This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, Recall, and F1-score, showed that our extraction method can be applied to the images of most satellites and different environments. We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April. The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide, and uncertainty for the predicted growth curve was estimated. The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15, respectively, and the accumulative cover area was expected to be approximately 1190.90–1191.21 km².

We found that significant errors occurred when diurnal data instead of diurnal–nocturnal data were used to calculate the daily sea-air CO₂ flux (F). As the errors were mainly associated with the partial pressure of CO₂ in seawater (pCO_2w) and the sea surface temperature (SST) in the control experiment, pCO_2w and SST equations were established, which are called the nocturnal effect of the CO₂ flux. The root-mean-square error between the real daily CO₂ flux (F_real) and the daily CO₂ flux corrected for the nocturnal effect (F_com) was 11.93 mmol m⁻² d⁻¹, which was significantly lower than that between the F_real value and the diurnal CO₂ flux (F_day) (46.32 mmol m⁻² d⁻¹). Thus, the errors associated with using diurnal data to calculate the CO₂ flux can be reduced by accounting for the nocturnal effect. The mean global daily CO₂ flux estimated based on the nocturnal effect and the sub-regional pCO_2w algorithm (cor_F_com) was −6.86 mol m⁻² y⁻¹ (September 2020–August 2021), which was greater by 0.75 mol m⁻² y⁻¹ than that based solely on the sub-regional pCO_2w algorithm (day_F_com = −7.61 mol m⁻² y⁻¹). That is, compared with cor_F_com, the global day_F_com value overestimated the CO₂ sink of the global ocean by 10.89%.

We believe that several sentences in the description of the source (sink) changes of CO₂ are prone to ambiguity and are not particularly well presented