Advances in Space Research最新文献

{"title":"ENSO-influenced Mekong plume extension revealed by causality between estuarine water level and GRACE-derived oceanic height","authors":"Hok Sum Fok ,&nbsp;Zhongtian Ma","doi":"10.1016/j.asr.2026.01.020","DOIUrl":"10.1016/j.asr.2026.01.020","url":null,"abstract":"<div><div>Understanding river plume extension is essential for resolving freshwater connectivity and biogeochemical fluxes in the open ocean. However, it remains poorly understood due to the limited detectable transport ranges of isotope measurements, numerical models, and satellite methods. Rather than cross-correlating runoff with <em>GRACE</em>-derived oceanic equivalent water height (OEWH) as in previous studies, this study cross-correlates estuarine water level—served as a runoff proxy—with <em>GRACE</em>-derived OEWH/Ocean bottom pressure. Granger and Liang causality tests are further applied to examine whether estuarine output potentially drives the <em>GRACE</em>-derived oceanic height changes, thereby validating the inferred spatiotemporal extent of far-field plume transport across the Sunda Shelf Sea (SSS) and South China Sea (SCS). Isolating interannual variability via multi-wavelet analyses, we found that while La Niña tends to enhance offshore extension across SSS and SCS, alternating El Niño-La Niña phases stagnate transport in central SSS. Comparison with isotope-estimated plume ages confirms the method’s potential applicability. Notably, the inferred Mekong plume might extend to ∼18°N in SCS that represents an exceptionally long range of the plume transport. These findings provide a potential alternative for determining extended plume transport duration patterns and highlight the role of climate variability in reshaping estuary-to-shelf transport.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 6675-6691"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ionospheric disturbances as precursor signals of the March 28, 2025, Myanmar earthquake","authors":"C. Pansong ,&nbsp;S. Ruttanaburee ,&nbsp;P. Pornsopin ,&nbsp;P. Kenpankho","doi":"10.1016/j.asr.2026.01.070","DOIUrl":"10.1016/j.asr.2026.01.070","url":null,"abstract":"<div><div>This study examined the ionospheric response associated with the Mw 7.7 Myanmar earthquake on March 28, 2025, using GPS-derived Total Electron Content (TEC) data from seven GNSS stations across Thailand. TEC variations were analyzed alongside Dst and Kp indices, as well as ionosonde-derived parameters, namely the critical frequency of the F2 layer (<em>foF</em>2), the peak height of the F2 layer (<em>hmF</em>2), the disturbances in <em>N</em>m<em>F</em>2, and the slab thickness (τ), which were obtained from three IGS-supported stations. We detected abnormal variations in TEC approximately 15 days before the earthquake (13–27 March 2025), characterized by alternating positive and negative deviations. The TEC exhibited alternating positive and negative deviations throughout the analysis period, reflecting ionospheric variability prior to the earthquake. During the early period (13–18 March), the deviations remained within approximately ±6 TECU. However, from 19 to 21 and 23–24 March, moderate fluctuations were observed, particularly at mid- and low-latitude stations (UTHG, THBK, THCP, and THPK), where ΔTEC ranged from ±6 to 10 TECU. The TEC decrease occurred on 25 March under weak geomagnetic conditions (Dst &gt;  −30 nT) at the northern stations MAEH (−18.40 TECU), THCM (−15.65 TECU), and NANN (−15.73 TECU), marking the most pronounced negative anomaly observed during the study period. Subsequently, on 26–27 March, TEC values recovered to positive anomalies of +4 to +10 TECU, indicating a return to normal ionospheric conditions. To objectively identify pre-seismic ionospheric anomalies, a Median Absolute Deviation (MAD) approach was applied using a ±1.34MAD threshold. This statistical technique effectively detects subtle deviations while minimizing transient noise. The results reveal coherent TEC depletions across multiple stations on 25 March, suggesting the presence of localized ionospheric disturbances potentially related to seismo-ionospheric processes rather than geomagnetic effects. Furthermore, concurrent anomalous increases in <em>foF</em>2 and <em>hmF</em>2, along with a reduction in slab thickness near the epicentral region, indicate vertical uplift of the F2 layer, consistent with possible Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanisms.