Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation |
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| Institution: | 1. Department Geoscience and Remote Sensing (GRS), Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands;2. Faculty of Geo-Information Science & Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands;1. School of Geodesy and Geomatics, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China;2. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu 210008, China;3. Center for Spatial Information Science and Systems, George Mason University, VA 22030, USA;4. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;1. Departament de Física de la Terra i Termodinàmica, Facultat de Física, Universitat de València, Dr. Moliner, 50, Burjassot 46100, València, Spain;2. Image Processing Laboratory (IPL), Universitat de València, Catedrático A. Escardino, 9, Paterna 46980, València, Spain;3. Institute for Electromagnetic Sensing of the Environment, Italian National Research Council, Via Bassini 15, Milan 20133, Italy |
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| Abstract: | Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory for various modelling exercises and predicting the precise farm productivity. Present study compared the two retrieval approach based on canopy radiative transfer (CRT) method and empirical method using four vegetation indices (VI) (e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observations available at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboard Indian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performed over 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 simulation 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 CRT model PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on minimization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Two consecutive 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 second growing season data used for validation. Among all the models, LAI-NDVI empirical model showed the least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. The comparison of PROSAIL retrieved LAI with in situ measurements of 2006–2007 over the two agro-climatic regions produced substantially less RMSE of 0.34 and 0.41 having more R2 of 0.91 and 0.95 for TGPR and CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value of errors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential for operational implementation to determine the regional crop LAI and can be extendible to other regions after rigorous validation exercise. |
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| Keywords: | LAI retrieval Canopy radiative transfer Satellite Crop |
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