International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"Variation mechanisms of suspended sediment concentration in complex estuary determined through remote sensing, observation and modeling coupling","authors":"Mingliang Zhang ,&nbsp;Zixuan Lang ,&nbsp;Yuling Liu","doi":"10.1016/j.jag.2025.104539","DOIUrl":"10.1016/j.jag.2025.104539","url":null,"abstract":"<div><div>Suspended sediments are vital indicators of water quality, so understanding the dynamic processes and influence factors of suspended sediment concentration (SSC) is crucial in estuaries or coastal waters. This study presents a comprehensive strategy for coupling several methods of remote sensing, field observation, and numerical simulation to systematically study spatio-temporal variability of suspended sediment in the Liao River Estuary (LRE) and Daliao River Estuary (DLRE), and then driving forces are analyzed. The SSC retrieval algorithm is suitable to interpret the dynamic SSC changes in shallow and turbid waters of the LRE and the DLRE. The results indicated that the SSC is higher during the muximum period of flood and ebb tide velocities but lower near the periods of flood and ebb slacks. SSC of the LRE and the DLRE is higher in spring tide cycle than that in neap tide under similar wind conditions. Wind conditions and waves caused by wind markedly influenced the SSC distribution in the LRE and the DLRE. Specifically, continuous turbidity zones are often formed due to sediment resuspension caused by larger bed shear stress under the action of strong southwest winds. The effect of river discharge on SSC in dry season is almost negligible in these study regions, and the river discharge during wet season leads to an increase of SSC. These results provide a fresh perspective on the complex sedimentary processes in estuary waters.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104539"},"PeriodicalIF":7.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tree species classification using intensity patterns from individual tree point clouds","authors":"Andreas Tockner,&nbsp;Ralf Kraßnitzer,&nbsp;Christoph Gollob,&nbsp;Sarah Witzmann,&nbsp;Tim Ritter,&nbsp;Arne Nothdurft","doi":"10.1016/j.jag.2025.104502","DOIUrl":"10.1016/j.jag.2025.104502","url":null,"abstract":"<div><div>Personal laser scanning has evolved into a cutting-edge technology for obtaining fast and accurate biometric measurements of individual trees in a forest. However, recent studies assessing tree species labels on single tree point clouds have been insufficiently accurate in complex forest ecosystems; moreover, explainability of machine-learning methods used in published studies has been insufficient. Whether the predictions of black-box models suffer from over-fitting or whether they are based on characteristic species traits often remains unclear. To solve this problem, we present a simple classifier combining random forest models with decision rules, trained on 9 common tree species in Central Europe. Explainable elements are a soft classifier on classification probabilities and detailed analysis of variable importance and minimal variable depth. The overall classification accuracy was 89.8% for nine species, with greater values for the four major species (spruce, pine, oak, and beech). Intensity measures in the upper tree section and tree geometry ratios were the most important predictors. The method proposed in this study can potentially be used to analyze forest ecosystems in more spatial detail by addressing species-specific research questions to an unprecedented degree.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104502"},"PeriodicalIF":7.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting for temporal and spatial autocorrelation to examine the effects of climate change on vegetation greenness trend in China","authors":"Lingwei Chen ,&nbsp;Likai Zhu ,&nbsp;Cuiyutong Yang ,&nbsp;Zizhen Dong ,&nbsp;Rui Huang ,&nbsp;Jijun Meng ,&nbsp;Min Liu","doi":"10.1016/j.jag.2025.104548","DOIUrl":"10.1016/j.jag.2025.104548","url":null,"abstract":"<div><div>Trend and attribution analysis of vegetation greenness is crucial to explain and predict ecosystem responses to climate change. The common practice to detect and explain greenness pattern from remote sensing time series is mostly based on pixel-by-pixel analysis, which often fails to account for spatial autocorrelation and may lead to spurious patterns. Here we applied the Partitioned Autoregressive Time Series (PARTS) method to the Normalized Difference Vegetation Index-3rd generation (NDVI3g) data and multiple climate datasets, and examined the climate effects on greenness trend in China. This method considers temporal and spatial autocorrelation structure, and aggregates pixel information to rigorously test the hypotheses about regional patterns. The results showed that greenness trends were strongly impacted by climate change, environmental background and their interactions. In regions with lower greenness, higher temperature, more precipitation and soil moisture, and lower vapor pressure deficit (VPD), the greening rate tends to be higher. For the whole China, long-term trends of temperature (P &lt; 0.05) and soil moisture (P &lt; 0.05) made significantly negative effects on greenness trend, while trend of precipitation (P &lt; 0.05) and VPD (P &lt; 0.001) made significantly positive impacts. But their effects strongly interacted with environmental background. The overall positive VPD impact was significantly enhanced with an increase in VPD level (P &lt; 0.001), which was also supported by the significantly positive VPD impact in the northwestern arid regions (high VPD) and the significantly negative impact in the tropical and subtropical areas (low VPD). In the cold ecosystems, the change in soil moisture made significantly negative effect on greenness trend. This study provides new insights into the driving mechanisms of greenness change, which is useful to inform ecosystem modeling to make accurate predictions. Moreover, the analysis framework with PARTS method could be effectively applied to other regions or to analyzing other ecosystem responses to climate change.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104548"},"PeriodicalIF":7.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatially predicting ecosystem service patterns in boreal drained peatlands forests using multisource satellite data","authors":"Kaapro Keränen,&nbsp;Anwarul Islam Chowdhury,&nbsp;Parvez Rana","doi":"10.1016/j.jag.2025.104545","DOIUrl":"10.1016/j.jag.2025.104545","url":null,"abstract":"<div><div>Boreal drained-peatland forests provide diverse, interlinked ecosystem services (ESs), critical for informed decision-making in forest management. We mapped five ESs: bilberry yield, visual amenity, biodiversity conservation, carbon storage, and timber production using Landsat 8–9, Sentinel-2, and PlanetScope data. By combining these five ESs variables, we calculated a summed-ESs variable to capture overall ESs in drained peatland forests. Our objectives included assessing the influence of sensor resolution, auxiliary data, and the feasibility of scaling ESs predictions across varying canopy covers (closed, partial, and open). Using spectral bands and indices, we applied random forest regression, achieving explained variances (R²) of 13–75 % for single ESs and 58–67 % for summed ESs. Sensor performance varied, with Landsat (R² 22–69 %), Sentinel-2 (R² 25–75 %), and PlanetScope (R² 13–65 %). Incorporating auxiliary variables from seven-year-old LiDAR data improved model R² value by 1–24 %. We successfully scaled ESs predictions to map spatial distributions across the study area, with high ESs value in closed-canopy areas. These findings demonstrate satellite imagery’s effectiveness for spatial ESs prediction, supporting sustainable drained-peatland forest management.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104545"},"PeriodicalIF":7.6,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery","authors":"Wantai Chen ,&nbsp;Yinfei Zhou ,&nbsp;Xiaofeng Li","doi":"10.1016/j.jag.2025.104550","DOIUrl":"10.1016/j.jag.2025.104550","url":null,"abstract":"<div><div>This study introduces a flood index-enhanced deep learning model for coastal inundation mapping leveraging dual-polarization and bitemporal Sentinel-1 synthetic aperture radar (SAR) imagery. The proposed model, FIE-Net, introduces two key innovations to improve flood mapping accuracy. First, it integrates the Ratio Image (RI), a primary flood index derived from bitemporal SAR data, which provides a reliable reference for precise inundation area delineation. The index offers a clear, flood-specific signal that enhances segmentation precision. Second, the model employs a dual-branch U-Net framework, augmented with specialized modules to enhance feature extraction and better integrate diverse data sources. This combination enables the model to handle complex flood scenarios more effectively, thereby boosting overall performance. Using data from the Copernicus Emergency Management Service, 16 pairs of representative SAR images from Madagascar’s Tropical Cyclones 2018 AVA and 2023 Cheneso were collected under various terrain conditions. After semi-automatic labeling and cropping, 4350 sample pairs were processed, with 2784/696/870 used for model training/validation/testing. The proposed model achieved the highest Intersection over Union (IOU) of 79.44%, outperforming the state-of-the-art models across all evaluation metrics. The experiments also demonstrate that each introduced innovation contributes to improved accuracy, with the flood index making the most significant impact. Furthermore, the model’s applicability was further confirmed with three independent flooded areas (689 samples, not included in training) from the event Cheneso. IOUs in all three scenes consistently exceeded 75%, underscoring the model’s reliability and robustness in real-world scenarios.