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Comparison of principal component inversion with VI-empirical approach for LAI estimation using simulated reflectance data

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A simulated canopy reflectance dataset for a total of six channels in visible, near-infrared (NIR) and shortwave-infrared (SWIR) region, corresponding to Landsat Thematic Mapper (TM) was generated using the PROSAIL (PROSPECT+SAIL) model and a range of Leaf Area Index (LAI), soil backgrounds, leaf chlorophyll, leaf inclination and viewing geometry inputs. This dataset was used to develop and evaluate approaches for LAI estimation, namely, standard two-band nonlinear empirical vegetation index (VI)-LAI formulation (using Normalized Difference Vegetation Index/simple ratio (NDVI/SR)) and a multi-band principal component inversion (PCI) approach. The analysis indicated that the multi-band PCI approach had a smaller rms error (RMSE=0.380) than the NDVI and SR approaches (RMSE=2.28, 0.88), for an independently generated test dataset.
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Document Type: Research Article

Affiliations: Crop Inventory and Modeling Division, Agricultural Resource Group, RESIPA, Space Applications Centre ISRO Ahmedabad--380015 India

Publication date: July 1, 2004

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