| The primary task of crop growth spectral monitoring is to determine sensitive wavebands and characteristic parameters for reflecting crop growth status, and establish quantitative relationship between agronomic variables and spectral indices. In the past few years, the newly emerged hyperspectral remote sensing, with the characteristics of high resolution, consecutive wavebands and rich data, can significantly enhance the ability of detecting specific crop variables related to physiology and biochemistry, and have opened up new possibilities for quantifying single biochemical index in plant. In this study, five field experiments were conducted with different nitrogen (N) rates and rice cultivars across three growing seasons at different eco-sites. Based on analysis of leaf spectral information and assay of physico-chemical index in rice plant, the characteristics of leaf hyperspectral reflectance under different conditions and their correlation to leaf and canopy nitrogen status and correlative biochemical components in rice were quantified in this paper, then some new spectral indices and quantitative regression models were developed for estimating single leaf and canopy N and chlorophyll (Ch1) concentration. The prospective results would provide technical basis for non-destructive monitoring and precise diagnosis of wheat growth.Comparison of variation pattern in leaf reflectance under different N supply rates and leaf N concentration (LNC) showed that with increasing soil N supply rates and LNC, reflectance at visible band were decreased, presenting significant negative correlation; but reflectance at near infrared flat region (NIR,750-1300nm) were increased slightly, with weakly positive correlation. The correlation of leaf first derivative spectra to LNC indicated that highly significant correlation were reached at many bands located in visible and red edge regions, correlation spectrum presented obvious peak-valley characteristics. These changed patterns of leaf spectra and N status at different experimental condition provided a basis for analyzing and constructing quantitative relationships of leaf N nutrition to hyperspectral characters of leaf reflectance in rice. Based on technique of leaf hyperspectra analysis, many characteristic bands and derived spectral indices were obtained. The quantitative relationships of LNC and leaf protein N concentration (LPNC) to leaf reflectance spectra, first derivatives, and all combinations of two wavebands between 350 and 2500 nm as simple ratio spectral indices (SRs), normalized difference spectral indices (NDs) and simple difference spectral indices (SDs) were developed. The results indicated that the sensitivity bands mostly occurred in 520-590 nm within green light region and 695-715 nm within red edge region, and a close correlation existed between red-edge region and LNC and LPNC. Comparison of prediction ability of different algorithmic spectral indices indicated that the SRs were the most effective approach for predicting LNC and LPNC in the top leaves of rice plant, especially with the ratio of reflectances in the NIR to reflectances centered at 700-702 nm, and next to reflectances centered at 583-587 nm within yellow region. Two narrow bands spectral indices as SR(R.78o, R702) and SR(R770, R700) were developed to estimate LNC and LPNC, respectively; and further, based on sensitivity analysis of SRs located in effective combined region, tow broad band spectral indices as SR[AR(763-860), AR(697-707)] and SR[AR(746-815), AR(697-705)] were also determined to estimate LNC and LPNC, respectively, giving similar sensitivity and prediction ability with narrow SRs, indicated that selective bandwidth in effective region had little effect on prediction accuracy. The optimal SDs and NDs for estimating LNC and LPNC only located in a small regions of 740-755 nm, the wavebands for constructing spectral indices were adjacent, and close to optimal first derivatives, but they were all inferior to SRs.The relationships of leaf Ch1 concentration to new SRs and NDs with two wavelengths combinations, and existing Ch1 sensitive spectral indices were systematically analyzed, some sensitive spectral indices and monitoring equations were put forward for leaf Ch1 concentration estimation. Analysis showed that the best indicators for estimating leaf Ch1 concentration in rice were SRs and NDs calculated in the red edge region. The sensitive region for estimating of chlorophyll a (Ch1a) and total chlorophyll (Ch1a+b) concentration were consistent, the best SRs were uniform as SR(R730, R710), and the best NDs were ND(R780, R710) and ND(R780, R712), respectively; the best SRs and NDs for estimating chlorophyll b (Ch1b) were SR(R780, R725) and ND(R780, R725), respectively. In addition, modifying the above spectral indices with the reflectance at 445 nm could reduce the predicting error of models, and increased the extrapolation potential