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 7002-7021"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlabeled data assisted domain adaptation for cross-scene image classification","authors":"Shuyue Wang ,&nbsp;Jiawei Niu ,&nbsp;Mohammed Bennamoun","doi":"10.1016/j.asr.2026.01.028","DOIUrl":"10.1016/j.asr.2026.01.028","url":null,"abstract":"<div><div>Domain adaptation (DA) is crucial in cross-scene image classification, enabling models to generalize across domains with varying data distributions. Existing approaches rely on abundant and diverse labeled source data to learn discriminative and transferable features for cross-domain alignment. However, such labeled data are often expensive and limited in remote sensing applications. In contrast, abundant task-relevant unlabeled data are more accessible but remain underutilized, despite containing domain-specific feature distributions that can enhance feature learning. To address this gap, we propose an Unlabeled data Assisted Domain Adaptation (UADA) framework for cross-scene image classification. UADA incorporates task-relevant unlabeled data as an auxiliary source alongside labeled source data to enrich feature diversity and improve the model’s adaptability to the target domain. Specifically, we introduce a progressive pseudo-label optimization strategy that iteratively refines pseudo-labels for unlabeled data through confidence-aware self-labeling. We then employ weight-shared feature extractors to jointly encode labeled and unlabeled source data, enabling the model to learn a unified feature space that captures diverse semantic representations for robust feature alignment. Finally, we construct domain-specific classifiers for each source and adaptively fuse their predictions, effectively harnessing complementary semantic cues for robust target classification. Extensive experiments across multiple tasks show that UADA outperforms existing methods. The code will be released at <span><span>https://github.com/Morrie0804/UADA.git</span><svg><path></path></svg></span> upon acceptance.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 6747-6759"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of Sentinel-2 simplified level 2 prototype (SL2P) processor in retrieving leaf chlorophyll concentration over dusty environment","authors":"Avinash Kumar Ranjan ,&nbsp;Bikash Ranjan Parida ,&nbsp;Jadunandan Dash ,&nbsp;Amit Kumar Gorai","doi":"10.1016/j.asr.2026.01.036","DOIUrl":"10.1016/j.asr.2026.01.036","url":null,"abstract":"<div><div>Reliable information on leaf chlorophyll concentration (LCC) in mining-impacted regions is critical for vegetation assessment and management. However, the presence of foliar dust (FD) significantly alters canopy reflectance, introducing uncertainty in satellite-based chlorophyll estimation. The present study, possibly for the first time, aims to evaluate the performance of the globally trained Sentinel-2 Simplified Level 2 Prototype Processor (SL2P) against locally calibrated empirical models and in-situ measurements for estimating LCC in FD-affected mining landscape. Furthermore, this study explores the LCC-FD nexus based on in-situ observations. In-situ LCC measurements were collected using a handheld chlorophyll meter across 40 sites over an industrial region in India. Sentinel-2B spectral bands (surface reflectance) and vegetation indices (VIs) were used to develop empirical models, while SL2P-derived LCC estimates were validated against in-situ measurements. The findings of the study revealed a non-linear FD–LCC relationship, indicating distinct vegetation responses across low, intermediate, and high dust loads. Furthermore, the study evidenced that SL2P consistently underestimates LCC under both dusty and non-dusty conditions, exhibiting substantial negative bias (–8.18 to –14.61 µg/cm²) and high uncertainty (RMSE = 10.10–15.09 µg/cm²), indicating limited reliability for LCC retrieval. In contrast, several empirical models demonstrated improved performance, particularly under dusty conditions. Band-based models using the red-edge (RE2), red, and near-infrared (NIR) bands achieved low dispersion (MAD ≈ 2.1–3.2 µg/cm²) and low relative uncertainty (nRMSE ≈ 7–8%). Among VIs, the Transformed Soil-Adjusted Vegetation Index (TSAVI) showed stable performance in dusty environments (MAD ≈ 2.0 µg/cm²; nRMSE ≈ 9%), while Global Environmental Monitoring Index (GEMI) and Modified Chlorophyll Absorption in Reflectance Index (MCARI) exhibited lower transferability across conditions. These results highlight the limited accuracy of globally trained biophysical algorithms across diverse environments, and advocate for locally calibrated, adaptive models for improved LCC estimation accuracy.