File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.3390/rs70201798
- Scopus: eid_2-s2.0-84928777398
- WOS: WOS:000352277400001
Supplementary
- Citations:
- Appears in Collections:
Article: Comparison of spatiotemporal fusion models: A review
Title | Comparison of spatiotemporal fusion models: A review |
---|---|
Authors | |
Keywords | Comparison Assessment Spatiotemporal fusion Prediction modes |
Issue Date | 2015 |
Citation | Remote Sensing, 2015, v. 7, n. 2, p. 1798-1835 How to Cite? |
Abstract | 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. |
Persistent Identifier | http://hdl.handle.net/10722/299519 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Bin | - |
dc.contributor.author | Huang, Bo | - |
dc.contributor.author | Xu, Bing | - |
dc.date.accessioned | 2021-05-21T03:34:35Z | - |
dc.date.available | 2021-05-21T03:34:35Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Remote Sensing, 2015, v. 7, n. 2, p. 1798-1835 | - |
dc.identifier.uri | http://hdl.handle.net/10722/299519 | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.relation.ispartof | Remote Sensing | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Comparison | - |
dc.subject | Assessment | - |
dc.subject | Spatiotemporal fusion | - |
dc.subject | Prediction modes | - |
dc.title | Comparison of spatiotemporal fusion models: A review | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3390/rs70201798 | - |
dc.identifier.scopus | eid_2-s2.0-84928777398 | - |
dc.identifier.volume | 7 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 1798 | - |
dc.identifier.epage | 1835 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.identifier.isi | WOS:000352277400001 | - |