Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPP_MOD) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPP_MOD, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPP_RF) agreed well with the eddy covariance (EC)-derived GPP (GPP_EC), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m⁻² d⁻¹. Therefore, it was deemed reliable to upscale GPP_EC to regional scales through the RF model. The upscaled cumulative seasonal GPP_RF was higher for rice (924 g C m⁻²) than that for wheat (532 g C m⁻²). By comparing GPP_MOD and GPP_EC, we found that GPP_MOD performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPP_MOD was calibrated by GPP_RF, and the error range of GPP_MOD (GPP_RF minus GPP_MOD) was found to be 2.5–3.25 g C m⁻² d⁻¹ for rice and 0.75–1.25 g C m⁻² d⁻¹ for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.

The drag coefficient is essential for calculating the aerodynamic friction between air and sea. In this study, we regress a set of relationships between the drag coefficient and the wind speed for different wind ranges using an observational dataset that consists of 5941 estimates of the mean flow and fluxes from 11 aircraft turbulent measurements over the sea surface. Results show that: (1) the drag coefficient is a power function of wind speed over smooth sea surface when it is no greater than 4.5 ms⁻¹, and the drag coefficient decreases with the increase of wind speed; and (2) for rough sea surface, when the wind speed is greater than 4.5 ms⁻¹ and less than or equal to 10.5 ms⁻¹, the drag coefficient increases linearly with the increase of horizontal wind speed; when the wind speed is greater than 10.5 ms⁻¹ and less than or equal to 33.5 ms⁻¹, the drag coefficient changes parabolically with the increase of wind speed; when the wind speed is greater than 33.5 ms⁻¹, the drag coefficient is constant. Additionally, regressed from drag coefficient, the saturated wind speed threshold is 23 ms⁻¹. Parameterizations of turbulent heat transfer coefficient ($C_h$) and water vapor transfer coefficient ($C_e$) are also investigated.