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 6791-6810"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and control of multi-stage spacecraft with turntable and active pointing ultra-quiet platform","authors":"Mingren Han,&nbsp;Liang Tang,&nbsp;Xin Guan,&nbsp;Youyi Wang,&nbsp;Xiao Feng,&nbsp;Renjian Hao,&nbsp;Kebei Zhang","doi":"10.1016/j.asr.2026.01.051","DOIUrl":"10.1016/j.asr.2026.01.051","url":null,"abstract":"<div><div>Constellation communication requires directing communication payloads toward other satellites or switching between multiple satellites, necessitating that satellites possess ultra-high Agility, Stability, and Precision Control (ASPC) and wide-range payload pointing tracking capabilities. However, spacecraft with conventional configurations struggle to simultaneously meet the demands of ASPC and wide-ranging payload pointing control. The maneuverability metrics, including speed and acceleration, are also limited by the capabilities of the onboard attitude control actuators. To address these limitations, an innovative multi-stage pointing control system for spacecraft is proposed in this study, which integrates a two-dimensional turntable with an Active Pointing Ultra-Quiet Platform (PQP). Initially, the dynamics of this multi-stage system are rigorously modeled, leading to the proposition of a multi-stage pointing control structure with separate controllers for each stage. Subsequently, to address the issue of disturbance torques that degrade tracking accuracy, a nonlinear disturbance observer is developed to estimate the disturbance torques acting on the spacecraft body and the turntable, which are introduced into the controller as feedforward compensations. Furthermore, a stability analysis of the closed-loop control system is conducted to ensure its reliability and performance. Finally, simulation scenarios for agile, stable, precise, and wide-ranging pointing control of constellation communication are designed. The feasibility and effectiveness of the proposed control methodology are validated through numerical simulations.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 7424-7442"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergizing GNSS, MODIS, and ERA5 for high-resolution PWV retrieval: a two-stage machine learning approach over Hong Kong","authors":"Guanmei Chen ,&nbsp;Aigong Xu ,&nbsp;Zongqiu Xu ,&nbsp;Zhiguo Deng ,&nbsp;Longjiang Tang ,&nbsp;Congying Shao ,&nbsp;Nannan Yang ,&nbsp;Xiang Gao ,&nbsp;Meiqi Zhang","doi":"10.1016/j.asr.2026.01.035","DOIUrl":"10.1016/j.asr.2026.01.035","url":null,"abstract":"<div><div>Atmospheric water vapor is a fundamental driver of the Earth’s energy balance and climate system, making precipitable water vapor (PWV) a key indicator for meteorological monitoring and extreme weather forecasting. This study introduces an innovative two-stage machine learning framework, named the Decision Tree and Random Forest PWV Fusion (DTRP) framework, to generate a high-precision, spatiotemporally continuous PWV product over Hong Kong by fusing multi-source data, including GNSS, Moderate Resolution Imaging Spectroradiometer (MODIS), and ERA5. GNSS PWV derived from 18 Continuously Operating Reference Stations (CORS) served as the benchmark. In the initial stage of the DTRP framework, a regression tree model is employed to correct systematic deviations in MODIS Near-Infrared (NIR) PWV. The model exploits the differences between ERA5 and MYD NIR PWV, together with spatiotemporal features, to estimate GNSS–MYD deviations, thereby yielding an enhanced PWV product (EMYD). Building on this, the second stage of DTRP applies a random forest framework to integrate EMYD with original ERA5 PWV along with meteorological covariates, generating the final fused product (RF PWV). Statistical evaluation confirms the initial correction successfully eliminated the systematic negative bias in MYD NIR PWV. The RMSE decreased to 3.45 mm with an R² of 0.95, corresponding to improvements of 55.8% in accuracy and 0.17 in explanatory power over the original data. The subsequent fusion stage achieved a further refined RMSE of 3.29 mm, outperforming the MYD, ERA5, and EMYD products by 57.9%, 30.4%, and 4.6%. The final RF PWV product demonstrates nearly unbiased estimation capability and maintains strong agreement with reference data, achieving an R² of 0.96. Spatiotemporal and correlation analyses confirmed the superior consistency and reliability of the RF PWV under diverse conditions and its effectiveness in decoupling errors from geographic and atmospheric influences. The proposed DTRP algorithm presents a valuable framework for predicting severe convective weather.