Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series

References

• Amoros-Lopez, J., L. Gomez-Chova, L. Alonso, L. Guanter, R. Zurita-Milla, J. Moreno, and G. Camps-Valls. 2013. “Multitemporal Fusion of Landsat/TM and ENVISAT/MERIS for Crop Monitoring.” International Journal of Applied Earth Observation and Geoinformation 23: 132–141. doi:10.1016/j.jag.2012.12.004.
   Web of Science ®Google Scholar
• Emelyanova, I. V., T. R. McVicar, T. G. Van Niel, L. T. Li, and V. D. Aijm. 2013. “Assessing the Accuracy of Blending Landsat-MODIS Surface Reflectances in Two Landscapes with Contrasting Spatial and Temporal Dynamics: A Framework for Algorithm Selection.” Remote Sensing of Environment 133: 193–209. doi:10.1016/j.rse.2013.02.007.
   Web of Science ®Google Scholar
• Fischer, A. 1994. “A Model for the Seasonal Variations of Vegetation Indices in Coarse Resolution Data and Its Inversion to Extract Crop Parameters.” Remote Sensing of Environment 48 (2): 220–230. doi:10.1016/0034-4257(94)90143-0.
   Web of Science ®Google Scholar
• Gao, F., J. Masek, M. Schwaller, and F. Hall. 2006. “On the Blending of the Landsat and MODIS Surface Reflectance: Predicting Daily Landsat Surface Reflectance.” IEEE Transactions on Geoscience and Remote Sensing 44 (8): 2207–2218. doi:10.1109/tgrs.2006.872081.
   Web of Science ®Google Scholar
• Garrigues, S., D. Allard, F. Baret, and M. Weiss. 2006. “Influence of Landscape Spatial Heterogeneity on the Non-Linear Estimation of Leaf Area Index from Moderate Spatial Resolution Remote Sensing Data.” Remote Sensing of Environment 105 (4): 286–298. doi:10.1016/j.rse.2006.07.013.
   Web of Science ®Google Scholar
• Gevaert, C. M., and F. Javier Garcia-Haro. 2015. “A Comparison of STARFM and an Unmixing-Based Algorithm for Landsat and MODIS Data Fusion.” Remote Sensing of Environment 156: 34–44. doi:10.1016/j.rse.2014.09.012.
   Web of Science ®Google Scholar
• Huang, B., and H. H. Song. 2012. “Spatiotemporal Reflectance Fusion via Sparse Representation.” IEEE Transactions on Geoscience and Remote Sensing 50 (10): 3707–3716. doi:10.1109/tgrs.2012.2186638.
   Web of Science ®Google Scholar
• Jarihani, A. A., T. R. McVicar, T. G. Van Niel, I. V. Emelyanova, J. N. Callow, and K. Johansen. 2014. “Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-Then-Blend” and “Blend-Then-Index” Approaches.” Remote Sensing 6 (10): 9213–9238. doi:10.3390/rs6109213.
   Web of Science ®Google Scholar
• Liao, L. M., J. L. Song, J. D. Wang, Z. Q. Xiao, and J. Wang. 2016. “Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI.” Remote Sensing 8 (6): 23. doi:10.3390/rs8060452.
   Web of Science ®Google Scholar
• Matthew, M. W., S. M. Adler-Golden, A. Berk, S. C. Richtsmeier, R. Y. Levine, L. S. Bernstein, P. K. Acharya, et al. 2000. “Status of Atmospheric Correction Using a MODTRAN4-based Algorithm.” In Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery Vi, edited by. S. S. Shen and M. R. Descour, 199–207. Orlando, FL: The international society for optics and photonics.
   Google Scholar
• Mousivand, A., M. Menenti, B. Gorte, and W. Verhoef. 2015. “Multi-Temporal, Multi-Sensor Retrieval of Terrestrial Vegetation Properties from Spectral-Directional Radiometric Data.” Remote Sensing of Environment 158: 311–330. doi:10.1016/j.rse.2014.10.030.
   Web of Science ®Google Scholar
• Navon, I. M. 2009. “Data Assimilation for Numerical Weather Prediction: A Review.” In Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, edited by S. K. Park and L. Xu, 21–65. Berlin: Springer.
   Google Scholar
• Rao, Y. H., X. L. Zhu, J. Chen, and J. M. Wang. 2015. “An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM/ETM Plus Images.” Remote Sensing 7 (6): 7865–7891. doi:10.3390/rs70607865.
   Web of Science ®Google Scholar
• Savoy, P., and D. S. Mackay. 2015. “Modeling the Seasonal Dynamics of Leaf Area Index Based on Environmental Constraints to Canopy Development.” Agricultural and Forest Meteorology 200: 46–56. doi:10.1016/j.agrformet.2014.09.019.
