The accurate analysis of temporal and spatial variation characteristics is significant for understanding the marine ecological system. Owing to the comprehensive effects of physical environment and biogeochemical effects in the South China Sea

chlorophyll distribution is characterized by a complicated

multi-temporal

and spatial scale. The South China Sea is often covered by clouds; especially in summer and autumn

cloud coverage is up to 80%. This characteristic causes the low coverage of optical sensor data in the South China Sea. Single optical sensor data cannot meet the demand of the study on the temporal and spatial characteristics of chlorophyll. The present study evaluates the performance of different merging methods with the chlorophyll data products of MODIS-aqua

MODIS-terra

and MERIS in the South China Sea. Long-term

continuous

and high-quality chlorophyll-a data are also provided for the study on the changes in ecological environment and biogeochemical cycle.Three methods of ocean color data merging were used on the data products of MODIS-aqua

MODIS-terra

and MERIS to obtain the chlorophyll distribution in the South China Sea. The performance of the three merging methods was evaluated with in situ match-up data. An empirical inversion algorithm of the chlorophyll concentration was developed with the in situ measurements in the South China Sea. The algorithm was applied to retrieve the chlorophyll concentration from MODIS-aqua

MODIS-terra

and MERIS data. The three merging methods of averaging

bio-optical model

and optimal interpolation were used on the chlorophyll data from the three ocean color sensors in the South China Sea. The merging results were assessed with the in situ measurements and the previously known knowledge.Merging products from the three merging methods have good consistency with the in situ match-up data. The accuracies of the merging products from the three methods are obviously different. Coverage of the merging data is significantly improved for all the three methods. Coverage of the data from averaging method and bio-optical model is similar

and the average of optimal interpolation is up to 100%. The running time of the three methods present a significant difference; the running times of averaging method and bio-optical model are similar

and are both approximately 40 times faster than optimal interpolation. Distributions of monthly chlorophyll products merged with MODIS-aqua

MODIS-terra

and MERIS from the three merging methods are in good agreement with previous studies.Coverage of the merged data is greatly increased

and the merged data have high reliability. The three merging methods have different performances. Bio-optical model has high running speed

while optimal interpolation has high coverage but low running speed. Averaging method and bio-optical model can keep considerable detailed information. In practice

the selection of merging method should depend on actual applications.