Indian Journal of Agricultural Sciences 83 (4): 380–5, April 2013/Article Retrieval of Leaf Area Index using IRS-P6, LISS-III data and validation of MODIS LAI product (MOD15 V5) over trans Gangetic Plains of India RAHUL TRIPATHI 1 , R N SAHOO 2 , V K GUPTA 3 , V K SEHGAL 4 and P M SAHOO 5 Indian Agricultural Research Institute, New Delhi 110 012 Received: 12 November 2012; Revised accepted: 5 Februaruy 2013 ABSTRACT The mapping of leaf area index (LAI) in large geographic area may be impossible when we rely on the field measurement. To solve this problem, there have been continuing efforts to develop methodologies to estimate LAI using remote sensor data. Different methods to estimate LAI from reflectance data have been developed and can be grouped into two approaches such as (1) statistical approach and (2) physical process based approach. In this study, statistical approach has been used to retrieve the LAI of a part of north western part of the country using LISS-III, IRS P6 sensor. Using physical process based approach (i e radiative transfer model) MODIS on-board Earth Observing System (EOS) Terra/Aqua platforms, now provide LAI as a standard product (MOD15) to the scientific community at 1 km resolution, every eight days. The validation of LAI products by comparison to reference field LAI values is necessary and has been carried out for many sites over USA, Africa but very few results are available over India, hence MOD 15 LAI products have been validated using the derived LAI of LISS-III which was aggregated to 1 km resolution. Results revealed positive correlation (R 2 = 0.62) between MODIS LAI product and aggregated LISS-III LAI but MODIS LAI values were found to be underestimated compared to the measured values. The overall RMSE of MODIS LAI is higher (ie 2.74) compared to LISS-III LAI (0.65). Key words: Leaf area index, LISS-III , MODIS, NDVI, Trans Gangetic Plain, Wheat Leaf Area Index (LAI) has been an important parameter that is directly related to the photosynthesis, evapotranspiration, and the productivity of plant ecosystem (Bonan et al. 1993). Measurement of LAI in the field is very difficult, and requires a great amount of time and efforts (Gower et al. 1999). The mapping of LAI in large geographic area may be impossible when we rely on the field measurement. To solve this problem, there have been continuing efforts to develop methodologies to estimate LAI using remote sensor data. The normalized difference vegetation index (NDVI) was the most commonly used (Chen and Cihlar 1996). Although empirical modeling is relatively easy and useful method for relating field measured LAI to remote sensor data, several factors have certain influence on empirical model (Cohen et al. 2003). Accurate estimates of leaf area index is essential in agricultural and forestry applications as LAI exhibits a major control on transpiration and uptake of CO 2 by the canopy. Remotely sensed data acts as a unique cost-effective source for a detailed knowledge of the spatial and temporal variations LAI. Different methods to estimate LAI from reflectance data have been developed and can be grouped into two approaches such as (1) statistical approach and (2) physical process based approach (using radiative transfer models) (Tripathi et al. 2012). Using statistical approach, many researchers have developed empirical relationships between vegetation indices (VIs) and canopy biophysical variables (Tripathi et al. 2013). All the existing VIs are based on the large contrast existing between vegetation reflectance observed in the red wavebands and the infrared wavebands. Such a contrast is not observed on other earth surfaces (bare soil, rocks, water bodies). As a consequence, this contrast is an indicator of vegetation presence and status. The potential of VIs for the determination of crop parameters have been demonstrated in numerous 1 Scientist (e mail: rahulcrri@gmail.com), Crop Production Division, Central Rice Research Institute, Cuttack, Odisha 753 006; 2 Senior Scientist (e mail: rnsahoo@iari.res.in), 3 Technical Officer (e mail: vkgupta@gmail.com), 4 Senior Scientist (e mail: sehgal@iari.res.in), Division of Agricultural Physics, 5 Senior Scientist (e mail: pmsahoo@gmail.com), Division of Sample Survey, Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi 110 012 16