It has a great significance to combine multi-source with different spatial resolution and temporal resolution to produce high spatiotemporal resolution Normalized Difference Vegetation Index (NDVI) time series data sets. In this study, four spatiotemporal fusion models were analyzed and compared with each other. The models included the spatial and temporal adaptive reflectance model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion model (FSDAF), and a spatiotemporal vegetation index image fusion model (STVIFM). The objective of is to: 1) compare four fusion models using Landsat-MODIS NDVI image from the Banan district, Chongqing Province; 2) analyze the prediction accuracy quantitatively and visually. Results indicate that STVIFM would be more suitable to produce NDVI time series data sets.
References
[1]
Busetto, L., Meroni, M. and Colombo, R. (2008) Combining Medium and Coarse Spatial Resolution Satellite Data to Improve the Estimation of Sub-Pixel NDVI Time Series. Remote Sens. Environ., 112, 118-131.
https://doi.org/10.1016/j.rse.2007.04.004
[2]
Tewes, A., Thonfeld, F., Schmidt, M., Oomen, R., Zhu, X., Dubovyk, O., Menz, G. and Schellberg, J. (2015) Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa. Remote Sens., 7, 6510-6534. https://doi.org/10.3390/rs70606510
[3]
Bhandari, S., Phinn, S. and Gill, T. (2012) Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia. Remote Sens., 4, 1856-1886. https://doi.org/10.3390/rs4061856
[4]
Gevaert, C.M. and García-Haro, F.J. (2015) A Comparison of STARFM and an Unmixing Based Algorithm for Landsat and MODIS Data Fusion. Remote Sensing of Environment, 156, 34-44. https://doi.org/10.1016/j.rse.2014.09.012
[5]
Schmidt, M., Udelhoven, T., Gill, T. and Röder, A. (2012) Long Term Data Fusion for a Dense Time Series Analysis with MODIS and Landsat Imagery in an Australian Savanna. J. Appl. Remote Sens., 6, 63512. https://doi.org/10.1117/1.JRS.6.063512
[6]
Fensholt, R. (2004) Earth Observation of Vegetation Status in the Sahelian and Sudanian West Africa: Comparison of Terra MODIS and NOAA AVHRR Satellite Data. Int. J. Remote Sens., 25, 1641-1659.
https://doi.org/10.1080/01431160310001598999
[7]
Zurita-Milla, R., Clevers, J., van Gijsel, J. and Schaepman, M. (2011) Using MERIS Fused Images for Classification Mapping and Vegetation Status Assessment in Heterogeneous Land-scapes. Int. J. Remote Sens., 32, 973-991.
https://doi.org/10.1080/01431160903505286
[8]
Gao, F., Masek, J., Schwaller, M. and Hall, F. (2006) On the Blending of the MODIS and Landsat ETM+ Surface Reflectance. IEEE Trans. Geosci. Remote Sens., 44, 2207-2218. https://doi.org/10.1109/TGRS.2006.872081
[9]
Zhu, X., Chen, J., Gao, F., Chen, X. and Masek, J.G. (2010) An enhanced Spatial and Temporal Adaptive Reflectance Fusion Model for Complex Heterogeneous Regions. Remote Sens. Environ., 114, 2610-2623. https://doi.org/10.1016/j.rse.2010.05.032
[10]
Zhu, X., Helmer, E.H., Gao, F., Liu, D., Chen, J. and Lefsky, M.A. (2016) A Flexible Spatiotemporal Method for Fusing Satellite Images with Different Resolutions. Remote Sens. Environ., 172, 165-177. https://doi.org/10.1016/j.rse.2015.11.016
[11]
Liao, C., Wang, J., Pritchard, I., et al. (2017) A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions. Remote Sensing, 9, 1125. https://doi.org/10.3390/rs9111125
[12]
Zhukov, B., Oertel, D. and Lanzl, F. (1999) Unmixing-Based Multisensor Multiresolution Image Fusion. IEEE Trans. Geosci. Remote Sens., 37, 1212-1226.
https://doi.org/10.1109/36.763276
