| 摘要: |
| 气候变化背景下，极端降水及其引发的洪涝灾害事件日趋严重。科学探究洪涝灾害监测领域的发展历程、技术和热点演变规律具有一定的指示意义。基于CNKI（China National Knowledge Infrastructure）与WOS（Web of Science）数据库，系统分析2000—2021年国内外洪涝遥感领域的发展和热点演变规律。主要结果为：（1）领域总发文量1693篇，2008年后呈稳定增长趋势。中国科学院、北京师范大学、武汉大学、意大利国家研究委员会和美国航空航天局为重要发文机构。（2）领域发展阶段性强，研究重心呈阶段性变化，2000—2009年以遥感影像应用、洪水模型开发等为重心；2010—2014年以遥感影像精确解译、多光谱影像应用、长时间序列检测为主；2015—2021年洪涝遥感正处在稳步发展阶段，海量可用数据增加，数据处理手段丰富，以水体指数改进、大数据融合、深度学习、无人机监测为主。（3）领域数据源、方法及应用方向的规律为：数据上，早期以雷达数据为主，近年来开始逐渐探索光学影像及其他多源数据的融合；算法上趋于深度学习、超像元、面向对象等方法的应用以提高洪涝识别精度；应用上，气候变化背景下，冰川融化、冰湖溃决带来的洪涝灾害的时空变化、风险识别以及预警监测研究，为近年来的一些热点研究。 |
| 关键词: 洪涝遥感 CiteSpace 综述 知识图谱分析 |
| DOI：10.7515/JEE221021 |
| CSTR： |
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| 基金项目:“天山英才”培养计划（2023TSYCCX0079）； 风云卫星应用先行计划（FY-APP-2022.0401）；中国沙漠气象科学研究基金项目（Sq?j2021002）；国家自然科学基金项目（42071075） |
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| Hot spot tracking of f lood remote sensing research over the past 22 years: abibliometric analysis using CiteSpace |
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HUO Hong, LIU Yan, LI Yang
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1. Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
2. Wulanwusu Ecology and Agrometeorology Observation and Research Station of Xinjiang, Urumqi 830002, China
3. Field Scientific Experiment Base of Akdala Atmospheric Background, China Meteorological Administration, Urumqi 830002, China
4. Meteorological and Technical Equipment Support Center of Xinjiang Uygur Autonomous Region, Xinjiang 830002, China
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| Abstract: |
| Background, aim, and scope In the context of climate change, extreme precipitation and resulting flooding events are becoming increasingly severe. Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range, periodic revisit capabilities, and continuous spatial coverage. These tools enable real-time and quantitative assessment of flood inundation. Over the past 20 years, the field of remote sensing for floods has seen significant advancements. Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions. Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing, both domestically and internationally during 2000—2021. Data from CNKI (China National Knowledge Infrastructure) and WOS (Web of Science) databases are utilized for this analysis. Results (1) A total of 1693 articles have been published in this field, showing a stable growth trend post-2008. Significant contributors include the Chinese Academy of Sciences, Beijing Normal University, Wuhan University, the Italian National Research Council, and National Aeronautics and Space Administration. (2) High-frequency keywords from 2000 to 2021 include “remote sensing” “flood” “model” “classification” “GIS” “climate change” “area”, and “MODIS”. (3) The most prominent keywords were “GIS” (8.65), “surface water” (7.16), “remote sensing” (7.07), “machine learning” (6.52), and “sentinel-2” (5.86). (4) Thirteen cluster labels were identified through clustering, divided into three phases: 2000—2009 (initial exploratory stage), 2010—2014 (period of rapid development), and 2015—2021 (steady development of remote sensing for floods and related disasters). Discussion The field exhibits strong phase-based development, with research focuses shifting over time. From 2000 to 2009, emphasis was on remote sensing image application and flood model development. From 2010 to 2014, the focus shifted to accurate interpretation of remote sensing images, multispectral image applications, and long time series detection. From 2015 to 2021, research concentrated on steady development, leveraging large datasets and advanced data processing techniques, including improvements in water body indices, big data fusion, deep learning, and drone monitoring. Early on, SAR data, known for its all-weather capability, was crucial for rapid flood hazard extraction and flood hydrological models. With the rise of high-quality optical satellites, optical remote sensing has become more prevalent, though algorithm accuracy and efficiency for water body index methods still require improvement. Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration. Algorithms now increasingly employ deep learning, super image elements, and object-oriented methods to enhance flood identification accuracy. Recent studies focus on spatial and temporal changes in flooding, risk identification, and early warning for climate change-related flooding, including glacial melting and lake outbursts. Recommendations and perspectives To enhance monitoring accuracy and timeliness, UAV technology should be further utilized. Strengthening multi-source data fusion and assimilation is crucial, as is analyzing long-term flood disaster sequences to better understand their mechanisms. |
| Key words: flood remote sensing CiteSpace review knowledge graph analysis |
