盐分对“棉花—土壤”系统水盐变化的影响及监测研究
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摘要
盐渍化是影响土地生产力的重要障碍因子,严重制约了粮食生产和农业可持续发展。棉花是盐碱地种植的先锋作物,随着耕地面积的减少,棉花种植逐渐向盐碱地集中,大力提高盐碱地棉花产量已经成为棉花生产的主攻方向。因此研究土壤盐分对棉花水、盐的影响及其监测研究是当今农业发发展的重要研究课题之一。本研究以耐盐品种中棉所44和盐敏感性品种苏棉12号为研究对象,基于2008-2009年在南京农业大学牌楼试验站地行的土壤盐分盆栽试验,综合运用红外测温技术、光谱辐射技术和电磁波谱技术,构建了不同土壤盐分下棉花水分胁迫指数模型,建立了基于光谱反射特征的棉花含水量、含盐量和土壤电导率监测模型。同时,研究不同电磁测量频率下土壤盐分对土壤介电特性的影响,确定土壤水、盐含量测量时与土壤离子电导相关的合理测量频率段,建立土壤介电常数对土壤水、盐含量的反演模型,为盐渍化土壤条件下棉花水、盐状况的实时监测和精确诊断提供有效技术支撑。
主要研究结果如下：
1.盐分条件下棉花水分胁迫指数模型的构建及相关生理特性的变化
随盐分水平的升高,棉花功能叶的蒸腾速率、含水量和净光合速率呈明显的下降趋势,以1.25dS m-1盐分处理作为充分灌水(即无水分胁迫)建立了棉花水分胁迫指数模型下基线方程,以此构建了不同土壤盐分下棉花水分胁迫指数模型。综合分析棉花水分胁迫指数与叶片含水量和净光合速率的关系,认为水分胁迫指数模型可以很好的反映棉花的受胁迫程度。
2.基于高光谱参数的盐土棉田棉花功能叶含水量监测模型研究
棉花功能叶含水量的敏感波段主要位于近红外和中红外波段,基于上述敏感波段,构建了最佳的(ratio spectral indices) RSI和(Normalized difference spectrum index)NDSI,以此建立了棉花功能叶含盐量的线性函数、幂函数和指数函数监测方程,比较发现,以1650/2220nm ratio, NDSI1(R1222, R2264)、NDSI2(R1347, R2307)、RSI1(R2264, R1321)和RSI2(R2307, R1347)为自变量构建的RWC和EWT线性回归方程的决定系数最大,标准误较小。利用2009年试验数据对所建立的模型进行检验发现：以NDSI2(R1347,R2307)和RSI2(R2307, R1347)为自变量构建的EWT监测模型的决定系数均较大,根均方差较小,拟合度均优于RWC监测模型,因此,以NDSI2(R1347, R2307)和RSI2(R2307, R1347)为自变量构建的EWT监测模型可以用来实时监测盐碱条件下棉花功能叶的水分状况。
3.基于高光谱参数的盐土棉田棉花功能叶含盐量监测模型研究
随土壤盐分水平的升高,棉花功能叶Na+,Cl-和SO42-含量逐渐上升,而K+和Ca2+呈相反的趋势。基于敏感波段,构建了最佳的RVI和NDSI,以此建立了棉花功能叶含盐量的线性函数、幂函数和指数函数监测方程,比较而言,以RSI(R2306,R1347)、RSI(R2276, R1343)、RSI(R2306,R1350)为自变量构建的K+,Na+,Ca2+线性回归方程、以RSI(R2202, R1361)、 RSI(R2317, R1154)为自变量构建的Mg2+, SO42幂函数方程、以RSI(R2264, R,335)为自变量构建的C1-指数函数方程、以NDSI(R1340, R2306)、NDSI(R1346, R2276)、NDSI(R1380,R2307)、 NDSI(R1200, R2211)、NDSI(R1154, R2317)为自变量构建的K+, Na+, Ca2+, Mg2+, SO42线性回归方程和以NDSI(R1300, R2250)为自变量构建的C1-幂函数方程的决定系数最大,标准误较小。利用2009年试验数据对所建立的模型进行检验发现：以RSI(R2306,R1347)、 RSI(R2276, R1343)、 RSI(R2306, R1350)为自变量构建的K+,Na+,Ca2+线性方程的监测模型、以RSI(R2202,R1361)、RSI(R2317, R1154)为自变量构建的Mg2+, SO42幂函数方程的监测模型和以RSI(R2264, R1335)为自变量构建的Cl-指数方程的监测模型的决定系数均大于0.69,根均方差较小,拟合度均优于以NDSI为自变量构建的的监测模型,因此,以RSI为自变量的监测模型可以用来实时监测盐碱条件下棉花功能叶的盐分状况。
4.基于棉花功能叶高光谱参数的土壤电导率监测模拟
