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Research Progress in urban thermal environment research towards public health assessment AI Introduction
Abstract：Against the backdrop of global climate change and rapid urbanization, the widespread replacement of natural land covers by impervious urban surfaces has substantially altered surface energy exchanges and intensified urban thermal stress. Urban heat environments have therefore become a critical challenge for sustainable urban development, with far-reaching implications for thermal comfort, heat exposure, and public health. Quantifying the spatiotemporal dynamics of urban thermal environments, identifying their health-related consequences, and developing effective mitigation and adaptation strategies are essential for advancing the United Nations Sustainable Development Goals, particularly those related to sustainable cities and communities, good health and well-being, and reduced inequalities. In recent years, research on urban heat environments has undergone a notable transition from physical monitoring of thermal conditions toward human-centered assessments of thermal perception, exposure inequality, adaptive capacity, and heat-related health risks. To clarify this evolving research paradigm, this paper reviews major advances in urban heat environment studies over the past two decades through bibliometric analysis and thematic synthesis. First, this review summarizes progress in urban heat environment monitoring based on in situ observations, satellite remote sensing, and numerical simulations. These approaches have improved the characterization of air temperature, land surface temperature, and urban microclimatic processes across multiple spatial and temporal scales. Nevertheless, existing monitoring frameworks remain constrained by limited spatial coverage, discontinuous temporal records, insufficient all-weather capability, and uncertainties in cross-scale integration. Second, this paper examines the growing emphasis on outdoor thermal comfort, urban heat exposure, and heat-related health risks. Research attention has increasingly shifted from the physical intensity of urban heat islands to the ways in which residents perceive, experience, and respond to thermal stress. However, fine-scale and dynamic assessments of heat exposure remain limited by the lack of globally consistent diurnal temperature data and short-term population mobility information, which constrains the evaluation of exposure inequality across different places, periods, and social groups. Finally, four future research directions are proposed. (1) integrated retrieval and reconstruction of multiple temperature parameters should be strengthened by combining ground observations, multisource satellite remote sensing, microwave data, data fusion, deep learning, and physically constrained modeling, thereby generating long-term, high-resolution, and all-weather temperature datasets for urban heat studies. (2) future research should further reveal the coupling relationships between urban heat exposure and urban morphology, ecological structure, infrastructure provision, population dynamics, socioeconomic conditions, and social development patterns, with particular attention to heat exposure inequality. (3) public health-oriented adaptation should be advanced through nature-based solutions, urban greening, blue-green infrastructure, vertical vegetation, landscape optimization, and climate-sensitive urban design, while fully considering exposure, vulnerability, and accessibility to cooling and medical resources. (4) under future warming and more frequent extreme heat events, interdisciplinary studies are needed to expand heat-health risk assessment across spatial and temporal scales, investigate the impacts of persistent heat on vulnerable populations, and support medical resource allocation, emergency response planning, and urban heat risk management.Keywords：Urban heat environment;thermal comfort;heat exposure;health risks of high temperatures;urban heatwaves;urban heat island196|44|0
<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-09-04Asymmetric dynamics between ratoon rice and double-season rice suitability under future climate scenarios in China AI Introduction
Abstract：Objective Double-season rice and ratoon rice represent two important rice-cropping systems in China's subtropical monsoon region. However, against the background of global warming, systematic insights into their relative climatic advantages remain limited. This study aims to clarify the adaptive characteristics of these two rice-cropping patterns under future climate change, thereby providing a scientific basis for maintaining regional and global food security.Method In this study, we employed climatic environmental variables derived from a combination of remote sensing satellite data and global weather station observations integrated with crop spatial occurrence records to evaluate the climatic suitability of double-season rice and ratoon rice. A set of 12 widely used species distribution models was tested, and high-performing models were selected on the basis of accuracy metrics. Ensemble predictions were then generated through weighted averaging using the true skill statistic (TSS), providing a robust assessment of current and future suitability patterns under multiple climate scenarios. Suitable areas were delineated using TSS-maximization thresholds, and within suitable areas, we further divided the suitability levels into three levels by equally partitioning the probability range. Comparative analyses of the suitability levels between the two rice systems allowed us to identify areas where ratoon rice displayed a higher suitability level than double-season rice did, thereby highlighting spatial and temporal asymmetries in the responses to climate change.Result The results indicate that under current climatic conditions, the suitable area of ratoon rice (137.91×10⁵ ha) is significantly broader than that of double-season rice (98.56×10⁵ ha). However, future climate change will drive an asymmetric spatiotemporal evolution of these two rice systems. In terms of the temporal dimension, the suitable area for double-season rice shows a continuous expansion trend under all the emission scenarios, with an approximate 27% increase by the end of this century under the SSP585 scenario. In contrast, the suitable area of ratoon rice initially expands but then decreases, with its suitable area projected to decrease by 10.29×105 ha by the end of this century under the SSP585 scenario compared with that during the current period. In terms of the spatial dimension, the area suitable for double-season rice exhibits unidirectional northward expansion with minimal shrinkage at low latitudes, whereas the area suitable for ratoon rice significantly decreases in low-latitude areas and expands in high-latitude regions and southwestern highlands. Despite this projected contraction, the advantageous area for ratoon rice remains significant. Even under the SSP585 scenario, by the end of the 21st century, the potential advantage area for ratoon rice is estimated to be 24.59×105 ha, far exceeding its current cultivation area of 12.4×105 ha.Conclusion By mitigating projection uncertainties through ensemble modeling and multiscenario analyses, this study provides a robust scientific basis for optimizing regional cropping layouts. Given its currently limited cultivation scale, ratoon rice has demonstrated expansion potential under both present and future climate scenarios. The asymmetric effects of climate change on double-season rice and ratoon rice underscore the necessity of developing targeted adaptation strategies to maintain food production stability under dynamic climatic conditions.Keywords：remote sensing application;climate change;suitability;ratoon rice;double-season rice;species distribution model194|30|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-09-04