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 6607-6628"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quasi-real-time retrieval of ERA5 precipitable water vapor over mainland China","authors":"Xiangshun Meng ,&nbsp;Yong Wang ,&nbsp;Yunlong Zhang ,&nbsp;Wei Du ,&nbsp;Yanping Liu ,&nbsp;Xiao Liu","doi":"10.1016/j.asr.2026.01.064","DOIUrl":"10.1016/j.asr.2026.01.064","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The occurrence of extreme weather events is closely related to the spatial distribution and temporal variation of atmospheric water vapor. High-precision and high-spatiotemporal-resolution precipitable water vapor (PWV) data can effectively capture such variations. However, current approaches for obtaining PWV data still have certain limitations: although PWV retrieved from Global Navigation Satellite System (GNSS) observations enables all-weather real-time monitoring, its sparse ground station distribution hinders the achievement of high spatial coverage; satellite remote sensing, while offering wide-area spatial coverage, suffers from low temporal resolution and is susceptible to cloud cover and meteorological events, limiting its ability to provide temporally continuous and spatially complete PWV fields. The fifth-generation global atmospheric reanalysis dataset (ERA5) from the European Centre for Medium-Range Weather Forecasts (ECMWF) offers high spatiotemporal resolution and has demonstrated significant potential in meteorological applications. However, it has a data latency of approximately 120 h. To address the real-time demand for water vapor data in severe weather forecasting, it is essential to develop predictive models for ERA5 water vapor. Taking mainland China as a case study, given its vast geographic span and significant climatic and geomorphological heterogeneity, the study area was divided into 13 regions based on climate types, latitude, and landform characteristics. The Fast Fourier Transform (FFT) was employed to extract the common period of the ERA5 meteorological elements. The best common period for each meteorological element was identified through correlation analysis, and a temporal sliding window, corresponding to the best common period, was constructed to enhance the representation of spatiotemporal heterogeneity among the elements. To address the temporal delay in ERA5 data acquisition, the Convolutional Long Short-Term Memory (ConvLSTM) network was employed to predict ERA5 PWV across different seasons. Results show that among the 13 subregions of mainland China, a medium-length common period (e.g., 83 h) yields the best predictive performance. Topographic and climatic characteristics have a significant impact on prediction accuracy: the plateau region demonstrates better predictive performance due to stable water vapor and low atmospheric pressure, whereas the tropical monsoon region exhibits strong variability driven by monsoon activity, with annual RMSE trends closely aligned with the seasonal variation of water vapor. The model was externally validated against GNSS-retrieved PWV (1 Mar 2020–28 Feb 2021). Driven by eight ERA5 variables (PWV, temperature, pressure and wind-related elements), it achieved Root Mean Square Error (RMSE)s of 2.83–8.08 mm—the minimum over the low-latitude first-step plateau mountains (LFA, plateau-monsoon) and the maximum over the mid-latitude second-step mountains (MSM, temperat","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 6952-6975"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A benchmark dataset for Landsat-to-Sentinel image generation using deep learning-driven super-resolution techniques","authors":"Peijuan Wang ,&nbsp;Samet Aksoy ,&nbsp;Elif Sertel","doi":"10.1016/j.asr.2026.01.049","DOIUrl":"10.1016/j.asr.2026.01.049","url":null,"abstract":"<div><div>High-resolution satellite imagery plays a vital role in accurately analyzing surface changes, vegetation dynamics, and land cover transitions for environmental monitoring and Earth science applications. While the Landsat satellite series provides long-term, high-coverage time-series data—essential for studying large-scale phenomena such as deforestation, urban expansion, and agricultural transformation—its 30-meter spatial resolution often falls short in applications requiring finer detail. To address this limitation, this study introduces Land2Sent, a novel remote sensing super-resolution dataset specifically designed for the Landsat 8/9 to Sentinel-2A/B image enhancement task. The Land2Sent dataset aims to upscale Landsat imagery from 30 m to 10 m by utilizing the higher-resolution Sentinel-2 images as reference. Both normalized 4-band (R, G, B, NIR) images and original 16-bit 4-band images are included to assess the impact of bit depth on model performance. Using this dataset, ten state-of-the-art deep learning models are evaluated for their ability to reconstruct super-resolved images from low-resolution Landsat inputs. The performance of these models is assessed using quantitative metrics across the full dataset, as well as through visual inspection and Normalized Difference Vegetation Index (NDVI) analysis of selected image patches.