Monitoring Canopy Leaf Nitrogen Concentration with Hyperspectrum Under Different Vegetation Coverage Conditions in Rice.

The objective of this study was to explore the optimum vegetation indices and quantitative models for estimating leaf nitrogen content (LNC) under different vegetation coverage conditions in rice. Based on the field experiments with different rice varieties, nitrogen rates and planting densities, a comprehensive analysis was made on the quantitative relationships between hyperspectral vegetation indices of rice canopy and its LNC. The aim was to develop a spectral index highly correlated to canopy LNC however less influenced by canopy leaf area index (LAI) and vegetation coverage (VC). The results showed that the simple ratio index SR(553, 537) which using two green bands and the three bands index (R605-R521-R682)/(R605+R521+R682) could be used to estimate canopy LNC with a good performance on eliminating the effects of LAI and VC. The best spectral index based on two first derivative bands for estimating canopy LNC was the difference index DI(D875, D645). In general, the hyperspectral indices based on spectral reflectance performed better than the spectral indices based on the first derivative spectra. The tests with independent dataset also indicated that the monitoring models based on SR(553, 537) and (R605-R521-R682)/(R605+R521+R682) had R2 of 0.69 and 0.72, RRMSE of 14% and 21%, yet (R605-R521-R682)/(R605+R521+R682) appeared instability among four years. Although NDVIg-b and ND(503, 483) had a good performance overall, the most of earlier published spectral indices showed poor performance on LNC estimation, but highly correlated with LAI and VC. Therefore, the newly developed spectral index SR(553, 537) could be reliably used for estimation of rice LNC. Key words: rice; canopy leaf nitrogen concentration; vegetation coverage; leaf area index; hyperspectral vegetation index