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.

This study introduces an innovative operational Bayesian neural network framework for high-precision joint retrieval of aerosol optical depth (AOD) and layer height (ALH) with physically-consistent uncertainty decomposition from TROPOMI hyperspectral measurements. Unlike conventional approaches, three different full-physics Bayesian neural network architectures (implemented via Bayes-by-Backprop, Dropout, and Batch Norm techniques) are developed to simultaneously estimate target parameters and their heteroscedastic aleatoric uncertainties while preserving radiative transfer constraints. Epistemic uncertainties are quantified via Monte Carlo sampling of stochastic forward propagation, enabling systematic separation of data-driven vs. model-driven uncertainties. A comprehensive validation demonstrates: (1) Synthetic experiments show epistemic uncertainties strongly correlate with retrieval errors, particularly for observing geometries outside the training data distribution, outperforming aleatoric estimates; (2) Analyses using TROPOMI measurements demonstrate that the framework delivers comparable accuracy to operational products while providing unique uncertainty diagnostics. The framework’s computational efficiency combined with its probabilistic outputs establishes a new paradigm for characterizing aerosol properties from satellite measurements, particularly valuable for climate and air quality applications.

1 Abstract-The cloud water path (CWP) has an important influence on the radiative effects of clouds and the water cycle in the Earth’s atmospheric system, serving as a key parameter in physical cloud processes. In this study, a novel method for retrieving CWP by leveraging the advantages of multisource and multiband active and passive satellite observations is proposed. A retrieval model to retrieve CWP that using Himawari-8/AHI) thermal infrared channels is established by learning from active radar (CloudSat) measurements, the model enables continuous CWP retrieval throughout the day. Compared with all-day CloudSat-CWP, our CWP products has has a higher retrieval accuracy that that of MODIS. The distribution of the monthly average CWP product based on the Himawari-8 full-disk dataset resembles that of CloudSat observations, with the highest average CWPs in equatorial region, followed by the CWPs in midlatitude regions. This spatial pattern of CWP is possibly due to the prevalence of strong convective systems in these areas, which facilitate the formation and progression of deep clouds, leading to higher CWP values. This algorithm can offer valuable data support for atmospheric-related analyses and has been integrated into the Cloud Remote Sensing, Atmospheric Radiation, and Renewable Energy Application (CARE) platform for atmospheric remote sensing algorithms.

Hourly Himawari-8 (H8) Advanced Himawari Imager Level 3 Ocean Color (L3 OC) products have been recently released; however, a thorough evaluation and uncertainty analysis of L3 OC data spanning full disk, as well as applicability to studies on photosynthetically active radiation (PAR) have not yet been conducted. This study evaluates the accuracy of L3 OC products, including normalized water-leaving radiance (Lwn) at 470, 510, and 640 nm, Chlorophyll-a concentration (Chlor-a), aerosol optical thickness (AOT) at 510 nm, and Ångström exponent (AE), by comparing them to ground-based measurements obtained from Ocean Color Component of the AErosol RObotic NETwork (AERONET-OC). Our results demonstrate a general agreement with the ground-based measurements, especially Lwn510. Chlor-a and AOT510 also demonstrate an overall consistency, whereas AE shows a larger discrepancy. Uncertainty analysis shows that Lwn remained accurate under different conditions, although increased uncertainties were observed in turbid water and periods of severe air pollution. Spatial analysis revealed that the distribution of L3 OC and Aqua-MODIS L2 OC products were strongly correlated. Lwn and Chlor-a in the Yellow and Bohai Seas exhibit seasonal variations, with both parameters decreasing in summer and increasing in winter. The impact of aerosols and Chlor-a on PAR calculations was investigated by developing a sophisticated algorithm for estimating PAR under clear-sky conditions using a coupled radiative transfer (RT) model. An analysis of the May 2021 dust event in the Southern Yellow Sea, which exhibited an AOT of 0.82, showed a notable increase in Chlor-a levels —one to two days later, while the average daytime PAR forcing was −42.469 W/m2 under clear-sky conditions.

Remote sensing of cloud properties based on multispectral or hyperspectral observations from satellites is important for earth radiation budget and climate change studies. Currently, most retrieval algorithms for the hyperspectral measurements are developed based on the O2-A band to derive cloud optical thickness (COT) and cloud top height (CTH) via the optimal estimation theory. Nevertheless, there are few studies on the retrieval of COT and CTH using the O4band, where the direct computation of slant column density and spectral information in the blue band provide a faster yet flexible inversion strategy. In this study, we develop a novel cloud retrieval algorithm based on neural networks using the O4band (CRANN-O4) for the simultaneous derivation of COT and CTH. CRANN-O4 employs a transfer learning strategy that combines the radiative transfer model (RTM) and multisource satellite data, for which the deep neural network module is pretrained based on the simulation data from RTM to enhance its adaptability and interpretability, following a fine-tuning scheme using multisource satellite data. To evaluate the CRANN-O4 performance, we apply CRANN-O4 to TROPOMI and make an intercomparison with its official products, which is generated based on the O2-A band. The results indicate that the CRANN-O4-derived spatial distributions of COT and CTH are generally similar to the official TROPOMI cloud product but are more consistent with the SNPP-VIIRS cloud product. The RMSEs of COT and CTH derived by CRANN-O4 are approximately 15.88 and 2.33 km, respectively, while those of the TROPOMI cloud product are 20.85 and 3.00 km, respectively. In addition, the validation of CRANN-O4-derived CTH using CALIOP measurements demonstrates better agreement than that of the TROPOMI official cloud product, with RMSE decreasing from 2.7 km to 2.2 km. The methodology presented in this study provides innovative insight into cloud parameter retrieval for hyperspectral instruments with O4channels, such as FY-3F/OMS.

