REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI 
    
OF IRRIGATED CROPS IN MURRY DARLING BASIN 
Indira Wittamperuma!, Mohsin Hafeez" 2, Mojtaba Pakparvar’and John Louis* 
!School of Environmental Sciences, Charles Sturt University, Wagga Wagga NSW 2678, Australia. 
Email: iwittamperuma@csu.edu.au 
GHD Pty Ltd, 201 Charlotte Street, Brisbane QLD, 4000 
3Faculty of Bioscience Engineering Gent University, 673 Cupour Links, Gent 9000, Belgium 
“School of Computing and Mathematics, Charles Sturt University, Wagga Wagga NSW 2678, Australia. 
KEY WORDS: GIS, LAL, 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 (LAG) 
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. 
1. INTRODUCTION 
LAI is a dimensionless vegetation biophysical parameter 
which defines the status of the vegetation growth and it is 
a key input parameter in crop growth and yield models 
(Doraiswamy et al., 2005), plant photosynthesis 
(Duchemin et al, 2006), evapotranspiration (ET) and 
carbon flux (Chen et al, 2007). Therefore, direct or 
indirect estimation of LAI measurements are key input in 
many ecosystem models. 
The direct method of LAI calculation involves steps 
including leaf collection, area determination and 
measurement of dry weight of leaves to derive the ratios 
of leaf area and mass per unit ground area (Zheng et al., 
2009). In many agricultural and forestry applications, 
LAI was directly estimated to assess crop growth and 
health of vegetation around the globe. Sarlikioti et al., 
(2011) measured LAI destructively to explore a way for 
the online estimation of LAI and PAR interception in two 
greenhouse grown crops (tomato and sweet paper). 
Casanova et al., (1998) monitored the rice crop status 
during the growing season using LAI at Ebro Delta in 
Spain. These researchers estimated LAI directly by 
measuring the leaf blade of the rice crop with a LI-300 
Area Meter. Even though the direct measurements of 
LAI provide more accurate estimation, it is time 
consuming and work intensive to use over large 
agricultural areas which are rapidly changing over the 
time. 
In the indirect method, LAI is estimated in terms of 
canopy gap fraction or gap size distribution. The hand 
held optical instruments such as plant canopy analysers 
(LI-COR LAI-2000) are usually used to measure canopy 
gap fraction. Liu et al, (2010) has used indirect 
measurements of LAI-2000 plant canopy analyzer to 
compare the LAI estimates derived from vertical gap 
fraction measurements obtained from digital colour 
photography over the top of canopy of corn, soybean and 
wheat canopies in Eastern Canada. The performance of 
indirect methods using hand held instruments can only 
provide reasonable estimates if the basic assumptions 
such as data is collected at dawn or dusk conditions are 
strictly followed (Wilhelm et al., 2000) and this method 
is not practical to collect LAI data over large vegetation 
areas. 
Stroppiana et al, (2006) estimated LAI directly and 
indirectly to evaluate the adequacy and the range of 
reliability of LAI-2000 estimates for rice in Northern 
Italy. Similarly, Liang et al, (2003) took LAI 
measurements using non-destructive methods to create an 
algorithm for estimating LAI using Advanced Land 
Imager (ALI) multi-spectral satellite images and it was 
validated using multiple small plots within large crop 
fields in the CIA, Australia. However, the direct 
comparison was not possible due to geo-location and 
registration uncertainties of the images; therefore, the 
average LAI value for each field in the CIA was 
calculated for the validation of the algorithm.