Next Article in Journal
Previous Article in Journal
Remote Sens. 2013, 5(10), 5346-5368; doi:10.3390/rs5105346
Article

An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data

1,2
, 2,* , 2
, 2
, 2
, 2
, 1
 and 3
Received: 31 August 2013; in revised form: 23 September 2013 / Accepted: 24 September 2013 / Published: 22 October 2013
View Full-Text   |   Download PDF [7707 KB, uploaded 19 June 2014]
Abstract: Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales.
Keywords: data fusion; linear spectral mixing model; multi-resolution segmentation; sliding window; temporal weight data fusion; linear spectral mixing model; multi-resolution segmentation; sliding window; temporal weight
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Zhang, W.; Li, A.; Jin, H.; Bian, J.; Zhang, Z.; Lei, G.; Qin, Z.; Huang, C. An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data. Remote Sens. 2013, 5, 5346-5368.

AMA Style

Zhang W, Li A, Jin H, Bian J, Zhang Z, Lei G, Qin Z, Huang C. An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data. Remote Sensing. 2013; 5(10):5346-5368.

Chicago/Turabian Style

Zhang, Wei; Li, Ainong; Jin, Huaan; Bian, Jinhu; Zhang, Zhengjian; Lei, Guangbin; Qin, Zhihao; Huang, Chengquan. 2013. "An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data." Remote Sens. 5, no. 10: 5346-5368.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert