Gross primary productivity (GPP), the amount of carbon plants fixed by photosynthesis, is pivotal for understanding the global carbon cycle and ecosystem functioning. Process-based models built on the knowledge of ecological processes are susceptible to biases stemming from their assumptions and approximations. These limitations potentially result in considerable uncertainties in global GPP estimation, which may pose significant challenges to our net zero goals. This study presents UFLUX v2.0, a process-informed model that integrates state-of-the-art ecological knowledge and advanced machine learning (ML) technique to reduce uncertainties in GPP estimation by learning the biases between process-based models and eddy covariance (EC) measurements. In our findings, UFLUX v2.0 demonstrated a substantial improvement in model accuracy, achieving an$R {^{{2}}}$of 0.79 with a reduced RMSE of 1.60 g$\cdot $Cm−2d−1, compared to the process-based model’s$R {^{{2}}}$of 0.51 and RMSE of 3.09 g$\cdot $Cm−2d−1. Our global GPP distribution analysis indicates that while UFLUX v2.0 and the process-based model achieved similar global total GPP (137.47 and 132.23 PgC, respectively), they exhibited large differences in spatial distribution, particularly in latitudinal gradients. These differences are very likely due to systematic biases in the process-based model and differing sensitivities to climate and environmental conditions. This study offers improved adaptability for GPP modeling across diverse ecosystems and further enhances our understanding of global carbon cycles and its responses to environmental changes.

In light of climate change, scaling up in situ eddy covariance (EC) fluxes with Earth observation data has been recognized as a viable strategy for estimating the global terrestrial ecosystem carbon uptake, specifically, gross primary productivity (GPP). Nevertheless, the significant uncertainty in estimation (100–150 PgCyr-1) necessitates the refinement of upscaling algorithms and the use of appropriate satellite data. This technological advancement is particularly sought after in underprivileged regions that are most susceptible to climate crises. Unfortunately, these regions are often constrained by insufficient financial resources and software engineering skills shortages. This study aims to evaluate satellite vegetation proxies [solar-induced fluorescence (SIF); near-infrared reflectance of vegetation (NIRv)] for upscaling GPP and to propose a cost-effective GPP estimation framework called unified FLUXes-GPP (UFLUX-GPP), which can be conveniently operated on a laptop while delivering outstanding performance. The results demonstrated that moderate resolution imaging spectroradiometer (MODIS) NIRv and OCO-2 CSIF exhibited superior performance in the upscaling of EC GPP, with a coefficient of determination ($R^{2}$) of 0.86 and a root mean square error (RMSE) of 1.55 gCm-2d-1. The integration of multiple satellite-derived vegetation proxies holds the potential to enhance the reliability of the model ($R^{2} =0.89$, RMSE =1.41 gCm-2d-1) with an uncertainty of 8 PgCyr-1, especially in tropical and polar regions. The UFLUX-GPP effectively preserved the ecological responses of GPP to the environment and showed promising potential for predicting future GPP. Although the spatiotemporal density of EC towers may occasionally impede the upscaling performance, UFLUX-GPP can convincingly advance a broader use of satellite remote sensing for GPP estimation.

The carbon dioxide (CO2) fertilisation effect has captured worldwide attention, owing to its tremendous potential to challenge existing predictions of future climate. However, quantifying the CO2fertilisation effect 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 CO2fertilisation effect from eddy covariance tower measurements. Building on earlier findings, this study presents an innovative approach that utilises explainable machine learning techniques — describing the partial dependence of the response variable to each explanatory variable — to quantify the global CO2fertilisation effect from remote sensing platforms with an averaged R2of 0.85. This study provides the first data-driven evidence of the global CO2fertilisation effect 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 CO2fertilisation 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 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.

Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ($\text{R}^{2}>$0.8 in polluted areas and uncertainty$\ll 5~\mu \text{g}/\text{m}^{3}$for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.

In contrast to the threats to urban human health, impacts of air pollutants on the ecosystem photosynthesis seem to be less concerned. The existence of aerosols could promote photosynthesis by increasing the ratio of diffuse to direct solar radiation; on the contrary, ozone (O3) could inhibit photosynthesis, as it is detrimental to leaf stomata. However, it is unknown whether these two opposite impacts worldwide cancel each other out. In the current mainstream methods, earth system models may show conflicts within situexperimental results due to their relatively coarse resolution. In virtue of satellite remote sensing and a global eddy covariance (EC) network, we studied ten years of data to explore the impacts of aerosol and O3on photosynthesis by fitting an explainable machine learning model. The impacts of aerosol on gross primary productivity (GPP) were positive in many cases, yet very weak. By means of the nitrogen dioxide (NO2) to formaldehyde (HCHO) ratio, O3was seen with positive impacts on photosynthesis under the NOx-sensitive regime, but the apparent positive impacts correlated with the plant phenology. Under the volatile organic compound (VOC)-sensitive regime, the impacts of O3on GPP were not obvious, which was likely due to the prioritized depletion of O3by NO2and VOCs. The impacts of air pollutants depended on many factors and results varied case by case, but the overall net impacts were negative.

