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Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-11 Lei Xu, Xihao Zhang, Xi Zhang, Tingtao Wu, Hongchu Yu, Wenying Du, Zeqiang Chen, Nengcheng Chen
Root zone soil moisture (RZSM) is an important hydrological variable for agricultural planning and water resources management. The Soil Moisture Active Passive Level 4 (SMAP L4) data demonstrates great value in RZSM estimation. Accurate sub-seasonal RZSM prediction based on SMAP L4 holds great significance for agricultural management and drought assessment. Current deep learning-based RZSM prediction
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SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-11 Shiyuan Jin, Changfeng Jing, Sheng Yao, Yushan Zhang, Pu Zhao, Jinlong Zhang
Deep learning based subway passenger flow prediction was widely employed to promote prediction accuracy, which is crucial for subway management and commercial infrastructure planning. However, the existing work ignored the semantic similarity inherent in the subway stations function, which can extract passengers and enhance prediction accuracy. In this work, a Semantic-Augmented Spatio-temporal Graph
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Decoding spatial consistency of multi-Source land cover products in China: Insights from heterogeneous landscapes Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-11 Yanglin Cui, Chunjiang Zhao, Yuchun Pan, Kai Ma, Xiaojun Liu, Xiaohe Gu
High-resolution land cover (LC) data are essential for ecological monitoring and resource management, especially in heterogeneous landscapes containing diverse LC types. With the growing of available LC products, a comprehensive evaluation of their classification accuracy and spatial consistency is important for users’ selection and application. In this study, we compared eight widely used LC products
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Improving crop condition monitoring using phenologically aligned vegetation index anomalies – A case study in central Iowa Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-11 Haoteng Zhao, Feng Gao, Martha Anderson, Richard Cirone, Geba Jisung Chang
Timely monitoring of crop conditions is essential for optimizing and assessing agricultural management. Vegetation indices (VIs) derived from remote sensing data can be useful for assessing crop conditions on a large spatial scale. Traditional crop condition assessments compare a VI in the current year to a baseline VI, averaged over multiple years. However, comparing VIs across years by calendar day
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Estimating water levels in reservoirs using Sentinel-2 derived time series of surface water areas: A case study of 20 reservoirs in Burkina Faso Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-11 Audrey Kantz Dossou Codjia, Komlavi Akpoti, Moctar Dembélé, Roland Yonaba, Tazen Fowe, Soumahila Sankande, Modeste G. Déo-Gratias Koissi, Sander J. Zwart
Reservoirs play a significant role in the mobilization of water resources in Burkina Faso, contributing to the management and availability of water for various purposes. Operational management of reservoirs requires accurate and timely water level information, which remote sensing can provide cost-effectively and with limited resources. In this study, the surface area of 20 reservoirs is first determined
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A SAR wave-enhanced method combining denoising and texture enhancement for bathymetric inversion Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-11 Aijun Cui, Yi Ma, Jingyu Zhang, Ruifu Wang
The wave phenomena in SAR images are able to provide water depth information. SAR ocean images are often characterized by unclear wave texture and strong speckle noise, which will hinder the bathymetric inversion. Denoising and texture enhancement are two strategies to improve image quality. However, noise reduction may blur textures, while texture enhancement may amplify noise. To address this, we
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Analyzing the impact of area of interest (AOI) size and endmember selection on evapotranspiration (ET) estimation through a contextual model (SEBAL) Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-11 Hamza Barguache, Jamal Ezzahar, Jamal Elfarkh, Said Khabba, Salah Er-Raki, Valerie Le Dantec, Mohamed Hakim Kharrou, Ghizlane Aouade, Abdelghani Chehbouni
Accurate estimation of evapotranspiration (ET) is essential for effective water resource management, particularly in arid and semi-arid areas. Advancements in remote sensing technology have made ET models indispensable, offering high-resolution spatial and temporal assessments. Contextual models such as the Surface Energy Balance Algorithm for Land (SEBAL) are particularly valuable for ET estimation
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Frequent drought and flood events in the Yellow River Basin, increasing future drought trends in the middle and upper reaches Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-11 Jianming Feng, Tianling Qin, Xizhi Lv, Shanshan Liu, Jie Wen, Juan Chen
