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The carbon dioxide (CO2) fertilization effect has captured worldwide attention, owing to its tremendous potential to challenge existing predictions of future climate. However, quantifying the CO2 fertilization effect (CFE) has proven to be challenging, given that it is closely entangled with other ecological and environmental processes. Recent years have witnessed significant advances with breakthroughs using theoretical methods to infer the CFE from eddy covariance tower measurements. Building on earlier findings, this study presents an innovative approach that utilizes explainable machine-learning techniques—describing the partial dependence of the response variable to each explanatory variable—to quantify the global CFE from remote-sensing platforms with an averaged $R ^{2}$ of 0.85. This study provides the first data-driven evidence of the global CFE and confirms the potential for extrapolation to the globe. The findings suggest that: 1) the employment of satellite vegetation proxies contributed to more than 50% of the fitting of gross primary productivity (GPP) and 2) the manifestation of the CO2 fertilization impact demonstrated heterogeneity among various types of ecosystems, and in some cases, an adverse effect was detected in broadleaf forests. Our results have significant implications for the preservation and protection of terrestrial ecosystems, particularly for a carbon-neutral future. This study, therefore, provides a valuable contribution to the growing body of knowledge in this area and highlights the potential of innovative analytical techniques to address complex ecological challenges. [ABSTRACT FROM AUTHOR]

Target tracking is a popular but challenging problem in computer vision field. Due to many aggravating factors such as position transformation, illumination, occlusion, it is difficult to achieve robust target tracking. According to the above constraints, an improved target tracking algorithm based on spatio-temporal context (STC) under occlusions is proposed. On the basis of STC, the proposed method introduces a novel mechanism for dealing with occlusion, the scale update, and the learning rate update to reduce the error update of the model and restrain error accumulation. As a consequence, the tracking performance can be improved efficiently. Extensive experimental results show that our algorithm outperforms the original STC algorithm and some other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) threatens human health and the natural environment. Estimating the near-ground PM2.5 concentrations accurately is of great significance in air quality research. Statistical and deep-learning models are widely used for estimating PM2.5 concentration based on remotely sensed aerosol optical depth (AOD) products. Deep-learning models can effectively express the nonlinear relationship between AOD, parameters, and PM2.5. This study proposed a capsule network model (CapsNet) to address the spatial differences in PM2.5 concentration distribution by introducing a capsule structure and dynamic routing algorithm for the first time, which integrates AOD, surface PM2.5 measurements, and auxiliary variables (e.g., normalized difference vegetation index (NDVI) and meteorological parameters). Moreover, we examined the longitude and latitude of pixels as input parameters to reflect spatial location information, and the results showed that the introduction of longitude (LON) and latitude (LAT) parameters improved the model fitting accuracy. The coefficient of determination (R2) increased by 0.05 ± 0.01, and the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) decreased by 3.30 ± 1.0 μg/m3, 8 ± 3%, and 1.40 ± 0.2 μg/m3, respectively. To verify the accuracy of our proposed CapsNet, the deep neural network (DNN) model was executed. The results indicated that the R2 values of the validation dataset using CapsNet improved by 4 ± 2%, and RMSE, MRE, and MAE decreased by 1.50 ± 0.4 μg/m3, ~5%, and 0.60 ± 0.2 μg/m3, respectively. Finally, the effects of seasons and spatial region on the fitting accuracy were examined separately from 2018 to 2020. With respect to seasons, the model performed more robustly in the cold season. In terms of spatial region, the R2 values exceeded 0.9 in the central-eastern region, while the accuracy was lower in the western and coastal regions. This study proposed the CapsNet model to estimate PM2.5 concentrations for the first time and achieved good accuracy, which could be used for the estimation of other air contaminants. [ABSTRACT FROM AUTHOR]