Monitoring Growth Characters Of Different Canopy Height With Hyperspectral Remote Sensing In Rice

As an important crop in China and even in the world,the yield and quality of rice are not only related to livelihood issues,and an improtant basis for food security.With the growth stage change,different growth parameters showed obvious spatial and temporal distribution characteristics in rice at the canopy level.In recent years,the rapid development of quantitative remote sensing inversion technology of crop biochemeical components based on hyperspectral reflectance has made it play an important role in monitoring crop growth,managing fertilizer and water,and estimation of productivity.However,existing inversion methods of growth parameters(leaf area index,leaf nitrogen concentration,leaf dry mater et al.)took the canopy as a uniformity whole and ignored the heterogeneity of the vertical distribution of growth parameters within the canopy,which not only affected the estimation accuracy of the canopy level growth parameters,but also failed to accurately obtain the growth potential and nutrition status of functional leaf layers at different spatial positions of the canopy.Therefore,quantitative study of the spatial and temporal distribution characteristics of different growth parameters and their effects on canopy spectral reflectance is significant for precise crop management.Thus,paddy field experiments with different treatments were carried out in different years.The spectral reflectance of the canopy before and after removing the panicles and different leaf layers were obtained,the spatial and temporal distribution characteristics of different growth parameters in rice canopy were quantified,and the effects of different components such as panicle,stem,leaf,and soil background on the spectral reflectance of rice canopy were studied.Thus,an accurate remote sensing inversion model of rice growth parameters of different canopy heights and nitrogen nutrition diagnostic indicators suitable for remote sensing monitoring were constructed.Furthermore,it provides a theoretical basis and technical support for the accurate diagnosis of crop growth status.First of all,based on the field experiment observation data with different rice cultivars,nitrogen application levels,and transplanting densities for 3 consecutive years from 2014 to 2016,the spatial and temporal variation of different growth parameters in rice canopy were systematically analyzed,the relationship between growth parameters at different leaf layer level and canopy level was studied,and the variation of reflectance of rice canopy and vegetation indices(VIs)before and after removal of panicles and leaf layers were investigated.The results showed that the vertical distribution characteristics of different growth parameters in the canopy were different.Within the rice canopy,leaf area index(LAI),leaf dry weight(LDW),leaf nitrogen accumulation(LNA),and aboveground biomass(AGB)generally increased first and then decreased from the top to the bottom of the canopy,leaf nitrogen concentration(LNC)showed a trend of gradual decline.Specific leaf nitrogen(SLN)showed a trend of decreasing first and then increasing.Moreover,different plant type characteristics significantly affected the vertical distribution trend of LAI,LDW,and AGB within the rice canopy.At the same time,the correlation between the growth parameters of the upper leaf layers(L1,L2,AL2)and the canopy level was significantly higher than that of the lower leaf layers,which indicated the growth parameters of the middle and upper part could better represent these of the canopy level.After the removal of different proportions of leaf layers and panicles,the reflectance of the canopy changed significantly.The variation increased with the increase of wavelength in the visible light range,especially at the red band(675 nm),then presented a certain downward trend and tended to be stable in the near-infrared band.Therefore,the vegetation indices including visible band and sensitivity to biomass and chlorophyll,had apparent changes.With the increase of canopy depth,the estimation accuracy of VIs for structural parameters such as AGB,LAI,LDW of single leaf layer level showed a trend of first increasing and then decreasing,and the accuracy was the highest for estimation of these structural parameters in L2 and AL2.However,the accuracy of nitrogen nutrition parameters,such as LNC and LNA,decreased with the increase of canopy depth,and the accuracy of the top leaf layer was the highest.The estimation accuracy of the growth parameters in the middle and upper leaf layer level(L2,AL2)was higher than in the canopy level by using vegetation indices.Moreover,the accuracy for estimating the growth parameters of the entire canopy decreased significantly after heading stages.Then,we took LNC as an example to construct the LNC vertical distribution