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Dense Satellite Image Time Series Analysis: Opportunities, Challenges, and Future Directions

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New Thinking in GIScience

Abstract

Earth observation satellites provide important data for monitoring land surface dynamics. In recent years, with the development of new satellite constellations, supercomputing, artificial intelligence, and cloud computing, remote sensing studies of land surface changes have been gradually shifted from sparse time series analysis to dense time series anslysis. Dense satellite image time series dramatically improve our capability for capturing frequent changes in the land surface. It has changed the research questions, data processing techniques, and applications compared with the traditional sparse time series analysis. This chapter discussed the opportunities, challenges, and future directions of dense satellite time series data analysis. It can help researchers from the remote sensing community or other disciplines apply dense satellite time series analysis to solve real-world problems.

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References

  • Ahmad, S. K., Hossain, F., Eldardiry, H., & Pavelsky, T. M. (2020). A fusion approach for water area classification using visible, near infrared and synthetic aperture radar for south asian conditions. IEEE Transactions on Geoscience and Remote Sensing, 58, 2471–2480.

    Article  Google Scholar 

  • Aybar, C., Wu, Q., Bautista, L., Yali, R., & Barja, A. (2020). rgee: An R package for interacting with Google Earth Engine. Journal of Open Source Software, 5, 2272.

    Article  Google Scholar 

  • Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., & Friedl, M. A. (2020). Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sensing of Environment, 240, 111685.

    Article  Google Scholar 

  • Cai, S., & Liu, D. (2018). Mapping Land cover trajectories using monthly MODIS time series from 2001 to 2010. In Q. Weng (Ed.), Remote Sensing time series image processing (pp. 137–155). CRC Press.

    Chapter  Google Scholar 

  • Cai, S., & Liu, D. (2015). Detecting change dates from dense satellite time series using a sub-annual change detection algorithm. Remote Sensing, 7(7), 8705–8727.

    Article  Google Scholar 

  • Claverie, M., Masek, J. G., Junchang, J., & Dungan, J. L. (2017). Harmonized Landsat-8 Sentinel-2 (HLS) Product User’s Guide 2, pp. 1–17.

    Google Scholar 

  • Deng, L., Mao, Z., Li, X., Hu, Z., Duan, F., & Yan, Y. (2018). UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 124–136.

    Article  Google Scholar 

  • Foody, G. M., & Arora, M. K. (1997). An evaluation of some factors affecting the accuracy of classification by an artificial neural network. International Journal of Remote Sensing, 18, 799–810.

    Article  Google Scholar 

  • Gomes, V. C. F., Queiroz, G. R., & Ferreira, K. R. (2020). An overview of platforms for big earth observation data management and analysis. Remote Sensing, 12, 1253.

    Article  Google Scholar 

  • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.

    Article  Google Scholar 

  • Hansen, M. C., & Loveland, T. R. (2012). A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 66–74.

    Article  Google Scholar 

  • Ju, J., & Roy, D. P. (2008). The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally. Remote Sensing of Environment, 112, 1196–1211.

    Article  Google Scholar 

  • Kennedy, R. E., Yang, Z., Gorelick, N., Cohen, W. B., & Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing, 10(5), 691.

    Article  Google Scholar 

  • Li, J., & Roy, D. P. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9(9), 902.

    Article  Google Scholar 

  • Liu, D., & Cai, S. (2012). A Spatial-Temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery. Annals of the Association of American Geographers, 102, 1329–1347.

    Article  Google Scholar 

  • Qiu, Y., Zhou, J., Chen, J., & Chen, X. (2021). Spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series ( SSFIT ). International Journal of Applied Earth Observations and Geoinformation, 100, 102333.

    Article  Google Scholar 

  • Schneider, A. (2012). Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment, 124, 689–704.

    Article  Google Scholar 

  • Tian, J., Zhu, X., Chen, J., Wang, C., Shen, M., Yang, W., Tan, X., Xu, S., & Li, Z. (2021). Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency. ISPRS Journal of Photogrammetry and Remote Sensing, 180, 29–44.

    Article  Google Scholar 

  • Wang, J., Yang, D., Detto, M., Nelson, B. W., Chen, M., Guan, K., Wu, S., Yan, Z., & Wu, J. (2020). Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest. Remote Sensing of Environment, 246, 111865.

    Article  Google Scholar 

  • Weisberg, P. J., Dilts, T. E., Greenberg, J. A., Johnson, K. N., Pai, H., Sladek, C., Kratt, C., Tyler, S. W., & Ready, A. (2021). Phenology-based classification of invasive annual grasses to the species level. Remote Sensing of Environment, 263, 112568.

    Google Scholar 

  • Wu, Q. (2021). Leafmap: A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. Journal of Open Source Software, 6, 3414.

    Article  Google Scholar 

  • Xu, S., Zhu, X., Helmer, E. H., Tan, X., Tian, J., & Chen, X. (2021). The damage of urban vegetation from super typhoon is associated with landscape factors: Evidence from Sentinel-2 imagery. International Journal of Applied Earth Observations and Geoinformation, 104, 102536.

    Article  Google Scholar 

  • Zhu, X., Cai, F., Tian, J., & Williams, T. (2018). Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions. Remote Sensing, 10, 527.

    Article  Google Scholar 

  • Zhu, X., & Helmer, E. H. (2018). An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions. Remote Sensing of Environment, 214, 135–153.

    Article  Google Scholar 

  • Zhu, X., & Liu, D. (2019). Investigating the impact of land parcelization on forest composition and structure in southeastern Ohio using multi-source remotely sensed data. Remote Sensing, 11(19), 2195.

    Article  Google Scholar 

  • Zhu, X., & Liu, D. (2015). Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 222–231.

    Article  Google Scholar 

  • Zhu, X., & Liu, D. (2014). Accurate mapping of forest types using dense seasonal landsat time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 1–11.

    Article  Google Scholar 

  • Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370–384.

    Article  Google Scholar 

  • Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94.

    Article  Google Scholar 

  • Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171.

    Google Scholar 

  • Zhu, Z., Zhou, Y., Seto, K. C., Stokes, E. C., Deng, C., Pickett, S. T. A., & Taubenböck, H. (2019). Understanding an urbanizing planet: Strategic directions for remote sensing. Remote Sensing of Environment, 228, 164–182.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Geography, The Ohio State University, Columbus, OH, 43210, USA

    Desheng Liu

  2. Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong, 999077, China

    Xiaolin Zhu

Authors
  1. Desheng Liu
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  2. Xiaolin Zhu
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Corresponding author

Correspondence to Desheng Liu .

Editor information

Editors and Affiliations

  1. Department of Geography and Environmental Studies, Central Michigan University, Mount Pleasant, SC, USA

    Bin Li

  2. Department of Geography, Dartmouth College, Hanover, NH, USA

    Xun Shi

  3. Department of Geography, University of Wisconsin-Madison, Madison, WI, USA

    A-Xing Zhu

  4. Department of Geography, University of South Carolina, Columbia, SC, USA

    Cuizhen Wang

  5. School of Geography and Environment, Jiangxi Normal University, Nanchang, Jiangxi, China

    Hui Lin

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Liu, D., Zhu, X. (2022). Dense Satellite Image Time Series Analysis: Opportunities, Challenges, and Future Directions. In: Li, B., Shi, X., Zhu, AX., Wang, C., Lin, H. (eds) New Thinking in GIScience. Springer, Singapore. https://doi.org/10.1007/978-981-19-3816-0_25

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