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104550"},"PeriodicalIF":7.6,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving subpixel impervious surface estimation based on point of interest (POI) data","authors":"Junzhe Wang ,&nbsp;Wang Jin ,&nbsp;Zheng Cao ,&nbsp;Zhiyi Pan ,&nbsp;Guang Yang ,&nbsp;Yaolong Zhao","doi":"10.1016/j.jag.2025.104538","DOIUrl":"10.1016/j.jag.2025.104538","url":null,"abstract":"<div><div>Accurate estimation of impervious surface area (ISA) at the subpixel level is essential for understanding urbanization and its environmental impacts. In recent years, point-of-interest (POI) data has demonstrated unique value for urban studies. However, its potential for improving subpixel ISA estimation has yet to be fully realized. This research seeks to overcome the challenges of fusing POI data with remote sensing imagery and improve subpixel ISA estimation. To form an integrated sample dataset for subpixel ISA estimation, POI data were processed using kernel density analysis and transformed into continuous raster layers compatible with remote sensing imageries. The proposed method was tested in two study areas with distinctly different urban land patterns: Shenzhen, China, and Chicago, USA. Two widely used machine learning models, Classification and Regression Tree (CART) and Convolutional Neural Network (CNN), were developed based on the integrated sample dataset. The results show POI data significantly improved both models. Incorporating POI data reduced MAE by 52.75% for CART and 56.68% for CNN, and RMSE by 45.39% and 48.54%, respectively, compared to models without POI data. The fully trained POI-integrated CNN achieved the highest accuracy (MAE: 2.95, RMSE: 5.12, R²: 0.99). By achieving accurate subpixel ISA estimation with minimal additional procedures, the proposed method is expected to offer an objective and repeatable approach, providing reliable basic data for urban environmental research and planning.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104538"},"PeriodicalIF":7.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying key indicators of essential biodiversity variables for mangrove species in response to hydro-meteorological factors","authors":"Hang Yao ,&nbsp;Bolin Fu ,&nbsp;Weiwei Sun ,&nbsp;Yuyu Zhou ,&nbsp;Yeqiao Wang ,&nbsp;Weiguo Jiang ,&nbsp;Hongchang He ,&nbsp;Zhili Chen ,&nbsp;Yiji Song","doi":"10.1016/j.jag.2025.104535","DOIUrl":"10.1016/j.jag.2025.104535","url":null,"abstract":"<div><div>Mangroves are critical for climate mitigation and biodiversity conservation, yet their spatiotemporal dynamics and physiological responses to hydrometeorological drivers remain poorly understood. This study extracted three essential biodiversity variables (area distribution, phenology, and physiological traits) and further revealed their dependencies on hydrometeorological conditions. We developed a continuous time-series monitoring method (CTSM) to enhance the Detect-Monitor-Predict detection framework for accurately tracking mangrove spatial succession in the Beibu Gulf from 2000 to 2021. We combined Continuous Change Detection and Classification with Harmonic Analysis of Time Series (HANTS) methods to capture the seasonal changes of physiological traits of dominant mangrove species. This study utilized HANTS-PLSR (partial least squares regression) response models and structural equation models to explore the seasonal responses of physiological trait to hydro-meteorological factors. The results indicated that (1) the improved detect component delineated fine-scale expansion patterns of mangroves, with area-hydrometeorology coupling evolving from uncoordinated to highly coordination during 2000–2021. (2) The start, peak and end of the growing season for mangroves are in March-April, June-September and January-February of the following year, respectively. The mangroves in different regions exhibit relatively delayed growth periods. (3) <em>Aegiceras corniculatum</em> exhibited bimodal phenological trajectories, contrasting with unimodal patterns in three co-occurring species. (4) The physiological traits displayed a positive correlation with water/air temperature and sunshine duration. The phenological changes of four mangrove species are driven by the interaction between hydrological and meteorological variables, with meteorological factors dominating (path coefficient &gt; 0.50, <em>p</em> &lt; 0.001). The findings provide insights into mangrove conservation and biodiversity monitoring.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104535"},"PeriodicalIF":7.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data","authors":"Yilin Bao ,&nbsp;Xiangtian Meng ,&nbsp;Weimin Ruan ,&nbsp;Huanjun Liu ,&nbsp;Mingchang Wang ,&nbsp;Abdul Mounem Mouazen","doi":"10.1016/j.jag.2025.104513","DOIUrl":"10.1016/j.jag.2025.104513","url":null,"abstract":"<div><div>Precision management of soil organic carbon (SOC) is crucial for regulating the global carbon cycle and ensuring food security. Currently, SOC prediction remains challenging due to unresolved moisture disturbances, underutilized multimodal remote sensing data, and uncertain model transferability. To address these challenges, a new paradigm integrating moisture elimination with advanced hybrid deep learning has been developed. Firstly, this research employs optimal cluster analysis based on soil moisture spatial characteristics, followed by the application of the Kubelka-Munk radiative transfer model to construct a moisture elimination strategy (MES). Next, a hybrid deep learning model, Multimodal Transformer Mechanism-Convolutional Neural Network-Convolutional Long Short-Term Memory (MT-CNN-ConvLSTM, MCCL), is constructed to enhance predictive accuracy and generalizability. The MCCL model was compared to other machine learning and deep learning models, including LSTM, Random Forest (RF), CNN, Artificial Neural Network (ANN), Support Vector Machine (SVM) and