The derivation of ice water path (IWP) from microwave radiometer measurements is challenging. This study presents a deep learning framework for global retrieval of IWP using observations from the Microwave Humidity Sounder-II (MWHS-II) aboard the FengYun-3D (FY-3D) satellites. Two deep learning models, Deep Forest (DF21) and Quantile Regression Neural Network (QRNN) are constructed to detect ice cloud flags and retrieve IWP. By collocating MWHS-II observations with 2C-ICE, a joint product of CloudSat and CALIPSO, deep learning models learn the characteristics of IWP from MWHS-II brightness temperatures. The test results show that the MWHS-II channels provide more information on IWP than the MWHS channels, particularly the 89 GHz channel and the 118 GHz channels with an offset of ≥ 0.8 GHz. Combining the QRNN and DF21 models, the IWP retrieval results in an RMSE of 707.346 g/m2, MAPE of 65.122%, MBE of -104 g/m2, determination coefficient (R2) of 0.683, and Pearson correlation coefficient (PCC) of 0.831. Application of the models to MWHS-II observations of Tropical Cyclone CILIDA shows better agreement with 2C-ICE. All datasets exhibit a similar feature on the monthly mean scale, but the magnitudes of IWP differ. Compared to GMI-GPROF, MODIS, and ERA5 IWP products, MWHS-II results are closest to 2C-ICE. Similar results are also shown for the zonal mean data. These results show that deep learning methods efficiently and probabilistically retrieve IWP from long-term observation data of MWHS/MWHS-II.

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.

Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties (cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)). The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forests method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients (R) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, wherein the CTT R value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT R value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed herein capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia-Pacific region.

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$5 ~\mu \text {g/m}^{3}$.

Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination). To overcome these shortcomings, a novel linearized subspace analysis technique with spatial-spectral manifold alignment is developed for a semisupervised hyperspectral dimensionality reduction (HDR), called joint and progressive subspace analysis (JPSA). The JPSA learns a high-level, semantically meaningful, joint spatial-spectral feature representation from hyperspectral (HS) data by: 1) jointly learning latent subspaces and a linear classifier to find an effective projection direction favorable for classification; 2) progressively searching several intermediate states of subspaces to approach an optimal mapping from the original space to a potential more discriminative subspace; and 3) spatially and spectrally aligning a manifold structure in each learned latent subspace in order to preserve the same or similar topological property between the compressed data and the original data. A simple but effective classifier, that is, nearest neighbor (NN), is explored as a potential application for validating the algorithm performance of different HDR approaches. Extensive experiments are conducted to demonstrate the superiority and effectiveness of the proposed JPSA on two widely used HS datasets: 1) Indian Pines (92.98%) and 2) the University of Houston (86.09%) in comparison with previous state-of-the-art HDR methods. The demo of this basic work (i.e., ECCV2018) is openly available at https://github.com/danfenghong/ECCV2018_J-Play.

In this article, we developed a hybrid method to estimate surface shortwave radiation (SSR) for the new-generation Himawari-8 geostationary satellite. This hybrid method combines the advantages of a deep neural network (DNN) with high speed and radiative transfer model (RTM) to achieve high accuracy: the RTM provides training data for the DNN under various cloud and aerosol conditions (including heavy aerosol loadings). Moreover, our hybrid method can simultaneously output the byproducts of photosynthetically active radiation (PAR), ultraviolet A (UVA), and Ultraviolet B (UVB), the direct and diffuse components at the surface, and the upward solar radiation at the top-of-atmosphere (TOA). The trained DNN was applied to the Himawari-8 satellite atmospheric products for 2016 and comprehensively validated using a total of 118 stations from four networks located in the full-disk regions of Himawari-8. The results showed an RMSE of 125.9 Wm-2for instantaneous SSR, 105.4 Wm-2for hourly SSR, 31.9 Wm-2for daily SSR, and respective mean bias error (MBE) scores of 8.1, 27.6, and 12.3 Wm-2. The hybrid method developed in this study performed well, achieving high accuracy and high speed, and it is capable of providing near-real-time SSR estimates for many applied energy fields.

Recently, several new generation instruments for remote sensing of the Earth's atmosphere from space and balloon have been launched and planned. The German Aerospace Center (DLR) has engaged in research activities for a number of missions, e.g. GOME/GOME-2 (Global Ozone Monitoring Experiment), Sentinel-4/-5P, and TELIS (TErahertz and submillimeter LImb Sounder). Inverse problems occurring in atmospheric science aim to estimate atmospheric state parameters from these remote sensing data. On the subject of these ill-posed inverse problems, the major challenge concerns the choice of the inversion algorithm balancing up accuracy and efficiency. The objective of this study is to look into the effectiveness of the regularized nonlinear iteration scheme and the neural network-based scheme. Practical implementations pertaining to ozone profiling from spaceborne and balloon-borne measurements are addressed.