Over the past two decades, despite significant fluctuations in China's soybean planting area, high-resolution assessments quantifying the spatially heterogeneous and nonlinear impacts of compounding economic, climatic, and policy drivers remain scarce. To fill this gap, this study uses satellite remote sensing data (2000–2022) to map 1 km gridded soybean areas across China’s three main producing regions: the Northeast China Plain (NEP), Huang-Huai and Middle-Lower Yangtze Plains (HH-MLYP), and Sichuan Basin (SCB). Integrating yield and cost‒price data, we calculated the comparative economic benefit (CEB) between soybean and maize. A random forest model with SHapley additive explanations (SHAP) quantified the contributions of CEB, climatic, and topographical variables. Results revealed that CEB, growing season precipitation, and growing degree days were the most influential drivers, explaining 14.88%, 14.47%, and 13.70% of area variation, respectively. These impacts exhibit strong spatial heterogeneity: economic factors dominate in the NEP, whereas climate is more critical in the HH-MLYP and SCB. Subsidy policies effectively expanded planting in the NEP, despite diminishing marginal effects. These findings provide a scientific basis for optimizing planting strategies and designing targeted subsidies.

Satellite-derived land surface phenology (LSP) metrics are widely used to monitor vegetation phenology at large scales. However, current LSP products have uncertainties from limited satellite revisit cycles and adverse weather. This study integrated satellite and near-surface camera data to construct a satellite-camera system. Compared to traditional satellite products, near-surface camera-calibrated products more accurately determine vegetation phenological phases. Our analysis shows satellite-camera fused vegetation index time series strongly agree with satellite data while retaining near-surface cameras' high temporal resolution. The multi-cycle double logistic model (MDLM) was proposed to overcome conventional models' limitations in capturing only single-peak patterns, effectively characterizing multi-peak dynamics. We corrected the 2022 and 2023 vegetation phenological metrics (PMs) across the contiguous United States through interpolation, using high-quality ground reference benchmarks for validation. The corrected satellite-camera phenological data demonstrate improved performance compared to satellite-derived products. Analysis of different data sources reveals general consistency in identifying PMs, with higher accuracy for growth than senescence stages. Studies on three representative land types (grassland, forest, and cultivated land) also demonstrate that integrating satellite imagery with near-surface camera data enhances LSP monitoring and improves the temporal accuracy of vegetation phenology, which is crucial for optimizing agricultural management and advancing ecological and environmental research.

Tropospheric delay remains a critical error source in time-series interferometric synthetic aperture radar (TS-InSAR), particularly in mountainous and plateau regions where seasonal stratification and stochastic turbulence coexist. Current correction methods based on global atmospheric models (GAM) often underestimate the turbulent delay and fail to effectively address its spatiotemporal variability. We propose a novel GAM-based tropospheric correction method termed GAM-STTD that simultaneously models stratified and turbulent delays. The method integrates (1) the Prophet forecasting model to capture atmospheric variability in the temporal domain and (2) an adaptive kernel density estimation (AKDE) strategy to optimize three-dimensional zenith total delay (3D-ZTD) sampling according to the terrain gradient. We incorporated GAM-STTD into the TS-InSAR framework and validated it using both simulated and RADARSAT-2 datasets over Lijiang Basin, China. The results showed that the GAM-STTD model overcomes the underestimation of stochastic turbulent delay observed in existing atmospheric models, with a mean bias of 0.29 cm relative to the ERA5 reference ZTD. The model reduced the average phase standard deviation (STD) across 125 interferograms from 2.88 rad to 2.51 rad. In addition, the GAM-STTD model reduces the turbulence-induced interferometric phase semi-variance from 2.38 rad² to 1.49 rad², which further improves the accuracy of the TS-InSAR deformation solution.

