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

In this dataset, we used the MODIS-derived chlorophyll-<em>a</em> 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 <em>R</em>²=0.84, root mean square error<em> RMSE</em>=11.81 μmol L^-1, unbiased percent difference<em> UPD</em>=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 <em>RMSE</em> = 12.83 μmol L^-1, non-bias percentage deviation <em>UPD</em> = 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 <em>RMSE</em> &lt; 13.24 μmol L^-1, non-bias percentage deviation <em>UPD</em> &lt; 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, <em>p</em> &lt;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.