Abstract：Objective Large-scale identification of idle rural residential land remains challenging in mountainous regions because of complex topographic conditions, fragmented spatial patterns, and heterogeneous land-use states. This study aims to develop a multi-source remote sensing framework for regional-scale identification of idle risk in rural residential land by integrating terrain-constrained spatial extraction with temporal indicators of human activity and vegetation dynamics.Method Zhangjiakou, Hebei Province, China, was selected as the study area. An improved U-Net semantic segmentation model was developed by incorporating a Digital Elevation Model (DEM) channel and an attention mechanism into the conventional U-Net architecture to enhance the extraction of rural residential land under complex terrain. Sentinel-2 imagery and DEM data were jointly incorporated to strengthen the representation of spectral, spatial, and topographic information. Urban built-up area boundaries were subsequently introduced to exclude urban pixels and construct a rural residential land mask. Multi-source temporal remote sensing features were then derived from SDGSAT-1 low-light nighttime imagery and multi-temporal Sentinel-2 imagery. Nighttime light statistics were used to characterize human activity intensity and temporal variability, whereas Normalized Difference Vegetation Index (NDVI) statistics were employed to characterize vegetation conditions and spatial heterogeneity. Correlation analysis was performed to reduce feature redundancy, and ADASYN combined with SMOTE-Tomek was employed to alleviate class imbalance. Random Forest, XGBoost, and Light Gradient Boosting Machine (LightGBM) were comparatively evaluated, and the optimal model was used to estimate the posterior probability of rural residential land being idle. The resulting probabilities were spatially smoothed and classified into five risk levels, followed by independent stratified validation.Result The improved U-Net achieved an Intersection over Union (IoU) of 0.8994 and an F1-score of 0.9470, while object-level evaluation yielded a patch integrity of 0.91, a relative area error of 0.11%, and an average boundary offset of 4.8 m. Among the machine learning models, LightGBM achieved the best overall performance, with an accuracy of 0.813, an area under the receiver operating characteristic curve (AUC) of 0.874, a recall of 0.867, a precision of 0.789, and an F1-score of 0.826. Feature importance analysis indicated that NDVI and nighttime light features provided complementary information for idle-risk identification. The integrated feature set achieved an AUC of 0.874, compared with 0.813 and 0.843 for nighttime light-only and vegetation-only features, respectively. Validation based on 500 independent stratified samples yielded an accuracy of 0.783, a precision of 0.745, a recall of 0.830, and an F1-score of 0.786. The proportion of samples exhibiting clear idle characteristics increased progressively from 18% in the low-risk class to 82% in the high-risk class. Global Moran’s I increased from 0.842 before spatial smoothing to 0.927 after smoothing, indicating significant positive spatial clustering of the estimated idle probabilities.Conclusion The proposed framework integrates terrain-constrained deep learning, multi-source temporal remote sensing, and machine learning to achieve regional-scale identification and probabilistic characterization of idle rural residential land. The synergistic use of nighttime light and vegetation features effectively captures complementary signals associated with the attenuation of human activity and the enhancement of vegetation cover. The posterior probability provides a continuous spatial representation of idle status and reveals distinct spatial differentiation across Zhangjiakou. Relatively high idle-risk areas are mainly concentrated in the Bashang Plateau and some mountainous areas, whereas areas with stronger urban influence and better transportation accessibility generally exhibit lower risk. The proposed framework provides an effective remote sensing-based approach for monitoring inefficient rural residential land and supporting spatially differentiated land-use management and rural land consolidation.Keywords：Rural residential land;Idle risk;DEM terrain constraints;SDGSAT-1 low-light attenuation;NDVI time-series vegetation enhancement;U-Net174|34|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-09-04Long-term water color evolution mechanisms of large shallow lakes under anthropogenic disturbance： A case study of Lake Chaohu AI Introduction
Abstract：Large shallow lakes are important for regional water supply and ecological security, and their long-term water quality dynamics are of great scientific and practical importance for watershed management and ecological protection. However, these lakes are highly sensitive to external disturbances and ecologically vulnerable. Under the combined effects of intensive human activities and continuous environmental management, the extent to which their water quality exhibits staged and nonlinear responses remains poorly understood. In this study, Lake Chaohu, the fifth-largest freshwater lake in China, was selected as a representative large shallow lake. The Forel-Ule Index (FUI), which provides an integrated measure of water optical properties and water quality status, was used to characterize long-term water quality changes. Based on MODIS long-term remote sensing data from 2000 to 2024, a high-accuracy improved water color inversion model was applied to generate a long-term water color series, with an overall validation mean relative error (MRE) of 1.38% and root mean square error (RMSE) of 3.32°. The spatiotemporal patterns of water color and their driving factors under long-term human disturbance were then systematically examined. The results showed that the multi-year mean FUI of Lake Chaohu ranged from 12 to 15, with a mean hue angle (α) of 211.67° ± 2.58°, indicating an overall green-to-yellow water color. Spatially, a clear west-to-east decreasing gradient was observed, with mean α values of 213.13° ± 2.22°, 211.67° ± 2.58°, and 209.79° ± 2.16° in the western, central, and eastern lake regions, respectively. This spatial pattern was largely consistent with the distribution of major inflowing rivers and their differences in pollution loads. At the seasonal scale, water color showed a distinct bimodal pattern, with the highest α value in autumn (214.23°), indicating the most turbid conditions, and the lowest value in summer (208.74°), indicating the clearest conditions. Seasonal variations in natural factors, particularly wind speed, precipitation, normalized difference vegetation index (NDVI), and temperature, were found to regulate these intra-annual changes. Over the long term, the annual mean α increased at a rate of 0.15° yr⁻¹ from 2000 to 2024, with the largest increase occurring in the eastern lake region (0.26° yr⁻¹). The Mann-Kendall and Pettitt tests jointly identified around 2014 as a key transition point. The decreasing trend during 2000–2014 (-0.13° yr⁻¹) changed to a significant increasing trend during 2014–2024 (0.44° yr⁻¹), indicating a clear shift from an earlier improvement to later deterioration in the water environment. SHAP-based attribution further showed that anthropogenic factors, including gross domestic product (GDP), impervious surface area, nighttime light, and population, contributed substantially more to long-term water color changes than natural climatic factors and were identified as the dominant drivers. Early watershed pollution control and ecological restoration measures effectively reduced environmental pressure and promoted water color improvement. However, as urbanization and socioeconomic activities continued to intensify, management gains gradually weakened, resulting in an imbalance in the lake's pressure-response relationship and a subsequent rebound in water color deterioration. Meanwhile, the Yangtze-to-Huaihe River Diversion Project and extreme climate events further intensified hydrodynamic disturbances and external pollutant inputs, increasing interannual variability and the risk of short-term water color deterioration. Overall, the results revealed distinct staged, nonlinear, and spatially heterogeneous patterns in the long-term water color evolution of large shallow lakes under sustained human disturbance. These findings provide a scientific basis for long-term water environmental management and adaptive regulation of Lake Chaohu and other large shallow lakes.Keywords：shallow lake;Chaohu Lake;water color index;long-term remote sensing;anthropogenic disturbance146|30|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-09-04Seasonal Dynamics-Aware Quantification of Traffic Sign Visibility and Simulation of Street Tree Pruning AI Introduction
