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

{"title":"Using street view imagery and localized crowdsourcing survey to model perceived safety of the visual built environment by gender","authors":"Hanlin Zhou ,&nbsp;Jue Wang ,&nbsp;Kathi Wilson ,&nbsp;Michael Widener ,&nbsp;Devin Yongzhao Wu ,&nbsp;Eric Xu","doi":"10.1016/j.jag.2025.104421","DOIUrl":"10.1016/j.jag.2025.104421","url":null,"abstract":"<div><div>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 for model training. Furthermore, these models are rarely validated by onsite perception evaluations, which undermines their credibility.</div><div>Given these gaps, this study builds a localized crowdsourcing survey to train crime-related and barrier-related perceived safety of the VBE captured by SVIs, and compares model-predicted perceptions with onsite perceptions. This study specifically focuses on their ability to represent onsite perceptions and examines gender differences as a test case in safety perception. This study recruits over 1,800 participants living in the Greater Toronto Area to rate SVIs in terms of crime-related and barrier-related perceived safety.</div><div>Pearson correlation coefficients reveal a positive but weak correlation between female and male safety perceptions, indicating some consistency while highlighting potential gender differences in safety perceptions. Machine-learning perception models are then trained using this localized SVI survey. Model-predicted perceptions are further validated to assess their alignments with onsite perceptions at sampling locations. The results show that model-predicted perceptions do not exactly match onsite perceptions but align better when less stringent criteria are applied (within ± 1 scale point).</div><div>In short, this study underscores the necessity of gender inclusivity and a clear definition of safety terms when using SVIs to model perceptions. While SVI-based perception models are cost-effective, the predicted perceptions cannot yet fully substitute onsite perceptions, necessitating broader research to refine the effectiveness.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104421"},"PeriodicalIF":7.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer learning for enhancing the generality of leaf spectroscopic models in estimating crop foliar nutrients across growth stages","authors":"Yurong Huang ,&nbsp;Wenqian Chen ,&nbsp;Wei Tan ,&nbsp;Yujia Deng ,&nbsp;Cuihong Yang ,&nbsp;Xiguang Zhu ,&nbsp;Jian Shen ,&nbsp;Nanfeng Liu","doi":"10.1016/j.jag.2025.104481","DOIUrl":"10.1016/j.jag.2025.104481","url":null,"abstract":"<div><div>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 by factors such as growth stages and planting cultivars. Transfer learning offers a powerful solution by utilizing knowledge acquired from one task to enhance performance in related one, addressing challenges in model generalizability. Here, we investigated the potential of integrating various transfer learning techniques with partial least squares regression (PLSR) for retrieving three key potato foliar nutrients (nitrogen, phosphorus and potassium) across five growth stages (emergence, tuber initiation, early tuber bulking, mid-tuber bulking and tuber maturation). Three categories of transfer learning techniques were examined: 1) instance-based, including PLSR-KMM (kernel mean matching) and PLSR-TrAdaBoostR2 (transfer adaptive boosting for regression); 2) feature-based, including PLSR-TCA (transfer component analysis); and 3) parameter-based, including PLSR-parameter-based. We found that: 1) The combination of transfer learning techniques with PLSR could generally enhance the model transferability across growth stages, with a decrease in the normalized root mean squared error (nRMSE of 1–10 % for nitrogen, 3–60 % for phosphorous, and 1–15 % potassium; 2) The ranking of transfer learning techniques for improving model generalizability was: PLSR-TrAdaBootR2 &gt; PLSR-parameter based &gt; PLSR-recalibrated &gt; PLSR-TCA &gt; PLSR-KMM; 3) Foliar nitrogen demonstrated the highest transferability, followed by potassium and phosphorus; 4) PLSR models integrated with transfer learning techniques more effectively leveraged the absorption features of foliar biochemistry (e.g., chlorophyll, water and dry matters) to predict nutrients.