Research Status and Prospect of Spatiotemporal Fusion of Multi-source Satellite Remote Sensing Imagery

doi:10.11947/j.AGCS.2017.20170376

Abstract

Abstract: High spatial resolution monitoring of land surface and atmospheric environment dynamics requires high spatiotemporal resolution satellite remote sensing imagery as data support. However, the efficient, low-cost, and feasible solution is to blend the multi-source images with high-spatial and high-temporal resolution respectively to produce the desired high spatiotemporal resolution imagery required by different research or applications, which is subject to the limitations of satellite sensor' hardware technology and the budget constraints of launching more satellites. Although plenty of spatiotemporal image fusion research has been conducted, they are limited to specific data types, algorithm principles, application purposes, etc. Furthermore, the development of spatiotemporal image fusion algorithm presents a phenomenon of disorder. This study summarizes and generalizes the existing mainstream spatiotemporal fusion methods and classified them into four categories:①spatiotemporal fusion based on land components; ②spatiotemporal fusion based on spatial information; ③spatiotemporal fusion based on temporal changes; ④combined spatiotemporal fusion. Meanwhile, the study analyzes the problems and challenges faced by spatiotemporal fusion; and informs the prospects of the future development of spatiotemporal fusion method.

Key words: multi-source remote sensing imagery, spatiotemporal resolution compromise, spatiotemporal fusion, land component, spatial information, temporal change

CLC Number:

P236

HUANG Bo, ZHAO Yongquan. Research Status and Prospect of Spatiotemporal Fusion of Multi-source Satellite Remote Sensing Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1492-1499.

