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Selection of the Most Sensitive Waveband Reflectance for Normalized Difference Vegetation Index Calculation to Predict Rice Crop Growth and Grain Yield


A split-plot designed experiment including four rice varieties and 10 nitrogen levels was conducted in 2003 at the Experimental Farm of Seoul National University, Suwon, Korea. Before heading, hyperspectral canopy reflectance (300-1100nm with 1.55nm step) and nine crop variables such as shoot fresh weight (SFW), leaf area index, leaf dry weight, shoot dry weight, leaf N concentration, shoot N concentration, leaf N density, shoot N density and N nutrition index were measured at 54 and 72 days after transplanting. Grain yield, total number of spikelets, number of filled spikelets and 1000-grain weight were measured at harvest. 14,635 narrow-band NDVIs as combinations of reflectances at wavelength ${\lambda}l\;and\;{\lambda}2$ were correlated to the nine crop variables. One NDVI with the highest correlation coefficient with a given crop variable was selected as the NDVI of the best fit for this crop variable. As expected, models to predict crop variables before heading using the NDVI of the best fit had higher $r^2$ (>10\%)$ than those using common broad- band NDVI red or NDVI green. The models with the narrow-band NDVI of the best fit overcame broad- band NDVI saturation at high LAI values as frequently reported. Models using NDVIs of the best fit at booting showed higher predictive capacity for yield and yield component than models using crop variables.

참고문헌 (21)

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이 논문을 인용한 문헌 (1)

  1. Kang, Seong-Soo ; Gong, Hyo-Young ; Jung, Hyun-Cheol ; Kim, Yi-Hyun ; Hong, Suk-Young ; Hong, Soon-Dal 2010. "Evaluation of Biomass and Nitrogen Status in Paddy Rice Using Ground-Based Remote Sensors" 韓國土壤肥料學會誌 = Korean journal of Soil Science and Fertilizer, 43(6): 954~961 


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