Wetland ecosystems have suffered from prolonged and severe global degradation. In recent years, wetland restoration has made significant contributions to addressing this issue. However, restoration interventions have induced complex and dynamic shifts in plant community composition. Conventional remote sensing approaches often fail to achieve fine-scale monitoring of wetland plant restoration when access to high-cost remote sensing data, such as hyperspectral imagery, is limited. To address this challenge, this study proposes a fine-scale emergent plant mapping method that integrates spatiotemporal-spectral fusion for resolution enhancement with a transformer-based classifier utilizing high-dimensional features. The study employs a spatiotemporal-spectral fusion model, TemPanSharpening net, to improve the spatial resolution of long-term multispectral image sequences. Subsequently, multiple spectral features are selected and conveyed to a Transformer variant classification model. This approach is applied to map 2 m resolution annual dynamics of emergent plant communities in the Honghu Lake South, China. Compared to conventional approaches, our method significantly enhances mapping granularity with an overall accuracy of 88.21%, and reveals that 9.5% of the carbon storage might be overlooked. This research overcomes the limitations of fine-scale emergent plant monitoring under constrained imaging conditions. It provides technical support for accurately monitoring the effectiveness of wetland plant restoration.

ABSTRACT The littoral zone is crucial in the aquatic ecosystem of lakes, and water depth is often insufficient when using the traditional zigzag echo-sounding method. This study evaluated the applicability of Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data for lake water depth reconstruction on the Tibetan Plateau (TP). The ICESat-2 photon effectively penetrated shallow water and showed the underwater topography clearly. The water depth at the 549 intersection points across the 33 lakes ranged from 0.4 to 19.25 m, with an average of 4.62 m, and the RMSE of water depths from the ICESat-2 data was 0.42 m comparing with 33 lakes with in situ bathymetric data. The ICESat-2 data since 2018 covered 1392 large lakes, and the total observation days for 677 lakes was >20 days. Water transparency affects the accuracy of water depth reconstruction. This research suggests that ICESat-2 data has substantial potential for reconstructing lake water depth on the TP, which is useful for lake water storage estimation and water resource management.

Intelligent mapping technology has become more popular in the era of AI. However, traditional mapping accuracy evaluation methods, algebraic geometric methods (Hausdorff distance), and AI accuracy evaluation indicators based on IOU and its derivative methods, due to theoretical design flaws, are all unable to accurately calculate the true error values of the automated mapping results. Therefore, this paper proposes a theoretical accuracy evaluation method for intelligent mapping results based on automatic contour matching and Riemann integration. Firstly, we calculated the inflection-point matching relationship between the mapping contour and the real reference contour based on the proposed contour matching method. Next, using the inflection point matching relationship between the two contours, the two vector contours are divided into several groups of mutually matching edges. The distances between the matching edges were calculated using the proposed calculation method, and the weighted average distance of these distances based on the lengths of the matching edges was considered as the error of the mapping contour. Experiments were conducted on an ideal dataset and two groups of mapping-contour data generated by different automatic mapping algorithms. The experiments show that the accuracy evaluation effect of the proposed method is significantly better than that of the existing mapping accuracy evaluation methods.

Accurate quantification of anthropogenic CO₂ emissions at fine scales is essential for effective climate mitigation but remains challenging because of spatial–temporal heterogeneity and data limitations. This study presents a spatio-temporal random forest (STRF) model as an inventory enhanced framework, integrating multisource remote sensing datasets to estimate monthly gridded anthropogenic CO₂ emissions across China at 0.1° × 0.1° resolution from 2019 to 2022. Compared with conventional models, the STRF model explicitly incorporates spatial autocorrelation and temporal continuity through spatio-temporal weighting and dynamic feature selection, achieving superior accuracy. A comparison against existing emission inventories reveals strong agreement with the Multi-resolution Emission Inventory for China (MEIC), highlighting the model’s reliability. Feature importance further identifies nighttime light, tropospheric NO₂ and CO concentrations, and XCO₂ anomalies as the dominant predictors. The results reveal that high-emission hotspots are consistently concentrated in industrial and urban agglomerations. Temporally, emissions display distinct seasonal variability, with peaks in winter (driven by heating demand) and summer (fuelled by cooling energy needs). The COVID-19 pandemic temporarily reduced emissions by approximately 30% in 2020, followed by a rapid rebound thereafter. These findings underscore the ability of STRF model to provide high-resolution, dynamic CO₂ emission estimates via multisource remote sensing data, offering valuable insights for targeted, season-specific mitigation strategies aligned with China’s dual carbon goals.