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104481"},"PeriodicalIF":7.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Free satellite data and open-source tools for urban green spaces and temperature pattern analysis in Algiers","authors":"Nadia Mekhloufi ,&nbsp;Mariella Aquilino ,&nbsp;Amel Baziz ,&nbsp;Chiara Richiardi ,&nbsp;Maria Adamo","doi":"10.1016/j.jag.2025.104482","DOIUrl":"10.1016/j.jag.2025.104482","url":null,"abstract":"<div><div>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 of UGSs-Land Surface Temperature (LST) dynamics in a North African context. It combines spatial pattern analysis with temporal trend detection to comprehensively evaluate UGSs-LST relationships in Algiers from 2004 to 2022, addressing a common limitation in the literature where these approaches are often treated separately. Using freely available satellite data and open-source tools—including Landsat data from Google Earth Engine (GEE), QGIS, and RStudio—we integrate supervised classification, landscape metrics (LMs) computation via our custom PyQGIS LMs calculator, and Mann-Kendall trend analysis to quantify the static spatial configuration of green spaces and their dynamic thermal impacts over time. Findings reveal a 38% decrease in green spaces and a 10% reduction in agricultural land, accompanied by increased urbanization. Strong negative correlations between some LMs and LST were observed, with PLAND (Percentage of Landscape) explaining 61% of LST variability at an optimal 600-meter scale. This medium-sized scale differs from previous findings in other regions, highlighting the importance of context-specific analysis. LST trend analysis identified specific heat-resistant zones characterized by large, contiguous green patches. Despite greening initiatives, UGSs in Algiers continue to decline, underlining the need to preserve and strategically expand UGSs to combat rising temperatures.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104482"},"PeriodicalIF":7.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A practical and efficient model for benthic habitat parameters retrieval in optically shallow waters from four-band multispectral imagery","authors":"Yunhan Ma ,&nbsp;Tong Han ,&nbsp;Enze Wang ,&nbsp;Zhongping Lee ,&nbsp;Surendra Prasad ,&nbsp;Gandercillar Wainiqolo Vosaki ,&nbsp;Wenting Cao ,&nbsp;Dongling Li ,&nbsp;Juan Wang ,&nbsp;Xiulin Lou ,&nbsp;Huaguo Zhang","doi":"10.1016/j.jag.2025.104475","DOIUrl":"10.1016/j.jag.2025.104475","url":null,"abstract":"<div><div>Water depth (<em>H</em>), bottom reflectance (<span><math><mrow><msub><mi>R</mi><mi>b</mi></msub><mrow><mfenced><mrow><mi>λ</mi></mrow></mfenced></mrow></mrow></math></span>), chlorophyll-a concentration (<em>Chl</em>) and seawater transparency (<em>SDD</em>) are key parameters in assessing shallow benthic habitats. However, due to limited in-situ data, restricted hyperspectral imagery or dynamic water quality, it is challenging to develop a generalized remote sensing model for decoupling water column and benthic signals across diverse regions and time periods. In this study, a Modified Log rotation Ratio and Semi-analytical (MLR-S) model is proposed for diffuse attenuation coefficient <span><math><mrow><msub><mi>K</mi><mi>d</mi></msub><mrow><mfenced><mrow><mi>λ</mi></mrow></mfenced></mrow></mrow></math></span> retrieval by adjusting inherent optical properties (<em>P, G, X</em>), from widely used four-band multispectral images without relying on truth data. A new Binary Quadratic Polynomial Relationship (BQPR) between <em>P</em> and the logarithmic values of blue-green bands was developed upon a sparse set of self-inferred points (<em>SIPs</em>). Then, large-scale and optimal <span><math><mrow><msub><mi>K</mi><mi>d</mi></msub><mrow><mfenced><mrow><mi>λ</mi></mrow></mfenced></mrow></mrow></math></span> was determined. Finally, using the monthly mean water column reflectance dataset <span><math><mrow><msub><mrow><mi>MMCR</mi></mrow><mi>w</mi></msub><mrow><mfenced><mrow><mi>λ</mi></mrow></mfenced></mrow></mrow></math></span> constructed