Yuejie Zhang , Qinghong Sheng , Kerui Li , Bo Wang , Jun Li , Xiao Ling , Fan Gao
{"title":"青藏高原地表温度反演中地形效应误差分析与减小","authors":"Yuejie Zhang , Qinghong Sheng , Kerui Li , Bo Wang , Jun Li , Xiao Ling , Fan Gao","doi":"10.1016/j.jag.2025.104637","DOIUrl":null,"url":null,"abstract":"<div><div>Land Surface Temperature (LST) plays a pivotal role in representing ground energy balance and understanding climate change. LST over the Tibetan Plateau (TP) significantly influences regional climate and environmental dynamics. Accurate LST data in the TP is vital for ecological monitoring and climate studies. However, most retrieval algorithms assume a flat-surface thermal infrared radiation transfer equation (TIRTE), which introduces inevitable topographic induced in the TP’s complex terrain. Additionally, the limited and sparse ground stations hinder pixel-level error analysis. These limitations restrict accurate characterization of topographic effects on LST errors and impede the effective application of current LST datasets. This study proposed a method to quantify the LST retrieval errors at a pixel level and innovatively introduced the radiation-topographic bias correction term (RTBC). The effectiveness of RTBC in reducing LST retrieval errors with only one atmospheric parameter was demonstrated theoretically. Random forest (RF) models were employed to assess the contribution of topographic effects to these errors. The sky view factor (SVF) was employed as an indicator of surface ruggedness. The results demonstrated that LST retrieval errors were predominantly due to topographic effect when surface ruggedness was high (SVF ≤ 0.25), with an R<sup>2</sup> value reaching up to 0.86. RTBC emerged as the primary factor influencing LST retrieval errors at SVF ≤ 0.25. In-situ LST analysis showed that when SVF decreased to 0.738, RTBC effectively reduced the root mean square error (RMSE) and the mean absolute error (MAE) and by an average of 1.2 K and 1.1 K, respectively. In comparison experiments with conventional methods, RTBC achieved approximately a 50 % reduction in RMSE. These findings highlight the significant impact of topography on LST retrieval accuracy and demonstrate the effectiveness of RTBC in reducing terrain-induced errors.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"141 ","pages":"Article 104637"},"PeriodicalIF":8.6000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis and reduction of topographic effect induced errors in land surface temperature retrieval over the Tibetan Plateau\",\"authors\":\"Yuejie Zhang , Qinghong Sheng , Kerui Li , Bo Wang , Jun Li , Xiao Ling , Fan Gao\",\"doi\":\"10.1016/j.jag.2025.104637\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Land Surface Temperature (LST) plays a pivotal role in representing ground energy balance and understanding climate change. LST over the Tibetan Plateau (TP) significantly influences regional climate and environmental dynamics. Accurate LST data in the TP is vital for ecological monitoring and climate studies. However, most retrieval algorithms assume a flat-surface thermal infrared radiation transfer equation (TIRTE), which introduces inevitable topographic induced in the TP’s complex terrain. Additionally, the limited and sparse ground stations hinder pixel-level error analysis. These limitations restrict accurate characterization of topographic effects on LST errors and impede the effective application of current LST datasets. This study proposed a method to quantify the LST retrieval errors at a pixel level and innovatively introduced the radiation-topographic bias correction term (RTBC). The effectiveness of RTBC in reducing LST retrieval errors with only one atmospheric parameter was demonstrated theoretically. Random forest (RF) models were employed to assess the contribution of topographic effects to these errors. The sky view factor (SVF) was employed as an indicator of surface ruggedness. The results demonstrated that LST retrieval errors were predominantly due to topographic effect when surface ruggedness was high (SVF ≤ 0.25), with an R<sup>2</sup> value reaching up to 0.86. RTBC emerged as the primary factor influencing LST retrieval errors at SVF ≤ 0.25. In-situ LST analysis showed that when SVF decreased to 0.738, RTBC effectively reduced the root mean square error (RMSE) and the mean absolute error (MAE) and by an average of 1.2 K and 1.1 K, respectively. In comparison experiments with conventional methods, RTBC achieved approximately a 50 % reduction in RMSE. These findings highlight the significant impact of topography on LST retrieval accuracy and demonstrate the effectiveness of RTBC in reducing terrain-induced errors.</div></div>\",\"PeriodicalId\":73423,\"journal\":{\"name\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"volume\":\"141 \",\"pages\":\"Article 104637\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2025-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1569843225002845\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843225002845","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Analysis and reduction of topographic effect induced errors in land surface temperature retrieval over the Tibetan Plateau
Land Surface Temperature (LST) plays a pivotal role in representing ground energy balance and understanding climate change. LST over the Tibetan Plateau (TP) significantly influences regional climate and environmental dynamics. Accurate LST data in the TP is vital for ecological monitoring and climate studies. However, most retrieval algorithms assume a flat-surface thermal infrared radiation transfer equation (TIRTE), which introduces inevitable topographic induced in the TP’s complex terrain. Additionally, the limited and sparse ground stations hinder pixel-level error analysis. These limitations restrict accurate characterization of topographic effects on LST errors and impede the effective application of current LST datasets. This study proposed a method to quantify the LST retrieval errors at a pixel level and innovatively introduced the radiation-topographic bias correction term (RTBC). The effectiveness of RTBC in reducing LST retrieval errors with only one atmospheric parameter was demonstrated theoretically. Random forest (RF) models were employed to assess the contribution of topographic effects to these errors. The sky view factor (SVF) was employed as an indicator of surface ruggedness. The results demonstrated that LST retrieval errors were predominantly due to topographic effect when surface ruggedness was high (SVF ≤ 0.25), with an R2 value reaching up to 0.86. RTBC emerged as the primary factor influencing LST retrieval errors at SVF ≤ 0.25. In-situ LST analysis showed that when SVF decreased to 0.738, RTBC effectively reduced the root mean square error (RMSE) and the mean absolute error (MAE) and by an average of 1.2 K and 1.1 K, respectively. In comparison experiments with conventional methods, RTBC achieved approximately a 50 % reduction in RMSE. These findings highlight the significant impact of topography on LST retrieval accuracy and demonstrate the effectiveness of RTBC in reducing terrain-induced errors.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.