{"title":"Beyond clouds: Seamless flood mapping using Harmonized Landsat and Sentinel-2 time series imagery and water occurrence data","authors":"Zhiwei Li , Shaofen Xu , Qihao Weng","doi":"10.1016/j.isprsjprs.2024.07.022","DOIUrl":null,"url":null,"abstract":"<div><p>Floods are among the most devastating natural disasters, posing significant risks to life, property, and infrastructure globally. Earth observation satellites provide data for continuous and extensive flood monitoring, yet limitations exist in the spatial completeness of monitoring using optical images due to cloud cover. Recent studies have developed gap-filling methods for reconstructing cloud-covered areas in water maps. However, these methods are not tailored for and validated in cloudy and rainy flooding scenarios with rapid water extent changes and limited clear-sky observations, leaving room for further improvements. This study investigated and developed a novel reconstruction method for time series flood extent mapping, supporting spatially seamless monitoring of flood extents. The proposed method first identified surface water from time series images using a fine-tuned large foundation model. Then, the cloud-covered areas in the water maps were reconstructed, adhering to the introduced submaximal stability assumption, on the basis of the prior water occurrence data in the Global Surface Water dataset. The reconstructed time series water maps were refined through spatiotemporal Markov random field modeling for the final delineation of flooding areas. The effectiveness of the proposed method was evaluated with Harmonized Landsat and Sentinel-2 datasets under varying cloud cover conditions, enabling seamless flood mapping at 2–3-day frequency and 30 m resolution. Experiments at four global sites confirmed the superiority of the proposed method. It achieved higher reconstruction accuracy with average F1-scores of 0.931 during floods and 0.903 before/after floods, outperforming the typical gap-filling method with average F1-scores of 0.871 and 0.772, respectively. Additionally, the maximum flood extent maps and flood duration maps, which were composed on the basis of the reconstructed water maps, were more accurate than those using the original cloud-contaminated water maps. The benefits of synthetic aperture radar images (e.g., Sentinel-1) for enhancing flood mapping under cloud cover conditions were also discussed. The method proposed in this paper provided an effective way for flood monitoring in cloudy and rainy scenarios, supporting emergency response and disaster management. The code and datasets used in this study have been made available online (<span><span>https://github.com/dr-lizhiwei/SeamlessFloodMapper</span><svg><path></path></svg></span>).</p></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"216 ","pages":"Pages 185-199"},"PeriodicalIF":10.6000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0924271624002892/pdfft?md5=01f4ede2e5789a2d4f709563c851664a&pid=1-s2.0-S0924271624002892-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS Journal of Photogrammetry and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0924271624002892","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Floods are among the most devastating natural disasters, posing significant risks to life, property, and infrastructure globally. Earth observation satellites provide data for continuous and extensive flood monitoring, yet limitations exist in the spatial completeness of monitoring using optical images due to cloud cover. Recent studies have developed gap-filling methods for reconstructing cloud-covered areas in water maps. However, these methods are not tailored for and validated in cloudy and rainy flooding scenarios with rapid water extent changes and limited clear-sky observations, leaving room for further improvements. This study investigated and developed a novel reconstruction method for time series flood extent mapping, supporting spatially seamless monitoring of flood extents. The proposed method first identified surface water from time series images using a fine-tuned large foundation model. Then, the cloud-covered areas in the water maps were reconstructed, adhering to the introduced submaximal stability assumption, on the basis of the prior water occurrence data in the Global Surface Water dataset. The reconstructed time series water maps were refined through spatiotemporal Markov random field modeling for the final delineation of flooding areas. The effectiveness of the proposed method was evaluated with Harmonized Landsat and Sentinel-2 datasets under varying cloud cover conditions, enabling seamless flood mapping at 2–3-day frequency and 30 m resolution. Experiments at four global sites confirmed the superiority of the proposed method. It achieved higher reconstruction accuracy with average F1-scores of 0.931 during floods and 0.903 before/after floods, outperforming the typical gap-filling method with average F1-scores of 0.871 and 0.772, respectively. Additionally, the maximum flood extent maps and flood duration maps, which were composed on the basis of the reconstructed water maps, were more accurate than those using the original cloud-contaminated water maps. The benefits of synthetic aperture radar images (e.g., Sentinel-1) for enhancing flood mapping under cloud cover conditions were also discussed. The method proposed in this paper provided an effective way for flood monitoring in cloudy and rainy scenarios, supporting emergency response and disaster management. The code and datasets used in this study have been made available online (https://github.com/dr-lizhiwei/SeamlessFloodMapper).
期刊介绍:
The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive.
P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields.
In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.