Improving modeling of ecosystem gross primary productivity through re-optimizing temperature restrictions on photosynthesis

Improving light use efficiency models via the introduction of both the diffuse fraction and radiation scalar

Surface solar radiation, both its components and intensity, is pivotal in determining vegetation light use efficiency (LUE) and is essential for accurately estimating gross primary production (GPP) in ecosystems. This study introduces two key parameters: the diffuse photosynthetic photon flux density fraction (fdPPFD) to account for the diffuse fertilization effect (DFE) and the radiation scalar to reflect the impact of radiation intensity on leaf-level LUE. Leveraging these parameters, we developed two novel LUE models: the Big-leaf Diffuse-fraction Radiation-scalar LUE (BDR-LUE) model, adapted from traditional big-leaf LUE models, and the Two-leaf Diffuse-fraction Radiation-scalar LUE (TDR-LUE) model, based on conventional two-leaf LUE frameworks. These models were calibrated and validated using data from 32 FLUXNET sites representing six vegetation types with available diffuse PPFD measurements. The results reveal the following key findings: (1) Both new models, particularly the TDR-LUE model, deliver superior performance compared to conventional big-leaf and two-leaf LUE models; (2) The BDR-LUE model reduces the root mean square error (RMSE) by at least 12.75 % compared to the conventional Eddy Covariance-LUE (EC-LUE) model; (3) The TDR-LUE model achieves an RMSE reduction of at least 13.54 % compared to the established Two-Leaf LUE (TL-LUE) model; (4) Both models effectively capture annual and monthly variations in vegetation GPP; (5) While the BDR-LUE model outperforms the TDR-LUE model for croplands, it shows lower accuracy for other vegetation types. These findings underscore the importance of integrating fdPPFD and the radiation scalar into LUE models. The proposed models demonstrate substantial potential for enhancing GPP estimates in terrestrial ecosystems.

Widespread decline in winds promoted the growth of vegetation

Vegetation dynamics are sensitive to climate change. Wind is an important climate factor that can affect carbon fluxes by altering carbon uptake and emission rates; however, the impact of wind has not been fully considered in previous studies; therefore, exploring the characteristics of vegetation responses to wind speed is crucial to sustainable natural resource utilization and ecological restoration. In this study, the global leaf area index (LAI) from 1984 to 2013 was used to investigate the vegetation spatial heterogeneities, change processes, and relative contributions of climate change. The differences in vegetation responses to climate factors, such as precipitation (PRE), temperature (TEM), and wind speed (WD), were compared by considering the effects of wind. The results revealed that (1) the global vegetation (86.24%) exhibited a greening trend, among which evergreen broad-leaved forests (0.0052 a^-1) changed the most. (2) The wind speed explained 31.54% of the vegetation variations, which is higher than the contribution of other factors. (3) Reduction of wind speed had a positive impact on vegetation changes. The contribution of climate to vegetation growth increased by 8.14% when considering the effects wind speed, particularly in India and South America. Wind speed effects were essential for enhancing the vegetation dynamics assessment and improving the prediction accuracy of the model.

Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product

Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPPMOD) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPPMOD, 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 (GPPRF) agreed well with the eddy covariance (EC)-derived GPP (GPPEC), 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 GPPEC to regional scales through the RF model. The upscaled cumulative seasonal GPPRF was higher for rice (924 g C m−2) than that for wheat (532 g C m−2). By comparing GPPMOD and GPPEC, we found that GPPMOD performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPPMOD was calibrated by GPPRF, and the error range of GPPMOD (GPPRF minus GPPMOD) 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.