International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"Empirical methods to determine surface air temperature from satellite-retrieved data","authors":"Joan Vedrí, Raquel Niclòs, Lluís Pérez-Planells, Enric Valor, Yolanda Luna, María José Estrela","doi":"10.1016/j.jag.2025.104380","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104380","url":null,"abstract":"Surface air temperature (SAT) is an essential climate variable (ECV). Models based on remote sensing data allow us to study SAT, without the need for a large network of meteorological stations. Therefore, it allows monitoring the climate in remote and extensive areas. Niclos et al. (2014) proposed parametric equations for the SAT retrieval over the Spanish Mediterranean basins. In this study, we evaluated those equations, but in a larger area and period of study. In addition, we proposed several linear regression models and nonlinear models based on decision tree methods, non-parametric methods and neuronal networks. These models relate SAT to land surface temperature, vegetation indexes and albedo from MODIS data. Moreover, meteorological reanalysis data, from ERA5-Land database, and geographical parameters were used. The accuracy of each model was evaluated against data from meteorological stations operated by AEMET in the Spanish Mediterranean basins, during the period 2021–2022. The equations of Niclos et al. (2014) obtained a robust root mean square error (RRMSE) of 3.1 K at daytime and 1.9 K at nighttime. For the linear regression models, the RRMSE decreased to 2.3 K (1.5 K) at daytime (nighttime). Finally, the nonlinear methods, in particular XGBoost model, showed an RRMSE of 1.5 K for daytime and 1.0 K at nighttime. Therefore, the comparison between methods showed that nonlinear models, in particular those based on decision tree methods, offered the best results in SAT retrieval in our study.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"78 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IceEB: An ensemble-based method to map river ice type from radar images","authors":"Plante Lévesque Valérie, Chokmani Karem, Gauthier Yves, Bernier Monique","doi":"10.1016/j.jag.2024.104317","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104317","url":null,"abstract":"This paper introduces IceEB, i.e., an innovative ensemble-based method that is designed to automate mapping of river ice types using radar imagery. Its goal is the merger of outcomes from three classifiers (IceMAP-R, RIACT, and IceBC) through ensemble-estimation, resulting in a highly performant and fully automated river ice-type map, which is applicable under all meteorological conditions. The first step of our research is the development of a <ce:italic>meta</ce:italic>-classifier and a confidence estimation index, then we validate our method using ground-truth datasets and finally compare the performance between IceEB and the original classifiers. The anticipated outcome was a map exhibiting superior results compared to individual classifiers. Validation and comparison of IceEB employed six RADARSAT-2 HH-HV C-band images that were selected from historical datasets of Quebec and Alberta rivers (Canada). IceEB integrates RADARSAT-2 satellite imagery, a digital elevation model, and a river mask, undergoing preprocessing tasks before activating the three initial classifiers. The <ce:italic>meta</ce:italic>-classifier then performs ensemble-based classification, yielding a legend comprised of water, sheet ice and rubble ice. This approach facilitates broad participation in validation data collection, differentiation between ice covers and ice jams, and minimization of assumptions regarding ice formation. We conclude that IceEB successfully combines existing radar remote sensing ice- classification models to create accurate river ice-type maps. IceEB’s ensemble-based approach outperforms individual classifiers, achieving overall accuracy &gt;91 % for each class. Shortcomings of the original classifiers are effectively offset through parallel use, resulting in marked improvements in automation and generalizability across diverse Canadian meteorological conditions.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"48 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual U–Vision–Transformer for reconstructing the three-dimensional eddy-resolving oceanic physical parameters from satellite observations","authors":"Huarong Xie, Changming Dong, Qing Xu","doi":"10.1016/j.jag.2025.104382","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104382","url":null,"abstract":"Three-dimensional (3-D) observations are crucial for understanding structures and evolution of ocean dynamic processes. This study proposes a dual U–Vision–Transformer (DUViT) model to reconstruct 3-D, eddy-resolving ocean temperature, salinity, and horizontal current fields from multi-resolution data observed by satellites at the sea surface. Daily parameter profiles from a reanalysis product with a 0.083° grid serve as labels for establishing and evaluating the model. Results demonstrate that the DUViT model can reproduce 3-D physical variables on scales from the basin scale to the mesoscale within the upper 2000 m of the South China Sea. Average root mean square errors between model estimations and reanalysis data are 0.039 ℃, 0.017 psu, and 0.012 m/s for temperature, salinity, and current profiles, respectively, with correlation coefficients above 0.9. Moreover, the reconstructed temperature and salinity profiles and their evolution agree well with <ce:italic>in-situ</ce:italic> observations with the correlation coefficients above 0.94. Case studies indicate that the DUViT model can effectively reproduce the 3-D structures of the circulation in the upper ocean and mesoscale eddies and distinguish mode-water eddies from general anticyclonic eddies. The integration of high-resolution satellite observations with the DUViT model enables accurate 3-D reconstruction of ocean phenomena across scales, which will offer new insights into the dynamics of marginal seas and open oceans.