from 23-year MODIS data, <span><math><mrow><msub><mi>R</mi><mi>b</mi></msub><mrow><mfenced><mrow><mi>λ</mi></mrow></mfenced></mrow></mrow></math></span>, <em>Chl</em> and <em>SDD</em> can be efficiently derived. Synchronous validation at Dazhou Island confirmed the effectiveness of the MLR-S model, with mean relative errors of 23.77%, 15.26% and 9.91% for blue, green and red <span><math><mrow><msub><mi>R</mi><mi>b</mi></msub></mrow></math></span>, respectively, and 36.69% for <em>Chl</em>. Further validation of <span><math><mrow><msub><mi>R</mi><mi>b</mi></msub><mrow><mfenced><mrow><mi>λ</mi></mrow></mfenced></mrow></mrow></math></span> in Heron Reef, Aitutaki Island and Buck Island demonstrated the model’s robustness across diverse habitats. Compared to the ICESat-2 Along Track Benthic Reflectance (ATBR) model, the MLR-S model yielded more reasonable and reliable <span><math><mrow><msub><mi>R</mi><mi>b</mi></msub><mrow><mfenced><mrow><mi>λ</mi></mrow></mfenced></mrow></mrow></math></span> in deeper waters. Additionally, it outperformed the traditional semi-analytical model, achieving greater accuracy while being over 100 times more computationally efficient. This model offers promising potential for large-scale, high-resolution monitoring of benthic habitats using four-band multispectral data, supporting improved understanding of reef health and informed decision-making.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104475"},"PeriodicalIF":7.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Large-Area DEM modeling of GF-7 stereo imagery: Integrating ICESat-2 data with Multi-characteristic constraint filtering and terrain matching correction","authors":"Kai Chen ,&nbsp;Wen Dai ,&nbsp;Fayuan Li ,&nbsp;Sijin Li ,&nbsp;Chun Wang","doi":"10.1016/j.jag.2025.104485","DOIUrl":"10.1016/j.jag.2025.104485","url":null,"abstract":"<div><div>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 the BA fusion outcomes during the matching process of the two datasets. To tackle this issue, this paper proposes a method aimed at enhancing the accuracy of the BA process. Initially, the multi-characteristic constraint is used to filter the ICESat-2 ATL08 product to obtain control points and check points. Subsequently, the Terrain Matching Correction is applied to control points, and then integrated with the GF-7 OPSSI for BA to generate DEM. Ultimately, the check points are employed to assess the accuracy of the established DEM. Experiments in a 2,000 km² test area in the Wuding River Basin show that: (1) The inclusion of ICESat-2 data has remarkably enhanced the accuracy of DEM modeling utilizing GF-7 OPSSI, and the Root Mean Square Error (RMSE) has been reduced from the range of 5–10 m to 2–6 m. (2) Multi-characteristic constraint filtering is crucial for the identification of high quality ICESat-2 control points in flat and low relief areas. When implementing this filtering method, the established criteria should comprehensively consider both the quantity and the spatial distribution of control points to ensure optimal results. (3) Terrain Matching Correction on ICESat-2 data has effectively elevated the vertical accuracy of DEM modeling, particularly in regions with flat terrain. The RMSE of the vertical accuracy in such areas can be decreased by 1–3 m. In summary, the integration of spaceborne laser altimeter data with OPSSI holds immense significance for the production of large-scale and high-accuracy DEMs, offering a promising solution for terrain modeling and analysis on regional scales.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"138 ","pages":"Article 104485"},"PeriodicalIF":7.