State Key Laboratory of Remote Sensing Science

Abstract Context Climate change is increasing the frequency and severity of droughts worldwide, with profound consequences for forest ecosystems. Although previous studies suggest that taller forests are more vulnerable during drought, their capacity to recover following drought remains poorly understood, creating substantial uncertainty for forest management. Objectives This study aimed to quantify how canopy height influences post-drought forest recovery at a regional scale. Methods We investigated evergreen broad-leaved forests in southwest China following a severe drought in 2010. We used the MODIS Enhanced Vegetation Index (EVI) to derive forest recovery metrics, the Standardized Precipitation Evapotranspiration Index (SPEI) to classify drought severity, and a LiDAR-derived canopy height map. We then used linear regression and ANOVA to evaluate relationships between forest recovery and canopy height. Results Short-term (1 year) recovery increased with the intensity of the preceding drought. Under severe and extreme drought conditions, recovery increased significantly with canopy height ( p < 0.001). In contrast, long-term (3 years) recovery did not differ significantly among canopy height classes. Conclusions Our results indicated that although tall forests may experience greater drought impacts, they exhibit stronger short-term recovery, resulting in comparable long-term resilience across canopy-height classes. This size-dependent recovery dynamic is a previously underappreciated mechanism that should be incorporated into forest dynamics models and climate-adaptive management strategies.

Large-scale agricultural remote sensing monitoring is challenged by pronounced spatial heterogeneity arising from fragmented terrain, complex climatic backgrounds, and diverse cropping structures. However, existing agricultural zoning schemes generally lack an integrated consideration of remote sensing imaging mechanisms and key variable conditions such as atmospheric interference and crop phenology, limiting their direct utility in guiding region-specific sensor selection and classification algorithm calibration. To address this limitation, this study integrates multi-source earth observation data and agricultural statistical information to construct an Agricultural Remote-sensing Classification Difficulty Index (ARCDI) from multiple dimensions, including image availability, cropping structure, cropland fragmentation, and topographic environment. On this basis, a graph theory-based spatially constrained Skater clustering algorithm is introduced to establish a two-tier “cropland–major cereal crops” zoning framework oriented toward remote sensing applications. The results indicate that the proposed framework delineates five distinct first-tier cropland classification difficulty zones across China. This zoning scheme effectively quantifies the regional heterogeneities in monitoring challenges. Building upon this first-tier zoning, the framework is further refined into 50 second-tier major cereal crop classification difficulty zones, including 13 winter wheat zones, 21 maize zones, and 16 rice zones. Statistical tests and spatial analyses demonstrate that the proposed zoning scheme significantly outperforms conventional clustering approaches in balancing within-zone homogeneity and spatial continuity. This advantage is quantitatively reflected by consistently lower residual spatial autocorrelation (residual Moran’s I ≈ 0.10–0.11) and an approximately 20% reduction in within-zone variance compared with other spatially constrained methods. Extensive field-sample validation provides preliminary evidence of an inverse relationship between crop-type classification difficulty and accuracy. These results confirm the framework’s reliability in identifying regional difficulty and its decision-support value for selecting remote sensing strategies. Overall, this study systematically elucidates the spatial differentiation patterns of remote sensing classification difficulty for cropland and major cereal crops across China. The proposed framework provides robust scientific support for data selection, algorithm optimization, and differentiated strategy formulation in national-scale agricultural monitoring, thereby facilitating the operationalization of regional agricultural remote sensing applications.

