Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring

被引:141
作者
Amoros-Lopez, Julia [1 ]
Gomez-Chova, Luis [1 ]
Alonso, Luis [1 ]
Guanter, Luis [2 ]
Zurita-Milla, Raul [3 ]
Moreno, Jose [1 ]
Camps-Valls, Gustavo [1 ]
机构
[1] Univ Valencia, Image Proc Lab IPL, Valencia 46980, Spain
[2] Free Univ Berlin, Inst Space Sci, D-12165 Berlin, Germany
[3] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7500 AE Enschede, Netherlands
关键词
Image fusion; Regularized spatial unmixing; Point-spread function; Multi-temporal NDVI; Crop monitoring; SPATIAL-RESOLUTION IMPROVEMENT; IMAGE FUSION; SURFACE REFLECTANCE; MERIS IMAGES; TIME-SERIES; ALGORITHMS; TM;
D O I
10.1016/j.jag.2012.12.004
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Monitoring Earth dynamics using current and future satellites is one of the most important objectives of the remote sensing community. The exploitation of image time series from sensors with different characteristics provides new opportunities to increase the knowledge about environmental changes and to support many operational applications. This paper presents an image fusion approach based on multiresolution and multisensor regularized spatial unmixing. The approach yields a composite image with the spatial resolution of the high spatial resolution image while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested using images from Landsat/TM and ENVISAT/MERIS instruments, but is general enough to be applied to other sensor pairs. The potential of the proposed spatial unmixing approach is illustrated in an agricultural monitoring application where Landsat temporal profiles from images acquired over Albacete, Spain, in 2004 and 2009 are complemented with MERIS fused images. The resulting spatial resolution from Landsat allows monitoring small and medium size crops at the required scale while the fine spectral and temporal resolution from MERIS allow a more accurate determination of the crop type and phenology as well as capturing rapidly varying land-cover changes. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:132 / 141
页数:10
相关论文
共 41 条
  • [1] ACRI-ST and ESA, 2007, POIDACRGS0003
  • [2] Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S data-fusion contest
    Alparone, Luciano
    Wald, Lucien
    Chanussot, Jocelyn
    Thomas, Claire
    Gamba, Paolo
    Bruce, Lori Mann
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2007, 45 (10): : 3012 - 3021
  • [3] A Global Quality Measurement of Pan-Sharpened Multispectral Imagery
    Alparone, Luciano
    Baronti, Stefano
    Garzelli, Andrea
    Nencini, Filippo
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2004, 1 (04) : 313 - 317
  • [4] Soft vector quantization and the EM algorithm
    Alpaydin, E
    [J]. NEURAL NETWORKS, 1998, 11 (03) : 467 - 477
  • [5] Regularized Multiresolution Spatial Unmixing for ENVISAT/MERIS and Landsat/TM Image Fusion
    Amoros-Lopez, Julia
    Gomez-Chova, Luis
    Alonso, Luis
    Guanter, Luis
    Moreno, Jose
    Camps-Valls, Gustavo
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2011, 8 (05) : 844 - 848
  • [6] [Anonymous], 2000, P 3 C FUS EARTH DAT
  • [7] [Anonymous], 2011, IEEE GEOSCI REMOTE S
  • [8] Arino O., 2007, P ESA LIV PLAN S HEL, P1
  • [9] Camps-Valls G., 2011, REMOTE SENSING IMAGE
  • [10] Multitemporal MERIS images for land-cover mapping at a national scale: a case study of Portugal
    Carrao, H.
    Araujo, A.
    Goncalves, P.
    Caetano, M.
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2010, 31 (08) : 2063 - 2082