model(LNCLi=a*exp(bHr))of rice canopy based on the relative canopy height(Hr),and combined with the hyperspectral reflectance of rice canopy,studied the quantitative relationship between different VIs and the parameters of the LNC vertical distribution model.We also compared the following three methods for estimating the LNC in different leaf layers in rice canopy:(1)estimating the LNCCanopy by VIs and then estimating the LNCLi based on the relationship between the LNCLi and LNCcanopy;(2)estimating the LNC in any leaf layer of the rice canopy by VIs,inputting the result into the LNC vertical distribution model to obtain the parameters of the model(a and b),and then estimating the LNCLi using the LNC vertical distribution model;(3)estimating the model parameters by using VIs directly and then estimating the LNCLi by the LNC vertical distribution model.The results showed that the LNC in the bottom of rice canopy was more susceptible to different N rates,and changes in the LNC with the relative canopy height could be simulated by an exponential model.R705/(R717+R491)(R2=0.763)and the renormalized difference vegetation index(RDVI)(1340,730)(R2=0.747)were able to estimate the parameter "a" of the LNC vertical distribution model in indica rice and japonica rice,respectively.In addition,method(2)was the best choice for estimating the LNCLi(R2=0.768,0.700,0.623,and 0.549 for LNCL1,LNCL2,LNCL3,and LNCL4,respectively).These results provide technical support for the rapid,accurate and non-destructive identification of the spatial and temporal distribution of nitrogen in rice canopies.At the same time,based on the vertical distribution characteristics of nitrogen and AGB in the rice canopy,the effect of heterogeneity of vertical distribution of growth parameters on nitrogen nutrition index(NNI)was further analyzed.For the first time,critical N dilution curves of different leaf layers were constructed based on aboveground biomass(AGB,t hm-2).The estimation accuracy of different remote sensing methods(direct inversion of the vegetation index;indirect inversion of the biomass and plant nitrogen concentration,and indirect inversion of the critical nitrogen content(Nc,%))were subsequently compared in estimations of different NNI.The results revealed that the lower canopy was more susceptible to N transfer than the upper canopy,while the upper and middle leaf layers(L2 and AL2)were found to be optimal for remote sensing analyses of N status in rice.The critical nitrogen dilution curve models of L2 and AL2 were NcL2=5.1824AGB-0.427 and NcAL2=6.0487AGB-0.484,respectively,with NNI ranging from 0.45-1.27(NNIL2)and 0.47-1.36(NNIAL2),respectively.Compared with the traditional canopy level NNI calculation method,the NNI(NNIL2 and NNIAL2)calculated by the critical nitrogen concentration curve of the upper leaf layer that constructed in this study could more accurately indicate the current nitrogen nutrition status of rice.NDVI(1113,527)and CI(1113,738)were the optimal vegetation indices for direct estimations of NNIL2 and NNIAL2 with coefficients of determination(R2)of 0.58 and 0.71,respectively,higher than the two indirect NNI estimation methods.Overall,these findings suggest that NNIAL2 is the optimal remote sensing indicator of N nutrition in rice.Moreover,the emergence of rice panicle substantially changes the spectral reflectance of rice canopy and,as a result,decreases the accuracy of leaf area index(LAI)that was derived from vegetation indices(VIs).The spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated.A rice"panicle line"—graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles.Subsequently,a panicle-adjusted renormalized difference vegetation index(PRDVI)that was based on the "panicle line" and the renormalized difference vegetation index(RDVI)was developed to reduce the effects of rice panicles and background.The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region.The red band(670 nm)was the most affected by panicles,while the red-edge bands(720-740 nm)were less affected.In addition,a combination of near-infrared and red-edge bands was for the one that best predicted LAI,and the difference vegetation index(DI)(976,733)performed the best,although it had relatively low estimation accuracy(R2=0.60,RMSE=1.41 m2 m-2).From these findings,correcting the near-infrared band in the RDVI by the panicle adjustment factor(θ)developed the PRDVI,which was obtained while using the "panicle line",and the less-affected red-edge band replaced the red band.Verification data from an unmanned aerial vehicle(UAV)showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI(R2=0.77;RMSE=1.01 m2 m-2)than other VIs during the post-heading stage.Moreover,of all the assessed VIs,the PRDVI yielded the highest R2(0.71)over the entire growth period,with an RMSE of 1.31(m2 m-2).These results suggest that the PRDVI is an efficient and suitable LAI estimation index.