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Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures

文献类型：期刊论文

作者Liu, Zihan2; Zhan, Wenfeng2,3; Lai, Jiameng2; Hong, Falu2; Quan, Jinling1; Bechtel, Benjamin4; Huang, Fan2; Zou, Zhaoxu2
刊名ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
出版日期2019-05-01
卷号151页码:189-206
关键词Land surface temperature Annual temperature cycle LST dynamics Prediction accuracy Generalization ability
ISSN号0924-2716
DOI10.1016/j.isprsjprs.2019.03.013
英文摘要Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or on generalization ability and a flexible ATC modelling framework for different numbers of thermal observations is lacking. Here, we propose a hybrid ATC model (ATCH) that considers both prediction accuracy and generalization ability; our approach combines multiple harmonics with a linear function of LST-related factors, including surface air temperature (SAT), NDVI, albedo, soil moisture, and relative humidity. Based on the proposed ATCH, various parameter-reduction approaches (PRAs) are designed to provide model derivatives which can be adapted to different scenarios. Using Terra/MODIS daily LST products as evaluation data, the ATCH is compared with the original sinusoidal ATC model (termed the ATCO) and its variants, and with two frequently-used gap-filling methods (Regression Kriging Interpolation (RKI) and the Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST)), under clear-sky conditions. In addition, under overcast conditions, the LSTs generated by ATCH are directly compared with in-situ LST measurements. The comparisons demonstrate that the ATCH increases the prediction accuracy and the overall RMSE is reduced by 1.8 and 0.7 K when compared with the ATCO during daytime and nighttime, respectively. Moreover, the ATCH shows better generalization ability than the RKI and behaves better than the RSDAST when the LST gap size is spatially large and/or temporally long. By employing LST-related controls (e.g., the SAT and relative humidity) under overcast conditions, the ATCH can better predict the LSTs under clouds than approaches that only adopt clear-sky information as model inputs. Further attribution analysis implies that incorporating a sinusoidal function (ASF), the SAT, NDVI, and other LST-related factors, provides respective contributions of around 16%, 40%, 15%, and 30% to the improved accuracy. Our analysis is potentially useful for designing PRAs for various practical needs, by reducing the smallest contribution factor each time. We conclude that the ATCH is valuable for further improving the quality of LST products and can potentially enhance the time series analysis of land surfaces and other applications.
WOS关键词AIR-TEMPERATURE ; MODIS LST ; SOLAR-RADIATION ; GLOBAL CLIMATE ; URBAN AREAS ; CYCLE ; RECONSTRUCTION ; RESOLUTION ; CLEAR ; INTERPOLATION
资助项目National Natural Science Foundation of China[41671420] ; DengFeng Program-B of Nanjing University ; NASA's Earth Science Data Systems (ESDS) Program for the Earth Observing System Data and Information System (EOSDIS) ; Key Research and Development Programs for Global Change and Adaptation of China[2017YFA0603604] ; Key Research and Development Programs for Global Change and Adaptation of China[2016YFA0600201] ; Jiangsu Provincial Natural Science Foundation[BK20180009] ; Fundamental Research Funds for the Central Universities of China[090414380017] ; Cluster of Excellence 'CliSAP', University of Hamburg through the German Science Foundation (DFG), Germany[EXC177]
WOS研究方向Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000469306300014
出版者ELSEVIER SCIENCE BV
源URL[http://ir.igsnrr.ac.cn/handle/311030/59149]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
4.Univ Hamburg, Ctr Earth Syst Res & Sustainabil, D-20146 Hamburg, Germany
推荐引用方式
GB/T 7714		 Liu, Zihan,Zhan, Wenfeng,Lai, Jiameng,et al. Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2019,151:189-206.
APA Liu, Zihan.,Zhan, Wenfeng.,Lai, Jiameng.,Hong, Falu.,Quan, Jinling.,...&Zou, Zhaoxu.(2019).Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,151,189-206.
MLA Liu, Zihan,et al."Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 151(2019):189-206.

入库方式： OAI收割

来源：地理科学与资源研究所

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