Under global warming, the Yellow River Basin (YRB), serving as an ecological barrier and climate-sensitive region in northern China, faces severe challenges such as frequent extreme droughts and floods, as well as intensifying water resource supply–demand conflicts. To systematically assess the evolution of droughts and floods in the YRB, this study utilizes observational data from 137 meteorological
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Assessing change point detection methods to enable robust detection of early stage Artisanal and Small-Scale mining (ASM) in the tropics using Sentinel-1 time series data Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-09 Mensah Isaac Obour, Barrett Brian, Cahalane Conor
Artisanal and Small-Scale mining (ASM) provides essential livelihoods for many in developing countries but often lacks regulation, leading to environmental issues such as water pollution and deforestation. Timely and accurate mapping of ASM activities is vital for responsible mining that benefits the environment and local communities. Synthetic Aperture Radar (SAR) is crucial for regular ASM monitoring
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Large-area urban TomoSAR method with limited a priori knowledge and a complex deep learning model Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-08 Haoxuan Duan, Yuzhou Liu, Hong Zhang, Peifeng Ma, Zhongqi Shi, Zihuan Guo, Yixian Tang, Fan Wu, Chao Wang
Buildings are crucial to cities, and tomographic synthetic aperture radar (TomoSAR) is an important tool for monitoring the heights, linear deformations and thermal amplitudes of buildings. However, existing TomoSAR height inversion methods do not fully leverage a priori knowledge, compromising the accuracy of deformation estimation; deep learning-based methods involve the integration of multiple steps
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DMP-PUNet: A novel network for two-dimensional InSAR phase unwrapping under severe noise and complex fringes conditions Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-08 Yu Chen, Shuai Wang, Yandong Gao, Yanjian Sun, Jinqi Zhao, Kun Tan, Peijun Du
In the processing of Interferometric synthetic aperture radar (InSAR) data, two-dimensional (2-D) phase unwrapping (PU) is crucial for ensuring the quality of InSAR data inversion. Traditional methods, based on the assumption of phase continuity, often struggle with abrupt terrain changes and the influence of severe noise, leading to poor performance or failure. To address these challenges, this paper
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Seamless global daily soil moisture mapping using deep learning based spatiotemporal fusion Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-08 Menghui Jiang, Tian Qiu, Ting Wang, Chao Zeng, Boxuan Zhang, Huanfeng Shen
Soil moisture products with long-term, high spatial continuity, and high accuracy are essential for meteorological management and hydrological monitoring. Microwave remote sensing retrieval and land surface model simulation are the two primary sources of global-scale soil moisture data, but each has inherent limitations, making it difficult to balance accuracy and spatial coverage. In this paper, to
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ESMII-Net: An edge-synergy and multidimensional information interaction network for remote sensing change detection Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-08 Yixin Chen, Xiaogang Ning, Ruiqian Zhang, Hanchao Zhang, Xiao Huang, You He
In recent advancements, deep learning-based methods for change detection have demonstrated rapid capabilities to identify alterations across extensive regions, underscoring significant research and application potential in remote sensing change detection. Nonetheless, these methods currently encounter limitations in feature extraction, often leading to blurred edges and challenges in identifying small-scale
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BANet: A bilateral attention network for extracting changed buildings between remote sensing imagery and cadastral maps Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-08 Qingyu Li, Lichao Mou, Yilei Shi, Xiao Xiang Zhu
Up-to-date cadastral maps are vital to local governments in administrating real estate in cities. With its growing availability, remote sensing imagery is the cost-effective data for updating semantic contents on cadastral maps. In this study, we address the problem of updating buildings on cadastral maps, as city renewal is mainly characterized by new construction and demolition. While previous works
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Estimating and mapping tailings properties of the largest iron cluster in China for resource potential and reuse: A new perspective from interpretable CNN model and proposed spectral index based on hyperspectral satellite imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-05 Haimei Lei, Nisha Bao, Moli Yu, Yue Cao
Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO2). Spatially characterizing the physical and chemical properties of iron tailings is greatly important for optimal utilization and proper disposal of tailings. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR; 350–2500 nm) spectroscopy offers a rapid, non-destructive, and cost-effective method for
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WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-04 Ningjing Wang, Xinyu Wang, Yang Pan, Wanqiang Yao, Yanfei Zhong
Efficient and accurate extraction of road networks from high-resolution satellite images is essential for urban planning, construction, and traffic management. Recently, various road datasets and advances in deep learning models have greatly enhanced road extraction techniques. However, challenges remain when trying to apply existing research to rural areas. Specifically, most public road datasets
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S2-IFNet: A spatial-semantic information fusion network integrated with boundary feature enhancement for forest land extraction from Sentinel-2 data Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-03 Junyang Xie, Mengyao Zhang, Hao Wu, Anqi Lin, Marcos Adami, Abdul Rashid Mohamed Shariff, Yahui Guo
Accurately extracting forest land and understanding its spatial distribution are crucial for forest monitoring and management. However, variations in tree species, human activities, and natural disturbances create diverse and distinct forest land characteristics in remote sensing images, posing challenges for precise forest land extraction. To address these challenges, we propose a spatial-semantic
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Occlusion mapping reveals the impact of flight and sensing parameters on vertical forest structure exploration with cost-effective UAV based laser scanning Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-03 Matthias Gassilloud, Barbara Koch, Anna Göritz
Recent studies have demonstrated the potential of light detection and ranging (LiDAR) from uncrewed aerial vehicles (UAVs) for assessing forest structures. Maximizing data completeness and representativeness is essential to accurately retrieve key structural parameters. However, knowledge on how data acquisition approaches affect canopy volume exploration is sparse. This study investigated the effects
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Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2 Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-02 Tatenda Dzurume, Roshanak Darvishzadeh, Timothy Dube, T.S. Amjath Babu, Mutasim Billah, Syed Nurul Alam, Mustafa Kamal, Md. Harun-Or-Rashid, Badal Chandra Biswas, Md. Ashraf Uddin, Md. Abdul Muyeed, Md. Mostafizur Rahman Shah, Timothy J. Krupnik, Andrew Nelson
Fall Armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), poses a significant risk to global food and income security by attacking various crops, particularly maize. Early detection and management of FAW infestation are crucial for mitigating its impact on crop yields. This study investigated the effect of FAW infestation on the spectral signature of maize fields and classified
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D[formula omitted]GNN: Double dual dynamic graph neural network for multisource remote sensing data classification Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-04-01 Teng Yang, Song Xiao, Jiahui Qu
Convolutional Neural Network (CNN) has garnered attention due to its outstanding performance in multisource remote sensing (RS) image classification. However, classical CNN-based methods primarily concentrate on information within a fixed-size neighborhood and a standard square region, neglecting long-range and global information. As non-Euclidean data, the topological structure enables flexible construction
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SAM-CTMapper: Utilizing segment anything model and scale-aware mixed CNN-Transformer facilitates coastal wetland hyperspectral image classification Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-31 Jiaqi Zou, Wei He, Haifeng Wang, Hongyan Zhang
Accurate and effective coastal wetland classification using hyperspectral remote sensing technology is crucial for their conservation, restoration, and sustainable development. However, the large scale variance of land covers in complex wetland scenes poses challenges for existing methods and leads to misclassifications. Additionally, existing methods encounter difficulties in practical wetland classification
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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data—a case study of Southwest basin Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-24 Zehui Zhou, Jiaxin Jin, Bin Yong, Weidong Huang, Lei Yu, Peiqi Yang, Dianchen Sun
The normalized difference vegetation index (NDVI) is a vital metric for assessing vegetation growth, yet accurate prediction remains challenging, particularly in regions with complex geographic and climatic conditions. Machine learning methods offer promise but are often hindered by sensitivity to model structure, input parameters, and training samples. To address these limitations, this study developed
