International Journal of Remote Sensing最新文献

{"title":"Recommendations for temporal aggregation of water quality data from multi-platform satellite constellations.","authors":"Megan M Coffer, Blake A Schaeffer, Wilson B Salls, Jeffrey M Minucci, Olivia Cronin-Golomb","doi":"10.1080/01431161.2025.2575515","DOIUrl":"10.1080/01431161.2025.2575515","url":null,"abstract":"<p><p>Satellite constellations often launch platforms over several years, increasing observational frequency and capturing additional, potentially more extreme, events. Consequently, reported changes in satellite-derived data may inadvertently capture variations in observational frequency rather than true environmental trends. This study used the Sentinel-3 Cyanobacteria Index (CI-cyano) to assess impacts of varying observational frequency on data distributions and trends. Daily CI-cyano was temporally aggregated into weekly composites using maximum, mean, and median values as both continuous and ordinal observations. Sentinel-3A, Sentinel-3B, and combined Sentinel-3A & -3B were compared using the Wilcoxon signed-rank test. For continuous observations, temporally aggregating via the maximum value showed a large 9% increase for combined Sentinel-3A & -3B versus Sentinel-3A or Sentinel-3B individually, compared to a small 1% decrease for temporal aggregation via the mean and negligible differences via the median. For ordinal observations, temporal aggregation via the maximum and mean showed large increases of up to 25% for combined Sentinel-3A & -3B, while the median showed small decreases of up to 5%. The seasonal Mann-Kendall trend test was then applied to Sentinel-3 imagery from 2016 to 2023, with and without observations from Sentinel-3B. Temporal aggregation via the maximum showed a moderate 20% increase with Sentinel-3B compared to a small 8% increase without Sentinel-3B; mean and median showed negligible trends. An abbreviated assessment using Sentinel-2 had similar results, with large increases for combined Sentinel-2A & -2B via the maximum, but small and moderate decreases via mean and median. Results suggest that temporal aggregation impacts multi-platform datasets. For more consistent summaries, continuous datasets should be temporally aggregated using the mean or median, and ordinal datasets using the median. Results are applicable to any satellite-derived water quality datasets with varied observational frequency. This study addresses a critical gap in the remote sensing community, ensuring relevant statistical concepts are appropriately applied in multi-platform analyses.</p>","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"47 1","pages":"177-199"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12927116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147283654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing predictions of environmental variables and species distributions on tidal flats by combining Sentinel-2 images and their deep-learning features with OBIA.","authors":"Logambal Madhuanand, Catharina J M Philippart, Wiebe Nijland, Steven M de Jong, Allert I Bijleveld, Elisabeth A Addink","doi":"10.1080/01431161.2024.2423909","DOIUrl":"10.1080/01431161.2024.2423909","url":null,"abstract":"<p><p>Tidal flat ecosystems, are under steady decline due to anthropogenic pressures including sea level rise and climate change. Monitoring and managing these coastal systems requires accurate and up-to-date mapping. Sediment characteristics and macrozoobenthos are major indicators of the environmental status of tidal flats. Field monitoring of these indicators is often restricted by low accessibility and high costs. Despite limitations in spectral contrast, integrating remote sensing with deep learning proved efficient for deriving macrozoobenthos and sediment properties. In this study, we combined deep-learning features derived from Sentinel-2 images and Object-Based Image Analysis (OBIA) to explicitly include spatial aspects in the prediction of tsediment and macrozoobenthos properties of tidal flats , as well as the distribution of four benthic species. The deep-learning features extracted from a convolutional autoencoder model were analysed with OBIA to include spatial, textural, and contextual information. Object sets of varying sizes and shapes based on the spectral bands and/or the deep-learning features, served as the spatial units. These object sets and the field-collected points were used to train the Random Forest prediction model. Predictions were made for the tidal basins Pinkegat and Zoutkamperlaag in the Dutch Wadden Sea for 2018 to 2020. The overall prediction scores of the environmental variables ranged between 0.31 and 0.54. The species-distribution prediction model achieved accuracies ranging from 0.54 to 0.68 for the four benthic species). There was an average improvement of 21% points on predictions using objects with deep learning features compared to the pixel-based predictions with just the spectral bands. The mean spatial unit that captured the patterns best ranged between 0.3 ha and 13 ha for the different variables. Overall, using both OBIA and deep-learning features consistently improved the predictions, making it a valuable combination for monitoring these important environmental variables of coastal regions.