Abstract：Objective Occlusion of traffic signs by street trees poses a threat to road traffic safety. However, existing studies mostly stop at static occlusion detection and lack a technical pathway that bridges occlusion quantification and pruning decision-making.Method This paper constructs a decision framework that connects seasonal occlusion dynamic quantification with multi-intensity pruning simulation and evaluation. An 8.0 km road section in Zhangjiang Hi-Tech Park, Shanghai, was selected as the study area, where 74 cantilever traffic signs and 1,524 street trees were analyzed. First, high-precision street tree models were reconstructed from the point clouds (mean error of 2.0 cm), and virtual leaves were incorporated to simulate seasonal variations. Second, a view frustum spatial analysis method was employed to quantify the visibility of traffic signs from the pavement in winter and summer. Finally, pruning simulations were conducted under two schemes with branch diameter thresholds of 5 cm and 10 cm, respectively, and the pruning effects were evaluated from three aspects: pruning cost, ecological loss, and landscape impact.Result The results show that the average visibility of traffic signs from the pavement is 91.15% in winter and decreases to 85.26% in summer, representing a reduction of 5.89%. Sign #7 exhibits the largest decrease of 41.45%, indicating the most significant seasonal impact. Signs with shorter cantilevers are more prone to occlusion. Under the 5 cm pruning scheme, branch volume loss is 6.20% and leaf loss is 20.70%; under the 10 cm scheme, branch volume loss increases to 19.20% and leaf loss to 39.21%, while the number of pruning points decreases from 558 to 219. Although high-intensity pruning reduces costs, it may cause severe structural damage to trees (e.g., a single tree near sign #43 experiences a branch loss of 57.69%, with some trees nearly reduced to trunks).Conclusion This study demonstrates that the proposed framework, by mapping occlusion quantification results into pruning threshold selection criteria and revealing the asymmetric cost relationship between the reduction of pruning points and the structural damage to trees, provides a scientific basis for the coordinated management of street trees that balances traffic safety, ecological conservation, and landscape preservation.Keywords：Mobile laser scanning point cloud;street tree;traffic sign;occlusion analysis;pruning simulation;traffic safety71|11|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-09-04Research and Remote Sensing Applications of Vegetation's Impact on Land Surface Energy Partitioning： Progress and Prospects AI Introduction
Abstract：Accelerated warming and intensified human activities have drastically altered global vegetation cover, profoundly affecting regional and global climate. Among these effects, vegetation change critically influences surface energy partitioning, which is the division of all-wave net radiation into latent, sensible, and soil heat fluxes. This partitioning directly regulates near-surface temperature, atmospheric stability, and boundary layer dynamics. Thus, clarifying vegetation's role in energy partitioning is essential for understanding the climatic impacts of land cover change and for improving climate models and land management. However, quantifying this response independently from background climate variability remains challenging. Nonlinear interactions, strong spatiotemporal heterogeneity, and limited long-term observational data hinder the separation of vegetation signals from climatic noise. Furthermore, the magnitude and even direction of vegetation-induced energy shifts depend on latitude, season, aridity, and ecosystem type, precluding simple generalizations. Focusing on vegetation–energy partitioning, this review systematically synthesizes studies over three decades, emphasizing remote sensing applications. Vegetation affects land surface energy partitioning via three interconnected pathways: (1) physiological traits (leaf area, stomatal conductance, root depth) regulating surface–atmosphere water and heat exchange; (2) modifying the surface radiation budget via albedo and emissivity changes; and (3) influencing the hydrological cycle through interception, transpiration, and soil moisture extraction, thereby altering latent and sensible heat fluxes. These processes are modulated by human activities (deforestation, irrigation, ecological restoration, urbanization) and natural disturbances (wildfires, extreme climate events). Energy flux interactions may amplify or offset one another; for example, increased latent heat typically suppresses sensible heat, cooling growing seasons, whereas reduced evaporation can worsen heatwaves in dry regions. Such complexity yields distinct climatic responses across regions and scales, demanding integrated assessments. Methodologically, numerical models, from land-surface schemes to Earth system models, still dominate, but observational evidence is gaining importance given model uncertainties in parameterization and forcing. Satellite remote sensing, with broad coverage, long time series, and multiple resolutions, overcomes the spatial sparsity of in situ networks. It supplies key variables (leaf area index, albedo, land surface temperature, evapotranspiration) as critical model inputs and constraints. Consequently, remote sensing has expanded vegetation–energy studies to global scales, enabling attribution of greening effects on energy balance and providing quantitative evidence for policy. Future research should prioritize several directions. First, enhancing the spatiotemporal resolution and accuracy of remote sensing products, through emerging techniques like remote sensing foundation models that merge deep learning with physical constraints, will better capture fine-scale heterogeneity and rapid land cover changes. Second, greater attention is needed for vegetation transition zones and complex terrains, including arid/semi-arid regions, urban areas, and mountains, where energy partitioning responses are especially sensitive and poorly understood. Third, incorporating coupled biogeochemical effects, particularly the links between carbon uptake, nutrient cycling, and energy exchange, will support more holistic assessments of vegetation–climate interactions. Fourth, fostering multi-platform, multi-sensor data fusion and developing robust causality attribution methods are critical for reducing uncertainties and guiding actionable strategies. Together, these advances will deepen mechanistic understanding of vegetation change and its regional climatic consequences from the energy partitioning perspective, ultimately contributing to sustainable ecosystem management and climate adaptation policies.Keywords：vegetation;surface energy partitioning;surface radiation budget;remote sensing;latent heat flux;sensible heat flux;land-atmosphere interactions;climate response128|21|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-09-04LSENet： Local Subdomain-aware and Edge-enhance Dynamic Fusion Network for Semantic Segmentation of Airborne LiDAR Point Cloud AI Introduction
Abstract：Objective Airborne LiDAR point cloud semantic segmentation is fundamental to 3D scene understanding and has important applications in smart cities, urban modeling, infrastructure extraction, land surveying, and environmental monitoring. To address the insufficient coordination between local geometric representation and contextual modeling in existing methods, we propose LSENet (Local Subdomain-aware and Edge-enhanced Dynamic Fusion Network). LSENet extracts fine-grained geometric features through adaptive local subdomain partitioning, enhances information exchange via cross-subdomain interactions, and improves feature representation and boundary discrimination through an edge-enhanced dual-attention fusion module. Experiments on the Vaihingen 3D and DFC2019 datasets achieve Overall Accuracy (OA) values of 83.9% and 97.8%, and mean F1-scores of 70.5% and 89.1%, demonstrating its effectiveness and cross-scene applicability.MethodLSENet adopts a U-Net-style encoder–decoder architecture with three core modules. The Local Subdomain Space-aware feature Encoding Aggregation module (LSSEA) constructs an adaptive local coordinate system using PCA and encodes relative position, spatial distribution, orientation, and curvature information for fine-grained geometric