Spatial enhanced spatiotemporal reflectance fusion model for heterogeneous regions with land cover change

References

• Cai J, Huang B, Fung T. 2022. Progressive spatiotemporal image fusion with deep neural networks. Int J Appl Earth Obs Geoinf. 108:102745. doi: 10.1016/j.jag.2022.102745.
   Web of Science ®Google Scholar
• Chen B, Huang B, Xu B. 2017. Multi-source remotely sensed data fusion for improving land cover classification. ISPRS J Photogramm Remote Sens. 124:27–39. doi: 10.1016/j.isprsjprs.2016.12.008.
   Web of Science ®Google Scholar
• Chen S, Wang J, Gong P. 2023. ROBOT: a spatiotemporal fusion model toward seamless data cube for global remote sensing applications. Remote Sens Environ. 294:113616. doi: 10.1016/j.rse.2023.113616.
   Web of Science ®Google Scholar
• Chen X, Du X, Yan Z, Zhu J, Xu S, Fan X. 2022. VSDF: a variation-based spatiotemporal data fusion method. Remote Sens Environ. 283:113309. doi: 10.1016/j.rse.2022.113309.
   Web of Science ®Google Scholar
• Emelyanova I, McVicar T, Van Niel T, Li L, M, van Dijk A. 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 Sens Environ. 133:193–209. doi: 10.1016/j.rse.2013.02.007.
   Web of Science ®Google Scholar
• Fu D, Chen B, Wang J, Zhu X, Hilker T. 2013. An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model. Remote Sens. 5(12):6346–6360. doi: 10.3390/rs5126346.
   Google Scholar
• Gao F, Masek J, Schwaller M, Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens, 44(12):2207–2218. doi: 10.1109/TGRS.2006.872081.
   Google Scholar
• Gao F, Anderson MC, Zhang X, Yang Z, Alfieri JG, Kustas WP, Mueller R, Johnson DM, Prueger JH. 2017. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sens Environ. 188:9–25. doi: 10.1016/j.rse.2016.11.004.
   Web of Science ®Google Scholar
• Gao H, Zhu X, Guan Q, Yang X, Yao Y, Zeng W, Peng X. 2022. cuFSDAF: an Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units. IEEE Trans Geosci Remote Sens. 60:1–16. doi: 10.1109/TGRS.2021.3080384.
   Web of Science ®Google Scholar
• Gong P, Wang J, Yu L, Zhao YC, Zhao YY, Liang L, Niu Z, Huang X, Fu H, Liu S, et al. 2013. Finer resolution observation and monitoring of global land cover: first mapping results with landsat TM and ETM + data. Int J Remote Sens. 34(7):2607–2654. doi: 10.1080/01431161.2012.748992.
   Web of Science ®Google Scholar
• Gong P, Li X, Wang J, Bai Y, Chen B, Hu T, Liu X, Xu B, Yang J, Zhang W, et al. 2020. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sens. Environ. 236:111510. doi: 10.1016/j.rse.2019.111510.
   Web of Science ®Google Scholar
• Gevaert CM, García-Haro FJ. 2015. A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote Sens Environ. 156:34–44. doi: 10.1016/j.rse.2014.09.012.
   Web of Science ®Google Scholar
• Ghosh R, Gupta PK, Tolpekin V, Srivastav SK. 2020. An enhanced spatiotemporal fusion method – implications for coal fire monitoring using satellite imagery. Int J Appl Earth Obs Geoinf. 88:102056. doi: 10.1016/j.jag.2020.102056.
   Web of Science ®Google Scholar
• Guo D, Shi W, Hao M, Zhu X. 2020. FSDAF 2.0: improving the performance of retrieving land cover changes and preserving spatial details. Remote Sens Environ. 248:111973. doi: 10.1016/j.rse.2020.111973.
   Web of Science ®Google Scholar
• Hilker T, Wulder MA, Coops NC, Linke J, McDermid G, Masek JG, Gao F, White JC. 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sens Environ. 113(8):1613–1627. doi: 10.1016/j.rse.2009.03.007.
   Web of Science ®Google Scholar
• Hou S, Sun W, Guo B, Li C, Li X, Shao Y, Zhang J. 2020. Adaptive-SFSDAF for spatiotemporal image fusion that selectively uses class abundance change information. Remote Sens. 12(23):3979. doi: 10.3390/rs12233979.
   Google Scholar
• Huang B, Song H. 2012. Spatiotemporal Reflectance Fusion via Sparse Representation. IEEE Trans Geosci Remote Sens. 50(10):3707–3716. doi: 10.1109/TGRS.2012.2186638.
   Web of Science ®Google Scholar
• Jafarzadeh H, Hasanlou M. 2019. An Unsupervised Binary and Multiple Change Detection Approach for Hyperspectral Imagery Based on Spectral Unmixing. IEEE J Sel Top Appl Earth Observat Remote Sens. 12(12):4888–4906. doi: 10.1109/JSTARS.2019.2939133.
