[Object] To setup the hyperspectral sensing models for estimating SPAD value of cotton leaves under waterlogging stress.[Method] Irrigation and drainage controllable plots were introduced to simulate the waterlogging stress treatment in the flowering and boll forming stage,during which the change characteristics of the cotton leaf spectral reflectance and SPAD value were observed after 1 d,3 d,6 d,9 d waterlogging,respectively.To find out the hyperspectral sensing models for estimating SPAD value of cotton leaves under waterlogging stress,the correlation and regression relationships between SPAD value and spectrum parameters were analyzed.[Result] (1) The SPAD value of the fourth cotton leaf from the top was significantly lower than control when suffers from waterlogging for 3 d,when waterlogged 9 d the SPAD value decreased by around 15％ compared with the control.(2) The cotton suffering from waterlogged damage in the flowering and boll forming stage caused the reflection peak in green light wave band became steep,while the near infrared spectral reflectance increased,and caused the reduction of red absorption and red edge position "blue shifts",the red edge position drifts towards short wave with 4 ～5 nm when suffers from waterlogging for 9 d.With increase of the waterlogged days,the red edge slope and red edge area increased with a maximum value at 6 d of waterlogging,meanwhile,the skewness and kurtosis of red edge increased.(3) After waterlogging,the SPAD value of the fourth cotton leaf from the top (chlorophyll content) had a remarkable correlation with red edge slope(Dr),red edge position(λr),green peak reflection(Rg),green peak position(λg),red well position(λo),blue edge area(SDb),yellow edge skewness(Sy),yellow edge kurtosis(Ky),red edge skewness(Sr),red edge kurtosis(Kr),etc.An experience linear,polynomial and exponential models for estimating SPAD value had been built through using the Sy,Sr,Kr as independent variables,respectively,their determination coefficient (R2) were greater than 0.9,and the root mean square error (RMSE) were less than 1;and an experience binary linear regression equation for estimating SPAD value had been built through multivariate regression using the λg,SDr/SDb (VI3),Sb,Sy,Kyas independent variables,the R2 was as high as 0.973,and the RMSEwas 0.393.[Conclusion] The model can be remote sensing model used as estimating leaf SPAD of cotton value under waterlogging stress.%[目的]建立淹水胁迫下棉花叶片SPAD的高光谱模型.[方法]利用灌排可控的试验田在棉花花铃期模拟淹水处理,分析受涝1d、3d、6d、9d后棉花叶片光谱反射率、SPAD值的变化特征;并对淹水胁迫后棉花叶片高光谱特征参数与SPAD值进行相关与回归分析,探寻用于估算淹水胁迫下棉花叶片SPAD变化的高光谱模型.[结果](1)受涝3d时棉花倒4叶SPAD值就显著低于对照,到受涝9d时SPAD值比对照减少15％左右.(2)棉花花铃期受涝后倒4叶绿光波段反射峰变陡,近红外短波段的反射率升高.花铃期受涝使棉花倒4叶红边位置“蓝移”,涝害持续9d时红边位置向短波方向移动了4～5 nm,红边幅度和红边面积呈先增大后减小趋势,在受涝6d时达到最大,红边偏度和红边峰度增大.(3)涝后棉花叶片SPAD值与红边幅值(Dr)、红边位置(λr)、绿峰反射率(Rg)、绿峰位置(λg)、红谷位置(λo)、蓝边面积(SDb)、黄边偏度(Sy)、黄边峰度(Ky)、红边偏度(Sr)、红边峰度(Kr)等光谱特征参数极显著相关;分别以Sy、Sr、Kr为自变量的一元线性、多项式和指数模型估算SPAD值较优,其决定系数(R2)均大于0.9,均方根误差(RMSE)均小于1;多元逐步回归分析发现以λg、SDr/SDb (VI3)、Sb、Ky、Ky为自变量的多元线性模型估算SPAD值较优,R2高达0.973,RMSE为0.393.[结论]该模型可以作为估算淹水胁迫棉花叶片SPAD值的遥感模型.
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