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ISSN: 2469-4134

Journal of Remote Sensing & GIS
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Research Article

Retrieving Leaf Area Index from Remotely Sensed Data Using Advanced Statistical Approaches

Gowda PH1,*, Oommen T2, Misra D3, Schwartz RC4, Howell TA4 and Wagle P5
1USDA-ARS Grazinglands Research Laboratory, El Reno, OK 73036, USA
2Department of Geological and Mining Engineering and Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI, USA
3Department of Mining and Geological Engineering, University of Alaska Fairbanks, P.O. Box 755800, Fairbanks, AK, USA
4USDA-ARS Conservation and Production Research Laboratory, P.O. Drawer 10, Bushland, TX, USA
5Department of Microbiology and Plant Biology, and Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA
Corresponding Author : Gowda PH
USDA-ARS Grazinglands Research Laboratory
El Reno, OK 73036, USA
Tel: 405-262-5291
E-Mail: [email protected]
Received: December 01, 2015; Accepted: December 04, 2015; Published: December 07, 2015
Citation:Gowda PH, Oommen T, Misra D, Schwartz RC, Howell TA, et al. (2015) Retrieving Leaf Area Index from Remotely Sensed Data Using Advanced Statistical Approaches. J Remote Sensing & GIS 4:156. doi:10.4172/2469-4134.1000156
Copyright: © 2015 Gowda PH, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Abstract

Mapping and monitoring leaf area index (LAI) is critical to model surface energy balance, evapotranspiration, and vegetation productivity. Remote sensing helps in rapid collection of LAI on individual fields over large areas, in a time and cost-effective manner using empirical regression between LAI and spectral vegetation indices (SVI). However, these relationships may be ineffective when sun-surface sensor geometry, background reflectance and atmosphereinduced variations on canopy reflectance are larger than variations in the canopy itself. This requires development of superior and region-specific LAI-SVI models. In recent years, statistical learning methods such as support vector machines (SVM) and relevant vector machines (RVM) have been successful over the ordinary least square (OLS) regression models for complex processes. The objective of this study is to develop and compare OLS, SVM, and RVMbased reflectance models to estimate LAI for major summer crops in the Texas High Plains. The LAI was measured in 47 randomly selected commercial fields in Moore and Ochiltree counties. Data collection was made to coincide with Landsat 5 satellite overpasses on the study area. Numerous derivations of SVIs were examined for estimating LAI using OLS, SVM, and RVM models. Analysis of the results indicated that the SVI-LAI models based on the ratio of TM bands 4 and 3, and normalized difference vegetation index (NDVI) are most sensitive to LAI. The R2 values for selected models varied from 0.79 to 0.96 with the SVM model producing the best results. However, accuracy of reported LAI models needs further evaluation that accounts for in-field spatial variability in the LAI for wider applicability.

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