Reducing background effects in orchards through spectral vegetation index correction

International Journal of Applied Earth Observation and Geoinformation

Volumn 34, Issue 1, 2015, Pages 167-177

  van Beek, Jonathan ^a   Tits, Laurent ^a   Somers, Ben ^a   Deckers, Tom ^b   Janssens, Pieter ^c   Coppin, Pol ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b Pcfruit research station   (Belgium)

^c Soil Service of Belgium   (Belgium)

Author keywords

Biophysical variables; Canopy cover fraction; Mixture problem; Orchards; Signal unmixinga; Vegetation indices

Indexed keywords

CORRECTION; ORCHARD; REMOTE SENSING; SATELLITE IMAGERY; VEGETATION INDEX; WORLDVIEW;

EID: 84920673774     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.08.009     Document Type: Article

References (39)

1. Acevedo-Opazo, C., Tisseyre, B., Guillaume, S., Ojeda, H., 2008. The potential of highspatial resolution information to define within-vineyard zones related to vinewater status. Precis. Agric. 9 (5), 285-302.
2. Adler-Golden, S.M., Berk, A., Bernstein, L.S., Richtsmeier, S., Acharya, P.K., Matthew,M.W., Anderson, G.P., et al., 1998. FLAASH, a MODTRAN4 atmospheric correctionpackage for hyperspectral data retrievals and simulations. In: Green, R.O. (Ed.),Summaries of the Seventh JPL Airborne Earth Science Workshop. January 12-16,1998, p. 442.
3. Aksoy, S., Yalniz, I.Z., Tasdemir, K., 2012. Automatic detection and segmentation oforchards using very high resolution imagery. IEEE Trans. Geosci. Remote Sens.50 (8), 3117-3131.
4. Delalieux, S., Somers, B., Hereijgers, S., Verstraeten, W., Keulemans, W., Coppin, P.,2008. A near-infrared narrow-waveband ratio to determine leaf area index inorchards. Remote Sens. Environ. 112 (10), 3762-3772.
5. Dorigo, W.A., Zurita-Milla, R., de Wit, A.J.W., Brazile, J., Singh, R., Schaepman, M.E.,2007. A review on reflective remote sensing and data assimilation techniquesfor enhanced agroecosystem modeling. Int. J. Appl. Earth Observ. Geoinf. 9 (2),165-193.
6. FAO, 1988. Soils Map of the World: Revised Legend. Food and Agriculture Organiza-tion of the United Nations, Rome.
7. Gitelson, A.A., Merzlyak, M.N., 1996. Signature analysis of leaf reflectance spectra:algorithm development for remote sensing of chlorophyll. J. Plant Physiol. 148(3-4), 494-500.
8. Govender, M., Dye, P.J., Weiersbye, I.M., Witkowski, E.T.F., Ahmed, F., 2009. Reviewof commonly used remote sensing and ground-based technologies to measureplant water stress. Water SA 35 (5), 741-752.
9. Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., 2002.Integrated narrow-band vegetation indices for prediction of crop chlorophyllcontent for application to precision agriculture. Remote Sens. Environ. 81 (2-3),416-426.
10. Hamada, Y., Stow, D.A., Roberts, D.A., 2011. Estimating life-form cover fractions inCalifornia sage scrub communities using multispectral remote sensing. RemoteSens. Environ. 115 (12), 3056-3068.
11. Hosgood, B., Jacquemoud, S., Andreoli, G., Verdebout, J., Pedrini, A., Schmuck, G.,1994. Leaf Optical Properties EXperiment 93 (LOPEX93). Report EUR 16095 EN.European Commission - Joint Research Centre, Ispra.
12. Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25,295-309.
13. Jacquemoud, S., Baret, F., 1990. Prospect: a model of leaf optical properties spectra.Remote Sens. Environ. 34, 75-91.
14. Laben, C.A., Brower, B. V., 2000. Process for enhancing the spatial resolution of multi-spectral imagery using pan-sharpening. United States Eastman Kodak Company.US Patent 6011875.
15. Laurikkala, J., Juhola, M., Kentala, E., 2000. Informal identification of outliers inmedical data. In: Fifth International Workshop on Intelligent Data Analy-sis in Medicine and Pharmacology IDAMAP-2000. Organized as a workshopof the 14th European Conference on Artificial Intelligence ECAI-2000, Berlin,22 August.
16. Marchisio, G., Pacifici, F., Padwick, C., 2010. On the relative predictive value of thenew spectral bands in the WorldView-2 sensor. In: IGARSS, pp. 2723-2726.
17. Moran, S., Fitzgerald, G., Rango, A., Walthall, C., Barnes, E., Bausch, W., Clarke, T.,et al., 2003. Sensor development and radiometric correction for agriculturalapplications. Photogramm. Eng. Remote Sens. 69 (6), 705-718.
