{"title":"利用多通道奇异光谱分析重建沙漠地区昼夜 MODIS 陆面温度","authors":"Fahime Arabi Aliabad , Mohammad Zare , Hamidreza Ghafarian Malamiri , Amanehalsadat Pouriyeh , Himan Shahabi , Ebrahim Ghaderpour , Paolo Mazzanti","doi":"10.1016/j.ecoinf.2024.102830","DOIUrl":null,"url":null,"abstract":"<div><div>The availability of continuous spatiotemporal land surface temperature (LST) with high resolution is critical for many disciplines including hydrology, meteorology, ecology, and geology. Like other remote sensing data, satellite–based LST is also encountered with the cloud issue. In this research, over 5000 daytime and nighttime MODIS–LST images are utilized during 2014–2020 for Yazd–Ardakan plain in Yazd, Iran. The multi–channel singular spectrum analysis (MSSA) model is employed to reconstruct missing values due to dusts, clouds, and sensor defect. The selection of eigenvalues is based on the Monte Carlo test and the spectral analysis of eigenvalues. It is found that enlarging the window size has no effect on the number of significant components of the signal which account for the most variance of the data. However, data variance changes for all the three components. Employing two images per day, window sizes 60, 180, 360, and 720 are examined for reconstructing one year LST, where these selections are based on monthly, seasonal, semi-annual, and annual LST cycles, respectively. The results show that window size 60 had the least computational cost and the highest accuracy with RMSE (root mean square error) of 2.6 °C for the entire study region and 1.4 °C for a selected pixel. The gap–filling performance of MSSA is also compared with the one by the harmonic analysis of time series (HANTS) model, showing the superiority of MSSA with an improved RMSE of about 2.7 °C for the study region. In addition, daytime and nighttime LST series for different land covers are compared. Lastly, the maximum, minimum, and average LST for each day and night as well as average and standard deviation of LST images in the seven-year-long time series are also computed.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102830"},"PeriodicalIF":5.8000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reconstructing daytime and nighttime MODIS land surface temperature in desert areas using multi-channel singular spectrum analysis\",\"authors\":\"Fahime Arabi Aliabad , Mohammad Zare , Hamidreza Ghafarian Malamiri , Amanehalsadat Pouriyeh , Himan Shahabi , Ebrahim Ghaderpour , Paolo Mazzanti\",\"doi\":\"10.1016/j.ecoinf.2024.102830\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The availability of continuous spatiotemporal land surface temperature (LST) with high resolution is critical for many disciplines including hydrology, meteorology, ecology, and geology. Like other remote sensing data, satellite–based LST is also encountered with the cloud issue. In this research, over 5000 daytime and nighttime MODIS–LST images are utilized during 2014–2020 for Yazd–Ardakan plain in Yazd, Iran. The multi–channel singular spectrum analysis (MSSA) model is employed to reconstruct missing values due to dusts, clouds, and sensor defect. The selection of eigenvalues is based on the Monte Carlo test and the spectral analysis of eigenvalues. It is found that enlarging the window size has no effect on the number of significant components of the signal which account for the most variance of the data. However, data variance changes for all the three components. Employing two images per day, window sizes 60, 180, 360, and 720 are examined for reconstructing one year LST, where these selections are based on monthly, seasonal, semi-annual, and annual LST cycles, respectively. The results show that window size 60 had the least computational cost and the highest accuracy with RMSE (root mean square error) of 2.6 °C for the entire study region and 1.4 °C for a selected pixel. The gap–filling performance of MSSA is also compared with the one by the harmonic analysis of time series (HANTS) model, showing the superiority of MSSA with an improved RMSE of about 2.7 °C for the study region. In addition, daytime and nighttime LST series for different land covers are compared. Lastly, the maximum, minimum, and average LST for each day and night as well as average and standard deviation of LST images in the seven-year-long time series are also computed.</div></div>\",\"PeriodicalId\":51024,\"journal\":{\"name\":\"Ecological Informatics\",\"volume\":\"83 \",\"pages\":\"Article 102830\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Informatics\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574954124003728\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574954124003728","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Reconstructing daytime and nighttime MODIS land surface temperature in desert areas using multi-channel singular spectrum analysis
The availability of continuous spatiotemporal land surface temperature (LST) with high resolution is critical for many disciplines including hydrology, meteorology, ecology, and geology. Like other remote sensing data, satellite–based LST is also encountered with the cloud issue. In this research, over 5000 daytime and nighttime MODIS–LST images are utilized during 2014–2020 for Yazd–Ardakan plain in Yazd, Iran. The multi–channel singular spectrum analysis (MSSA) model is employed to reconstruct missing values due to dusts, clouds, and sensor defect. The selection of eigenvalues is based on the Monte Carlo test and the spectral analysis of eigenvalues. It is found that enlarging the window size has no effect on the number of significant components of the signal which account for the most variance of the data. However, data variance changes for all the three components. Employing two images per day, window sizes 60, 180, 360, and 720 are examined for reconstructing one year LST, where these selections are based on monthly, seasonal, semi-annual, and annual LST cycles, respectively. The results show that window size 60 had the least computational cost and the highest accuracy with RMSE (root mean square error) of 2.6 °C for the entire study region and 1.4 °C for a selected pixel. The gap–filling performance of MSSA is also compared with the one by the harmonic analysis of time series (HANTS) model, showing the superiority of MSSA with an improved RMSE of about 2.7 °C for the study region. In addition, daytime and nighttime LST series for different land covers are compared. Lastly, the maximum, minimum, and average LST for each day and night as well as average and standard deviation of LST images in the seven-year-long time series are also computed.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.