The hybrid nature of the mid-infrared (MIR) spectrum complicates the separation of reflected solar irradiance from total energy. Consequently, existing studies rarely use MIR satellite data alone for retrieving land surface temperature (LST) and land surface emissivity (LSE). In this study, we developed What we believe to be a novel physics-based approach to retrieve LSE and LST using MIR channel data from the MEdium Resolution Spectral Imager II (MERSI-II) onboard China’s new-generation polar-orbiting meteorological satellite Fengyun-3D (FY-3D). MERSI-II includes two MIR channels (channels 20 and 21) with a spatial resolution of 1 km, suitable for applying the split-window (SW) algorithm. First, considering the unequal but linearly related land surface bidirectional reflectivity (LSR) in channels 20 and 21, we propose an improved nonlinear SW algorithm. This algorithm, combined with the radiative transfer equation (RTE), accurately retrieves LSR from MIR data. Second, using a kernel-driven bidirectional reflectance distribution function (BRDF) model, the RossThick-LiSparse-R model, we estimate hemispherical directional reflectance from the time series of LSRs (10 days) and subsequently retrieve LSE based on Kirchhoff's law. Atmospheric correction is performed using ERA-5 atmospheric reanalysis data with the radiative transfer (RT) code (MODTRAN 5.2). Finally, LST is retrieved using the RTE in the MIR spectral region. The retrieved LSR was compared with those fitted using the BRDF model, yielding a root mean square error (RMSE) < 0.006 and a bias < 0.003. Cross-validation using the MODIS LSE and LST products (MYD11C1) as a reference showed that the RMSE of the retrieved LSE over 10 days was < 0.027 with a bias < 0.023. For the retrieved LST, the RMSE was < 1.8 K with a bias < 0.7 K. Overall, the proposed method demonstrates potential for retrieving global LSE and LST from MERSI-II MIR data, contributing to advancements in related applications.

Large-scale and high spatiotemporal resolution sea surface temperature (SST) products can provide important support for monitoring dynamic changes in the marine environment, energy development, and assimilation models. We develop an algorithm to obtain the high spatiotemporal resolution SST product by fusion of the high spatial resolution SST product from the Medium Resolution Spectral Imager (MERSI) on the FY-3E satellite and the high temporal SST product from the Advanced Geosynchronous Radiation Imager (AGRI) on the FY-4A satellite. During data preprocessing, MERSI products have large data volumes stored in chunks, we use the multicore computer for batch re-projection to generate the matrix of temperature values and satellite observation times. The matrix has high spatial coverage and remove the anomalous data, increasing the stability of the algorithm. AGRI data has gaps due to cloud cover, we build a pixel-by-pixel linear fitting model to extract more valid change information from multi-hours data, which also improves the coverage of fusion results. During the fusion process, we calculate the spectral and temporal weighting factors in real time, divide data into regular blocks for multi-cores fusion to get 1km/1hour sea surface temperature fusion product covering most areas of the Eastern Hemisphere. Using Argo buoy data to verify the accuracy of the results for 15 days, the root mean square error is 1.089°C and the average deviation is 0.867°C. Multi-core parallel computing makes algorithm fast and efficient. The spatiotemporal resolution of results is improved significantly and the effect of missing original data of the results is reduced.

China’s new generation of polar-orbiting meteorological satellite, Fengyun-3D (FY-3D), is equipped with the MEdium Resolution Spectral Imager II (MERSI-II) sensor, which greatly enhances Chinese capacity for comprehensive space environment detection and meteorological remote sensing. MERSI-II has two mid-infrared (MIR) channels with the potential to apply split-window (SW) algorithms. We have developed an improved nonlinear SW (NSW) algorithm to retrieve the ground brightness temperature T⁰_gb without the contribution of the solar direct beam from MERSI-II MIR data, enabling the accurate retrieval of surface bidirectional reflectivity ρ_b based on the radiative transfer equation (RTE). Compared with the traditional estimation method, this paper assumed a linear relationship between ρ_b of MIR channels 20 and 21, and believed that under dry-cool atmospheric conditions, T⁰_gb is not equal. Considering different solar zenith angles (SZAs), by iterating the combination of atmospheric and surface parameters under reasonable variations, the MODerate spectral resolution atmospheric TRANsmittance (MODTRAN) model version 5.2 was used to obtain the simulation datasets for determining the NSW coefficients. In the practical retrieval, the precise algorithm coefficients are only relevant to SZA. The R² fitted by the NSW algorithm is 0.99, and the root mean square error (RMSE) is less than 0.58 K for different SZAs. Under different SZAs, comparing the actual value of ρ_b with the proposed method, the bias is less than 1.57×10^-3, and the RMSE is less than 2.21×10^-3. A detailed sensitivity analysis found that the errors in atmospheric water vapor content (WVC), CO₂ concentrations, O₃ concentrations, horizontal visibilities, and instrumental noise were below the order of 10-4, and their effects on the retrieval of ρ_b were negligible. This study employed two methods to validate T⁰_gb, thereby indirectly validating ρ_b. First, the MODIS land surface emissivity (LSE) product MYD11C1 and the ERA5-Land land surface temperature (LST) product were used to validate T⁰_gb, yielding a bias of 0.9 K and an RMSE of 1.13 K. Second, in situ measurements from the SURFRAD sites were used to validate T⁰_gb, resulting in a bias of 0.74 K and an RMSE of 1.83 K. Overall, results showed that the proposed algorithm has good accuracy and can improve their applications in relevant fields.

