A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion

Remote Sensing of Environment

Volumn 156, Issue , 2015, Pages 34-44

  Gevaert, Caroline M ^a,b   García Haro, F Javier ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

^b UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

Data fusion; NDVI temporal profiles; STARFM; STRUM; Unmixing based data fusion

Indexed keywords

LANDSAT; NDVI TEMPORAL PROFILES; STARFM; STRUM;

DATA FUSION;

ALGORITHM; BAYESIAN ANALYSIS; DATA ASSIMILATION; IMAGE RESOLUTION; LANDSAT; MODIS; NDVI; REFLECTANCE; SATELLITE IMAGERY; SENSOR; SPATIAL RESOLUTION;

EID: 84907963084     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2014.09.012     Document Type: Article

References (44)

1. Amorós-López J., Gómez-Chova L., Alonso L., Guanter L., Zurita-Milla R., Moreno J., et al. Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring. International Journal of Applied Earth Observation and Geoinformation 2013, 23:132-141. 10.1016/j.jag.2012.12.004.
2. Anderson M.C., Kustas W.P., Norman J.M., Hain C.R., Mecikalski J.R., Schultz L., et al. Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrology and Earth System Sciences 2011, 15(1):223-239. 10.5194/hess-15-223-2011.
3. Arai E., Shimabukuro Y.E., Pereira G., Vijaykumar N.L. Remote sensing. Remote Sensing 2011, 3:1943-1956. 10.3390/rs3091943.
4. Bhandari S., Phinn S., Gill T. Preparing Landsat Image Time Series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia. Remote Sensing 2012, 4(12):1856-1886. 10.3390/rs4061856.
5. Bioucas-Dias J.M., Plaza A., Dobigeon N., Parente M., Du Q., Member S., et al. Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2012, 5(2):354-379. 10.1109/JSTARS.2012.2194696.
6. Canty M.J., Nielsen A.a. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sensing of Environment 2008, 112(3):1025-1036. 10.1016/j.rse.2007.07.013.
7. Chavez P.S.J. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 1988, 24(3):459-479. 10.1016/0034-4257(88)90019-3.
8. Ehlers M. Multisensor image fusion techniques in remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 1991, 46(1):19-30. 10.1016/0924-2716(91)90003-E.
9. Emelyanova I.V., McVicar T.R., Van Niel T.G., Tao Li L., Van Dijk A.I.J.M. Remote sensing of environment assessing the accuracy of blending Landsat - MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics : A framework for algorithm selection. Remote Sensing of Environment 2013, 133:193-209. 10.1016/j.rse.2013.02.007.
10. Fasbender D., Radoux J., Bogaert P. Bayesian data fusion for adaptable image pansharpening. IEEE Transactions on Geoscience and Remote Sensing 2008, 46(6):1847-1857. 10.1109/TGRS.2008.917131.
11. Feng M., Sexton J.O., Huang C., Masek J.G., Vermote E.F., Gao F., et al. Global surface reflectance products from Landsat: Assessment using coincident MODIS observations. Remote Sensing of Environment 2013, 134:276-293. 10.1016/j.rse.2013.02.031.
12. Gao F., Anderson M.C., Kustas W.P., Wang Y. Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference. Journal of Applied Remote Sensing 2012, 6(1):063554. 10.1117/1.JRS.6.063554.
13. Gao F., Masek J., Schwaller M., Hall F. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing 2006, 44(8):2207-2218. 10.1109/TGRS.2006.872081.
14. García-Haro F.J., Sommer S., Kemper T. A new tool for variable multiple endmember spectral mixture analysis (VMESMA). International Journal of Remote Sensing 2005, 26(10):2135-2162. 10.1080/01431160512331337817.
15. Gómez-chova L., Zurita-milla R., Alonso L., Amorós-lópez J., Guanter L., Camps-valls G. Gridding artifacts on medium-resolution satellite image time series: MERIS case study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2011, 49(7):2601-2611. 10.1109/TGRS.2011.2108660.
16. Gu J., Li X., Huang C., Okin G.S. A simplified data assimilation method for reconstructing time-series MODIS NDVI data. Advances in Space Research 2009, 44(4):501-509. 10.1016/j.asr.2009.05.009.
17. Hilker T., Wulder M.a., Coops N.C., Linke J., McDermid G., Masek J.G., et al. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment 2009, 113(8):1613-1627. 10.1016/j.rse.2009.03.007.
18. Hmimina G., Dufrêne E., Pontailler J.-Y., Delpierre N., Aubinet M., Caquet B., et al. Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sensing of Environment 2013, 132:145-158. 10.1016/j.rse.2013.01.010.
19. Hwang T., Song C., Bolstad P.V., Band L.E. Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain. Remote Sensing of Environment 2011, 115(10):2499-2512. 10.1016/j.rse.2011.05.010.
20. Jiang Z., Huete A.R., Chen J., Li J., Yan G., Zhang X. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment 2006, 101(1):366-378.
21. Lillo-Saavedra M., Gonzalo C., Arquero A., Martinez E. Fusion of multispectral and panchromatic satellite sensor imagery based on tailored filtering in the Fourier domain. International Journal of Remote Sensing 2005, 26(6):1263-1268. 10.1080/01431160412331330239.