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"26 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)","authors":"Forough Fendereski, Shizhou Ma, Sassan Mohammady, Christopher Spence, Charles G. Trick, Irena F. Creed","doi":"10.1016/j.jag.2025.104376","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104376","url":null,"abstract":"Wetlandscapes—hydrologically connected networks of wetlands—vary over time, causing changes in their provision of hydrological, biogeochemical, and ecological functions to landscapes. Here, we developed a method for mapping wetlands and extracting wetlandscape properties from Landsat-derived inundation data and applied this method to the Lake Winnipeg Watershed (LWW). We first mapped the annual (1984–2020) time series of inundated areas using a fusion of two Landsat-derived inundation products, Global Surface Water Extent (GSWE) and Dynamic Surface Water Extent (DSWE), finding that this fusion reduced omission errors from 17 % for GSWE and 18 % for DSWE to 8 % overall. We then used the inundated area maps to identify the topological structure of the wetlandscape, i.e., networks composed of nodes (representing wetlands) and their links (representing hydrological connectivity among wetlands). The time series of the wetlandscape properties (number, size, and connectivity of wetlands) showed coherence with a concurrent increase in precipitation over the watershed. The LWW is transitioning to a more extensive wetland area consisting of a greater number of larger wetlands with increased connections among them (<ce:italic>p</ce:italic> &lt; 0.1). With Landsat-derived inundation products widely available globally, we suggest using the method developed here to analyze changes in wetlandscape properties in other regions worldwide.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"27 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the next generation of Geospatial Artificial Intelligence","authors":"Gengchen Mai, Yiqun Xie, Xiaowei Jia, Ni Lao, Jinmeng Rao, Qing Zhu, Zeping Liu, Yao-Yi Chiang, Junfeng Jiao","doi":"10.1016/j.jag.2025.104368","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104368","url":null,"abstract":"Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"3 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landslide susceptibility assessment of the Wanzhou district: Merging landslide susceptibility modelling (LSM) with InSAR-derived ground deformation map","authors":"Chao Zhou, Lulu Gan, Ying Cao, Yue Wang, Samuele Segoni, Xuguo Shi, Mahdi Motagh, Ramesh P. Singhc","doi":"10.1016/j.jag.2025.104365","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104365","url":null,"abstract":"The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The landslide catalog and multiple EML methods are used for the preparation of the preliminary susceptibility map. We have also compared and analyzed the impact of ensemble strategies (homogeneous and heterogeneous ensemble) and base-learners on the modelling performance. Subsequently, Sentinel-1 data from 2018 to 2020, analyzed using MT-InSAR approach, are used to map ground deformation rates. We outline the active slopes and deduce the relationship between the deformation of Matou landslide and triggering factors. The final susceptibility map is generated by coupling catalog-based susceptibility and ground deformation rate maps through an empirical assessment matrix. Our results show that the causal factors of distance to rivers, distance to faults, annual rainfall and distance to roads are basic parameters for landslide spatial development; Heterogeneous EML methods outperform the homogeneous, and the more base-learner types provide better performance. InSAR-acquired deformation rates corrected overestimation and underestimation errors in the landslide susceptibility map produced by catalog-based method. Our proposed method is capable of improving the accuracy and timeliness of susceptibility map, providing a useful instrument to better assess landslide risk scenarios in rapidly changing environments.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"57 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HUTDNet: A joint unmixing and target detection network for underwater hyperspectral imagery","authors":"Qi Li, Xingyuan Zu, Ming Zhang, Jinghua Li, Yan Feng","doi":"10.1016/j.jag.2025.104374","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104374","url":null,"abstract":"Underwater hyperspectral target detection (HTD) technology holds pivotal value in enhancing maritime military power. However, the absorption and scattering properties of the water bodies result in the inevitable issue of mixed pixels in underwater hyperspectral images (HSIs). To address the issue, a joint hyperspectral unmixing and target detection network for underwater HSI is proposed, denoted as HUTDNet, which utilizes the material type and abundance information for downstream semantic tasks. Specifically, a nonlinear underwater unmixing network is designed to extract pure underwater endmembers and their associated abundance information, which is essential in assisting the subsequent target detection task. The network also extracts underwater virtual endmembers and their abundance values to reconstruct a more realistic underwater HSI. Then, the abundance weighting module determines the abundance weighting factor by calculating the spectral distance between a priori target spectra and the estimated underwater pure endmembers, generating a weighted abundance map. Finally, due to the inherent limitations in the characterization capabilities of abundance maps and endmembers, the detection network extracts key spectral feature maps from the input underwater HSI. These feature maps serve as complementary terms, fused with the original and weighted abundance maps. Subsequently, convolutional and fully connected layers are employed to extract deeper features and generate the target detection maps. Experiments on both real and synthetic datasets demonstrate the superior performance and efficiency of the proposed method in this paper compared to other state-of-the-art methods.