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 6855-6880"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A diversity-based strategy for asteroid tour design","authors":"Jan Grabowski,&nbsp;Andrea Bellome,&nbsp;Leonard Felicetti","doi":"10.1016/j.asr.2026.01.075","DOIUrl":"10.1016/j.asr.2026.01.075","url":null,"abstract":"<div><div>The design of multi-target tours among the celestial bodies of the Solar System is one of the key drivers for scientific return in space exploration missions. Such missions could bring valuable insights into the evolution of our Solar System and the available resources by prospecting many targets with a single spacecraft. The generation of a sequence of encountered bodies is a combinatorial optimization problem that can be addressed with heuristic search algorithms. However, solving the optimization problem does not consider the diversity of the body population and heuristic search techniques often produce tours with the same encountered bodies. In this paper, a new search strategy based on the diversity of visited bodies is described. An asteroid tour diversity score is defined to serve as a metric for a new search strategy in the combinatorial optimization problem. The standard Beam Search algorithm is compared to diversity-based optimization for the generation of asteroid tours in the Main Asteroid Belt and Near-Earth Asteroid populations. Unlike Beam Search, the proposed diversity-based strategy does not generate duplicate sequences and facilitates the encounter of more asteroids at higher eccentricities and inclinations, at the cost of higher <span><math><mrow><mi>Δ</mi><mi>v</mi></mrow></math></span>. A hybrid search strategy is shown to find better balance between asteroid tour diversity and <span><math><mrow><mi>Δ</mi><mi>v</mi></mrow></math></span> cost. These study cases illustrate the performance of diversity-based search for the preliminary mission design of future asteroid tour missions.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 7039-7053"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the cooling effects of built-up areas for mitigating thermal discomfort in semi-arid urban environments: a case study of Tehran","authors":"Firouz Aghazadeh ,&nbsp;Akbar Rahimi ,&nbsp;Mohammad Karimi Firozjaei ,&nbsp;Vladimir Ondrejicka ,&nbsp;Maros Finka","doi":"10.1016/j.asr.2026.01.025","DOIUrl":"10.1016/j.asr.2026.01.025","url":null,"abstract":"<div><div>Urbanization has significantly transformed land-use patterns and continues to influence thermal comfort in cities. This study explores how built-up areas contribute to reducing thermal discomfort in semi-arid regions, focusing on Tehran from 2000 to 2024. Landsat 7 and 8 satellite data were used to classify land use, derive Land Surface Temperature (LST), and assess thermal stress through the Discomfort Index (DI). To evaluate cooling performance, several indicators were applied, including Temperature and Discomfort Index variations for both Urban Green Spaces (UGS) and built-up areas (Temperature Urban Green Space (TUGS), Temperature Built-up (TBU), Discomfort Index Urban Green Space (DIUGS), and Discomfort Index Built-up (DIBU)), as well as Apparent Temperature differences (Apparent Temperature Urban Green Space (ATUGS), and Apparent Temperature Built-up (ATBU)). During the study period, built-up areas expanded from 55% to 68%, while UGS coverage peaked at 18% in 2014 before declining to 14% in 2024. Despite their ecological importance, green spaces showed only a slight cooling influence, with mean LST decreasing by just −0.13°C. However, DI dropped more noticeably (−0.51°C), indicating the importance of humidity in shaping thermal comfort. Meanwhile, UGS temperatures increased from 36.0°C to 38.7°C, whereas built-up zones experienced a minor cooling trend. Cooling indices revealed a gradual loss of cooling benefits from UGS and an enhanced moderating effect from built-up structures. TUGS decreased from 2.92 to −0.40, while TBU increased from 1.09 to 2.56. Similarly, ATUGS weakened from −3.54 to 0.36, whereas ATBU improved from −1.09 to −2.77. Correlation analysis showed that larger UGS patches still offer better cooling, although patch complexity has limited impact. Overall, the findings underscore growing urban heat pressure in Tehran and the weakening role of green spaces in temperature regulation. At the same time, they highlight the emerging significance of built-up morphology and shading features in improving thermal comfort across a rapidly urbanizing landscape.</div></div>","PeriodicalId":50850,"journal":{"name":"Advances in Space Research","volume":"77 6","pages":"Pages 6710-6733"},"PeriodicalIF":2.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147388072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}