22. Liu H., Weng Q. Enhancing temporal resolution of satellite imagery for public health studies: A case study of West Nile Virus outbreak in Los Angeles in 2007. Remote Sensing of Environment 2012, 117:57-71. 10.1016/j.rse.2011.06.023.
23. Lunetta R.S., Lyon J.G., Guindon B., Elvidge C.D. North American landscape characterization dataset development and data fusion issues. Photogrammetric Enginnering & Remote Sensing 1998, 64(8):821-829.
24. Lunetta R.S., Shao Y., Ediriwickrema J., Lyon J.G. Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data. International Journal of Applied Earth Observation and Geoinformation 2010, 12(2):81-88. 10.1016/j.jag.2009.11.005.
25. Mello M., Vieira C., Rudorff B., Aplin P., Santos R., Aguiar D. STARS: A new method for multitemporal remote sensing. IEEE Transactions on Geoscience and Remote Sensing 2013, 51(4):1897-1913. 10.1109/TGRS.2012.2215332.
26. Middleton E.M., Ungar S.G., Mandl D.J., Ong L., Frye S.W., Campbell P.E., et al. The Earth Observing One (EO-1) satellite mission: Over a decade in space. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2013, 6(2):243-256. 10.1109/JSTARS.2013.2249496.
27. Murphy K. Machine learning: A probabilistic perspective 2012, 1098. MIT Press, Cambridge, Massachusetts, USA, (Retrieved from http://dl.acm.org/citation.cfm?id=2380985).
28. Nielsen A.A. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing 2007, 16(2):463-478. (Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/17269639).
29. Pohl C., Van Genderen J.L. Review article. Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing 1998, 19:823-854. 10.1080/014311698215748.
30. Roy D.P., Ju J., Lewis P., Schaaf C., Gao F., Hansen M., et al. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment 2008, 112(6):3112-3130. 10.1016/j.rse.2008.03.009.
31. Singh D. Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data. International Journal of Applied Earth Observation and Geoinformation 2011, 13(1):59-69. 10.1016/j.jag.2010.06.007.
32. Steven M.D., Malthus T.J., Baret F., Xu H., Chopping M.J. Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment 2003, 88(4):412-422. 10.1016/j.rse.2003.08.010.
33. Su Z., Timmermans W., Gieske a., Jia L., Elbers J.a., Olioso a., et al. Quantification of land-atmosphere exchanges of water, energy and carbon dioxide in space and time over the heterogeneous Barrax site. International Journal of Remote Sensing 2008, 29(17-18):5215-5235. 10.1080/01431160802326099.
34. Tan B., Woodcock C.E., Hu J., Zhang P., Ozdogan M., Huang D., et al. The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions. Remote Sensing of Environment 2006, 105(2):98-114. 10.1016/j.rse.2006.06.008.
35. Teillet P.M., Fedosejevs G., Thome K., Barker J.L. Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain. Remote Sensing of Environment 2007, 110(3):393-409. 10.1016/j.rse.2007.03.003.
36. Van Leeuwen W.J.D., Orr B.J., Marsh S.E., Herrmann S.M. Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications. Remote Sensing of Environment 2006, 100(1):67-81. 10.1016/j.rse.2005.10.002.
37. Villa a, Chanussot J., Benediktsson J.a, Jutten C., Dambreville R. Unsupervised methods for the classification of hyperspectral images with low spatial resolution. Pattern Recognition 2013, 46(6):1556-1568. 10.1016/j.patcog.2012.10.030.
38. Wald L. Data fusion: Definitions and architectures: Fusion of images of different spatial resolutions 2002, 198. Presses des MINES, Paris, France.
39. Walker J.J., de Beurs K.M., Wynne R.H., Gao F. Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology. Remote Sensing of Environment 2012, 117:381-393. 10.1016/j.rse.2011.10.014.
40. Xin Q., Olofsson P., Zhu Z., Tan B., Woodcock C.E. Remote sensing of environment toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sensing of Environment 2013, 135:234-247. 10.1016/j.rse.2013.04.002.
41. Zhu X., Chen J., Gao F., Chen X., Masek J.G. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment 2010, 114(11):2610-2623. 10.1016/j.rse.2010.05.032.
42. Zurita-Milla R., Clevers J.G.P.W., Schaepman M.E. Unmixing-based Landsat TM and MERIS FR data fusion. IEEE Geoscience and Remote Sensing Letters 2008, 5(3):453-457. 10.1109/LGRS.2008.919685.
43. Zurita-Milla R., Gómez-Chova L., Guanter L., Clevers J.G.P.W., Camps-Valls G. Multitemporal unmixing of medium-spatial-resolution satellite images: A case study using MERIS images for land-cover mapping. IEEE Transactions on Geoscience and Remote Sensing 2011, 49(11):4308-4317. 10.1109/TGRS.2011.2158320.
44. Zurita-Milla R., Kaiser G., Clevers J.G.P.W., Schneider W., Schaepman M.E. Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics. Remote Sensing of Environment 2009, 113(9):1874-1885. 10.1016/j.rse.2009.04.011.