{"title":"多时间分辨率空间海表温度数据的两种时空重建方法比较","authors":"Xuehua Ma;Junyu He;Shuangyan He;Yanzhen Gu;Anzhou Cao;Peiliang Li;Feng Zhou","doi":"10.1109/JSTARS.2024.3453508","DOIUrl":null,"url":null,"abstract":"The satellite remote sensing sea surface temperature (SST) plays a crucial role in global climate change and ocean–atmosphere interactions. With a notably severe issue of missing data due to clouds and rainfall, data reconstruction methods have been developed to effectively enhance the spatiotemporal completeness of satellite-derived SST data products in recent years. However, few studies have focused on performance comparisons between these different data reconstruction methods, which limits further improvement and application of reconstructed data products. In this study, two representative methods, the data interpolating empirical orthogonal functions (DINEOF) and a spatiotemporal geostatistical method of Bayesian maximum entropy (BME), were used to reconstruct satellite SST data in four regions, and their reconstruction performance under various temporal resolutions (hourly, daily, and monthly) and missing data rates were evaluated and compared. Our results demonstrate that BME consistently outperforms DINEOF. As the missing data rate increases from 10% to 90%, especially when it exceeds 70%, DINEOF reconstruction results exhibit significant increasing noises and reconstruction errors, while BME demonstrates stable precise reconstruction results. Compared with DINEOF method, the results of BME method are less influenced by missing data rates, spatiotemporal resolutions, temporal length, and regions of input data series by different remote sensing sensors, rendering it more applicable and robust in reconstructing multisensor SST data with different temporal resolutions. The BME method holds promising implications in reconstructing high-quality gap-filled data using noisy and high-missing-rate multisensor data in regional areas with high dynamics.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670006","citationCount":"0","resultStr":"{\"title\":\"Comparison of Two Spatiotemporal Reconstruction Methods for Spaceborne Sea Surface Temperature Data at Multiple Temporal Resolutions\",\"authors\":\"Xuehua Ma;Junyu He;Shuangyan He;Yanzhen Gu;Anzhou Cao;Peiliang Li;Feng Zhou\",\"doi\":\"10.1109/JSTARS.2024.3453508\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The satellite remote sensing sea surface temperature (SST) plays a crucial role in global climate change and ocean–atmosphere interactions. With a notably severe issue of missing data due to clouds and rainfall, data reconstruction methods have been developed to effectively enhance the spatiotemporal completeness of satellite-derived SST data products in recent years. However, few studies have focused on performance comparisons between these different data reconstruction methods, which limits further improvement and application of reconstructed data products. In this study, two representative methods, the data interpolating empirical orthogonal functions (DINEOF) and a spatiotemporal geostatistical method of Bayesian maximum entropy (BME), were used to reconstruct satellite SST data in four regions, and their reconstruction performance under various temporal resolutions (hourly, daily, and monthly) and missing data rates were evaluated and compared. Our results demonstrate that BME consistently outperforms DINEOF. As the missing data rate increases from 10% to 90%, especially when it exceeds 70%, DINEOF reconstruction results exhibit significant increasing noises and reconstruction errors, while BME demonstrates stable precise reconstruction results. Compared with DINEOF method, the results of BME method are less influenced by missing data rates, spatiotemporal resolutions, temporal length, and regions of input data series by different remote sensing sensors, rendering it more applicable and robust in reconstructing multisensor SST data with different temporal resolutions. The BME method holds promising implications in reconstructing high-quality gap-filled data using noisy and high-missing-rate multisensor data in regional areas with high dynamics.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670006\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10670006/\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10670006/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Comparison of Two Spatiotemporal Reconstruction Methods for Spaceborne Sea Surface Temperature Data at Multiple Temporal Resolutions
The satellite remote sensing sea surface temperature (SST) plays a crucial role in global climate change and ocean–atmosphere interactions. With a notably severe issue of missing data due to clouds and rainfall, data reconstruction methods have been developed to effectively enhance the spatiotemporal completeness of satellite-derived SST data products in recent years. However, few studies have focused on performance comparisons between these different data reconstruction methods, which limits further improvement and application of reconstructed data products. In this study, two representative methods, the data interpolating empirical orthogonal functions (DINEOF) and a spatiotemporal geostatistical method of Bayesian maximum entropy (BME), were used to reconstruct satellite SST data in four regions, and their reconstruction performance under various temporal resolutions (hourly, daily, and monthly) and missing data rates were evaluated and compared. Our results demonstrate that BME consistently outperforms DINEOF. As the missing data rate increases from 10% to 90%, especially when it exceeds 70%, DINEOF reconstruction results exhibit significant increasing noises and reconstruction errors, while BME demonstrates stable precise reconstruction results. Compared with DINEOF method, the results of BME method are less influenced by missing data rates, spatiotemporal resolutions, temporal length, and regions of input data series by different remote sensing sensors, rendering it more applicable and robust in reconstructing multisensor SST data with different temporal resolutions. The BME method holds promising implications in reconstructing high-quality gap-filled data using noisy and high-missing-rate multisensor data in regional areas with high dynamics.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.