</p>","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"46 2","pages":"811-834"},"PeriodicalIF":3.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11755323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature extraction via 3-D homogeneous attribute decomposition for hyperspectral imagery classification","authors":"Yong Zhang, Yishu Peng, Guoyun Zhang, Wujin Li","doi":"10.1080/01431161.2024.2394234","DOIUrl":"https://doi.org/10.1080/01431161.2024.2394234","url":null,"abstract":"Feature extraction is a core aspect in hyperspectral image classification, which can extract key information closely related to ground cover from complex scene, thus improving classification accura...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"11 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic region growing approach for leaf-wood separation of individual trees based on geometric features and growing patterns","authors":"Wen Hao, Maoxue Ran","doi":"10.1080/01431161.2024.2394235","DOIUrl":"https://doi.org/10.1080/01431161.2024.2394235","url":null,"abstract":"The separation of leaf and wood points remains challenging due to the diversity of tree species and structures. We propose an automatic leaf-wood separation method from tree point clouds, leveragin...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"3 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of yield loss due to fall armyworm in maize using high-resolution multispectral spaceborne remote sensing","authors":"Prabhakar Mathyam, Gopinath Kodigal A, Ravi Kumar Nakka, Thirupathi Merugu, Sai Sravan Uppu, Srasvan Kumar Golla, Samba Siva Gutti, Chandana Pebbeti, Suryakala Adhikari, Vinod Kumar Singh","doi":"10.1080/01431161.2024.2394233","DOIUrl":"https://doi.org/10.1080/01431161.2024.2394233","url":null,"abstract":"The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), invasion endangered the maize production worldwide, including India. The objective of this study was to quantify the FAW damage severity...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"53 79 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural graph learning method for hyperspectral band selection","authors":"Shuying Li, Zhe Liu, Long Fang, Qiang Li","doi":"10.1080/01431161.2024.2394231","DOIUrl":"https://doi.org/10.1080/01431161.2024.2394231","url":null,"abstract":"Recently, graph learning-based hyperspectral band selection algorithms illustrate impressive performance for hyperspectral image (HSI) processing, whose goal is to select an optimal band combinatio...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"168 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing high-resolution remote sensing: a compact and powerful approach to semantic segmentation","authors":"Hua Zhang, Zhengang Jiang, Jun Xu, Xin Pan","doi":"10.1080/01431161.2024.2398226","DOIUrl":"https://doi.org/10.1080/01431161.2024.2398226","url":null,"abstract":"Deep learning (DL)-based approaches are notable for their ability to establish feature associations without relying on physical constraints, unlike traditional strategies that are complex and depen...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"9 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyper-Parameter Optimization-based multi-source fusion for remote sensing inversion of non-photosensitive water quality parameters","authors":"Yuhao Yuan, Zhiping Lin, Xinhao Jiang, Zhongmou Fan","doi":"10.1080/01431161.2024.2388878","DOIUrl":"https://doi.org/10.1080/01431161.2024.2388878","url":null,"abstract":"The constraints of spatiotemporal heterogeneity and spatial resolution constitute two crucial challenges in the establishment of remote sensing inversion models. Spatiotemporal heterogeneity gives ...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"9 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"All weather land surface temperature estimation by combining thermal infrared and passive microwave radiometry: a study over India","authors":"Rahul Harod, Eswar Rajasekaran","doi":"10.1080/01431161.2024.2394232","DOIUrl":"https://doi.org/10.1080/01431161.2024.2394232","url":null,"abstract":"Land Surface Temperature (LST) plays a crucial role in water and energy cycle studies. However, clouds pose a significant challenge in obtaining continuous LST time series from Thermal Infrared (TI...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"1 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OccFaçade: enabling precise building façade parsing in large urban scenes with occlusion","authors":"Yongjun Zhang, Dongdong Yue, Xinyi Liu, Siyuan Zou, Weiwei Fan, Zihang Liu","doi":"10.1080/01431161.2024.2391589","DOIUrl":"https://doi.org/10.1080/01431161.2024.2391589","url":null,"abstract":"Building façade parsing is to recognize the building façade image into different categories of individuals including walls, doors, windows, balconies, etc. However, obstructions such as trees prese...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"189 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}