representation. The Local Subdomain feature Enhancement and Interaction Attention module (LSEIA) enhances intra-subdomain features and establishes cross-subdomain interactions to improve local contextual modeling. The Edge-enhanced Grouped Multi-scale Dual-Attention dynamic fusion module (EGMDA) integrates edge-enhanced grouped channel attention and multi-scale spatial attention through dynamic fusion, strengthening boundary representation and classification accuracy.Result Comparative experiments were conducted on two widely used benchmark datasets, ISPRS Vaihingen 3D and DFC2019. On Vaihingen 3D, LSENet achieved an OA of 83.9% and a mean F1-score of 70.5%, obtaining the best OA among compared methods. Significant improvements were observed in challenging categories such as power lines, roofs, and trees, demonstrating the effectiveness of the proposed modules in preserving complex structures and clear boundaries. On DFC2019, LSENet achieved an OA of 97.8% and a mean F1-score of 89.1%, ranking first in OA and demonstrating strong cross-scene generalization. Visualization results further show that LSENet produces more coherent regions and clearer boundaries than the baseline.ConclusionLSENet effectively integrates local geometric awareness, cross-subdomain interaction, and edge-preserving dual attention with dynamic fusion to overcome limitations of existing point cloud segmentation networks. Results on two airborne LiDAR benchmarks validate its effectiveness, robustness, and generalization capability. Future work will focus on developing more discriminative geometric descriptors, lightweight dynamic inference strategies, and evaluating the proposed method on newly released large-scale and diverse airborne LiDAR datasets to improve its applicability in real-world scenarios.Keywords：airborne LiDAR point clouds;deep learning;semantic segmentation;local subdomain awareness;dynamic fusion;edge-enhance97|15|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-09-04Fine-Tuning and High-Frequency Enhanced Multi-Scale Segment Anything Model for Remote Sensing Image Semantic Segmentation AI Introduction
Abstract：The Segment Anything Model (SAM), as a representative vision foundation model, has demonstrated remarkable performance and strong generalization capability in various downstream computer vision tasks. However, directly applying SAM to remote sensing image semantic segmentation remains challenging due to the substantial domain gap between natural images and remote sensing scenes. Specifically, SAM is primarily trained on large-scale natural image datasets, making it difficult to effectively adapt to the complex characteristics of remote sensing images, such as significant scale variations, diverse spatial distributions, and heterogeneous land-cover patterns. Moreover, the image encoder of SAM is constructed based on the Vision Transformer (ViT) architecture, which mainly focuses on global contextual representation through self-attention mechanisms but has limited capability in capturing local textures and high-frequency spatial details. Consequently, important boundary information and fine-grained structural features of remote sensing objects may be weakened during feature extraction, resulting in blurred object boundaries and degraded segmentation accuracy. To address these limitations, this study proposes a Multi-Scale SAM with Fine-Tuning and High-Frequency Enhancement (MSHF-SAM) for high-resolution remote sensing image semantic segmentation. The proposed framework aims to improve the adaptability of SAM to remote sensing scenarios by enhancing high-frequency feature representation, optimizing model transfer strategies, and designing a more effective segmentation decoder. Specifically, in the encoder stage, a high-frequency enhancement module is introduced into the original SAM image encoder to strengthen the extraction and preservation of high-frequency spatial information, thereby improving the model’s ability to characterize fine-grained textures and object boundaries. Meanwhile, a parameter-efficient fine-tuning strategy is incorporated to alleviate the feature distribution discrepancy between the pretrained SAM model and remote sensing images, enabling effective domain adaptation with only a small number of trainable parameters. In the decoder stage, the original SAM mask decoder is replaced with a MACU-Net-based decoder to achieve more accurate multi-scale feature reconstruction. Furthermore, a lightweight boundary refinement branch is developed to enhance the representation of object contours and improve segmentation details, while maintaining computational efficiency. Through the collaborative optimization of the encoder and decoder, the proposed method can effectively integrate global semantic information with local spatial details, providing more precise segmentation results for complex remote sensing scenes. Extensive experiments are conducted on two widely used high-resolution remote sensing semantic segmentation datasets, namely LoveDA and ISPRS Vaihingen. The experimental results demonstrate that MSHF-SAM achieves superior performance compared with several state-of-the-art segmentation methods in terms of mean Intersection over Union (mIoU), F1-score, and other evaluation metrics. Compared with the classical UnetFormer model, the proposed method improves the mIoU by 4.33% on the LoveDA dataset and by 2.85% on the Vaihingen dataset. These results verify the effectiveness of the proposed high-frequency enhancement strategy, parameter-efficient fine-tuning scheme, and multi-scale decoding architecture. The proposed framework provides a promising solution for adapting large-scale vision foundation models to remote sensing semantic segmentation tasks and demonstrates the potential of foundation models in intelligent remote sensing image interpretation.Keywords：deep learning;Segment Anything Model(SAM);semantic segmentation;high-frequency enhancement;parameter-efficient fine-tuning;remote sensing images140|23|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-09-04Rotation-Transform-Based Scattering Characteristics Extraction and Classification in Agricultural Areas Using Dual-Polarization SAR Data AI Introduction
Abstract：Objective In the field of polarimetric synthetic aperture radar (PolSAR) remote sensing, the capability to conduct all-weather, day-and-night observations makes it a pivotal tool for crop classification. Currently, establishing correlations between decomposition features and crop types via machine learning algorithms has become a mainstream approach. However, existing dual-polarimetric decomposition methodologies, such as Stokes or Cloude decompositions, are constrained by their reliance on predefined and fixed scattering models. These conventional approaches are limited to extracting discrete scattering features, which often fail to sufficiently characterize the geometric structures and scattering processes of complex agricultural vegetation, thereby hindering the achievement of higher classification accuracy.Method To effectively address these issues, the paper proposes a rotation transformation methodology specifically designed for dual-polarimetric data. By using the polarimetric scattering angleSynergistic use of satellite and UAV imagery for continuous monitoring of field-scale paddy rice NDVI AI Introduction