The realization of Digital Earth requires not only macro-scale geospatial mapping but also the micro-scale synchronization of massive dynamic entities (e.g. crowds, logistics). However, current solutions often face trade-offs between precision, coverage, and deployment cost. To cope with this gap, we propose GeoLocTrack, a cost-effective, edge-native system serving as the synchronization layer for Digital Twins. Distinct from traditional fiducial markers, our proposed Color-Texture Fiducial Marker (CTFM) supports ID mapping onto arbitrary geometries without altering physical structures. To decode these patterns, Texture-Preserving YOLO (TP-YOLO) integrates a Texture-Preserving Feature Pyramid (TP-FPN) and Texture-Guided Attention (TGA) to explicitly capture high-frequency synthetic texture details, ensuring robust multi-angle recognition. Via marker multiplexing and fusing 2D-3D tracking framework, Grouping-based Trajectory Similarity Tracking (GTST) eliminates reliance on active positioning hardware and achieves near real-time tracking. Alternatively, Hybrid Active-Passive Tracking (HAPT) achieves real-time tracking utilizing sparse passive localizations to correct active localization drift. Comprehensive validations across real-world VR and large-scale simulation experiments demonstrate that GeoLocTrack delivers decimeter-level accuracy (10-20 cm) and supports over 100 concurrent targets on commercial edge devices. Crucially, the system’s robustness has been verified through long-term field deployment in multiple VR scenes, offering a proven, ‘infrastructure-light’ solution for bridging the physical-digital divide.

The ocean exhibits strongly multi-scale dynamics that require flexible discretization across a wide range of spatial scales. Although Discrete Global Grid System (DGGS) has been introduced to overcome limitations of conventional grids through its unified hierarchical tessellation, existing applications mainly focus on single-level or problem-specific simulations, while its capability for generalized multi-scale ocean modelling has yet to be fully explored. This study develops a DGGS-based global multi-nested grid framework using ISEA4T tessellation. The framework constructs nested meshes across multiple DGGS resolution levels and introduces a resolution-adaptive refinement scheme. By integrating DGGS with oceanographic considerations, it enables systematic grid configuration from coarse global grids to locally refined regions. Global simulations with the FVCOM model are conducted to evaluate the framework and different refinement schemes. Results show that DGGS-based nested grids reproduce major global circulation features while improving regional flow representation through targeted refinement. Rossby-radius-based refinement improves open-ocean mesoscale representation, whereas coastal and topographic refinement enhances nearshore dynamics. These results suggest that DGGS can support multi-scale ocean discretization within a unified hierarchical framework and provide a valuable reference for applying DGGS structures into multi-scale ocean modelling, with the potential to be extended to broader Earth system modelling in the future.

China's Environmental Disaster Reduction Satellite 2 (HJ-2) recently launched charge-coupled device (CCD) sensors, designed to monitor environmental and ecological changes. This study marks the first application of HJ-2A/B CCD imagery for quantifying chlorophyll-a (Chl-a) in lakes with diverse optical properties and trophic statuses, highlighting its potential for comprehensive water quality monitoring. Chl-a, a key indicator of algae biomass and nitrogen levels, was evaluated using empirical algorithms (EMs), semi-analytical algorithms (e.g. quasi-analytical algorithms (QAAs) and data-driven machine learning (ML) algorithms. Results showed that EMs struggled with lake-specific characteristics, while QAA demonstrated reliability (R² > 0.75, RPD > 2). ML algorithms, leveraging their data-driven adaptability, outperformed both the EM and the QAA, with CatBoost (CB) achieving the highest accuracy (R² = 0.97, RMSE = 6.69 μg/L, MAE = 4.78 μg/L, RPD = 5.16). CB-generated spatial Chl-a distribution maps highlighted HJ-2A/B CCD's significant potential for practical water quality monitoring across lakes with varying optical and trophic conditions. This study not only validates HJ-2A/B CCD's utility in Chl-a quantification but also underscores the superiority of ML approaches in handling complex, data-driven challenges. The findings provide a robust foundation for future large-scale and long-term applications of HJ-2A/B CCD imagery, offering valuable insights for environmental managers and researchers.