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"13 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying algal bloom types and analyzing their diurnal variations using GOCI-Ⅱ data","authors":"Renhu Li, Fang Shen, Yuan Zhang, Zhaoxin Li, Songyu Chen","doi":"10.1016/j.jag.2025.104377","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104377","url":null,"abstract":"Frequent algal blooms pose a serious threat to the marine ecosystem of the East China Sea. The Geostationary Ocean Color Imager-Ⅱ (GOCI-Ⅱ), a second-generation geostationary satellite sensor, is crucial for monitoring marine environmental dynamics. To evaluate the potential of GOCI-II for identifying and monitoring the diurnal variation of algal blooms in the East China Sea, we combined a coupled ocean–atmosphere model with the eXtreme Gradient Boosting (XGBoost) method to develop an atmospheric correction algorithm for coastal waters (XGB-CW). Validation showed that this algorithm derived remote sensing reflectance (<ce:italic>R</ce:italic><ce:inf loc=\"post\">rs</ce:inf>) from GOCI-Ⅱ with higher accuracy than those provided by the National Ocean Satellite Center of South Korea (NOSC). To further evaluate GOCI-Ⅱ’s potential for algal bloom types identification, we compared three identification algorithms’ (Bloom Index (BI), Diatom Index (DI), and R<ce:inf loc=\"post\">slope</ce:inf>) results with <ce:italic>R</ce:italic><ce:inf loc=\"post\">rs</ce:inf> data derived by XGB-CW. And the BI algorithm performed best in distinguishing the diatoms and dinoflagellates blooms, while R<ce:inf loc=\"post\">slope</ce:inf> was effective under high biomass conditions. The DI algorithm was good for diatoms blooms but less effective for dinoflagellates. Using Photosynthetically Available Radiation (PAR) and Sea Surface Temperature (SST) data, we analyzed the influence of these factors on the daily variations and characteristics of <ce:italic>Akashiwo sanguinea</ce:italic> (Dinoflagellate) and <ce:italic>Chaetoceros curvisetus</ce:italic> (Diatom). The results showed more pronounced daily variations in <ce:italic>A. sanguinea</ce:italic> compared to <ce:italic>C. curvisetus</ce:italic>. GOCI-Ⅱ, combined with accurate atmospheric correction and identification algorithms, plays a crucial role in algal bloom monitoring.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"23 19 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning reveals hotspots of global oceanic oxygen changes from 2003 to 2020","authors":"Dongliang Ma, Fang Zhao, Likai Zhu, Xiaofei Li, Jine Wei, Xi Chen, Lijun Hou, Ye Li, Min Liu","doi":"10.1016/j.jag.2025.104363","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104363","url":null,"abstract":"The decrease in global oceanic dissolved oxygen (DO) has exerted a profound impact on marine ecosystems and biogeochemical processes. However, our comprehension of DO distribution and its global change patterns remains hindered by sparse measurements and coarse-resolution simulations. Here we presented Oxyformer, a deep learning method that accurately learns DO-related information and estimates high-resolution global DO concentration. The results derived by Oxyformer demonstrate an accelerated decline in global oceanic DO content, estimated at approximately 1045 ± 665 Tmol decade<ce:sup loc=\"post\">−1</ce:sup> from 2003 to 2020. The observed trends exhibit considerable variability across different regions and depths, with some new hotspots of recent DO change including the Equatorial Indian Ocean, the South Pacific Ocean, the North Atlantic Ocean, and the Western Coast of California. The unprecedented modeling approach provides a powerful tool to track changes in global DO contents and to facilitate the understanding of their influences on ocean ecosystems and biogeochemical processes.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"57 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NeRFOrtho: Orthographic Projection Images Generation based on Neural Radiance Fields","authors":"Dongdong Yue, Xinyi Liu, Yi Wan, Yongjun Zhang, Maoteng Zheng, Weiwei Fan, Jiachen Zhong","doi":"10.1016/j.jag.2025.104378","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104378","url":null,"abstract":"The application value of orthographic projection images is substantial, especially in the field of remote sensing for True Digital Orthophoto Map (TDOM) generation. Existing methods for orthographic projection image generation primarily involve geometric correction or explicit projection of photogrammetric mesh models. However, the former suffers from projection differences and stitching lines, while the latter is plagued by poor model quality and high costs. This paper presents NeRFOrtho, a new method for generating orthographic projection images from neural radiance fields at arbitrary angles. By constructing Neural Radiance Fields from multi-view images with known viewpoints and positions, the projection method is altered to render orthographic projection images on a plane where projection rays are parallel to each other. In comparison to existing orthographic projection image generation methods, this approach produces orthographic projection images devoid of projection differences and distortions, while offering superior texture details and higher precision. We also show the applicative potential of the method when rendering TDOM and the texture of building façade.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"7 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}