Zhang, CY, Hu, CM, Shang, SL, Muller-Karger, FE, Li, Y, Dai, MH, Huang, BQ, Ning, XR, Hong, HS (2006). Bridging between SeaWiFS and MODIS for continuity of chlorophyll-a concentration assessments off Southeastern China. REMOTE SENSING OF ENVIRONMENT, 102(4-Mar), 250-263.
Chlorophyll-a (Chl) concentration in the Taiwan Strait (TWS) and in the South China Sea (SCS) was estimated using time series of satellite data collected with the MODIS/Aqua and SeaWiFS instruments, and validated with in situ measurements from three cruises conducted in winter and summer 2004. For Chl between 0.1 and 10 mg m(-3), both SeaWiFS and MODIS agreed well with in situ data. Errors for turbid coastal waters were larger than for offshore waters but the overall RMS (root mean square) error in log scale was within 0.35. The percentage RMS error was much larger, varying between 60% and 170% for open ocean and most of the shallow (< 30 m), coastal regions. However, there was no large systematic error or significant bias in either satellite data set, and these numbers were comparable to those for other global oceans and not significantly larger than the algorithm noise (0.22 in RMS error in log scale). Further, SeaWiFS and MODIS showed similar spatial and temporal patterns between July 2002 and October 2004, as well as nearly identical concentrations for Chl between 0.1 and 4 mg m(-3). RMS difference between the two data sets of monthly mean Chl for several sub-regions was generally < 11% and < 0.05 (after logarithmic transformation). For each individual month, the statistics (mean, mode, median) of the two data sets for the entire study region (6-9 x 10(5) satellite pixels at similar to 1 km resolution) were very similar, with RMS differences typically between 30% and 40% and between 0.10 and 0.15 (after logarithmic transformation), where no significant bias was found. Therefore, it would be possible to continue the time series using only one sensor such as MODIS, in the eventual absence of SeaWiFS. Further research is needed to improve the remote sensing algorithms for application in turbid coastal waters. (c) 2006 Elsevier Inc. All rights reserved.