Terrestrial Biosphere and Hydrology Group EPSA

Ahmadābād, India

Terrestrial Biosphere and Hydrology Group EPSA

Ahmadābād, India
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Nigam R.,Terrestrial Biosphere and Hydrology Group EPSA | Bhattacharya B.K.,Terrestrial Biosphere and Hydrology Group EPSA | Vyas S.,Terrestrial Biosphere and Hydrology Group EPSA | Oza M.P.,Terrestrial Biosphere and Hydrology Group EPSA
International Journal of Applied Earth Observation and Geoinformation | Year: 2014

Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory forvarious modelling exercises and predicting the precise farm productivity. Present study compared the tworetrieval approach based on canopy radiative transfer (CRT) method and empirical method using four veg-etation indices (VI) (e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observationsavailable at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboardIndian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performedover two different wheat growing regions, situated in different agro-climatic settings/environments:Trans-Gangetic Plain Region (TGPR) and Central Plateau and Hill Region (CPHR). Forward simula-tion of canopy reflectances in four AWiFS bands viz. green (0.52-0.59 μm), red (0.62-0.68 μm), NIR(0.77-0.86 μm) and SWIR (1.55-1.70 μm) were carried out to generate the look up table (LUT) using CRTmodel PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on mini-mization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Twoconsecutive wheat growing seasons (November 2005-March 2006 and November 2006-March 2007)datasets were used in this study. The empirical models were developed from first season data and sec-ond growing season data used for validation. Among all the models, LAI-NDVI empirical model showedthe least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. Thecomparison of PROSAIL retrieved LAI with in situ measurements of 2006-2007 over the two agro-climaticregions produced substantially less RMSE of 0.34 and 0.41 having more R2of 0.91 and 0.95 for TGPRand CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value oferrors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential foroperational implementation to determine the regional crop LAI and can be extendible to other regionsafter rigorous validation exercise. © 2014 Elsevier B.V. All rights reserved.

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