Abstract
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.
Highlights
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)
The corresponding regression relationship between LAIG and NDVIMLS and LAIG and NDVIUS is shown in Figure 4 and 5
It is interesting to note that this regression relationship is not as good as either the Mid Latitude Summer (MLS) derived NDVI or United States (US) derived NDVI or NDVI derived from raw uncorrected reflectance at the top of the atmosphere
Summary
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). Direct or indirect estimation of LAI measurements are key input in many ecosystem models. In many agricultural and forestry applications, LAI was directly estimated to assess crop growth and health of vegetation around the globe. 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
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