• Proposed a novel combination method to mitigate DMSP-OLS saturation effects. • Developed a new deep learning framework to generate cross-sensor VIIRS-DNB. • Produced a global, long-term annual VIIRS-DNB at 500 m resolution (1992–2024). Nighttime light (NTL) remote sensing is a widely used proxy for quantifying human activities, urbanization and economic dynamics. The two most commonly used NTL archives are the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB). However, substantial differences in radiometric sensitivity, spatial resolution and measurement units between DMSP-OLS and VIIRS-DNB hinder the construction of continuous long-term NTL time series. To address this issue, we proposed and implemented a novel method for improving spatial resolution which combined a Landsat-based NDVI correction index (NDVI-corrected Nighttime Light Index, NWLI) to mitigate DMSP-OLS saturation and overflow effects, with a U-Net architecture augmented by a Transformer bottleneck (U-TransNet) for pixel-level cross-sensor mapping. The U-TransNet was trained on paired NWLI–VIIRS samples from 2013 to learn the nonlinear mapping from NWLI to VIIRS radiance, the trained model was then applied to predict NWLI for 1992–2012 on an annual basis to generate simulated VIIRS images. Finally, the simulated series were concatenated with observed VIIRS-DNB (2012–2024) to produce a continuous, VIIRS-compatible NTL dataset for 1992–2024 at 500 m resolution. Multi-scale validation indicated strong agreement with reference data, the pixel-level R 2 was 0.92, provincial/state-level and national-level R 2was 0.97 and 0.99, respectively. Comparisons with several radiometrically calibrated DMSP products (RNTL) also yield provincial R 2 values were greater than 0.90. The NTL time series products from 1992 to 2024 constitute a high-quality, spatio-temporally consistent observational baseline for long-term studies of urbanization, population dynamics and economic activity.

ABSTRACT Cloud horizontal heterogeneity within instrument footprints constitutes a major error source in ice cloud retrievals. We present a novel algorithm considering cloud horizontal heterogeneity based on lightweight convolutional neural network (LCNN) designed for Ice Cloud Imagers (ICI), which retrieves high‐resolution ice water path (IWP) by leveraging spatial correlations in low‐resolution satellite observations. Our approach employs a prior database generated through three‐dimensional radiative transfer simulations, containing collocated high‐resolution atmospheric profiles and cloud parameters, as well as the corresponding brightness temperature differences (BTDs). Then, the LCNN is trained and tested by the simulated prior database. Compared to Bayesian Monte Carlo integration (BMCI), the LCNN reduces mean absolute error (MAE) by 12.05 g/m 2 while tripling spatial resolution (from 16 to 5.3 km). This resolution enhancement mitigates errors induced by ice cloud horizontal heterogeneity, achieving a 23.6% reduction in MAE attributing to plane‐parallel cloud assumptions within footprints. The framework demonstrates the viability of deep learning for submillimeter‐wave cloud sensing, providing a pathway to resolve sub‐footprint cloud variability in future satellite missions.

Haze, as a critical factor affecting regional air quality and human health, necessitates accurate remote sensing identification for pollution monitoring and climate research. This study proposes a two-stage haze mapping algorithm (THMA), based on a backpropagation neural network and a random forest model, which achieves high-precision identification of haze, clouds, and clear air using FY-4A AGRI geostationary satellite data, with small misclassification rates and high F1 scores. Through detailed comparison with CALIOP observations, THMA performs well over most regions over Asia, successfully extending the traditional binary classification task of distinguishing only clouds and clear air. Notably, the model provides good classification capability in vertically overlapping areas of broken clouds and haze, with minimal misclassification even over bright surfaces such as deserts and ice/snow. Statistical analysis for the year 2022 shows that the annual average number of haze days is 51.3 in China. This study confirms the significant complementary value of satellite remote sensing and ground-based observations for haze monitoring.

With the intensification of environmental pollution and the increasingly prominent problem of industrial harmful gas emissions, existing mainstream gas detection technologies still have obvious limitations in terms of real-time performance, non-contact capability, detection accuracy, and multi-component identification. To address this demand, this paper proposes a lightweight and compact gas detection system based on passive Fourier Transform Infrared Spectroscopy (FTIR). The system innovatively integrates an improved parallel pendulum mirror interferometer and a low-noise signal preprocessing module, and simultaneously presents a novel oversampling method fusing equal time, equal optical path difference, and digital filtering, which effectively enhances the operational stability and sampling accuracy of the spectrometer. The system features excellent platform adaptability and can be flexibly mounted on various operation carriers. Combined with a two-dimensional rotating platform and an inertial navigation module, its monitoring range and application scenarios can be further expanded. Indoor sensitivity test results show that the detection limit of the system for sulfur hexafluoride (SF6) is less than 20 ppm; flight tests under real-world scenarios have successfully achieved accurate detection of SF6 gas, fully verifying the practical application effectiveness of the system. Based on the comprehensive results of indoor and outdoor tests, the system demonstrates core technical advantages of high sensitivity, strong flexibility, and excellent real-time performance. It is expected to be widely applied in gas monitoring tasks across multiple fields such as industrial safety monitoring, ecological environment monitoring, and transportation support in the future.