Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 367-373, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B8-367-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
30 Jul 2012
REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI OF IRRIGATED CROPS IN MURRY DARLING BASIN
I. Wittamperuma1, M. Hafeez2,1, M. Pakparvar3, and J. Louis4 1School of Environmental Sciences, Charles Sturt University, Wagga Wagga NSW 2678, Australia
2GHD Pty Ltd, 201 Charlotte Street, Brisbane QLD, 4000, Australia
3Faculty of Bioscience Engineering Gent University, 673 Cupour Links, Gent 9000, Belgium
4School of Computing and Mathematics, Charles Sturt University, Wagga Wagga NSW 2678, Australia
Keywords: GIS, LAI, LANDSAT TM, NDVI, Remote Sensing Abstract. Remote sensing is a rapid and reliable method for estimating crop growth data from individual plant to crops in irrigated agriculture ecosystem. The LAI is one of the important biophysical parameter for determining vegetation health, biomass, photosynthesis and evapotranspiration (ET) for the modelling of crop yield and water productivity. Ground measurement of this parameter is tedious and time-consuming due to heterogeneity across the landscape over time and space. This study deals with the development of remote-sensing based empirical relationships for the estimation of ground-based LAI (LAIG) using NDVI, modelled with and without atmospheric correction models for three irrigated crops (corn, wheat and rice) grown in irrigated farms within Coleambally Irrigation Area (CIA) which is located in southern Murray Darling basin, NSW in Australia. Extensive ground truthing campaigns were carried out to measure crop growth and to collect field samples of LAI using LAI- 2000 Plant Canopy Analyser and reflectance using CROPSCAN Multi Spectral Radiometer at several farms within the CIA. A Set of 12 cloud free Landsat 5 TM satellite images for the period of 2010-11 were downloaded and regression analysis was carried out to analyse the co-relationships between satellite and ground measured reflectance and to check the reliability of data sets for the crops. Among all the developed regression relationships between LAI and NDVI, the atmospheric correction process has significantly improved the relationship between LAI and NDVI for Landsat 5 TM images. The regression analysis also shows strong correlations for corn and wheat but weak correlations for rice which is currently being investigated.
Conference paper (PDF, 1004 KB)


Citation: Wittamperuma, I., Hafeez, M., Pakparvar, M., and Louis, J.: REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI OF IRRIGATED CROPS IN MURRY DARLING BASIN, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 367-373, https://doi.org/10.5194/isprsarchives-XXXIX-B8-367-2012, 2012.

BibTeX EndNote Reference Manager XML