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A model for the fusion of multi-source data to generate high temporal and spatial resolution VI data

  • role： First author第一作者
  • Affiliation：

    College of Water Conservancy and Architecture, Northeast Agricultural University, Harbin 150030, China

  • Email：julming@163.com
  • Introduction：杨军明，1992年生，男，硕士研究生，研究方向为多源遥感数据融合及其农业应用。E-mail：julming@163.com
YANG Junming1,  
  • Affiliation：

    College of Forestry, Northeast Forestry University, Harbin 150040, China

    Heilongjiang Agricultural Reclamation Survey and Research Institute, Harbin 150090, China

WU Yu23,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Water Conservancy and Architecture, Northeast Agricultural University, Harbin 150030, China

    Key Laboratory of High Efficiency Utilization of Agricultural Water Resources in Ministry of Agriculture, Harbin 150030, China

  • Email：wyx0915@163.com
  • Introduction：魏永霞，1964年生，女，教授，研究方向为农业水土资源高效利用与保护。E-mail：wyx0915@163.com
WEI Yongxia14*,  
  • Affiliation：

    College of Water Conservancy and Architecture, Northeast Agricultural University, Harbin 150030, China

WANG Bin1,  
  • Affiliation：

    College of Water Conservancy and Architecture, Northeast Agricultural University, Harbin 150030, China

RU Chen1,  
  • Affiliation：

    College of Water Conservancy and Architecture, Northeast Agricultural University, Harbin 150030, China

MA Yingying1,  
  • Affiliation：

    College of Water Conservancy and Architecture, Northeast Agricultural University, Harbin 150030, China

ZHANG Yi1

resumen

Vegetation Index (VI) data with high spatial and temporal resolution are highly important in the use of remote sensing technology to observe the earth. Owing to technological limitations, obtaining VI data that exhibits both high spatial and temporal resolution is impossible. The arrival of multi-source remote sensing data spatial and temporal fusion model enables the retrieval of data with high spatial and temporal resolution. The commonly used model does not have the ability to capture the intermediate change process of pixel value, and a certain regularity occurs in the change of the VI of the landmark. This study proposes a new multi-source data fusion model (FCMVISTFM) based on Fuzzy C-Mean algorithm (FCM). Making pixels that group together have similar VI values and law of VI changes throughout the period. FCMVISTFM uses FCM to divide land-cover types into certain categories based on multi-phase VI data, which are defined as subclasses of each land-cover class. Each land-cover class average value is calculated by using the linear unmixed model, and subclass average value is calculated by the law between land-cover class and subclass. The VI data are fused by Landsat8 OLI and MODIS data based on the assumption that the average VI value of subclass S is the same as the VI value of pixels that belong to subclass S. Results show that FCMVISTFM can achieve relatively high accuracy. The average values of correlation coefficient (R), RMSE, ERGAS, and variance are 0.9057, 0.0674, 1.9795, and 0.0045, respectively. With this level of accuracy, VI data can be used for vegetation research and observations of the earth. Commonly used line unmixed models, spatial and temporal adaptive reflectance fusion model (STARFM), and its improved models have the problem of uncertain ability to capture the intermediate change process of VI. Thus, FCMVISTFM is more accurate compared with STDFA and ESTARFM. FCMVISTFM is developed for obtaining high spatial and temporal resolution VI data, making it easier to capture the intermediate changes of VI, which can be applied where high spatial and temporal resolution VI data are needed. In this study, the accuracy of the multi-source data fusion model can be increased by the following aspects. (1) The models based on line unmixed model, regardless of pixel classes or subset S average value calculations, are based on the entire image. However, in the STARFM model and improved models based on STARFM, the data fusion based on high and low resolutions pixels in a certain window, cloud cover only affects the calculation of pixels near its coverage area. The acquisition of multiphase cloudless images is especially difficult when the study area is large. In this case, the STARFM model and improved models based on STARFM have more application advantages. (2) All of the multi-source remote sensing data fusion models are based on certain assumptions, though these assumptions are based on a certain theoretical and have certain rationality. Errors are mainly caused by assumptions. A complete model assumption is proposed as the main way to improve the accuracy of the multi-source remote sensing data fusion model. (3) The time sequence laws of the VI of various landmark are not disordered, but a certain regularity, such as the specific laws of the crop’s VI, occurs. If the multi-source remote sensing data fusion model is established based on these laws, then it can also improve the accuracy of fusion results to some extent.