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Scientists yet to consider spatial correlation in assessing uncertainty of spatial averages and totals Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-23 Alexandre M.J.-C. Wadoux, Gerard B.M. Heuvelink
High-resolution maps of climate and ecosystem variables are essential for supporting terrestrial carbon stocks and fluxes estimation, climate change mitigation, and ecosystem degradation assessment. These maps are usually created using remotely sensed data obtained from various types of imagery and sensors. The remote sensing data typically serve as covariates to deliver spatially explicit information
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Classification of tree mortality following drought-defoliation interaction using single date Landsat imagery and comparison to aerial detection surveys Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-22 Danielle N. Tanzer, Chandi Witharana, Robert T. Fahey
Forest disturbance regimes are changing across the globe under the influence of climate change and other global change factors, with potentially substantial consequences for tree mortality rates. Tree mortality has been assessed using field and aerial surveys and, more recently, frequently using satellite remote sensing-based techniques. Rapid detection of tree mortality is often important in decision-making
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Spatio-temporal-text fusion for hierarchical multi-label crop classification based on time-series remote sensing imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-22 Xiyao Li, Jiayi Li, Jie Jiang, Xiaofeng Pan, Xin Huang
Recent advances in deep learning have enhanced crop classification, yet current research still underutilizes the hierarchical information of crop types, limiting classification accuracy. As categories are subdivided, the sample imbalance intensifies, posing a challenge to fine classification of crops. To address this, we propose the Class Semantic Guided Hierarchical Segmentation Framework (SemHi framework)
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High-quality one-shot interactive segmentation for remote sensing images via hybrid adapter-enhanced foundation models Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-22 Zhili Zhang, Xiangyun Hu, Yue Yang, Bingnan Yang, Kai Deng, Hengming Dai, Mi Zhang
Interactive segmentation of remote sensing images enables the rapid generation of annotated samples, providing training samples for deep learning algorithms and facilitating high-quality extraction and classification for remote sensing objects. However, existing interactive segmentation methods, such as SAM, are primarily designed for natural images and show inefficiencies when applied to remote sensing
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Low Saturation Confidence Distribution-based Test-Time Adaptation for cross-domain remote sensing image classification Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-22 Yu Liang, Shilei Cao, Juepeng Zheng, Xiucheng Zhang, Jianxi Huang, Haohuan Fu
Unsupervised Domain Adaptation (UDA) has emerged as a powerful technique for addressing the distribution shift across various Remote Sensing (RS) applications. However, most UDA approaches require access to source data, which may be infeasible due to data privacy or transmission constraints. Source-free Domain Adaptation addresses the absence of source data but usually demands a large amount of target
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Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models: A GNSS perspective Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-21 Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai, Yuyan Wang, Xiaolong Mi, Tong Liu, Duojie Weng
The artificial intelligence (AI) weather forecast foundation models can infer and generate precise global atmospheric state forecasts on the user’s device and with speed over 10,000 times faster than the operational Integrated Forecasting System (IFS), and it is making increasingly significant contributions to geodetic applications represented by the Global Navigation Satellite System (GNSS). However
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Multi-source data joint processing framework for DEM calibration and fusion Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-20 Cuilin Yu, Qingsong Wang, Zibo Zhang, Zixuan Zhong, Yusheng Ding, Tao Lai, Haifeng Huang, Peng Shen
High-accuracy digital elevation models (DEMs) are essential for remote sensing and geospatial analysis, yet integrating multi-source data over large and complex terrains remains challenging. To address these challenges, this study presents the Multi-source Data Joint Processing (MDJP) framework. This framework establishes a systematic way for correcting DEM errors of varying quality and integrating
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Enhanced unsupervised domain adaptation with iterative pseudo-label refinement for inter-event oil spill segmentation in SAR images Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-20 Guangyan Cui, Jianchao Fan, Yarong Zou
