The eddy covariance method is widely used to investigate fluxes of energy, water, and carbon dioxide at landscape scales, providing important information on how ecological systems function. Flux measurements quantify ecosystem responses to environmental perturbations and management strategies, including nature‐based climate‐change mitigation measures. However, due to the high cost of conventional instrumentation, most eddy covariance studies employ a single system, limiting spatial representation to the flux footprint. Insufficient replication may be limiting our understanding of ecosystem behavior. To address this limitation, we deployed eight lower‐cost eddy covariance systems in two clusters around two conventional eddy covariance systems in the Chihuahuan Desert of North America for a period of 2 years. These dryland settings characterized by large temperature variations and relatively low carbon dioxide fluxes represented a challenging setting for eddy covariance. We found very good closure of energy and water balance across all systems (within ±9% of unity). We found very good correspondence between the lower‐cost and conventional systems' fluxes of sensible heat (with concordance correlation coefficient (CCC) of ≥0.87), latent energy (evapotranspiration; CCC ≥ 0.89), and useful correspondence in the net ecosystem exchange ((NEE); with CCC ≥ 0.4) at the daily temporal resolution. Relative to the conventional systems, the low‐frequency systems were characterized by a higher level of random error, particularly in the NEE fluxes. Lower‐cost systems can enable wider deployment affording better replication and sampling of spatiotemporal variability at the expense of greater measurement noise that might be limiting for certain applications. Replicated eddy covariance observations may be useful when addressing gaps in the existing monitoring of critical and underrepresented ecosystems and for measuring areas larger than a single flux footprint.

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 (PM _2.5 , PM ₁₀ , O ₃ , NO ₂ , SO ₂ , and CO), we propose a robust estimation framework called Learning Air Pollutants from Satellite Observations (LAPSO). The principle of LAPSO is to derive a non-linear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyses based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models, lower hardware requirements as well as 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 (R ² > 0.8 in polluted areas and uncertainty ≪ 5 μg/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, TROPOMI onboard the Sentinel-5P demonstrated marginally better performance for estimating PM _2.5 . Although the selection of satellite observations did not significantly affect the results of O ₃ estimation, the number and spatial sampling density of in-situ sites imposed large impacts on the O ₃ 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.

Continuously monitoring surface ozone (O ₃ ) 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 O ₃ estimation 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 O ₃ concentration by learning spatial patterns and a long short-term memory (LSTM) model to forecast O ₃ concentration by learning data from the past. We used sites of city-level in situ networks as the control sites to manifest short-distance O ₃ transportation. Satellite-based observations of O ₃ precursor indicators were incorporated to capture O ₃ photochemical reactions. DF21 explained a larger fraction of O ₃ variability (90%) with a mean bias error (MBE) of smaller than $1~\mu \text {g/m}^{3}$ . 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 ( $R^{2} > 0.85$ and bias $< 2~\mu \text {g/m}^{3}$ ). The forecast O ₃ concentration via LSTM showed a good and stable agreement ( $R^{2}\approx 0.85$ and bias $< 5~\mu \text {g/m}^{3}$ ) with ground-based measurements for 8-, 24-, 28-, and 72-h time periods, respectively. Overall, LESO aims to bring convenient functionality and reliable surface O ₃ estimates for broad users.

Quantifying spatiotemporal polar ozone changes can promote our understanding of global stratospheric ozone depletion, polar ozone-related chemical processes, and atmospheric dynamics. By means of ground-level measurements, satellite observations, and re-analyzed meteorology, the global spatial and temporal distribution characteristics of the total column ozone (TCO) and ozone profile can be quantitatively described. In this study, we evaluated the ozone datasets from CrIS/NOAA20, AIRS/Aqua, and ERA5/ECWMF for their performance in polar regions in 2020, along with the in situ observations of the Dobson, Brewer, and ozonesonde instruments, which are regarded as benchmarks. The results showed that the ERA5 reanalysis ozone field had good consistency with the ground observations (R > 0.95) and indicated whether the TCO or ozone profile was less affected by the site location. In contrast, both CrIS and AIRS could capture the ozone loss process resulting from the Antarctic/Arctic ozone hole at a monthly scale, but their ability to characterize the Arctic ozone hole was weaker than in the Antarctic. Specifically, the TCO values derived from AIRS were apparently higher in March 2020 than those of ERA5, which made it difficult to assess the area and depth of the ozone hole during this period. Moreover, the pattern of CrIS TCO was abnormal and tended to deviate from the pattern that characterized ERA5 and AIRS at the Alert site during the Arctic ozone loss process in 2020, which demonstrates that CrIS ozone products have limited applicability at this ground site. Furthermore, the validation of the ozone profile shows that AIRS and CrIS do not have good vertical representation in the polar regions and are not able to characterize the location and depth of ozone depletion. Overall, the results reveal the shortcomings of the ozone profiles derived from AIRS and CrIS observations and the reliability of the ERA5 reanalysis ozone field in polar applications. A more suitable prior method and detection sensitivity improvement on CrIS and AIRS ozone products would improve their reliability and applicability in polar regions.

To enable an evidence-based management of ecosystems to adapt to the climate crisis, we require fine spatiotemporal resolution estimates of carbon, water, and energy fluxes at the field scale. To overcome the limitations resulting from the coarse spatial resolution of existing flux products, e.g. 500 m ( Running et al., 2015 ), and the challenges in matching eddy covariance (EC) footprints with land use field scales, we for the first time investigate the influence of satellite resolution on flux estimation, which is to support the fine-scale extrapolation of EC fluxes from the tower footprint to the field scale. We validate the extrapolation at 206 FLUXNET2015 tower sites to pave the way for estimating field-scale fluxes extrapolated from three towers in a managed European grazing pasture on a fine-scale, 30 m spatial resolution. The findings suggest that (a) tower-level flux estimates from 30 m satellites were in agreement with fluxes estimated from moderate-resolution satellites, which are extensively employed in literature (R² difference ≪ 0.1); (b) flux estimates were in reasonable agreement with EC measurements (R²: 0.7 and annual bias < 2 Mg ha⁻¹ yr⁻¹ for carbon fluxes); (c) Sentinel-2 was advantageous in capturing land-use variability over other satellites in European pastures; (d) the machine-learning extrapolation algorithm was resistant to livestock grazing.