palabra clave

remote sensing;Vegetation Index (VI);data fusion;temporal and spatial resolution;fuzzy c-means algorithm;linear unmixed model

References

  1. 1.
    Bezdek J C, Ehrlich R and Full W. 1984. FCM: the fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2/3): 191–203
  2. 2.
    Cai X L and Cui Y L. 2009. Crop planting structure extraction in irrigated areas from multi-sensor and multi-temporal remote sensing data. Transactions of the CSAE, 25(8): 124–130
  3. 3.
    Cerasoli S, Costa e Silva F and Silva J M N. 2016. Temporal dynamics of spectral bioindicators evidence biological and ecological differences among functional types in a cork oak open woodland. International Journal of Biometeorology, 60(6): 813–825
  4. 4.
    Gao F, Masek J, Schwaller M and Hall F. 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
  5. 5.
    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
  6. 6.
    Hilker T, Wulder M A, Coops N C, Linke J, McDermid G, Masek J G and White J C. 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113(8): 1613–1627
  7. 7.
    Liao L M, Song J L, Wang J D, Xiao Z Q and Wang J. 2016. Bayesian method for building frequent Landsat-like NDVI datasets by integrating MODIS and Landsat NDVI. Remote Sensing, 8(6): 452
  8. 8.
    Luo Y, Xu J H and Yue W Z. 2005. Research on vegetation indices based on the remote sensing images. Ecologic Science, 24(1): 75–79
  9. 9.
    Ojeda-Magaña B, Quintanilla-Domínguez J, Ruelas R, Barba L G and Andina D. 2018. Improvement of the image sub-segmentation for identification and differentiation of atypical regions. International Journal of Pattern Recognition and Artificial Intelligence, 32(1): 1860011
  10. 10.
    Qiu Q L, Zhao H Y, Guo J, Song F and Wu Y Z. 2004. The applying RS vegetation indexes in monitoring agricultural environment. Journal of Agricultural Mechanization Research(6): 215–217
  11. 11.
    Rao Y H, Zhu X L, Chen J and Wang J M. 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 Sensing, 7(6): 7865–7891
  12. 12.
    Shi Y C, Yang G J, Li X C, Song J, Wang J H and Wang J D. 2015. Intercomparison of the different fusion methods for generating high spatial-temporal resolution data. Journal of Infrared and Millimeter Waves, 34(1): 92–99
  13. 13.
    Tian Q J and Min X J. 1998. Advances in study on vegetation indices. Advance in Earth Sciences, 13(4): 327–333
  14. 14.
    Wu M Q, Niu Z and Wang C Y. 2010. Mapping paddy fields by using spatial and temporal remote sensing data fusion technology. Transactions of the CSAE, 26(S2): 48–52
  15. 15.
    Wu M Q, Wang J, Niu Z, Zhao Y Q and Wang C Y. 2012. A model for spatial and temporal data fusion. Journal of Infrared and Millimeter Waves, 31(1): 80–84
  16. 16.
    Wu M Q, Wu C Y, Huang W J, Niu Z, Wang C Y, Li W and Hao P Y. 2016. An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery. Information Fusion, 31: 14–25
  17. 17.
    Xie D F, Zhang J S, Sun P J, Pan Y Z, Yun Y and Yuan Z M Q. 2016. Remote sensing data fusion by combining STARFM and downscaling mixed pixel algorithm. Journal of Remote Sensing, 20(1): 62–72
  18. 18.
    Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610–2623
  19. 19.
    Zhukov B, Oertel D, Lanzl F and Reinhackel G. 1999. Unmixing-based multisensor multiresolution image fusion. IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1212–1226

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