partial least squares regression (PLSR). Results indicate that (1) the proposed paradigm achieves optimal SOC content prediction accuracy in humid regions, with a root mean square error (RMSE) of 3.58 g kg⁻¹, a coefficient of determination (R²) of 0.76, a ratio of performance to interquartile distance (RPIQ) of 2.26, and a mean absolute error (MAE) of 4.73 g kg⁻¹. The model shows better performance in semi-humid regions, yielding an RMSE of 3.12 g kg⁻¹, R² of 0.77, RPIQ of 2.27, and MAE of 4.71 g kg⁻¹, indicating significant spatial transferability. (2) Under MES, multiple models showed improved R² using PLSR as the baseline: e.g., MCCL (41.4 %), LSTM (28.3 %), RF (17.2 %), CNN (14.1 %), ANN (8.1 %), and SVM (7.1 %). (3) The MES approach reduces RMSE by 1.06 g kg⁻¹ and MAE by 1.58 g kg⁻¹, while increasing R² by 18.75 %, and RPIQ by 0.82. Using the KM radiative transfer model without cluster partitioning decreases RMSE and MAE by 0.58 g kg⁻¹ and 0.23 g kg⁻¹, while increasing R² and RPIQ by 7.1 % and 0.3, respectively. Specifying the soil moisture gradient in the spectral correction process is crucial. The novel MES-MCCL paradigm proposed in this study is robust and provides promising insights into soil moisture masking’s spectral characterization and the potential of multimodal remote sensing for SOC monitoring.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104513"},"PeriodicalIF":7.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks","authors":"Yu Li,&nbsp;Patrick Matgen,&nbsp;Marco Chini","doi":"10.1016/j.jag.2025.104524","DOIUrl":"10.1016/j.jag.2025.104524","url":null,"abstract":"<div><div>Up-to-date mapping of built-up areas is of paramount importance for urban planning, environmental monitoring, and disaster management. In recent years, there has been a growing interest in employing supervised machine learning and deep learning methods to map built-up areas using satellite SAR and optical data. However, the laborious and expensive task of gathering and maintaining a vast array of diverse training data poses a challenge to the widespread adoption of these methods for large-scale built-up area mapping. This paper presents a two-step framework enabling an automated extraction of built-up areas using Sentinel-1 and Sentinel-2 data. Initially, training data for built-up and non-built-up classes are automatically sampled and labeled from Sentinel-1 and Sentinel-2 data for a given area of interest. Subsequently, a cross-fusion neural network is trained using the samples from the first step to produce a built-up map for the entire study area. To enhance the network’s resilience to label noise, a contextual virtual adversarial training (CVAT) regularization is introduced within the mean-teacher architecture. Our proposed framework was tested on 48 different study areas across the world. Both quantitative and qualitative evaluations demonstrate its robustness and effectiveness for large-scale built-up area extraction. The versatility of our framework in generating accurate and up-to-date built-up information, which is essential for monitoring urban environments and assessing economic losses resulting from natural disasters, is highlighted through comparisons with four state-of-the-art global built-up products: Global Human Settlement Built-up map based on 2018 Sentinel-2 composites (GHS-BUILT-S2), World Settlement Footprint 2019 (WSF 2019), ESA World Cover, and Dynamic World.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104524"},"PeriodicalIF":7.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation","authors":"Xiaohu Sun ,&nbsp;Yong Xue ,&nbsp;Lin Sun","doi":"10.1016/j.jag.2025.104534","DOIUrl":"10.1016/j.jag.2025.104534","url":null,"abstract":"<div><div>Satellite-based data can provide continuous aerosol observations but suffer from significant uncertainties across various regions. Transfer learning improves model generalization, yet its application in atmospheric research remains limited. Here, we introduce an innovative framework for retrieving global aerosol optical depth (AOD) which named the <strong>A</strong>erosol domain-<strong>Ada</strong>ptive <strong>N</strong>etwork (AAdaN). The framework utilizes a neural network to estimate mutual information, and aligns spatial covariate shift via a transfer loss term. Then, we assess the retrieval potential in unknown scenarios using independent land cover type, and the proposed model demonstrates satisfactory results. The cross-validation shows strong agreement with in-situ measurements, both in sample-based and site-based evaluations. Specifically, the site-based ten-fold cross-validation of our AOD retrievals indicate that all accuracy metrics are satisfactory, with a Pearson correlation of 0.766 and a Root-Mean-Square Error of 0.118, and that about 76.05 % of the retrievals meet the expected error criteria [±(0.05 + 20 %)]. Additionally, the proposed AAdaN achieves stable, high-accuracy aerosol retrievals across various surface and atmospheric conditions, and can generate spatially continuous AOD distributions. This study significantly improves spatial generalization and offers valuable insights for future model development.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104534"},"PeriodicalIF":7.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}