IGSNRR OpenIR
Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures
Liu, Zihan1; Zhan, Wenfeng1,2; Lai, Jiameng1; Hong, Falu1; Quan, Jinling3; Bechtel, Benjamin4; Huang, Fan1; Zou, Zhaoxu1
2019-05-01
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
Volume151Pages:189-206
Corresponding AuthorZhan, Wenfeng(zhanwenfeng@nju.edu.cn)
AbstractAnnual 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.
KeywordLand surface temperature Annual temperature cycle LST dynamics Prediction accuracy Generalization ability
DOI10.1016/j.isprsjprs.2019.03.013
WOS KeywordAIR-TEMPERATURE ; MODIS LST ; SOLAR-RADIATION ; GLOBAL CLIMATE ; URBAN AREAS ; CYCLE ; RECONSTRUCTION ; RESOLUTION ; CLEAR ; INTERPOLATION
Indexed BySCI
Language英语
Funding ProjectNASA'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] ; National Natural Science Foundation of China[41671420] ; Jiangsu Provincial Natural Science Foundation[BK20180009] ; Fundamental Research Funds for the Central Universities of China[090414380017] ; DengFeng Program-B of Nanjing University ; Cluster of Excellence 'CliSAP', University of Hamburg through the German Science Foundation (DFG), Germany[EXC177]
Funding OrganizationNASA'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 ; National Natural Science Foundation of China ; Jiangsu Provincial Natural Science Foundation ; Fundamental Research Funds for the Central Universities of China ; DengFeng Program-B of Nanjing University ; Cluster of Excellence 'CliSAP', University of Hamburg through the German Science Foundation (DFG), Germany
WOS Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000469306300014
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/59150
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhan, Wenfeng
Affiliation1.Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Univ Hamburg, Ctr Earth Syst Res & Sustainabil, D-20146 Hamburg, Germany
Recommended Citation
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.
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