for the model. Some broad bands spectral indices were further developed for Ch1a, Ch1b and Ch1a+6 concentration estimation, respectively, as SR[AR(720-740), AR(705-715)] for leaf Chla and Ch1a+b and SR[AR(750-850), AR(715-735)] for leaf Chlb concentration estimation, and ND[AR(750-850), AR(705-715)], ND[AR(750-850), AR(706-718)] and ND[AR(750-850), AR(715-735)] for leaf Chla, Chla+b and Chlb concentration estimation, respectively, each of them had similar sensitivity as narrow bands spectral indices. This would help to development of portable Ch1 monitoring instrument.Further analysis were conducted on the relationships of leaf reflectance derived from different positions or single leaf spectral combination to canopy LNC and LPNC, and proper leaf position, key hyperspectral indices and quantitative monitoring model for accurate prediction of canopy LNC and LPNC were determined. The performance of different leaf hyperspectral indices for estimating canopy leaf N status were different with changed leaf position, top 2nd and 3rd leaf were ideal sampling position for monitoring canopy LNC and LPNC, average reflectance of top 2nd and 3rd leaf (L23) could help to improve the sensitivity and stability of key hyperspectral parameters, as ideal combination of key leaf position. The SRs combined of 702±nm vs. NIR were the most effective approach for predicting canopy LNC and LPNC in rice, and next were 580±nm vs. NIR. Green ratio indices as SR(R780, R580) and SR[AR(750-850), AR(568-588)] were developed for estimating canopy LNC; and red edge ratio indices as SR(R780, R701) and SR[AR(750-850), AR(697-706)] were developed for estimating canopy LPNC. The effect of model simulation and test indicated that these spectral indices constructed with leaf hyperspectral data could be effectively used for accurate estimation of canopy LNC and LPNC in rice under different growing conditions.Quantificational relationship between canopy leaf Chi concentration and leaf spectral indices derived from top four leaves at different growth position or their certain combination were also analyzed, and some key hyperspectral indices were developed for accurate prediction of canopy leaf Ch1 concentration, thus quantitative monitoring model for canopy leaf Ch1 concentration were determined. The results indicated that the performance of estimating canopy leaf Ch1 concentration by leaf hyperspectral indices were different with changed leaf position, average reflectance of top 2nd and 3rd leaf (L23) was more effective than others, as ideal selection of leaf spectrum. Both SRs and NDs were effective approach for predicting canopy leaf Chla and Chla+b concentration in rice, but centre waveband of optimal combination were different. The NDs located in 560±10 nm vs. NIR and 710±6 nm vs. NIR, and the SRs located in 554±10 nm vs. NIR and 718±6 nm vs. NIR, which were more remarkable than other wavebands combination. Thus green NDs as ND(R776, R560) and ND [R(750-850), R(550-570)], green SRs as SR(R554, R776) and SR[R(544-564), R(750-850)], red edge NDs as ND(R780, R710) and ND[R(750-850), R(704-716)], and red edge SRs as SR(R718, R780) and SR[R(712-724), R(750-850)] were developed to estimate canopy leaf Ch1a and Chla+b concentration, especially red edge wavebands combination, with higher sensitivity were more advised.The change characteristics of single leaf carotenoid (Car) concentration and carotenoid/chlorophyll ratio (Car/Ch1) in rice with development stages and quantitative relationships to leaf reflectance spectra and derived spectral indices were investigated. The results indicated that the SRs using reflectance around 723 nm combined with NIR or the NDs using reflectance around 713 nm combined with NIR could be used to estimate leaf Car concentration, among which the SR(R723, R770) and ND(R770, R713) have the best performance. Broad bands combinations as SR[AR(715-729), AR(750-820)] and ND[AR(740-840), AR(707-719)] in red edge region also have a good correlation with leaf Car concentration. Tests with independent dataset showed that leaf Car concentration in rice could be predicted effectively by above hyperspectral indices. Because changes of leaf Car/Ch1 ratio were direct related to leaf senescence or stress, SR(R698, R712), ND(R716, R695), SR(R615, R713) and ND(R737, R622) were developed for estimating leaf Car/Ch1 ratio in the mature stage, especially in the process of leaf senescence, but much work also remains to be done to test and perfect it. Further, Relationships of leaf hyperspectral indices to canopy leaf Car concentration were also analyzed, results show that average spectral reflectance of top 2nd and 3rd leaf was more suitable for monitoring canopy leaf Car concentration, blue spectral indices as SR(R466, R496) and ND(R466, R496), with a good accuracy and precision, were presented for canopy leaf Car concentration estimation. |
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