   Web of Science ®Google Scholar
• Ju J, Roy DP. 2008. The availability of cloud-free Landsat ETM + data over the conterminous United States and globally. Remote Sens Environ. 112(3):1196–1211. doi: 10.1016/j.rse.2007.08.011.
   Web of Science ®Google Scholar
• Kong F, Li X, Wang H, Xie D, Li X, Bai Y. 2016. Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series. Remote Sens. 8(9):741. doi: 10.3390/rs8090741.
   Google Scholar
• Li A, Bo Y, Zhu Y, Guo P, Bi J, He Y. 2013. Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method. Remote Sens Environ. 135:52–63. doi: 10.1016/j.rse.2013.03.021.
   Web of Science ®Google Scholar
• Li Y, Huang C, Hou J, Gu J, Zhu G, Li X. 2017. Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River Basin, Northwest China. Agric For Meteorol. 244–245:82–97. doi: 10.1016/j.agrformet.2017.05.023.
   Web of Science ®Google Scholar
• Li X, Foody GM, Boyd DS, Ge Y, Zhang Y, Du Y, Ling F. 2020. SFSDAF: an enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion. Remote Sens Environ. 237:111537. doi: 10.1016/j.rse.2019.111537.
   Web of Science ®Google Scholar
• Liao L, Song J, Wang J, Xiao Z, Wang J. 2016. Bayesian method for building frequent landsat-like NDVI datasets by integrating MODIS and Landsat NDVI. Remote Sens. 8(6):452. doi: 10.3390/rs8060452.
   Google Scholar
• Liu S, Bruzzone L, Bovolo F, Du P. 2016. Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images. IEEE Trans Geosci Remote Sens. 54(5):2733–2748. doi: 10.1109/TGRS.2015.2505183.
   Web of Science ®Google Scholar
• Liu M, Yang W, Zhu X, Chen J, Chen X, Yang L, Helmer EH. 2019. An Improved Flexible Spatiotemporal Data Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series. Remote Sens Environ. 227:74–89. doi: 10.1016/j.rse.2019.03.012.
   Web of Science ®Google Scholar
• Liu W, Zeng Y, Li S, Huang W. 2020. Spectral unmixing based spatiotemporal downscaling fusion approach. Int J Appl Earth Obs Geoinf. 88:102054. doi: 10.1016/j.jag.2020.102054.
   Web of Science ®Google Scholar
• Liu W, Zeng Y, Li S, Pi X, Huang W. 2019. An improved spatiotemporal fusion approach based on multiple endmember spectral mixture analysis. Sensors. 19(11):2443. doi: 10.3390/s19112443.
   PubMed Web of Science ®Google Scholar
• Lu D, Batistella M, Moran E. 2004. Multitemporal spectral mixture analysis for Amazonian land-cover change detection. Can J Remote Sens. 30(1):87–100. doi: 10.5589/m03-055.
   Web of Science ®Google Scholar
• Mariana B, Alfred S. 2019. Spatiotemporal image fusion in remote sensing. Remote Sens. 11:818.
   Google Scholar
• Moosavi V, Talebi A, Mokhtari MH, Shamsi SRF, Niazi Y. 2015. A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature. Remote Sens Environ. 169:243–254. doi: 10.1016/j.rse.2015.08.015.
   Web of Science ®Google Scholar
• Peng K, Wang Q, Tang Y, Tong X, Atkinson PM. 2022. Geographically weighted spatial unmixing for spatiotemporal fusion. IEEE Trans Geosci Remote Sens. 60:1–17. doi: 10.1109/TGRS.2021.3115136.
   Web of Science ®Google Scholar
• Rao Y, Zhu X, Chen J, Wang J. 2015. An improved method for producing high spatial-resolution NDVI Time series datasets with multi-temporal MODIS NDVI data and Landsat TM/ETM + images. Remote Sens. 7(6):7865–7891. doi: 10.3390/rs70607865.
   Google Scholar
• Ridd M. 1995. Exploring a V–I–S (vegetation–impervious–surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int J Remote Sens. 16(12):2165–2185. doi: 10.1080/01431169508954549.
   Web of Science ®Google Scholar
• Semmens K, Anderson M, Kustas W, Gao F, Alfieri J, McKee L, Prueger J, Hain C, Cammalleri C, Yang Y, et al. 2016. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sens Environ. 185:155–170. doi: 10.1016/j.rse.2015.10.025.
   Web of Science ®Google Scholar
• Shi W, Guo D, Zhang H. 2022. A reliable and adaptive spatiotemporal data fusion method for blending multi-spatiotemporal-resolution satellite images. Remote Sens Environ. 268:112770. doi: 10.1016/j.rse.2021.112770.
   Web of Science ®Google Scholar
• Somers B, Asner G, Tits L, Coppin P. 2011. Endmember variability in spectral mixture analysis: a review. Remote Sens Environ. 115(7):1603–1616. doi: 10.1016/j.rse.2011.03.003.
   Web of Science ®Google Scholar
• Small C. 2003. High spatial resolution spectral mixture analysis of urban reflectance. Remote Sens Environ. 88(1-2):170–186. doi: 10.1016/j.rse.2003.04.008.
