International Journal of Remote Sensing最新文献

{"title":"Sentinel-2 for chlorophyll-a water quality monitoring: a review of validation evidence and application potential.","authors":"Sarah Goodrich, Blake Schaeffer, Kate Meyers, Wilson Salls, Tyler King, Bridget Seegers, Olivia Cronin-Golomb, David Demaree, Molly Reif","doi":"10.1080/01431161.2026.2637851","DOIUrl":"10.1080/01431161.2026.2637851","url":null,"abstract":"<p><p>Water quality monitoring is integral to preserving the health of freshwater ecosystems, and satellite remote sensing has emerged as one monitoring method. Sentinel-2, in particular, has been valuable for water quality monitoring due to its 5-day global temporal revisit time and spatial resolution that ranges from 10 to 60 metres. Sentinel-2 can be used to measure and monitor chlorophyll-a, which historically has been used as an indicator of water quality, eutrophication and harmful algal blooms. Our goal was to review aquatic chlorophyll-a Sentinel-2 research to assess the types of validation evidence reported. Validation evidence is defined here as the set of information key to assessing algorithm performance, and include the spatial and temporal scales of satellite validation, reported in situ sampling method context information, demonstration of validation results through plots, and appropriate algorithm performance metrics. We highlight how the body of literature collectively contributes to advancing a national scale chlorophyll-a product that could support future resource management applications. Our review of 122 published studies indicated that much of the validation evidence corresponded to early stages, as defined by the NASA data maturity framework, due to a limited focus on individual lakes and limited detail on methodology for reproducibility. Prioritizing data accessibility for both in situ data and satellite workflows used in published studies; reporting methods with transparency and consistency; and using standard algorithm performance metrics could provide a consistent framework to support and enhance the utility of satellite inland water quality research. These three quality assurance mechanisms can promote effective evaluation of approaches for remote sensing of chlorophyll-a. Adopting these quality criteria could enable the integration of validation evidence from multiple studies, supporting more spatially and temporally representative products that would advance these approaches towards maturation for broader application.</p>","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"47 9","pages":"3820-3845"},"PeriodicalIF":2.6,"publicationDate":"2026-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13266896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148264233","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":"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":"Impact of assimilation of DWR reflectivity and radial velocity on simulation of active monsoon precipitation over Indian region","authors":"Devajyoti Dutta, Kondapalli Niranjan Kumar, Ashish Routray, Sukhwinder Kaur, V. S. Prasad","doi":"10.1080/01431161.2025.2457133","DOIUrl":"https://doi.org/10.1080/01431161.2025.2457133","url":null,"abstract":"","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"46 7","pages":"2767-2792"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147920840","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":"Analysis of co-seismic ionospheric disturbance of the Qinghai Mw 7.4 earthquake on May 21, 2021","authors":"Qingshan Ruan, Hang Liu, Xitun Yuan, Shuyao Ge, Xi Cheng, Wu Wang","doi":"10.1080/01431161.2025.2450563","DOIUrl":"https://doi.org/10.1080/01431161.2025.2450563","url":null,"abstract":"","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"46 5","pages":"2233-2253"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147882451","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":"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}