Monitoring aerosols over the oceans is critical for understanding Earth’s climate and air quality. Although polarization can substantially reduce uncertainty in aerosol retrievals, current algorithms rely mainly on multi-view polarimeters, and no dedicated algorithm is available for single-view polarimeters over the ocean. Here, we present the first ocean algorithm for a spaceborne single-view polarimeter, demonstrated with the Particulate Observing Scanning Polarimeter (POSP) onboard the GF-5(02) satellite. Our algorithm combines multi-spectral polarization with machine-learning-accelerated radiative transfer calculation and seasonally clustered global aerosol models. Validation with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data demonstrates high accuracy, with RMSEs of 0.061, 0.479, and 0.037 for AOD₅₅₀, AE_670-870, and SSA₅₅₀ using AERONET, and 0.030 and 0.259 for AOD₅₅₀ and AE_670-870 using MAN, respectively. Comparison with retrievals from the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm confirms that our algorithm performs comparably to GRASP products. These results underscore the necessity and feasibility of developing specialized aerosol retrieval algorithms for single-view polarimeters, and pave the way for global aerosol over the ocean monitoring.

Ozone pollution suppresses plant photosynthesis and reduces crop yields, yet many global assessments still rely on indicators or models that are difficult to compare across regions. We propose a concise and scalable remote sensing index, called Ozone Vegetation Exposure Intensity ( VEIO₃ ), that combines near-surface ozone concentrations with the Normalized Difference Vegetation Index (NDVI) to characterize the spatiotemporal distribution of vegetation ozone exposure. Using global observations from 2003 to 2019, we find pronounced spatial and seasonal contrasts, with higher exposure in industrialized and densely vegetated regions, particularly during spring and summer in the Northern Hemisphere. VEIO₃ is consistently associated with independent vegetation metrics: regions of higher ozone exposure generally correspond to lower Solar-Induced Chlorophyll Fluorescence (SIF), indicating ozone-induced stress on vegetation activity. In site-level evaluations using the Community Land Model (CLM5.0) and FLUXNET eddy-covariance data, we identify a new potential critical ozone concentration of 42.38 ppb, which yields improved performance over the traditional AOT40 (Accumulated exposure Over a Threshold of 40 ppb) in broad, non-cropland contexts – suggesting it can complement existing ozone exposure indices. VEIO₃ can be operationalized for ozone risk management: for example, by screening hotspots to prioritize field inspections and expand monitoring networks; by serving alongside AOT40 as a metric for evaluating seasonal mitigation plans and issuing alerts during peak growing seasons; and by coupling with land-use and crop distribution data to support emission-control planning and scenario analysis. Key limitations of our current approach include reliance on monthly ozone and NDVI products (with uneven monitoring coverage), a predominantly linear statistical framework that does not capture time lags or nonlinear dose–response behavior, and the absence of explicit stomatal-flux representation. Priorities for future work include controlled experimental validation, integration of VEIO₃ with stomatal flux schemes in land-surface models, expansion of ground observations in data-sparse regions, and testing of alternative vegetation indices with stricter cloud and reflectance screening. VEIO₃ represents a new remote sensing index for detecting vegetation ozone exposure. The critical levels and model-improvement directions inferred here should be treated as potential references that, with further validation and refinement, can help bridge the gap between observations, models, and policy, ultimately providing a reliable remote-sensing-based decision support tool for managing risks to ecosystem productivity and food security.

Spaceborne hyperspectral imaging spectrometers enable refined retrieval and quantification of methane point-source emissions. However, the conventional matched filter (MF) systematically underestimates methane enhancements under high-concentration conditions and remains sensitive to spectral inconsistencies across varying observation scenarios. To address these limitations, we improve MF-based retrieval from two aspects: the observation model and the unit absorption spectrum (UAS) representation. First, a Levenberg–Marquardt matched filter (LMMF) is developed by extending the MF framework to a nonlinear retrieval formulation while retaining its data-driven and background-statistics-based characteristics. Specifically, the exponential absorption term is preserved, and methane enhancement is iteratively solved in the nonlinear domain, enabling a more physically consistent retrieval without requiring precise external prior knowledge. Building upon this framework, a spectrally corrected LMMF (SC-LMMF) is further proposed by introducing a lookup-table-based dynamic UAS correction to account for variations in observation geometry, surface elevation, and atmospheric state. Comprehensive validation using idealized and noise-perturbed simulations, end-to-end simulations, and controlled-release experiments demonstrates that the LMMF mitigates high-concentration underestimation relative to the MF. The SC-LMMF further reduces cross-scene systematic biases, shifting retrievals toward a near 1:1 relationship. In controlled-release experiments, the SC-LMMF increased the coefficient of determination (R²) by approximately 50% while reducing the root mean square error (RMSE) and mean absolute error (MAE) by approximately 70% relative to the MF. Overall, the proposed framework enhances the robustness and quantitative consistency of methane point-source retrievals across multisource hyperspectral satellite observations.