Montring Nitrogen Status And Grain Quality With Canopy Reflectance Spectra In Rice

Nitrogen is one of the most important nutrients plant growth, and plays key roles in grain yield and quality formation and cultural regulation in rice production. Remote sensing is an important tool for estimating crop growth characters, nitrogen status, yield and quality formation. In this study, a series of field experiments with rice including different varieties, nitrogen and water management practices and population treatments were carried out in five years, canopy spectral reflectance were measured with canopy multi-spectral radiometer (CROPSCAN) and hyper-spectral radiometer (FieldSpec Pro FR2500) over the whole growth periods. The characteristics of canopy multi-spectral and hyper-spectral reflectance under different experimental conditions and their correlations to nitrogen status and grain quality traits in rice were investigated, and the sensitive spectrum parameters and quantitative regression models were established for leaf pigment content, nitrogen status and grain quality traits. This work would provide theoretical basis and key techniques for non-destructive monitoring of nitrogen status and grain quality traits in rice plants with remote sensing technology. The main contents and results of the present study are summarized as follows.The change patterns of canopy reflectance under varied nitrogen rates, different eultivars and different growth stages were investigated based on integrated analysis of canopy multi-spectral and hyper-spectral reflectance features in rice. Results showed that reflectance at near infrared reflected flat increased with increasing nitrogen supply, whereas reflectance at visible band decreased, and green peak and red edge position of canopy reflectance spectra also respectively moved to direction of blue light(short wavelength) and red light (long wavelength). Reflectance at visible light initially decreased, then increased with growth progress after tillering, and the lowest value appeared at heading. Reflectance increased and reflectance peak also disappeared gradually in course of leaf yellowing. However, reflectance in near infrared range had opposite trend, which initially increased, then decreased to the lowest from anthesis to maturity. The canopy reflectance also differed with eultivars. These results provide theoretical basis for using canopy reflectance spectra to monitor of growth status, nitrogen status and grain qualities in rice. The relationships of leaf pigment contents to canopy spectral reflectance were quantified based on the experiment data. The results showed that the pigment contents of different position leaves increased with increasing nitrogen rates. Leaf chlorophyll a, chlorophyll b, total chlorophyll content and the ratio of total chlorophyll to carotenoid content were negatively correlated to reflectance at 350-710nm and 1400-2485 nm, and positively correlated at 750-1300 nm. Yet carotenoid content and the ratio of chlorophyll a to chlorophyll b content were opposite, negatively correlated to reflectance at 750-1300nm, and positively correlated at 350-710nm and 1400-2485 nm. It is proposed that both leaf chlorophyll a and total chlorophyll can be well monitored by vegetation index RVI(660, 460), chlorophyll b can be well monitored by NDVI(610, 460), and the ratio of total chlorophyll to carotenoid content can be well monitored by NDVI(950, 870). Leaf pigment contents were also significantly correlated with canopy hyper-spectral parameters WI and NDVI(610,460). The derived equations were established as Chla (mg.g^-1FW) =-0.5718×RVI (660,460) + 2.9335, Chlb (mg.g^-1FW) =-2.0667×NDVI (610,460) + 1.2819, Chlt (mg.g^-1FW)=-0.9075×RVI (660,460) + 4.0158, Chlt/Car =-59.051×NDVI (950,870)+ 4.322. Tests with other independent dataset showed the estimation accuracy of 0.930-1.062, and RMSE of 11.277-19.574% under varied growing conditions. It is concluded that the present model was feasible and reliable for estimating leaf pigment contents in rice with different cultivars and nitrogen levels.The quantitative relationships between leaf and plant nitrogen status and canopy reflectance spectra in rice were investigated. The results showed that NDVI of R₁₂₂₀ and R₇₁₀ is the best parameter for predicting leaf content (LNC), the RVI of R₁₁₀₀ and R₅₆₀ is the best parameter for predicting leaf N accumulation (LNA), and the DVI of R₁₂₂₀ and R₈₇₀ is the best parameter for predicting plant N accumulation (PNA). The three models were tested by the data from independent field experiments. The RMSEs between the estimated and observed values were all below 20%, and slopes were 0.908～1.016, which indicated the monitoring models were feasible and useful for predicting nitrogen content and accumulation in leaves and plant of rice grown under different cultivars and nitrogen levels.Based on the change patterns of leaf sugar and nitrogen content and C/N ratio under different nitrogen supply with growth stages, correlations of leaf C/N to reflectance of single bands, different vegetation indices, derivative indices and parameters normalized by the continuum were analyzed comprehensively. The results showed that there was consistent correlation between C/N ratio and canopy reflectance after jointing in rice. The spectral index of ND₆₇₂ was found to be the best parameter for predicting leaf C/N in rice. The derived equation, LCNRA=788.33×ND₆₇₂-4.4326, was tested with the observed data of seven growth stages in the field experiment. The estimation precision ranged 0.687-0.986, estimation accuracy 0.907-1.126, and RMSE 7.851-18.25, indicating a good fit between the predicted and observed values of leaf C/N. Tests with other independent dataset showed that the estimation precision was 0.857-0.967, estimation accuracy 0.970-1.049, and RMSE 7.07-16.01. Thus, the present hyper-spectral model was feasible and reliable for estimating leaf C/N in rice with different cultivars and nitrogen levels.The relationships of grain protein content to canopy reflectance spectra and leaf and plant N status at different growth stages in rice were quantified based on differences of protein content and accumulation under different years, cultivars and nitrogen rates. The results showed that there were significant negative correlation between grain protein content and canopy spectral reflectance at 460-710 nm and positive correlation at 760-1220 nm after jointing, with best performance from the relationship at 760 nm and booting stage. The differential vegetation index of R₁₅₀₀ and R₉₅₀ was found to be the best parameter for predicting grain protein content (GPC) in rice. The derived equation, GPC =0.15×DVI (1500, 950) + 3, was tested with the observed data from the other independent experiments. There was a good fit between the predicted and observed values of grain protein content. Thus, the present spectral index model is feasible and useful for estimating grain protein content in rice with different cultivars and nitrogen levels. In addition, grain protein index at maturity could be estimated by leaf and nitrogen status at prior stage, but estimating capacity and proper time were different for different nitrogen status. In addition, the proper time for estimating grain protein content and accumulation is different with the same plant nitrogen index. Overall, plant nitrogen status from booting to mid-filling could be used to estimating gratin protein index at maturity for different cultivar and different nitrogen levels.Relationship of grain amylase content and other quality traits to canopy reflectance spectra were investigated using multi-spectral remote sensing data from different experiments. Results showed that amylase content, chalkiness grain percentage and chalkiness degree could be quantitatively estimated by canopy reflectance spectra, but other quality traits were not satisfied. In addition, relationships of grain quality traits to leaf N status and different quality traits were also analyzed for identifying indirect approach to monitor integrated grain quality in rice. The results revealed that indirect estimation of other grain quality straits could be realized based on quantitative monitoring of grain protein content and mutual relationships of grain quality indices.