High-levels of surface ozone (O3) pollution threaten human and environmental health. Chongqing, a mountainous municipality located in southwest China, is exposed to serious O3 pollution and requires more studies. Due to its complex terrain and always foggy weather, it is difficult to maintain many in-situ sites in Chongqing, and Chemical Transportation Model (CTM) simulations are also challenged. The recently launched (in 2017) Sentinel-5p satellite provides O3 columns with advanced spatiotemporal resolution. Without the dependence on CTMs, we linked O3 columns and surface monitoring data from 2019 to 2021 in virtue of a deep forest machine-learning model. Compared with another widely used machine-learning model and previous studies, our results showed great advantages in estimating surface O3 on a daily scale. Validated against in-situ sites in Chongqing, averaged R2 of cross-validations reached 0.9 while the root mean squared error (RMSE) and mean bias error (MBE) were 13.57 and 0.37 μg/m3. We found out that the model performance is associated with relative height difference between training sites and the test site. The model performed stably when the height difference was lower than 200 m, but obvious performance degradation was seen when the height difference exceeding 400 m.

Continuously monitoring surface ozone (O3) spatial distribution and forecasting its variations are beneficial to improving air quality and ensuring public health in China, although achieving this goal faces challenges from currently available observations and retrieval techniques. Hence, we introduce a coupled surface O3estimation framework (LESO) to address these challenges by integrating ground-level observing networks and satellite remote sensing. LESO features easy-to-use deep learning algorithms, independence on chemical transportation models (CTMs), and consistent performance using data from different satellites. LESO includes a Deep Forest 21 (DF21) model to interpolate O3concentration by learning spatial patterns and a Long Short-Term Memory (LSTM) model to forecast O3concentration by learning data from the past. We used sites of city-levelin-situnetworks as the control sites to manifest short-distance O3transportation. Satellite-based observations of O3precursor indicators were incorporated to capture O3photochemical reactions. DF21 explained a larger fraction of O3variability (90 %) with a mean bias error of smaller than 1 μg/m3. We also investigated the impact of the number of training sites on the DF21 performance, which suggested that five training sites could ensure a good DF21 performance for the most areas (R2> 0.85 and bias < 2 μg/m3). The forecasted O3concentration via LSTM showed a good and stable agreement (R2≈ 0.85 and bias < 5 μg/m3) with ground-based measurements for 8-hour, 24-hour, 28-hour, and 72-hour time periods, respectively. Overall, LESO aims to bring convenient functionality and reliable surface O3estimates for broad users.

Continuous spatial knowledge is required to control the regional ozone pollution. Measurements from ground-level sites are beneficial to this goal, but their number is limited due to the huge expenses of site establishment, operation, and maintenance. Remote sensing seems a promising data source, but its application is challenged by bad weather conditions. Always covered by thick clouds, Chongqing, a populated industrial city in west China, is facing serious ozone pollution, but relevant studies here are relatively insufficient. Another alternative is estimating ozone by models. Well-performed models degrade in Chongqing partially due to the very complex terrain. Modeled hourly ozone does not agree with ground-level measurements. Therefore, an optimization approach is proposed to improve model estimates for such regions. This approach integrates the ground-level information (e.g., measured ozone and meteorology) through the employment of ResNet (Residual Network). ResNet overcomes the notorious vanishing gradient issue in classic neural networks, and the ability of learning complex systems is largely boosted. Ozone distribution is like a gray image that varies every second, which is not the case usually learned by ResNet. A color-image alike data structure is raised to address this “nonstill image” problem; according to the Taylor Expansion, polynomials can describe a complex system, and the errors are acceptable. To facilitate the usage in business operations, this approach is designed to be robust, inexpensive, and easy to use. The scheme of control site selection is discussed in detail. In cross-validations, this approach performs well, averaged$R^{2}$is higher than 0.9 and the error is less than$5 ~\mu \text {g/m}^{3}$.