The Estimation of Crop Leaf Area Index in Consideration of Texture Characteristics of SAR

doi:10.6046/gtzyyg.2010.03.08

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Abstract

The authors studied the feasibility of estimating Leaf Area Index (LAI) of the crop by using intensity and texture characteristics of SAR, and analyzed the texture characteristics of SAR which have relatively high correlation with LAI. In this study, six texture characteristics calculated from ENVISAT-ASAR image were selected and compared with measured LAI of the corn. The results show that the texture characteristics of HH polarization for gray level co-occurrence matrix have higher correlation with the LAI of corn than those of VV polarization. Dissimilarity of HH polarization and skewness and homogeneity of VV polarization are significantly related to LAI. In combination with backscattering coefficient, multiple regressions of two formulae were computed respectively, and the correlation coefficients are 0.68 for HH polarization and 0.87 for VV polarization. It is thus held that the methods discussed in this paper have potential application values in the estimation of the crop Leaf Area Index.

Keywords Mountain areas of Beijing Vegetation coverage Remote sensing mapping FCD mapping model Landscape pattern analysis

TP 79: S 127

Issue Date: 20 September 2010

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Cite this article:

GAO Shuai, NIU Zheng, LIU Xiang, WU Chao-Yang. The Estimation of Crop Leaf Area Index in Consideration of Texture Characteristics of SAR[J]. REMOTE SENSING FOR LAND & RESOURCES,2010, 22(3): 36-40.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.03.08 OR https://www.gtzyyg.com/EN/Y2010/V22/I3/36

［1］Paloscia S. An Empirical Approach to Estimating Leaf Area Index from Multifrequency SAR Data［J］.International Journal of Remote Sensing,1998,19(2):359-364．

［2］Inoue Y, Kurosu T, Maeno H, et al. Season-long Daily Measurements of Multifrequency (Ka, Ku, X, C, and L) and Full-polarization Backscatter Signatures over Paddy Rice Field and Their Relationship with Biological Variables［J］. Remote Sensing of Environment,2002,81(2):194-204．

［3］高帅，牛铮，刘晨洲．基于RADARSAT SAR估测热带人工林叶面积指数研究［J］．国土资源遥感，2008(4)：35-38．

［4］Ulaby F T, Allen C T, Eger III G, et al. Relating the Microwave Backscattering Coefficient to Leaf Area Index［J］. Remote Sensing of Environment,1984,14(1):113-133．

［5］Dente L, Satalino G, Mattia F, et al. Assimilation of Leaf Area Index Derived from ASAR and MERIS Data into CERES-Wheat Model to Map Wheat Yield［J］. Remote Sensing of Environment,2008,112(4):1395-1407．

［6］Dong P, Leblon B. Rock Unit Discrimination on Landsat TM, SIR-C and Radarsat Images Using Spectral and Textural Information［J］. International Journal of Remote Sensing,2004,25(18):3745-3768．

［7］Lu D, Batistella M, Moran E. Land-cover Classification in the Brazilian Amazon with the Integration of Landsat ETM+ and Radarsat Data［J］. International Journal of Remote Sensing,2007,28(24):5447-59．

［8］Champion I, Dubois-Fernandez P, Guyon D, et al. Radar Image Texture as A Function of Forest Stand Age［J］. International Journal of Remote Sensing,2008,29(6):1795-8000．

［9］Gonzalez R C, Wintz P A. Digital Image Processing［M］. Reading Mass: Addison-Wesley,1987．

［10］Haralick R M, Shanmuga K, Dinstein I. Textural Features for Image Classification［J］. IEEE Transactions on Systems Man and Cybernetics,1973,SMC3(6):610-21．

［11］李新，马明国，王建，等．黑河流域遥感—地面观测同步试验:科学目标与试验方案［J］．地球科学进展，2008(9)：897-914．

［12］Lopes A, Touzi R, Nezry E. Adaptive Dpeckle Filters and Scene Heterogeneity［J］. IEEE Transactions on Geoscience and Remote Sensing,1990,28(6):992-1000．

［13］Du L, Grunes M R, Lee J S. Unsupervised Segmentation of Dual-polarization SAR Images Based on Amplitude and Texture Characteristics［J］. International Journal of Remote Sensing,2002,23(20):4383-402．

［14］Baraldi A, Parmiggiani F. An Investigation of the Textural Characteristics Associated with Gray-level Coocurrence Matrix Statistical Parameters［J］. IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):293-304．

［15］Ulaby F, Sarabandi K, Mcdonald K, et al. Michigan Microwave Canopy Scattering Model［J］.International Journal of Remote Sensing,1990,11(7):1223-1253．

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