The imaging features of oil spills in synthetic aperture radar (SAR) images have significant differences due to factors such as marine environment, SAR sensors, oil film thickness and types, which makes it difficult to obtain a generalized model, and the limited number of SAR images obtained from new oil spill events hampers the effective training of deep learning networks. To solve these issues, an
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Multitemporal Sentinel and GEDI data integration for overstory and understory fuel type classification Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-20 Pegah Mohammadpour, Domingos Xavier Viegas, Alcides Pereira, Emilio Chuvieco
Wildfires significantly reshape the landscape of the Mediterranean basin, altering forest composition, structure, and diversity. Consequently, detailed fuel mapping is crucial for improving fire risk assessment and enhancing fire behavior modeling, as wildfires typically ignite from surface fuels and may spread vertically to canopy fuels due to canopy fuel continuity. This study generates a fuel type
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Assessing urban residents’ exposure to greenspace in daily travel from a dockless bike-sharing lens Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-19 Xijie Xu, Jie Wang, Stefan Poslad, Xiaoping Rui, Guangyuan Zhang, Yonglei Fan, Guangxia Yu
Considering the importance of greenspace for the health and life of urban citizens, different levels of greenspace exposure (GE) have received increasing attention. However, the understanding of human travel-related greenspace exposure is still limited, especially the lack of quantitative description of the fine-grained dynamics of greenspace exposure for active travel. Therefore, this study aims to
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Satellite retrieval of bottom reflectance from high-spatial-resolution multispectral imagery in shallow coral reef waters Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-19 Benqing Chen, Yanming Yang, Mingsen Lin, Bin Zou, Shuhan Chen, Erhui Huang, Wenfeng Xu, Yongqiang Tian
Under anthropogenic disturbances and global warming, coral reef ecosystems are degrading, and there is growing concern about the changes in benthic habitats in shallow coral reef waters. As an essential parameter, bottom reflectance can be used to indicate the health of benthic habitats in coral reefs. However, accurately determining bottom reflectance from satellite data remains challenging. This
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Near real-time land surface temperature reconstruction from FY-4A satellite using spatio-temporal attention network Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-19 Ruijie Li, Hequn Yang, Xu Zhang, Xin Xu, Liuqing Shao, Kaixu Bai
Land Surface Temperature (LST) is a critical parameter for climate studies and land surface process models as it indicates ground surface temperature variations across landscapes and timescales. However, satellite-based LST products derived from infrared sensors suffer from substantial missing values due to extensive cloud covers on the Earth’s surface. Traditional methods rely heavily on numerical
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Deep projective prediction of building facade footprints from ALS point cloud Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-19 Hengming Dai, Jiabo Xu, Xiangyun Hu, Zhen Shu, Wei Ma, Zhifang Zhao
The automated extraction of building facade footprints (BFFs) is a critical task in surveying and remote sensing. Existing methods primarily use mobile laser scanning point cloud as the data source, with limited methods utilizing airborne laser scanning (ALS) data. This is mainly because current methods require explicit building extraction and wall detection, and the facade points in ALS point clouds
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Using street view imagery and localized crowdsourcing survey to model perceived safety of the visual built environment by gender Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-18 Hanlin Zhou, Jue Wang, Kathi Wilson, Michael Widener, Devin Yongzhao Wu, Eric Xu
Scholars have documented that perceived safety of the visual built environment (VBE) can influence human behaviors. The dual developments of street view imagery (SVI) and deep learning techniques offer a cost-effective approach to measure perceived safety. However, current SVI-based perception models often lack specific definitions of perceived safety and demographic information when collecting data
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Free satellite data and open-source tools for urban green spaces and temperature pattern analysis in Algiers Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-17 Nadia Mekhloufi, Mariella Aquilino, Amel Baziz, Chiara Richiardi, Maria Adamo
Rapid urbanization and global climate change are intensifying the Urban Heat Island (UHI) effect in cities worldwide, with consequences for human health and well-being. Urban green spaces (UGSs) mitigate extreme temperatures, but their cooling potential depends on spatial configuration, size, shape, and distribution. This study fills a geographic gap by providing one of the first detailed analyses