Abstract：Objective The real-time and precise monitoring of crop growth status at the field scale is a critical component for achieving modern precision agriculture. Multi-source remote sensing technologies, including satellite platforms and unmanned aerial vehicles (UAVs), have emerged as effective non-destructive tools for this purpose. However, these data sources present a significant spatiotemporal trade-off, limiting their independent utility. Satellite remote sensing, while offering broad-area coverage, is often constrained by adverse weather conditions and insufficient spatial resolution to capture in-field variability. Conversely, UAV remote sensing provides exceptionally high spatial resolution but is hampered by limited battery endurance, making large-scale continuous (e.g., daily) monitoring challenging. Consequently, a single data source is inadequate for supporting the continuous monitoring of crop growth at the field scale. To address this critical gap, this study proposes a cross-platform spatiotemporal fusion method. The objective is to synergistically integrate satellite and UAV data, effectively leveraging their complementary temporal and spatial resolutions to generate a continuous, high-resolution dataset for precision agriculture.Method This research was based on a synergistic combination of multi-platform, multispectral remote sensing data, including high-resolution UAV imagery, Sentinel-2 data and PlanetScope SuperDove data. We developed an improved CACAO (Consistent Adjustment of the Climatology to Actual Observations) algorithm, adapting its core logic for cross-platform data fusion rather than its original climatological application. Two distinct data combination strategies were designed and tested: (1) a baseline “UAV+Sentinel-2” strategy and (2) an enhanced “SuperDove+Sentinel-2+UAV” strategy, which integrates high-frequency commercial satellite data. The CACAO framework was implemented using two distinct modes: a “forward prediction” (FP) mode, designed for near real-time applications, and a “backward updating” (BU) mode, which iteratively refines historical estimates as new data becomes available. The final output of the framework is a near real-time, daily 1-meter resolution normalized difference vegetation index (NDVI) time-series dataset. The accuracy of the fusion results was rigorously evaluated using two methods: (1) Leave-One-Out Cross-Validation (LOOCV), which assesses the model’s predictive power, and (2) a benchmark comparison against the established GLM-STF (Generalized Linear Model-based Spatiotemporal Fusion) algorithm.Result The prerequisite for data fusion was confirmed as NDVI data from the different platforms exhibited good consistency, with a strong correlation between Sentinel-2 and SuperDove (R = 0.97) and a reliable correlation was observed between UAV and satellite data (R > 0.75). In addition, the CACAO algorithm was proven to effectively reconstruct the phenological dynamics of the rice crop. A key finding was that the backward updating (BU) mode produced a significantly smoother and more robust NDVI time series than the forward prediction (FP) mode. Both CACAO-based data combination strategies achieved high overall accuracy (R > 0.94). Critically, the study demonstrated that introducing high-temporal-resolution SuperDove data during key phenological stages can substantially improve accuracy, with the correlation increasing from 0.51 to 0.67 in a specific validation case. Finally, in the comparative analysis, the CACAO algorithm demonstrated greater stability and slightly higher accuracy than the GLM-STF algorithm, particularly showing more robust performance across the entire growing season.Conclusion In conclusion, the cross-platform fusion framework proposed in this study, centered on the improved CACAO algorithm, is an effective and robust solution for generating continuous, high-precision (daily 1-meter) field-scale rice NDVI time series. This approach successfully overcomes the limitations of single-source data platforms. The framework provides strong technical support for the fine-grained monitoring of crop growth and the implementation of precision management strategies in modern agriculture.Keywords：PlanetScope;Sentinel-2;spatiotemporal data fusion;growth monitor;precision agriculture;field scale;phenological curve;near real-time monitoring1142|1140|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-03-17Unsupervised Automated Remote Sensing Monitoring of Crop Lodging for the Entire Pre- and Post-Disaster Process AI Introduction
Abstract：Objective Crop lodging poses a significant threat to agricultural productivity and food security, yet existing monitoring approaches often suffer from low automation, insufficient integration of pre- and post-disaster data, and a lack of systematic spatiotemporal coordination. To overcome these limitations, we developed an automated framework, StandardCurve-iForest-RF, which aims to establish a resilient crop growth baseline, distinguish true lodging from noise, and enable precise spatiotemporal mapping for disaster management.Method The approach utilizes time-series Sentinel-2 satellite data to construct a Crop Growth Standard Curve (CGSC) for the target crop. The Soft Dynamic Time Warping (Soft-DTW) algorithm is employed to create this curve, which serves as a resilient reference model capable of accommodating inter-annual climatic variations. To detect lodging, the method calculates cumulative multi-feature anomaly scores by comparing post-disaster satellite observations against the pre-established standard curve. An Isolation Forest (iForest) algorithm is applied for initial anomaly detection across multiple spectral features. Subsequently, a spatiotemporal joint decision mechanism is implemented to refine the results, effectively suppressing false alarms caused by persistent cloud cover, cloud shadows, and other environmental noise. Finally, a Random Forest (RF) classifier is used to accurately map the spatial extent and precise boundaries of the lodged areas, completing the fully automated workflow from dynamic detection to precise mapping.Result The method was validated using a case study of a lodging event that occurred on September 15, 2020, in Bohetai Township, Zhaoyuan County, Daqing City, Heilongjiang Province. It successfully identified the lodging event, demonstrating its effectiveness in distinguishing actual crop damage from noise. The overall detection accuracy reached 80.36%, with a Kappa coefficient of 0.60, confirming a substantial agreement between the automated detection results and ground reference data. The results clearly showed that the integrated use of the standard curve, the anomaly scoring mechanism, and the spatiotemporal decision rules significantly enhanced the reliability of lodging identification and minimized false positives. The entire process, from data processing to the final generation of the lodging map, was executed automatically without manual intervention.Conclusion The StandardCurve-iForest-RF framework presents a significant advancement in automated crop disaster monitoring. Its core innovation lies in the construction of a resilient growth standard curve and a sophisticated spatiotemporal analysis pipeline that effectively differentiates true lodging from interference. The successful application in a real-world case study confirms the method's practical utility and accuracy. This framework provides a valuable tool for agricultural departments and emergency management agencies, enabling rapid assessment of crop damage extent and supporting timely disaster response and loss estimation. The methodology is adaptable and holds promise for application in other regions and for monitoring other types of abrupt agricultural disasters.Keywords：Crop Lodging Monitoring;Standard Growth Curve;time-series analysis;Spatiotemporal Joint Detection;Sentinel-2519|1050|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-03-13Weakly Supervised Semantic Change Detection in Street View Imagery and Its Application in Urban Renewal Dynamics Mapping AI Introduction
Abstract：Objective Street view imagery (SVI) has emerged as an important geospatial big data source for perceiving the urban built environment. Accurately detecting facade-level changes and identifying their semantic categories is essential for monitoring urban renewal dynamics. However, existing change detection approaches struggle to separate temporal ownership of changed objects (change decomposition) and to directly provide semantic change information, leading to complex workflows and high data preparation costs. This study aims to develop a weakly supervised semantic change detection framework that integrates change decomposition and semantic labeling, and to apply it to dynamic mapping of urban renewal in Guangzhou, China.Method We propose Cross-C2PO, a novel dual-branch architecture designed to achieve end-to-end weakly supervised change decomposition. Unlike traditional single-branch models, Cross-C2PO introduces a cross-comparison mechanism to explicitly model asymmetric temporal differences, ensuring completeness and consistency regardless of input order. The model integrates a differentiable union operator to maintain consistency constraints during weak supervision and employs the proposed Cross-MTF feature fusion function to break commutativity for accurate temporal differentiation. Building on Cross-C2PO outputs, we design a semantic change detection workflow