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VCDFormer: Investigating cloud detection approaches in sub-second-level satellite videos","authors":"Xianyu Jin ,&nbsp;Jiang He ,&nbsp;Yi Xiao ,&nbsp;Ziyang Lihe ,&nbsp;Jie Li ,&nbsp;Qiangqiang Yuan","doi":"10.1016/j.jag.2025.104465","DOIUrl":"10.1016/j.jag.2025.104465","url":null,"abstract":"<div><div>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 a cloud removal algorithm can be utilized to recover the occluded regions, both of which inherently demand the precise detection of clouds. However, no existing methods are capable of robust cloud detection in satellite videos. We propose the first sub-second-level satellite video cloud detection model VCDFormer to handle this problem. In VCDFormer, a spatial–temporal-enhanced transformer consisting of a local spatial–temporal reconfiguration block and a spatial-enhanced block is introduced to explore global spatial–temporal correspondence efficiently. Additionally, we construct WHU-VCD, the first sub-second-level synthetic dataset specifically designed to capture the more realistic motion characteristics of both thick and thin clouds in satellite videos. Compared to the state-of-the-art cloud detection methods, VCDFormer achieves an approximate 10%–15% improvement in the IoU metric and a 5%–8% increase in the F1-Score on the simulated test set. Experimental evaluations on Jilin-1 satellite videos, involving both synthetic and real-world scenarios, demonstrate that our proposed VCDFormer achieves superior performance in satellite video cloud detection tasks. The source code is available at <span><span>https://github.com/XyJin99/VCDFormer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"138 ","pages":"Article 104465"},"PeriodicalIF":7.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A framework for montane forest canopy height estimation via integrating deep learning and multi-source remote sensing data","authors":"Hongbin Luo ,&nbsp;Guanglong Ou ,&nbsp;Cairong Yue ,&nbsp;Bodong Zhu ,&nbsp;Yong Wu ,&nbsp;Xiaoli Zhang ,&nbsp;Chi Lu ,&nbsp;Jing Tang","doi":"10.1016/j.jag.2025.104474","DOIUrl":"10.1016/j.jag.2025.104474","url":null,"abstract":"<div><div>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 to estimate canopy height in subtropical mountain forests. Our goal was to explore effective modeling techniques for this task. Additionally, we treated “slope” as a dummy variable and incorporated factors such as slope and geographic coordinates into the model. The results showed that optical remote sensing provided the highest estimation accuracy in mountainous terrain, significantly outperforming both GEDI and SAR data. The combination of multiple remote sensing datasets further enhanced the estimation accuracy. Incorporating slope and geographic location data also improved model performance. Among all methods, the RF model was most sensitive to topographic variations, whereas the DMR model consistently delivered excellent performance across different slope conditions. The R² of the DMR model was 0.772, the RMSE was 2.968 m, and the prediction accuracy approached 80 %.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"138 ","pages":"Article 104474"},"PeriodicalIF":7.6,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating high-resolution DEMs in mountainous regions using ICESat-2/ATLAS photons","authors":"Yi Zhao ,&nbsp;Bin Wu ,&nbsp;Gefei Kong ,&nbsp;He Zhang ,&nbsp;Jianping Wu ,&nbsp;Bailang Yu ,&nbsp;Jin Wu ,&nbsp;Hongchao Fan","doi":"10.1016/j.jag.2025.104461","DOIUrl":"10.1016/j.jag.2025.104461","url":null,"abstract":"<div><div>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 associated with high-resolution DEMs, these products are often unavailable or difficult to obtain in numerous countries and regions, particularly in less developed areas. Here, we introduced a novel method named the Spatial interpolation knowledge-constrained Conditional Generative Adversarial Network (SikCGAN). This method can generate high-resolution DEMs from publicly available data sources, specifically the photons collected by the Advanced Topographic Laser Altimeter System (ATLAS) carried by the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). SikCGAN takes ICESat-2/ATLAS photons as the single data source and incorporates spatial interpolation knowledge constraints into a Conditional Generative Adversarial Network (CGAN) to generate DEMs at a 10-m spatial resolution. A case study conducted in boreal mountainous regions demonstrates SikCGAN’s remarkable ability to produce high-resolution and highly accurate DEMs, with an MAE of 22.09 m and RMSE of 29.25 m, which reduced error by 37 %–46 % compared to benchmark methods. Additionally, the results reveal that SikCGAN has remarkable resiliece to interference, including variations in spatial distance, terrain slope, and ATL03 photon count, this further elucidates and substantiates the effectiveness of SikCGAN. These findings demonstrate that SikCGAN provides innovative solutions for generating new high-resolution DEMs products and potentially supplementing existing ones to overcome their limitations.