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Transfer learning for enhancing the generality of leaf spectroscopic models in estimating crop foliar nutrients across growth stages Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-17 Yurong Huang, Wenqian Chen, Wei Tan, Yujia Deng, Cuihong Yang, Xiguang Zhu, Jian Shen, Nanfeng Liu
China, despite being a leading producer of potatoes, has a potato yield below the global average, primarily due to inefficient nutrient management practices. Remote sensing provides a non-invasive and large-scale approach to monitor crop nutrient status, offering an efficient alternative to traditional plant tissue analysis. However, the generalization of foliar nutrient models is often constrained
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A framework for montane forest canopy height estimation via integrating deep learning and multi-source remote sensing data Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-15 Hongbin Luo, Guanglong Ou, Cairong Yue, Bodong Zhu, Yong Wu, Xiaoli Zhang, Chi Lu, Jing Tang
Quantitative remote sensing-based forest parameter estimation is challenging in tropical mountainous conditions with complex topography and vegetation. To address this issue, we conducted a study utilizing Landsat 8, ALOS-2 PALSAR, and GEDI data. We applied an effective deep learning framework—Deep Markov Regression (DMR)—along with Random Forest Regression (RF) and 3D Regression Kriging (3DRK) methods
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Enhancing Large-Area DEM modeling of GF-7 stereo imagery: Integrating ICESat-2 data with Multi-characteristic constraint filtering and terrain matching correction Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-15 Kai Chen, Wen Dai, Fayuan Li, Sijin Li, Chun Wang
The integration of Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data with Optical Photogrammetric Satellite Stereo Imagery (OPSSI) for Block Adjustment (BA) has emerged as a novel approach for generating large-area, high-accuracy Digital Elevation Models (DEMs). However, owing to the discrepancies between these two data platforms and the systematic errors of their sensors, errors arise in
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VCDFormer: Investigating cloud detection approaches in sub-second-level satellite videos Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-15 Xianyu Jin, Jiang He, Yi Xiao, Ziyang Lihe, Jie Li, Qiangqiang Yuan
Satellite video, as an emerging data source for Earth observation, enables dynamic monitoring and has wide-ranging applications in diverse fields. Nevertheless, cloud occlusion hinders the ability of satellite video to provide uninterrupted monitoring of the Earth’s surface. To mitigate the interference of clouds, cloud-free areas need to be selected before application, or an optimized solution like
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FAIR principles in workflows: A GIScience workflow management system for reproducible and replicable studies Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-14 Tao Hu, Taiping Liu, Venkat Sai Divyacharan Jarugumalli, Samuel Cheng, Chengbin Deng
Scientific workflow management systems (WfMS) provide a systematic way to streamline necessary processes in scientific research. The demand for FAIR (Findable, Accessible, Interoperable, and Reusable) workflows is increasing in the scientific community, particularly in GIScience, where data is not just an output but an integral part of iterative advanced processes. Traditional WfMS often lack the capability
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Generating high-resolution DEMs in mountainous regions using ICESat-2/ATLAS photons Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-14 Yi Zhao, Bin Wu, Gefei Kong, He Zhang, Jianping Wu, Bailang Yu, Jin Wu, Hongchao Fan
High-resolution (≤10 m) digital elevation models (DEMs) are essential for obtaining accurate terrain information and are integral to geographic analysis. However, a majority of currently available DEMs datasets possess a relatively coarse spatial resolution (≥30 m), which limits the terrain features and details that can be accurately represented. Furthermore, due to the substantial production costs
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High-resolution snow depth retrieval by passive microwave based on linear unmixing and machine learning stacking technique Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-13 Yanan Bai, Zhen Li, Ping Zhang, Lei Huang, Shuo Gao, Haiwei Qiao, Chang Liu, Shuang Liang, Huadong Hu
Accurate measurement of high-resolution snow depth (SD) is crucial for regional ecohydrology and climate studies. Passive microwave remote sensing is an effective technique for SD retrieval on global or regional scales. However, its low spatial resolution limits its application in various fields. Additionally, the complex effects of multiple factors in the microwave radiation process pose a significant