that leverages state-of-the-art segmentation models (e.g., DeepLabV3+) without requiring synthetic datasets. Finally, we introduce an urban renewal dynamic index to quantify facade-level changes and visualize renewal patterns across panoramic and directional views (front, back, left, right) for Guangzhou’s central districts (2013–2019), based on 11,431 pairs of Baidu street view panoramas.Result Traditional change detection methods fail to achieve end-to-end change decomposition and rely on complex multi-stage pipelines, limiting scalability and flexibility. In contrast, the proposed Cross-C2PO framework enables one-stage weakly supervised change decomposition and can seamlessly integrate with mainstream architectures, granting them both improved detection accuracy and the ability to perform temporal ownership splitting without additional labels. Experiments on multiple benchmark datasets demonstrate that our method consistently achieves state-of-the-art performance, outperforming existing approaches in both binary change detection and decomposition tasks. Ablation studies further validate the contribution of the cross-branch structure, Cross-MTF fusion, and the differentiable union operator. Applied to Guangzhou street view imagery, the workflow successfully produced urban renewal dynamic maps, revealing high-intensity updates clustered in Liwan and Baiyun industrial areas, while moderate changes dominate residential zones. Directional view analysis additionally highlights local disparities and micro-scale renewal patterns.Conclusion The proposed Cross-C2PO framework offers a simple yet effective solution for weakly supervised semantic change detection, enabling accurate change decomposition without additional synthetic labels. Combined with an interpretable urban renewal dynamic index, it provides a scalable and cost-effective approach for urban facade change analysis. This study bridges street view imagery and AI-based computer vision for urban analytics, offering new insights into spatiotemporal renewal dynamics. Future work will focus on optimizing computational efficiency and extending the method to multi-source data integration for large-scale applications.Keywords：urban renewal;street view imagery;semantic change detection;scene change detection;weak supervision;dynamic index763|926|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2026-03-06GF-1 Wide-Field Multi-temporal Water Body Extraction Dataset AI Introduction
Abstract：Objective This study aims to overcome the limitations of existing public water body datasets, such as single temporal resolution and low annotation accuracy. The objective is to construct a high-quality, multi-temporal lake extraction dataset based on the high-resolution wide-field multispectral imagery from the "GF-1" satellite, which offers improved temporal and spatial coverage of water bodies.Method To achieve this, three study areas with varying levels of dynamic change were selected: Poyang Lake (high dynamic change), Namtso Lake (moderate dynamic change), and Yangcheng Lake (low dynamic change). These areas were covered in four seasons of 2022. The GF-1 wide-field multispectral imagery underwent preprocessing, including radiometric correction, orthorectification, and quick atmospheric correction. For the annotation process, a hybrid strategy combining automated methods with manual visual interpretation was employed to ensure high annotation accuracy.Result The resulting dataset is characterized by multi-temporal data and high annotation accuracy, offering a significant improvement over existing datasets. The overall accuracy of the dataset for all three study areas and across all four seasons exceeded 94%.It provides reliable data for dynamic water body mapping and change monitoring across seasonal variations. Additionally, various water body extraction methods, including threshold segmentation, traditional machine learning algorithms, and deep learning techniques, were employed to validate the dataset’s practical utility. The results demonstrated that the dataset supports the effective training and evaluation of these methods.Conclusion The findings indicate that the constructed multi-temporal lake extraction dataset is highly reliable and can effectively support various water body extraction methods. It provides a robust data foundation for enhancing the performance of dynamic water body extraction algorithms, and contributes valuable data for research in dynamic water body monitoring and mapping using high-resolution remote sensing imagery.Keywords：Gaofen-1;Dynamic Water Body;water body extraction;dataset;feature extraction813|944|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-12-11
Abstract：Objective Quantitative analysis of the coupling coordination relationship between the ecological environment and socioeconomic development in China's poverty-alleviated counties, along with the identification of influencing factors, is of great significance for summarizing poverty alleviation experiences and consolidating the achievements of poverty eradication efforts.Method Based on long-term and high-resolution remote sensing datasets, this study constructed a comprehensive evaluation index system for both the ecological environment and socioeconomic development across 832 poverty-alleviated counties in China. It quantitatively assessed the development levels and coupling coordination status of these two dimensions in 2010, 2015, and 2020. Furthermore, a Boosted Regression Tree model was employed to identify the contribution rates of various indicators to the regional coupling coordination development.Result The results indicated that, overall, the ecological environment in poverty-alleviated counties exhibited a higher level of comprehensive development compared with socioeconomic factors. However, the socioeconomic development progressed at a faster pace than ecological improvements. In addition, the average annual growth rates of both dimensions from 2015 to 2020 were higher than those from 2010 to 2015. Spatially, the coupling coordination degree was the highest in northeastern counties and the lowest in the northwest, showing a distribution pattern of “high in the east and low in the west” and a trend of progressive improvement from coastal to inland areas. Most counties were categorized as “economically lagging”. Among all indicators, population size, gross domestic product, and nighttime light intensity made particularly significant contributions to the coupling coordination.Conclusion Drawing from the poverty alleviation paths and practical experiences of typical regions, the study concludes that implementing context-specific industrial poverty alleviation strategies is crucial for accelerating socioeconomic development in poverty-alleviated counties. Establishing diversified, locally distinctive industries is identified as a key approach for consolidating poverty alleviation achievements and preventing a return to poverty.Keywords：poverty-alleviated counties;ecological environment;socioeconomic development;Remote sensing dataset;coupling coordination;boosted regression tree model;contribution rate;spatial distribution pattern;industrial poverty alleviation613|440|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-11-04Theory and method of ground observation on satellite pixel scales-taking numerical land surface parameters as an example AI Introduction
