Monthly average satellite-estimated dataset of Lake Taihu's dissolved carbon dioxide concentration

In this dataset, we used the MODIS-derived chlorophyll-a concentration, lake surface temperature, diffuse attenuation coefficient of photosynthetically active radiation, and photosynthetically active radiation to estimate daily dissolved CO2 concentrations of Lake Taihu (the coefficient of determination R2=0.84, root mean square error RMSE=11.81 μmol L-1, unbiased percent difference UPD=22.46%). Then the daily CO2 concentrations were averaged on a monthly scale to obtain the monthly average satellite-estimated dataset of Lake Taihu’s dissolved CO2 concentration from July 2002 to December 2018. The uncertainty assessment results of the product show that under the influence of all input variables, the monthly CO2 concentration product would overestimate about 30%. The differences between CO2 concentrations of pixel-sample matchups were small in total (Root mean standard error RMSE = 12.83 μmol L-1, non-bias percentage deviation UPD = 24.03%). The annual average of CO2 concentrations estimated by field observation and MODIS were consistent with each other in different regions of Lake Taihu (Root mean standard error RMSE < 13.24 μmol L-1, non-bias percentage deviation UPD < 25.82%). Based on the monthly average dataset, the CO2 concentrations of Lake Taihu showed significant seasonal dynamics, which were was low in summer and autumn (Jun.-Nov.) and eastern region, and high in winter and spring (Dec.-May) and western region. Besides, the annual average CO2 concentrations showed a significant declining trend (0.80 μmol L-1 yr-1, p <0.01).

This monthly average dataset corresponds to the time scale of traditional limnological and ecological observations, which is suitable for comparison and analysis with traditional field datasets. Besides, the satellite dataset provides more spatial details of dissolved CO2. It is very enlightening for better understanding of the biogeochemical process associated with CO2 in Lake Taihu. We believed this dataset would be very worth promoting to all researchers focusing on Lake Taihu.