Monitoring Nitrogen Status And Growth Characters With Canopy Hyperspectal Remote Sensing In Wheat

Precision farming is a prime approach to realizing high yielding, good quality and low consumption in modem agricultural production. The operational precision farming has been hampered by a lack of timely distributed information of crop conditions, and remote sensing can rapidly determine the growth status of crop in the field, which offers important technical support for implementation of precision farming. In recent years remote sensing technology has been proved to easily monitor and forecast growth characters, nitrogen status, yield and quality formation of farm crop. The newly-emerged hyperspectral remote sensing is sensitive to specific crop parameters, with better estimation of various growth variables related to crop physiology and biochemistry.In this study, a series of field experiments with different wheat varieties and nitrogen levels were carried out in three years. Based on analysis of canopy spectral reflectance and assay of agronomic parameters and physico-chemical index, in wheat plant, the characteristics of canopy hyperspectral reflectance under different conditions and their correlation to nitrogen status, growth characters, and grain traits in wheat were quantified computed in this paper. The sensitive spectrum parameters and quantitive regression models of nitrogen status, pigment status, leaf area index, biomass were established. Based on the spectral monitoring of plant nitrogen status and the quantitative relationships between nitrogen status, nitrogen translocation, grain yield and protein content, the indirect approach predicting mature grain traits with reflectance spectra were developed, which provided technical basis for non-destructive monitoring and precise diagnosis of wheat growth.Comparison of variation patter in canopy reflectance under different nitrogen rates, growing stages and cultivars in wheat, showed that reflectance at near infrared flat (750-1300nm) increased with increasing nitrogen rates, whereas reflectance at visible band decreased. Reflectance at visible light initially decreased and then increased with growth progress after jointing, with the lowest value appeared around at heading. However, reflectance in near infrared had opposite trend, which initially increased and then decreased to the lowest from growth bloom stages to maturity. These results provide background spectral information for monitoring of growth characters, nitrogen status and grain formation with canopy reflectance spectra in wheat.Based on technique of hyperspectra analysis, many characteristic bands and derived spectral parameters were obtained. The quantitative relationships of leaf nitrogen status to canopy reflectance spectra, and the sensitive parameters and monitoring equations of leaf N status were put forward. The sensitivity bands occurred during visible light and near infrared region mostly, and a close correlation existed between red-edge district and learN status. The vegetable indices related most significantly to LNC differed from that of LNA. An integrated linear regression equation of LNC to REIPLE,λO and mND705 described the dynamic pattern of change in LNC in wheat, giving high determination of coefficients and low standard errors. MSS-SARVI, FD742 and PSSRb were linearly related significantly to LNA, with higher R2 for LNA than that for LNC. The new developed red edge parameters based on red edge double peak could well describe the dynamic pattern of LNC and LNA changes in wheat, and the best indices was ND[RSDr(REP_IG), LSDr(REP_IG)] for LNC, and LSDr(REP_LE) for LNA with both high R² and low SE by regression analysis.The change characteristics of different pigment forms in leaves with development stages and quantitative relationships to canopy reflectance spectra and derived spectral parameters in wheat were investigated. The red edge position (REP) was highly correlated with leaf chlorophyll concentrations, with high R² in REP_LE, but R² between carotenoid and different spectral indices all decreased significantly. The correlations of chlorophyll density to VOG2, VOG3, RVI(810,560), Dr/Db and SDr/SDb were higher. Testing the monitoring equations with independent datasets indicated that the red edge position were the best to predict leaf pigment concentrations, and VOG2, VOG3, Dr/Db and SDr/SDb were indicators of leaf pigment density. The overall results suggested that pigment concentrations and density in wheat leaf could be estimated by hyperspectral parameters selected, and the chlorophyll a and chlorophyll a+b status could be reliably estimated in wheat.The change patterns of leaf soluble sugar to nitrogen ratio with nitrogen levels, and the quantitative relationships to characteristic bands and sensitive parameters were analyzed. The proper time for monitoring leaf soluble sugar to nitrogen ratio should be from jointing to mid-filling, with best stage as anthesis. FWBI and Area980 of water-index were highly correlated with leaf soluble sugar to nitrogen ratio, and (R_750-800/R_695-740)-1 and VOG2 of pigment-index were also significantly related to leaf soluble sugar to nitrogen ratio, with the highest determination of coefficients from exponential equation. Testing of the monitoring models with independent dataset indicated that FWBI, Areal190, (R_750-800/R_695-740)-1 and VOG2 were the best indicators to estimate leaf soluble sugar to nitrogen ratio.Based on the change patterns of leaf area index and dry weight under different nitrogen rates with growth stages, correlations of LAI and leaf dry weight to canopy hyperspectral reflectance and spectral parameters were investigated, and sensitive spectral parameters and quantitative equations were developed to forecast LAI and leaf dry weight in wheat, with unified spectral parameters for LAI and leaf dry weight across a broad ranges of growth stages, nitrogen levels and growing seasons. Regression models with spectral variables as RVI(810,560), FD755, GMI, SARVI(MSS) and TC3 produced better estimation of leaf dry weight and LAI. Testing of the monitoring models with independent dataset indicated that the above spectral indices gave accurate growth estimation, with more reliable estimation from RVI(810,560), GMI and SARVI(MSS).Relation of leaf nitrogen status to grain protein index under different wheat cultivar and growth stages was revealed in this study. Results showed that grain protein character at maturity could be forecasted by plant nitrogen status of pre-maturity, with anthesis as ideal proper stage. Based on the technical route of key spectral parameters-leaf N indices-grain protein contents, total predicting models on grain protein content were constructed by linking the two set of models with leaf N nutrition as intersection, namely monitoring model on leaf nitrogen status with hyperspectral remote sensing and predicting model on grain protein content based on leaf nitrogen status. Testing of the predicting models with independent datasets indicated that the spectral indices of REP_LE, mND705, SDr/SDb, and FD742 at anthesis gave accurate estimation of grain protein contents in wheat, with more reliable estimation from mND705.Based on the biological mechanism of yield formation, relationship of leaf nitrogen status to grain yield was compared among different growth stages under different wheat cultivars across two years. The results showed that there were significant relationships of grain yield to leaf N accumulation and leaf area N index at initial filling, and the sum of leaf N content, leaf N accumulation and leaf area N index from booting to maturity were reliable indices for predicting yield. Based on the technical-route of characteristic spectral parameters-leaf N nutrition-grain yield, predicting models on grain yield were constructed with canopy hyper-spectral parameters at initial filling and cumulative value of key spectral parameters from booting to maturity in wheat by linking the two sets of models with leaf N nutrition as intersection, namely monitoring model on leaf nitrogen status with hyperspectral remote sensing and predicting model on grain yield based on leaf nitrogen status. Testing of the predicting models with independent two-year dataset indicated that the above linked models gave accurate yield estimation.The regression analyses between vegetation indices and plant N accumulation indicated that several key spectral parameters could accurately estimate the changes in plant N status across different growth stages, nitrogen levels and growing seasons, with same spectral parameters for each wheat cultivar. The cumulative value of plant N accumulation from anthesis to specific day were highly correlated with grain N accumulation at corresponding day, and monitoring models on grain N accumulation were constructed with plant N accumulation during filling. Total monitoring models on above-ground N accumulation during filling period were established using canopy hyper-spectral parameters by adding grain N accumulation to plant N accumulation. Tests with other independent dataset showed that several key spectral indices such as SDr/SDb, VOG2, VOG3, RVI(810,560), [(R_750-800)/(R_695-740)]-1 and Dr/Db could be used to predict above-ground nitrogen accumulation at both pre-anthesis and grain filling.