Abstract：(Objective)Ground observation is the foundation of remote sensing scientific research, providing important data support for the construction of quantitative remote sensing models, accurate and efficient inversion of remote sensing information, and validation of remote sensing products. In particular, with the entrance of era of artificial intelligence, ground observation has been combined with satellite data to drive deep learning models, generating remarkable research results in the field of remote sensing. However, with the combination of satellite data with ground observations, uncertainty is unavoidably introduced to the subsequent results and analysis. This is resulted from the representativeness errors of ground observation partly due to the scale differences between ground observations and satellite pixels and partly due to the complex spatial heterogeneity land surface itself. Ground observation only represents the true value of the measured object at the observation time and in the space it represents, but cannot be directly used as the true value at the scale of satellite pixels.(Method)How to improve the spatiotemporal representativeness of ground observations on satellite pixel scales and obtain the closest representation of reality has alway been the key issue in the field of remote sensing experiments. The acquisition of pixel scale ground truth involves the selection of sample areas, evaluation of spatial heterogeneity, optimization of ground sample layout, ground observation, and scale conversion. Although a large amount of research has been carried out for each aspect, there are still cases of conceptual ambiguity and insufficient understanding in each link, resulting in significant uncertainty in obtaining pixel scale ground truth. How to constrain and control the uncertainty of the pixel scale ground truth in the acquisition process and how to obtain the pixel scale ground “truth” with minimum uncertainty is currently a bottleneck problem that urgently needs to be solved. This article discusses the current challenges and possible solutions in obtaining pixel scale ground “truth”, aiming to provide new insights and theoretical guidance for remote sensing field observation experiments.(Result)Large spatial heterogeneity does not necessarily mean poor spatial representativeness of ground observations. Because representativeness error is not only related to spatial heterogeneity, but also to factors such as the number, location, and observation scale of ground stations. Spatial heterogeneity is the dominant factor affecting the representativeness error of ground observations without optimizing sampling. But it is almost unrelated to spatial representativeness error when the sampling was optimized. It is noteworthy that spatial heterogeneity show strong dependence on spatial scales. At a smaller scale, spatial heterogeneity caused by random factors cannot be ignored. As the sub-pixel scale increases, spatial heterogeneity is mainly influenced by structural factors. The influence of geolocation mismatch needs to be fully considered, whose effect can be eliminate by developing the methods to identify the exact spatial extent of validation pixel.(Conclusion)High-quality ground observation data and effective scale conversion methods are essential prerequisites for obtaining high-quality ground "truth" at the pixel scale. However, there is still a lack of high-precision scale conversion methods, especially for complex terrains such as mountainous regions. In terms of ground observations, it is not only necessary to establish a high-quality observation network but also to ensure effective collaboration among different networks, instruments, observation techniques, and data managers to construct a ground observation dataset with a unified quality standard. In terms of scale conversion, there is a need to develop more universal and accurate scale conversion models, aiming to fully utilize ground observation data globally to construct high-quality remote sensing pixel "truth" datasets.Keywords：Satellite pixel scale ground truth;ground observation;scale difference;spatial heterogeneity;representativeness errors;uncertainty analysis;validation;accuracy assessment;training sample717|1139|1<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-07-21Estimation and Spatiotemporal Evolution Analysis of Annual 500-m Anthropogenic Heat Flux in the Guanzhong Plain Urban Agglomeration AI Introduction
Abstract：Anthropogenic Heat Flux (AHF) refers to the total amount of human-generated heat emissions per unit area within a unit of time. As a key factor in the formation of the urban heat island (UHI) effect, anthropogenic heat emissions significantly influence urban thermal environments by directly releasing waste heat into the atmosphere through human activities. Therefore, studying the spatiotemporal distribution of AHF helps to understand the formation and evolutyion of urban thermal environments, and holds important theoretical and practical significance for mitigating and regulating urban ecological issues.In order to obtain the spatial distribution of anthropogenic heat emissions under limited sample data at a regional scale and to analyze the spatiotemporal evolution characteristics of different types of AHFs in the Guanzhong Plain urban agglomeration in China, this study first employed a modified emission inventory method to estimate the AHFs of prefecture-level cities. The modification primarily addresses the overestimation of residential building heat emissions by excluding private vehicle energy consumption, which is already accounted for in residential energy use. Additionally, it refines the calculation of transportation AHF by incorporating heat emissions from public transportation. Subsequently, using multi-source spatial data, including point of interest (POI) data, nighttime light data, building height data from the Global Human Settlement Layer (GHSL), and population distribution data from WorldPop, a multivariate linear regression model was constructed to estimate different types of anthropogenic heat emissions. This approach enabled the acquisition of annual anthropogenic heat emission data from 2016 to 2021 for the Guanzhong Plain urban agglomeration at a 500-meter spatial resolution, followed by a spatiotemporal analysis of emission characteristics.The study results show that (1) Multivariate linear regression is highly feasible for AHF gridding, as the fitted models achieve high accuracy, with R2 values all exceeding 0.9. Among them, the building AHF model has the highest accuracy, with an R2 of 0.98. (2) POI data contributes significantly to the gridded allocation of anthropogenic heat, effectively reflecting the spatial heterogeneity of different types of anthropogenic heat emissions. This makes it an important data source for estimating the spatial distribution of AHF from various heat sources. (3) The spatial distribution of AHF in the Guanzhong Plain urban agglomeration is uneven, with high-value areas concentrated in economically developed, flat, and highly urbanized central regions of the urban agglomeration, particularly in the northern central urban area of Xi’an. Temporally, AHF exhibits an overall upward trend.Keywords：Anthropogenic heat;point of interest (POI) data;nighttime light data;multi-source spatial data;human activities;multiple linear regression;inventory method;Guanzhong plain urban agglomeration;human activities836|1211|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-07-21Correlation analysis of the sea-surface features under eddy modulation in the global ocean using remote sensing data AI Introduction
Abstract：Mesoscale eddies are broadly distributed in the global ocean and have significant effects on the sea surface temperature (SST), sea surface height (SSH), chlorophyll (Chl), wind speed (WS), and other ocean parameters. Therefore, the coupling analysis of mesoscale eddies and ocean key parameters is an important part of ocean research. With the realization of individual eddy identification technology, the anti-cyclonic eddy (AE), cyclonic eddy (CE), and outside eddy (OE) can be better distinguished. This enables us to comprehensively study the distribution and difference of correlation of sea surface features by comparing OE with AE and CE, which provides a theoretical basis for further clarifying the modulation mechanism of the mesoscale eddy on the air-sea interface and improving ocean numerical simulation. On the basis of distinguishing AE, CE, and OE, this study calculated the Pearson correlation coefficient using sea surface temperature anomaly (SSTA), sea level anomaly (SLA), chlorophyll anomaly (CHLA), and wind speed abnormal (WSA) data from 2010 to 2019, and compared the smoothness of the correlation coefficient. The results show that the correlation distribution among the parameters has significant regional characteristics. In CE and AE, the correlation is ±0.5 in most areas of the ocean, and ±0.7 in the Northern Indian Ocean and the Equatorial Pacific. In OE, the correlation is ±0.2 in most regions, and ±0.4 in the Northern Indian Ocean and the Equatorial Pacific. In addition, when a positive (negative) correlation occurs in OE, it generally shows a large range of positive (negative) correlation, and there is a noticeable transition region between the two. However, under the influence of eddy, the extreme correlation regions are smaller and mainly present as scattered points, and the transition regions between positive and negative regions are very narrow. In terms of smoothness, the smoothness coefficient of the correlation coefficient of each parameter in OE is about 15, while the smoothness coefficient of AE and CE is about 450, which is much lower than that in OE area. We conclude that the influence of eddy on the correlation of each parameter is mainly reflected in the value, distribution and smoothness of the correlation coefficient. The correlation coefficient of each parameter in OE is about 0.2 lower than that in CE and AE, and the smoothness of the correlation coefficient in OE is about 30 times of that in AE and CE. The distribution of the correlation also has a strong point feature in the original distribution mode due to the modulation of the eddy.Keywords：Remote sensing observation;mesoscale eddy;coupling analysis;sea surface temperature;sea surface height;Chlorophyll concentration;wind speed448|1022|2<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-06-17
