Comparison of Spatiotemporal Fusion Models: A Review

作者:Chen, B (Chen, Bin)[ 1 ] ; Huang, B (Huang, Bo)[ 2,3 ] ; Xu, B (Xu, Bing)[ 1,4 ]

REMOTE SENSING

卷: 7  期: 2  页: 1798-1835

DOI: 10.3390/rs70201798

出版年: FEB 2015

摘要

Simultaneously capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Spatiotemporal fusion has gained wide interest in various applications for its superiority in integrating both fine spatial resolution and frequent temporal coverage. Though many advances have been made in spatiotemporal fusion model development and applications in the past decade, a unified comparison among existing fusion models is still limited. In this research, we classify the models into three categories: transformation-based, reconstruction-based, and learning-based models. The objective of this study is to (i) compare four fusion models (STARFM, ESTARFM, ISTAFM, and SPSTFM) under a one Landsat-MODIS (L-M) pair prediction mode and two L-M pair prediction mode using time-series datasets from the Coleambally irrigation area and Poyang Lake wetland; (ii) quantitatively assess prediction accuracy considering spatiotemporal comparability, landscape heterogeneity, and model parameter selection; and (iii) discuss the advantages and disadvantages of the three categories of spatiotemporal fusion models.

通讯作者地址: Xu, B (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China

[ 2 ] Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China

[ 3 ] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Shatin, Hong Kong, Peoples R China

[ 4 ] Tsinghua Univ, Minist Educ Key Lab Earth Syst Modelling, Ctr Earth Syst Sci, Beijing 100084, Peoples R China