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"138 ","pages":"Article 104461"},"PeriodicalIF":7.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAIR principles in workflows: A GIScience workflow management system for reproducible and replicable studies","authors":"Tao Hu ,&nbsp;Taiping Liu ,&nbsp;Venkat Sai Divyacharan Jarugumalli ,&nbsp;Samuel Cheng ,&nbsp;Chengbin Deng","doi":"10.1016/j.jag.2025.104477","DOIUrl":"10.1016/j.jag.2025.104477","url":null,"abstract":"<div><div>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 to ensure geospatial data and process transparency, leading to challenges in reproducibility and replicability of research findings. This paper proposes the conceptualization and development of FAIR-oriented GIScience WfMS, aiming to incorporate the FAIR principles into the entire lifecycle of geospatial data processing and analysis. To enhance the findability and accessibility of workflows, the WfMS utilizes Harvard Dataverse to share all workflow-related digital resources, organized into workflow datasets, nodes, and case studies. Each resource is assigned a unique DOI (Digital Object Identifier), ensuring easy access and discovery. More importantly, the WfMS complies with the Common Workflow Language (CWL) standard to guarantee interoperability and reproducibility of workflows. It also enables the integration of diverse tools and software, supporting complex analyses that require multiple processing steps. This paper demonstrates the prototype of the GIScience WfMS and illustrates two geospatial science case studies, reflecting its flexibility in selecting appropriate techniques for various datasets and research goals. The user-friendly workflow designer makes it accessible to users with different levels of technical expertise, promoting reusable, reproducible, and replicable GIScience studies.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"138 ","pages":"Article 104477"},"PeriodicalIF":7.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-resolution snow depth retrieval by passive microwave based on linear unmixing and machine learning stacking technique","authors":"Yanan Bai ,&nbsp;Zhen Li ,&nbsp;Ping Zhang ,&nbsp;Lei Huang ,&nbsp;Shuo Gao ,&nbsp;Haiwei Qiao ,&nbsp;Chang Liu ,&nbsp;Shuang Liang ,&nbsp;Huadong Hu","doi":"10.1016/j.jag.2025.104467","DOIUrl":"10.1016/j.jag.2025.104467","url":null,"abstract":"<div><div>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 challenge for accurate SD retrieval as SD increases. In this study, a high-resolution SD retrieval algorithm for passive microwave data was developed based on the linear unmixing method and machine learning (ML) stacking technique. Firstly, the 0.25° AMSR2 brightness temperature data were downscaled to 0.01° through linear unmixing. Then, combining the temporal and spatial features of the snowpack, the high-resolution SD was retrieved based on the ML stacking technique. This method combined the advantages of multiple base models for retrieving different depths of snow, which effectively improved the overall estimation performance of the algorithm. Compared with in situ observed SD at meteorological stations and field observation SD, the algorithm achieved an overall RMSE of 5.25 cm, which was lower than that of other coarse-resolution SD datasets and products, including the long-term series of daily SD dataset in China (7.40 cm), the ERA5-Land (9.71 cm), and JAXA AMSR2 Level 2 SD products (12.59 cm). Especially, it reduced the estimation error of deep snow with a depth exceeding 30 cm by 20.3 %, 21.5 %, and 24.9 %, respectively.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"138 ","pages":"Article 104467"},"PeriodicalIF":7.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}