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Multi-decadal Dutch coastal dynamic mapping with multi-source remote sensing imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-13 Bin Zhang, Ling Chang, Zhengbing Wang, Li Wang, Qinghua Ye, Alfred Stein
Tidal flats and their associated sandbanks are dynamic environments crucial for ecological balance and biodiversity. Monitoring their evolutionary history and topographic changes is important to better understand their dynamic mechanisms and predict their future status. Accurately mapping their evolution, however, remains challenging due to highly dynamic currents, suspended sediment variability, and
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Satellite-based flood mapping of coastal floods: The Senegal River estuary study case Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-12 E.T. Mendoza, E. Salameh, E.I. Turki, J. Deloffre, B. Laignel
This study employs an integrated approach, combining remote sensing and numerical modelling techniques, to characterize flood-prone regions resulting from the combined effects of extreme river water elevations and long-term sea-level rise in the Senegal River Estuary. Four different case scenarios of hydrodynamic conditions have been investigated to provide a quantitative assessment of flooding. Simultaneously
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FUELVISION: A multimodal data fusion and multimodel ensemble algorithm for wildfire fuels mapping Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-12 Riyaaz Uddien Shaik, Mohamad Alipour, Eric Rowell, Bharathan Balaji, Adam Watts, Ertugrul Taciroglu
Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources – including L8 optical imagery, S1 (C-band) Synthetic Aperture Radar (SAR) imagery, PL (L-band) SAR imagery, and terrain features – to capture comprehensive information about fuel types and distributions. An ensemble model
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Tracking diurnal variation of NO2 at high spatial resolution in China using a time-constrained machine learning model Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-11 Sicong He, Yanbin Yuan, Zhen Li, Heng Dong, Xiaopang Zhang, Zili Zhang, Lan Luo
The spatially continuous dynamic monitoring of near-surface NO2 concentrations on sub-daily scales would serve to enhance awareness of the current state of air pollution, which is crucial to improving regional air quality. Satellites, like OMI and TROPOMI, are capable of observing atmospheric NO2 column concentrations on a global scale. However, the fixed transit times of the satellites and severe
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Extracting a decadal deformation on Xiaolangdi upstream dam slope using seasonally inundated distributed scatterers InSAR (SIDS − InSAR) Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-10 Lei Xie, Wenbin Xu, Yosuke Aoki
Estimating deformation at the upstream dam slope from Interferometric Synthetic Aperture Radar (InSAR) is challenging due to the complete loss of coherence in seasonally inundated upstream slope. Here, we present an improved Distributed Scatterer-InSAR method that accounts for the seasonal decorrelation of upstream dam slopes and optimizes the interferogram pair selection with inter- and multi-annual
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Statistical models for urban growth forecasting: With application to the Baltimore–Washington area Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-10 Carlo Grillenzoni
Monitoring and governing the development of cities are the major concerns of urban planners, since involve physical and social aspects, such as land use and population trends. Models for spatial growth have been developed both from the mathematical and empirical viewpoints, with the aim of forecasting and decision-making. Statistical models require regular space–time datasets that are provided by recent
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Improved hourly all-sky land surface temperature estimation: Incorporating the temporal variability of cloud-radiation interactions Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-09 Dukwon Bae, Dongjin Cho, Jungho Im, Cheolhee Yoo, Yeonsu Lee, Siwoo Lee
Land surface temperature (LST) is an indispensable factor for comprehending of surface equilibrium state on the Earth. In particular, satellites can continuously provide LST data and support the large-scale monitoring of LST with a high temporal resolution; however, satellite data may be easily contaminated by clouds. Previous satellite-based all-sky LST reconstruction approaches have inherent limitations
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Historical habitat mapping from black-and-white aerial photography: A proof of concept for post World War II Switzerland Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-08 Nica Huber, Matthias Bürgi, Christian Ginzler, Birgit Eben, Andri Baltensweiler, Bronwyn Price
Information regarding the spatial arrangement and extent of past habitats is important for understanding present biodiversity, restoration potential, and fighting extinction-debt effects. European landscapes have changed profoundly over recent decades, with the trend accelerating following World War 2. We develop a proof of concept for mapping historic habitat distribution for Switzerland from black