   Web of Science ®Google Scholar
• Singh, D. 2011. “Generation and Evaluation of Gross Primary Productivity Using Landsat Data through Blending with MODIS Data.” International Journal of Applied Earth Observation and Geoinformation 13 (1): 59–69. doi:10.1016/j.jag.2010.06.007.
   Web of Science ®Google Scholar
• Sun, F. B., M. L. Roderick, G. D. Farquhar, W. H. Lim, Y. Q. Zhang, N. Bennett, and S. H. Roxburgh. 2010. “Partitioning the Variance between Space and Time.” Geophysical Research Letters 37. doi:10.1029/2010gl043323.
   Web of Science ®Google Scholar
• Tarantola, A. 2005. Inverse Problem Theory and Methods for Model Parameter Estimation. Paris: SIAM.
   Google Scholar
• Verger, A., F. Baret, M. Weiss, S. Kandasamy, and E. Vermote. 2013. “The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series.” IEEE Transactions on Geoscience and Remote Sensing 51 (4): 1963–1972. doi:10.1109/tgrs.2012.2228653.
   Web of Science ®Google Scholar
• Walker, J. J., K. M. De Beurs, R. H. Wynne, and F. Gao. 2012. “Evaluation of Landsat and MODIS Data Fusion Products for Analysis of Dryland Forest Phenology.” Remote Sensing of Environment 117: 381–393. doi:10.1016/j.rse.2011.10.014.
   Web of Science ®Google Scholar
• Wang, P. J., F. Gao, and J. G. Masek. 2014. “Operational Data Fusion Framework for Building Frequent Landsat-Like Imagery.” IEEE Transactions on Geoscience and Remote Sensing 52 (11): 7353–7365. doi:10.1109/tgrs.2014.2311445.
   Web of Science ®Google Scholar
• Weng, Q. H., P. Fu, and F. Gao. 2014. “Generating Daily Land Surface Temperature at Landsat Resolution by Fusing Landsat and MODIS Data.” Remote Sensing of Environment 145: 55–67. doi:10.1016/j.rse.2014.02.003.
   Web of Science ®Google Scholar
• Yin, G. F., A. N. Li, H. A. Jin, W. Zhao, J. H. Bian, Y. H. Qu, Y. L. Zeng, and B. D. Xu. 2017. “Derivation of Temporally Continuous LAI Reference Maps through Combining the LAINet Observation System with CACAO.” Agricultural and Forest Meteorology 233: 209–221. doi:10.1016/j.agrformet.2016.11.267.
   Web of Science ®Google Scholar
• Yin, G. F., J. Li, Q. H. Liu, L. H. Li, Y. L. Zeng, B. D. Xu, L. Yang, and J. Zhao. 2015. “Improving Leaf Area Index Retrieval over Heterogeneous Surface by Integrating Textural and Contextual Information: A Case Study in the Heihe River Basin.” IEEE Geoscience and Remote Sensing Letters 12 (2): 359–363. doi:10.1109/lgrs.2014.2341925.
   Web of Science ®Google Scholar
• Zhang, H. K., J. M. Chen, B. Huang, H. H. Song, and Y. R. Li. 2014. “Reconstructing Seasonal Variation of Landsat Vegetation Index Related to Leaf Area Index by Fusing with MODIS Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (3): 950–960. doi:10.1109/jstars.2013.2284528.
   Web of Science ®Google Scholar
• Zhu, X., E. H. Helmer, F. Gao, D. Liu, J. Chen, and M. A. Lefsky. 2016. “A Flexible Spatiotemporal Method for Fusing Satellite Images with Different Resolutions.” Remote Sensing of Environment 172: 165–177. doi:10.1016/j.rse.2015.11.016.
   Web of Science ®Google Scholar
• Zhu, X. L., J. Chen, F. Gao, X. H. Chen, and J. G. Masek. 2010. “An Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model for Complex Heterogeneous Regions.” Remote Sensing of Environment 114 (11): 2610–2623. doi:10.1016/j.rse.2010.05.032.
   Web of Science ®Google Scholar
• Zhukov, B., D. Oertel, F. Lanzl, and G. Reinhackel. 1999. “Unmixing-Based Multisensor Multiresolution Image Fusion.” IEEE Transactions on Geoscience and Remote Sensing 37 (3): 1212–1226. doi:10.1109/36.763276.
   Web of Science ®Google Scholar
• Zurita-Milla, R., J. Clevers, and M. E. Schdepman. 2008. “Unmixing-Based Landsat TM and MERIS FR Data Fusion.” IEEE Geoscience and Remote Sensing Letters 5 (3): 453–457. doi:10.1109/lgrs.2008.919685.
   Web of Science ®Google Scholar