Using FORMOSAT-2 Satellite Data to Estimate Leaf Area Index of Rice Crop

Estimation of plant growth over a large paddy field provides the needed information for site-specific management of crop including rice. With the estimated leaf area index (LAI) as input, growth status of rice crop may be evaluated and yield production at harvest may be assessed through functioning algorithms. This study measured the near-ground hyperspectral reflectance of rice canopy periodically on the dates of plant samplings during crop development, and then established the relationship between LAI(subscript measured) (i.e. the measured LAI) and NDVI(subscript NB) (i.e. normalized difference vegetation index calculated from narrowbands of hyperspectral reflectance) from the collected data. Rice plants of different planting densities, in the range of 0.28-2.78×10^5 hills ha^(-1), were grown in the field to produce varied values (m^2 m^(-2)) of LAI along plant growth for such purpose in the first and the second cropping seasons of 2006. A total of thirty-six multi-spectral images of the study area taken by Formosat-2 satellite on days of ground samplings were also acquired to calculate the broadband values of NDVI (NDVI(subscript BB)) and input to the LAI(subscript measured)-NDVI(subscript NB) relationship to obtain the estimated LAI (LAI(subscript BB)). Results indicate that the high-temporal and high-spatial-resolution images of Formosat-2 satellite are good source for monitoring plant growth of rice crop by providing reasonable estimated values of LAI. Such a capability of Formosat-2 spectral images enables their applicability in areas of precision farming.

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