Abstract：Objective：Land surface temperature (LST) is a key parameter in the physical process of surface radiant energy balance and water cycle. Obtaining LST data accurately and timely, and mastering its temporal and spatial changes are of great significance to climate change research. Thermal infrared(TIR) measurements are limited in practical applications due to cloud cover and other effects. Passive microwave (PMW) remote sensing measurements can penetrate clouds and are less affected by atmospheric interference, which has the advantage of obtaining all-weather surface radiation information. Among them, the microwave remote sensing data Advanced Microwave Scanning Radiometer for EOS(AMSR-E) can obtain all-weather LST, which can be used as a supplement to the missing LST information in thermal infrared (TIR) products under cloudy conditions. However, the AMSR-E data has the problem of lack of information due to the satellite orbit scanning gap of its own sensor, which causes the obtained AMSR-E LST data to be greatly restricted in practical applications. Therefore, it is necessary to propose an effective method for solving the problem. Method：Based on the superiority of deep learning in solving non-linear problems and the high dynamic variability of LST, this paper proposes a multi-temporal feature-connected convolutional neural network (MTFC-CNN) which uses specific input combinations of multi-temporal information and spatial fusion units. The network structure is based on the characteristics of the temporal and spatial distribution of missing track gaps in AMSR-E LST data and the reconstruction of missing LST values is carried out from the timing information. Result：In the simulation experiment, the 2010 annual data was divided into 8 data subsets in four seasons, day and night. The average root mean square error of the reconstructed LST value is about 1.0k and the coefficient of determination R2 is above 0.88. Compared with the other two methods: spline interpolation (Spline) and time multiple linear regression (Regress), the reconstruction effect of MTFC-CNN method performs better regardless of seasons or day and night, which proves that MTFC-CNN is better than Spline and Regress methods at mining the characteristics of temporal and spatial changes in LST. In real experiments, through comparison with MODIS LST products, the LST value reconstructed at the missing area is basically consistent with it at other areas in temporal and spatial distribution. The reconstruction results show that the LST in mainland China region shows a gradual increasing trend from January to July, and a gradual decreasing trend from August to December. Which is basically consistent with the temperature changes in the four seasons. The change of LST during the day is more significant than that at night. In summer, the temperature in Northwest China is significantly higher than that in other regions. In winter, the temperature in Northeast China is generally lower than that in other regions. At night, the difference between summer and winter is more obvious. The difference in LST changes at night in autumn is relatively close. Conclusion：The experimental results show that the MTFC-CNN method proposed in this paper mines the spatio-temporal variation information of LSTs more effectively than two traditional methods, and achieves better results in reconstructing the orbital gap ah-missing of AMSR-E LST data. It provides the possibility for the reconstruction of missing information from TIR LST data under the cloud. Keywords: Land surface temperature; AMSR-E LST; Reconstruction; Deep learning; MTFC-CNN;Keywords：land surface temperature;AMSR-E LST;reconstruction;deep learning;MTFC-CNN3036|1200|0<HTML><L-PDF><Meta-XML>Updated：2022-03-09
Abstract：With the global warming, land ecosystem productivity presents a substantial enhanced trend, which is primarily attributed to the prolonged growing season of terrestrial vegetation. However, the impact of summer greatest growing magnitude on vegetation productivity remains unclear. This study aims to explore the different impacts of growing season length (LOS) and magnitude (MAG) on the long-term trends and interannual variability of vegetation productivity, based on long time series of satellite images. Firstly, the GIMMS NDVI3g data was applied to derive the key phenology parameters, including start and end of growing season (SOS, EOS), summer growing peak. Summer vegetation productivity (Summary of Vegetation Index, VIsum) was obtained by integrating the area under growth curve. The long-term trends and interannual variability of SOS, EOS and VIsum was explored at pixel and land cover ecosystem level. Special attention was paid to the impacts of LOS and MAG on VIsum changes. The relative important method was used to quantify contribution of LOS and MAG to VIsum. The results indicate the overall vegetation productivity for Northeastern China do not behave a clear trend, but LOS has a decrease trend and MAG show an increase trend. VIsum presents a consistent fluctuation pattern with LOS, but changes asynchronously with MAG. There is a cycle of 10-years length for the trends and variability of LOS, MAG and VIsum. For the spatial distribution at pixel level, VIsum tends to increase in needle forest of northern and southern part, and the western grassland. The spatial distribution of LOS trend shows an opposite pattern with that of MAG. LOS is becoming shortened in the middle cropland and western grassland (81.5% of vegetated area). While MAG is enhanced in this ecosystem (16.5% of vegetated area). This reflects shortened LOS induces the increase of MAG. Across various land types, LOS contribute mostly to the long-term trends and interannual variability of VIsum (75%). LOS plays a key role in northern needle and eastern broad-leaved forest, MAG accounts for 27% of relative importance. The carbon flux data show a similar change pattern with satellite NDVI in the calculation of phenology parameters and vegetation productivity. Although LOS mainly control the trend and interannual variability, MAG may play a control role in the future as it keeps the increase trend.Keywords：植被物候;植被生产力;生长季长度;生长幅度;相对重要性;GIMMS NDVI3g1928|2359|0<HTML><L-PDF><Meta-XML>Updated：2021-06-11- Abstract：The water body extraction algorithm based on satellite remote sensing is mainly aimed at large and medium-sized lakes or large rivers. When applied to small water bodies, it is easy to misjudge. Sentinel 2 satellite multi-spectral remote sensing data spatial resolution of 10, 20, 60 meters, a double star time resolution of 5 days, and high temporal and spatial resolution. Therefore, this paper uses the sentinel-2 green light band (560nm), red edge band (705nm), near infrared band (842nm, 865nm) and short wave infrared band (2190) for remote sensing reflectance. A new water index algorithm (Red Edge based Water Index, RWI for short) is proposed for the extraction of finely water. The normalized remote sensing reflectivity of vegetation, shadow, building, mixed pixel, bare soil and water body is compared and analyzed. The mechanism explains why RWI has better effect of extracting fine water body than other water body indexes.Keywords：Water extraction;water body index;small water body;sentinel-2;RWI;MNDWI;MBWI;AWEI3458|3751|0<HTML><L-PDF><Meta-XML>Updated：2021-06-11