References

[1] TONG Xudong, ZHAO Wenbo, XING Jin, et al. Status and Development of China High-resolution Earth Observation System and Application[C]//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing:IEEE, 2016:3738-3741.
[2] ZHANG H K, HUANG Bo, ZHANG Ming, et al. A Generalization of Spatial and Temporal Fusion Methods for Remotely Sensed Surface Parameters[J]. International Journal of Remote Sensing, 2015, 36(17):4411-4445.
[3] HUANG Bo, SONG Huihui. Spatiotemporal Reflectance Fusion via Sparse Representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(10):3707-3716.
[4] SONG Huihui, HUANG Bo. Spatiotemporal Satellite Image Fusion Through One-pair Image Learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(4):1883-1896.
[5] HUANG Bo, ZHANG Hankui. Spatio-temporal Reflectance Fusion via Unmixing:Accounting for Both Phenological and Land-cover Changes[J]. International Journal of Remote Sensing, 2014, 35(16):6213-6233.
[6] IRONS J R, DWYER J L, BARSI J A. The Next Landsat Satellite:The Landsat Data Continuity Mission[J]. Remote Sensing of Environment, 2012, 122:11-21.
[7] CHANDER G, MARKHAM B L, HELDER D L. Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors[J]. Remote Sensing of Environment, 2009, 113(5):893-903.
[8] 赵英时. 遥感应用分析原理与方法[M]. 2版. 北京:科学出版社, 2013. ZHAO Yingshi. The Teory and Methods of Remote Sensing Application Analysis[M]. 2nd ed. Beijing:Science Press, 2013:108-136.
[9] LECKIE D G. Advances in Remote Sensing Technologies for Forest Surveys and Management[J]. Canadian Journal of Forest Research, 1990, 20(4):464-483.
[10] GAO Feng, MASEK J, SCHWALLER M, et al. On the Blending of the Landsat and MODIS Surface Reflectance:Predicting Daily Landsat Surface Reflectance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8):2207-2218.
[11] GAO Feng, HILKER T, ZHU Xiaolin, et al. Fusing Landsat and MODIS Data for Vegetation Monitoring[J]. IEEE Geoscience and Remote Sensing Magazine, 2015, 3(3):47-60.
[12] ZHU Xiaolin, CHEN Jin, GAO Feng, et al. An Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model for Complex Heterogeneous Regions[J]. Remote Sensing of Environment, 2010, 114(11):2610-2623.
[13] ZHU Xiaolin, HELMER E H, GAO Feng, et al. A Flexible Spatiotemporal Method for Fusing Satellite Images with Different Resolutions[J]. Remote Sensing of Environment, 2016, 172:165-177.
[14] HILKER T, WULDER M A, COOPS N C, et al. A New Data Fusion Model for High Spatial-and Temporal-Resolution Mapping of Forest Disturbance Based on Landsat and MODIS[J]. Remote Sensing of Environment, 2009, 113(8):1613-1627.
[15] HUANG Bo, WANG Juan, SONG Huihui, et al. Generating High Spatiotemporal Resolution Land Surface Temperature for Urban Heat Island Monitoring[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5):1011-1015.
[16] WU Penghai, SHEN Huanfeng, AI Tinghua, et al. Land-surface Temperature Retrieval at High Spatial and Temporal Resolutions Based on Multi-sensor Fusion[J]. International Journal of Digital Earth, 2013, 6(S1):113-133.
[17] WU Penghai, SHEN Huanfeng, ZHANG Liangpei, et al. Integrated Fusion of Multi-scale Polar-orbiting and Geostationary Satellite Observations for the Mapping of High Spatial and Temporal Resolution Land Surface Temperature[J]. Remote Sensing of Environment, 2015, 156:169-181.
[18] EMELYANOVA I V, MCVICAR T R, VAN NIEL T G, et al. Assessing the Accuracy of Blending Landsat-MODIS Surface Reflectances in Two Landscapes with Contrasting Spatial and Temporal Dynamics:A Framework for Algorithm Selection[J]. Remote Sensing of Environment, 2013, 133:193-209.
[19] KIM J, HOGUE T S. Evaluation and Sensitivity Testing of A Coupled Landsat-MODIS Downscaling Method for Land Surface Temperature and Vegetation Indices in Semi-arid Regions[J]. Journal of Applied Remote Sensing, 2012, 6(1):063569.
[20] HASSAN Q K, BOURQUE C P A, MENG Fanrui. Application of Landsat-7 ETM+ and MODIS Products in Mapping Seasonal Accumulation of Growing Degree Days at an Enhanced Resolution[J]. Journal of Applied Remote Sensing, 2007, 1(1):013539.
[21] OUYANG Wei, HAO Fanghua, SKIDMORE A K, et al. Integration of Multi-sensor Data to Assess Grassland Dynamics in a Yellow River Sub-watershed[J]. Ecological Indicators, 2012, 18:163-170.
[22] ZHANG Hankui, CHEN J M, HUANG Bo, et al. Reconstructing Seasonal Variation of Landsat Vegetation Index Related to Leaf Area Index by Fusing with MODIS Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(3):950-960.
[23] BHANDARI S, PHINN S, GILL T. Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia[J]. Remote Sensing, 2012, 4(6):1856-1886.
[24] SINGH D. Generation and Evaluation of Gross Primary Productivity Using Landsat Data Through Blending with MODIS Data[J]. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(1):59-69.
[25] GEVAERT C M, GARCíA-HARO F J. A Comparison of STARFM and an Unmixing-based Algorithm for Landsat and MODIS Data Fusion[J]. Remote Sensing of Environment, 2015, 156:34-44.
[26] GAO Feng, MASEK J G, WOLFE R E, et al. Building a Consistent Medium Resolution Satellite Data Set Using Moderate Resolution Imaging Spectroradiometer Products as Reference[J]. Journal of Applied Remote Sensing, 2010, 4(1):043526.
[27] WU Mingquan, NIU Zheng, WANG Changyao, et al. 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]. Journal of Applied Remote Sensing, 2012, 6(1):063507.
[28] TEILLET P M, FEDOSEJEVS G, THOME K J, et al. Impacts of Spectral Band Difference Effects on Radiometric Cross-calibration Between Satellite Sensors in the Solar-reflective Spectral Domain[J]. Remote Sensing of Environment, 2007, 110(3):393-409.
[29] ZHANG Hankui, HUANG Bo. Support Vector Regression-based Downscaling for Intercalibration of Multiresolution Satellite Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3):1114-1123.
[30] POLH C, VAN GENDEREN J L. Multisensor Image Fusion in Remote Sensing:Concepts, Methods and Applications[J]. International Journal of Remote Sensing, 1998, 19(5):823-854.
[31] LUO Yi, TRISHCHENKO A P, KHLOPENKOV K V. Developing Clear-sky, Cloud and Cloud Shadow Mask for Producing Clear-sky Composites at 250-Meter Spatial Resolution for the Seven MODIS Land Bands over Canada and North America[J]. Remote Sensing of Environment, 2008, 112(12):4167-4185.
[32] HAZAYMEH K, HASSAN Q K. Spatiotemporal Image-fusion Model for Enhancing the Temporal Resolution of Landsat-8 Surface Reflectance Images Using MODIS Images[J]. Journal of Applied Remote Sensing, 2015, 9(1):096095.
[33] WANG Juan, HUANG Bo, FU Dongjie, et al. Response of Urban Heat Island to Future Urban Expansion over the Beijing-Tianjin-Hebei Metropolitan Area[J]. Applied Geography, 2016, 70:26-36.