18. Perry, E.M., Dezzani, R.J., Seavert, C.F., Pierce, F.J., 2009. Spatial variation in treecharacteristics and yield in a pear orchard. Precis. Agric. 11 (1), 42-60.
19. Pharr, M., Humphreys, G., 2004. Physically Based Rendering, From Theory to Imple-mentation. Morgan Kaufmann, San Fransisco, CA, pp. 1019.
20. Pinter, P.J., Hatfield, J.L., Schepers, J.S., Barnes, E.M., Moran, M.S., Daughtry, C.S.T.,Upchurch, D.R., 2003. Remote sensing for crop management. Photogramm. Eng.Remote Sens. 69 (6), 647-664.
21. Roberts, D.A., Gardner, M., Church, R., Ustin, S., Scheer, G., 1998. Mapping chaparralin the Santa Monica mountains using multiple endmember spectral mixturemodels. Remote Sens. Environ. 65, 267-279.
22. Rondeaux, G., Steven, M., Baret, F., 1996. Optimization of soil-adjusted vegetationindices. Remote Sens. Environ. 107, 95-107.
23. Sansavini, S., Musacchi, S., 1994. Canopy architecture, training and pruning inthe modern European pear orchards: an overview. Acta Hortic. (ISHS) 367,152-172.
24. Somers, B., Asner, G.P., Tits, L., Coppin, P., 2011. Endmember variability in spectralmixture analysis: a review. Remote Sens. Environ. 115 (7), 1603-1616.
25. Somers, B., Delalieux, S., Verstraeten, W.W., Coppin, P., 2009. A conceptualframework for the simultaneous extraction of sub-pixel spatial extent andspectral characteristics of crops. Photogramm. Eng. Remote Sens. 75 (1),57-68.
26. Somers, B., Gysels, V., Verstraeten, W.W., Delalieux, S., Coppin, P., 2010. Modellingmoisture-induced soil reflectance changes in cultivated sandy soils: a case studyin citrus orchards. Eur. J. Soil Sci. 61 (6), 1091-1105.
27. Stagakis, S., González-Dugo, V., Cid, P., Guillén-Climent, M.L., Zarco-Tejada, P.J., 2012.Monitoring water stress and fruit quality in an orange orchard under regulateddeficit irrigation using narrow-band structural and physiological remote sensingindices. ISPRS J. Photogramm. Remote Sens. 71, 47-61.
28. Stuckens, J., Dzikiti, S., Verstraeten, W.W., Verreynne, S., Swennen, R., Coppin, P.,2011. Physiological interpretation of a hyperspectral time series in a citrusorchard. Agric. For. Meteorol. 151 (7), 1002-1015.
29. Stuckens, J., Somers, B., Albrigo, G.L., Dzikiti, S., Verstraeten, W.W., 2010. Off-nadirviewing for reducing spectral mixture issues in citrus orchards. Photogramm.Eng. Remote Sens. 76 (11), 1261-1274.
30. Stuckens, J., Somers, B., Delalieux, S., Verstraeten, W.W., Coppin, P., 2009. Theimpact of common assumptions on canopy radiative transfer simulations: a case study in Citrus orchards. J. Quant. Spectrosc. Radiat. Transfer 110 (1-2),1-21.
31. Tauler, R., Kowalski, B., Fleming, S., 1993. Multivariate curve resolution applied tospectral data from multiple runs of an industrial process. Anal. Chem. 65 (15),2040-2047.
32. Tits, L., Somers, B., Saeys, W., Coppin, P., 2013a. Alternating least-squares unmixingfor the extraction of sub-pixel information from agricultural areas. Proc. SPIE8887, 888706-888710.
33. Tits, L., Somers, B., Stuckens, J., Farifteh, J., Coppin, P., 2013b. Integration of in situmeasured soil status and remotely sensed hyperspectral data to improve plantproduction system monitoring: concept, perspectives and limitations. RemoteSens. Environ. 128, 197-211.
34. Tou, J.T., Gonzalez, R.C., 1974. Pattern Recognition Principles. Addison-Wesley Pub-lishing Company, Reading, MA, pp. 377.
35. Updike, T., Comp, C., 2010. Radiometric Use of WorldView-2 Imagery. TechnicalNote., pp. 16.
36. Van Beek, J., Tits, L., Somers, B., Coppin, P., 2013. Stem water potential monitoringin pear orchards through WorldView-2 multispectral imagery. Remote Sens. 5(12), 6647-6666.
37. Weber, J., Penn, J., 1995. Creation and rendering of realistic trees. In: Proceedings ofthe 22nd Annual Conference on Computer Graphics and Interactive Techniques,pp. 119-128.
38. Zarco-Tejada, P., Miller, J., Morales, A., Berjón, A., Agüera, J., 2004. Hyperspectralindices and model simulation for chlorophyll estimation in open-canopy treecrops. Remote Sens. Environ. 90 (4), 463-476.
39. van der Meer, F.D., van der Werff, H.M.A., van Ruitenbeek, F.J.A., 2014. Potential of ESA's Sentinel-2 for geological applications. Remote Sens. Environ. 148,124-133.