances. Fig. 6 Chlor_a_3 retrieval accuracy for MODIS Terra radiances. Match-ups Chlor_a_2 and Chlor_a_3 using in situ and MODIS Terra data By F.R. Chen 1, K. L. Carder 1, J. Patch 1, Jeremy Werdell 2, and Sean Bailey 2 1 College of Marine Science, University of South Florida, St. Petersburg, Florida USA 2 SeaBASS project, GSFC, NASA.