Research On Canopy Hyperspectral Difference And Monitoring Model Inwheat Based On Differnet Soil Textures

Canopy spectral information of crop has been obtained from hyperspectralremote sensing technology. The relationship between crop agriculture parameters andcanopy spectral indices were analyzed, so as to find the sensitive band andcharacteristic indices, on which quantitative relationship wasestablelished further. Thereal-time diagnosis and rapid monitoring of crop growth and nitrogen nutrition couldbe achieved by hyperspectral technology.Based on the hyperspectral information andmeasurement of agronomy parameters, this study investigated the respose differenceof canopy spectral in wheat with two field experiments consisting of three differentvarieties and five nitrogen (N) levels. Several kinds of hyperspectral indices includingdifference spectral indices (DSI), ratio spectral indices (RSI) and normalizeddifference spectral indices (NDSI) with all combinations of two wavebands between350and1050nm were calculated, their relationships to leaf dry weight, LAI, LNCand Chla+b concentration were analyzed, establelished the estimation model. Basedon the spectral monitoring of wheat LNC and grain protein content and grain yield,then prediction model were built. Which provided the reference and theoretical basisfor real-time diagnosis and nitrogen reasonable strategy in wheat under different soiltextures.The main research results were summarized as follows:1The change laws of wheat canopy spectral reflectance were studied under thecondition of three different soil textures (sand, loam and clay), the results ofexperiment showed that there were certain different in the spectra of canopyreflectance under different nitrogen levels and during different growth periods. Theywere0.01to0.64in clay,0.01to0.51in loam,0.01to0.46in sand. The furtheranalysis of the red edge parameters showed the location of minimum chlorophyllabsorption (Lo) presented the red shift and the blue shift with the growth of wheat.The variation range of Lo and the red edge width (Lwidth) were the biggest for sandand the smallest for clay. Red edge area (SDr) showed oppositely.2This study analysed the correlation between leaf dry weight and the spectral oforigina and first derivative in three soil textures, the correlation coefficient reachedabove0.70in the region of574-700nm and720-750nm. Several kinds of hyperspectral indices including difference spectral indices (DSI), ratio spectral indices(RSI) and normalized difference spectral indices (NDSI) with all combinations of twowavebands between350and1050nm were calculated, their relationships to leafweight were analyzed, compared with existing hyperspectral indices. The resultsshowed that RSI (R545,R550) and NDSI(R550,R545) were the best indicators to theintegrated modeling of leaf weight, with the modeling decision coefficient (R2) of0.78and0.77, the predictive determination coefficient (R2) of0.74and0.73respectively, and the root mean square error (RMSE) of0.03.3The higher correlation of LAI to the spectral of origina and first derivative. Onthis basis, new hyperspectral indices including difference spectral indices (DSI), ratiospectral indices (RSI) and normalized difference spectral indices (NDSI) with allcombinations of two wavebands between350and1050nm were calculated, theirrelationships to LAI were analyzed, and compared with existing hyperspectral indices.The results showed that DSI(R770,R755), RSI(R750,R770), NDSI(R770,R750) andDSI(FD750,FD890) were the best indicators to the integrated modeling of LAI, withthe decision coefficient of the modeling and prediction (R2) were higher than0.80,and the root mean square error (RMSE) of0.65,0.57,0.56and0.55.4The correlation for several kinds of hyperspectral indices including differencespectral indices (DSI), ratio spectral indices (RSI) and normalized difference spectralindices (NDSI) with all combinations of two wavebands between350and1050nm toLNC were analyzed, and the estimation models were establelished, then comparedwith the existing hyperspectral indices.DSI(R450,R455), DSI(FD755,FD600),RSI(FD690,FD715) and NDSI(FD710,FD700) were the best indicators to theintegrated modeling of LNC, with the predictive determination coefficient (R2) of0.76,0.79,0.74and0.75. Testing the above better spectral parameters of monitoringmodels with independent sample, the results reconfirmed that they were betterindicators, with the predictive determination coefficient (R2) of0.83,0.76,0.85and0.83respectively, and the root mean square error (RMSE) of0.38,0.54,0.31and0.34respectively.Therefore, DSI(R450,R455), DSI(FD755,FD600),RSI(FD690,FD715)and NDSI(FD710,FD700) could be used to predict wheat LNC asthe effective parameters.5This study analysed the correlation between canopy spectral reflectance andleaf chla+b content, the results showed that the correlation were higher in region of540-700nm and714-754nm, with the correlation coefficient higher than0.75. The new spectral parameter of DSI(R565,R695)and NDSI(FD695,FD735) could be betterestimated the wheat Chla+b in differnet soil textures, with the modeling decisioncoefficient (R2) were higher than0.75, the predictive determination coefficient (R2) of0.88and0.85respectively, and the root mean square error (RMSE) of0.43and.0.48respectively6Relationship of wheat LNC to grain protein concent and grain yield at maturityunder different growth stages was conducted in this study. Results showed that thegrain protein concent at maturity could be forecasted by LNC at anthesis, while thebest correlation between grain yield and LNC at anthesis and10DAA. On this basis,the regression analysis of the best parameters which can predict wheat LNC to grainprotein concent and yield, then the estimation model was establelished. The resultsshowed that the spectral indices of DSI(R450,R455) and NDSI(FD710,FD700) atanthesis gave better estimation model of GPC, with the modeling decision coefficient(R2) of0.63and0.66, the decision coefficient of the modeling and prediction (R2) of0.75and0.76, the root mean square error (RMSE) of0.75and0.78. Further analysedand compared the relationship of parameters between grain yield and LNC at anthesisand10DAA, the results showed that the prediction accuracy was better with anthesisas ideal proper stage. The performance was better based on DSI(R450,R455),DSI(FD755,FD600) and NDSI(FD710,FD700), the decision coefficient of themodeling and prediction (R2) of0.82,0.93and0.81, and the root mean square error(RMSE) of440.61,499.08and463.5.This study showed that using these spectralparameters could predict grain protein content and yield efficiently.