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A novel hyperspectral remote sensing estimation model for surface soil texture using AHSI/ZY1-02D satellite image Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-06 Qiang Shen, Kun Shang, Chenchao Xiao, Hongzhao Tang, Taixia Wu, Changkun Wang
Soil texture is an essential attribute of soil structure, which plays an important role in evaluating soil fertility and carrying out agricultural production. This study developed a novel soil texture estimation model using ZiYuan-1-02D (ZY1-02D) satellite Advanced Hyperspectral Imager (AHSI), based on the mechanism of soil spectral mixing, that enables simultaneous estimation of the three soil texture
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An operational Airborne-Ground Integrate observation scheme for validating land surface temperature over heterogeneous surface Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-06 Yajun Huang, Wenping Yu, Xujun Han, Jianguang Wen, Qing Xiao, Xufeng Wang, Jiayuan Lin, Zengjing Song, Dandan Li, Xiangyi Deng
At present, there are more than 30 satellite remote sensing Land Surface Temperature (LST) products from kilometers to hectometers resolutions. The accuracy of these products is the key issue for further application. The validation of LST products is mainly achieved through ground observations on homogeneous surfaces, but the accuracy of satellite products on heterogeneous surfaces is also an important
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Dynamic inference for on-orbit scene classification with the scale boosting model Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-06 Kunyang Yang, Naisen Yang, Hong Tang
Existing scene classification methods allocate the same computational resources, i.e., all model parameters in the neural network, to each remote sensing image whenever from any geographic scene. However, this might be redundant for images of certain scenes that are easy to discriminate, e.g., homogeneous scenes. This observation motivates us to propose an efficient method for on-orbit scene classification
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Evaluating Earth observation products for Catchment-Scale operational flood monitoring and risk management in a sparsely gauged to ungauged river basin in Nigeria Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-06 Dorcas Idowu, Brad G. Peter, Jessica Boakye, Sagy Cohen, Elizabeth Carter
With the persistent rise in intensity and magnitude of hydrological extremes globally, timely information from operational early flood warning systems provide lead times that translate into actionable strategies to monitor and mitigate flood risk. However, the situation is often different for flood-prone regions of the global south with sparse to no ground flood monitoring systems, where flood management
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GeoCode-GPT: A large language model for geospatial code generation Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-05 Shuyang Hou, Zhangxiao Shen, Anqi Zhao, Jianyuan Liang, Zhipeng Gui, Xuefeng Guan, Rui Li, Huayi Wu
The increasing demand for spatiotemporal data and modeling tasks in geosciences has made geospatial code generation technology a critical factor in enhancing productivity. Although large language models (LLMs) have demonstrated potential in code generation tasks, they often encounter issues such as refusal to code or hallucination in geospatial code generation due to a lack of domain-specific knowledge
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Emergency-oriented fine change detection of flood-damaged farmland from medium-resolution remote sensing images Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-05 Gang Qin, Shixin Wang, Futao Wang, Zhenqing Wang, Suju Li, Xingguang Gu, Kailong Hu, Longfei Liu
Flood disasters are characterized by frequent and sudden occurrences and obvious chain effects, posing a major threat to agricultural production. Government disaster relief and agricultural insurance are increasingly urgent in assessing losses to flood-damaged farmland. There are many challenges in assessing flood-damaged farmland. On the one hand, the historical data needed for the farmland loss assessment
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PatchOut: A novel patch-free approach based on a transformer-CNN hybrid framework for fine-grained land-cover classification on large-scale airborne hyperspectral images Int. J. Appl. Earth Obs. Geoinf. (IF 7.6) Pub Date : 2025-03-05 Renjie Ji, Kun Tan, Xue Wang, Shuwei Tang, Jin Sun, Chao Niu, Chen Pan
Airborne hyperspectral systems can provide high-resolution hyperspectral images (HSIs) covering large scenes, enabling fine-grained land-cover classification. However, the most popular patch-based methods are limited by low computational efficiency and broken classification results, which hinders the full utilization of this powerful technology in Earth observation applications. Therefore, in this