   Web of Science ®Google Scholar
• Small C. 2004. The Landsat ETM + spectral mixing space. Remote Sens Environ. 93(1–2):1–17. doi: 10.1016/j.rse.2004.06.007.
   Web of Science ®Google Scholar
• Singh D. 2011. Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data. Int J Appl Earth Obs Geoinf. 13(1):59–69. doi: 10.1016/j.jag.2010.06.007.
   Web of Science ®Google Scholar
• Tran T, De Beurs K, Julian J. 2016. Monitoring forest disturbances in Southeast Oklahoma using Landsat and MODIS images. Int J Appl Earth Obs Geoinf. 44:42–52. doi: 10.1016/j.jag.2015.07.001.
   Web of Science ®Google Scholar
• Wang Q, Ding X, Tong X, Atkinson P. 2021. Spatio-temporal spectral unmixing of time-series images. Remote Sens Environ. 259:112407. doi: 10.1016/j.rse.2021.112407.
   Web of Science ®Google Scholar
• Wang X, Jia K, Liang S, Li Q, Wei X, Yao Y, Zhang X, Tu Y. 2017. Estimating fractional vegetation cover from landsat-7 ETM + reflectance data based on a coupled radiative transfer and crop growth model. IEEE Trans Geosci Remote Sensing. 55(10):5539–5546. doi: 10.1109/TGRS.2017.2709803.
   Web of Science ®Google Scholar
• Wang Z, Bovik A, Sheikh H, Simoncelli E. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 13(4):600–612. doi: 10.1109/tip.2003.819861.
   PubMed Web of Science ®Google Scholar
• Watts JD, Powell SL, Lawrence RL, Hilker T. 2011. Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery. Remote Sens Environ. 115(1):66–75. doi: 10.1016/j.rse.2010.08.005.
   Web of Science ®Google Scholar
• Weng Q, Fu P, Gao F. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sens Environ. 145:55–67. doi: 10.1016/j.rse.2014.02.003.
   Web of Science ®Google Scholar
• Wu B, Huang B, Zhang L. 2015. An error-bound-regularized sparse coding for spatiotemporal reflectance fusion. IEEE Trans Geosci Remote Sensing. 53(12):6791–6803. doi: 10.1109/TGRS.2015.2448100.
   Web of Science ®Google Scholar
• Wu C, Murray AT. 2003. Estimating impervious surface distribution by spectral mixture analysis. Remote Sens Environ. 84(4):493–505. doi: 10.1016/S0034-4257(02)00136-0.
   Web of Science ®Google Scholar
• Wu M, Niu Z, Wang C, Wu C, Wang L. 2012. Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. J Appl Rem Sens. 6(01):1–14. doi: 10.1117/1.JRS.6.063507.
   Google Scholar
• Wu M, Huang W, Niu Z, Wang C, Li W, Yu B. 2018. Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach. Inf Fusion. 40:34–44. doi: 10.1016/j.inffus.2017.06.005.
   Web of Science ®Google Scholar
• Yang Y, Anderson M, Gao F, Hain C, Noormets A, Sun G, Wynne R, Thomas V, Sun L. 2020. Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data. Remote Sens Environ. 238:111018. doi: 10.1016/j.rse.2018.12.017.
   Web of Science ®Google Scholar
• Zhang B, Zhang L, Xie D, Yin X, Liu C, Liu G. 2016. Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation. Remote Sens. 8(1):10. doi: 10.3390/rs8010010.
   Google Scholar
• Zhang F, Zhu X, Liu D. 2014. Blending MODIS and Landsat images for urban flood mapping. Int J Remote Sens. 35(9):3237–3253. doi: 10.1080/01431161.2014.903351.
   Web of Science ®Google Scholar
• Zhang H, Huang B, Zhang M, Cao K, Yu L. 2015. A generalization of spatial and temporal fusion methods for remotely sensed surface parameters. Int J Remote Sens. 36(17):4411–4445. doi: 10.1080/01431161.2015.1083633.
   Web of Science ®Google Scholar
• Zhu X, Chen J, Gao F, Chen X, Masek JG. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens Environ. 114(11):2610–2623. doi: 10.1016/j.rse.2010.05.032.
   Web of Science ®Google Scholar
• Zhu X, Helmer EH, Gao F, Liu D, Chen J, Lefsky MA. 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sens Environ. 172:165–177. doi: 10.1016/j.rse.2015.11.016.
   Web of Science ®Google Scholar
• Zhu X, Cai F, Tian J, Williams TK-A. 2018. Spatiotemporal fusion of multisource remote sensing data: literature survey, taxonomy, principles, applications, and future directions. Remote Sens. 10(4):527. doi: 10.3390/rs10040527.
   Google Scholar
• Zhukov B, Oertel D, Lanzl F, Reinhackel G. 1999. Unmixing-based multisensor multiresolution image fusion. IEEE Trans Geosci Remote Sens. 37(3):1212–1226. doi: 10.1109/36.763276.
   Web of Science ®Google Scholar