棉花功能叶光谱反射率在近红外和中红外区域均随土壤盐分水平的升高而升高；以敏感波段1350nm和2307nm构建的归一化光谱指数NDSI(R1350, R2307)与土壤电导率的决定系数最高,基于此构建了基于NDSI(R1350, R2307)勺棉田土壤EC监测模型：EC=-42.899NDSI(R1350, R2307)+27.338;在光谱微分参数中,以TM影像的第5个波段(TM5-SWIR)与棉田土壤EC的决定系数最高,构建了基于TM5-SWIR的棉田土壤EC监测模型：EC=0.0574TM5-SWIR2-2.5928TM5-SWIR+30.021。模型检验的结果表明：分别以NDSI(R,350, R2307)和TM5-SWIR为自变量的监测模型的预测精度均较高,分别为0.887和0.8136,根均方差均较小,分别为1.09和1.29ds·m-1,表明利用棉花功能叶NDSI(R1350, R2307)和TM5-SWIR这两个高光谱参数均能较好地监测棉田土壤电导率。
5.基于土壤介电常数的盐土棉田土壤含水量、含盐量变化的反演研究
随土壤盐分水平的升高,土壤中可溶性Na+、K+、Ca2+、Mg2+、Cl-、SO42-和HCO3-含量上升,随生育期的推迟,同一盐分水平下土壤中可溶性Na+、K+、Ca2+、Mg2+、 C-、SO42-和HCO3含量呈下降趋势,综合分析土壤介电常数虚部与土壤体电导率土壤体电导率与土壤溶液电导率、土壤溶液电导率与土壤的离子浓度的一系列关系,最终建立了土壤介电常数的虚部模型。进一步利用2009年试验数据对土壤含水量、含盐量进行反演发现,Dobson的实部模型的R2较高(0.7428)、根均方差(RMSE)较小(1.16%),可以很好的反演土壤的含水量状况。在对含盐量进行反演时,频率较低(f<3Ghz)时,土壤Sc、Cl-、Ca2+的R2较高,分别为0.9417,0.6852,0.7965,RMSE较小分别为0.13g.kg-1,0.34g.kg-1,0.09g.kg-1;频率较高(f≥3Ghz)时,土壤Sc、Cl-、Ca2+、Na+的反演结果可以看出,Sc的检测结果最好,Na+次之,而Cl-、Ca2+的检验全结果较差,其R2及RMSE分别为0.8655,0.8408,0.8117,0.8217及0.25g.kg-1,0.15g.kg-1,0.02g.kg-1,0.21g.kg-1.总体上,模型检验结果较好,表明模型可以很好的对土壤水分、盐离子状况进行反演。
Salinity is considered to be one of the major limiting factors for plant growth and agricultural productivity. Cotton is one of the most important economic crops in China, which has been reported to be salt tolerant. With the reduction of field area, more and more cotton was planted in saline soil. Thus, study the effect and monitoring of soil salinity on water and salinity content is one of important research in development of agriculture. The objective of this study was to determine the crop water stress index, cotton water, salinity content, soil electrical conductivity monitoring model based on hyperspectral reflectance, ascertain the suitable frequency for monitoring soil water and salinity content and establish the imaginary part of dielectric constant models through comprehensive use of the infrared temperature measurement technology, spectral radiation technology and electromagnetic spectrum technology, on the basis of multiple pot experiments under varied soil salinity levels with Sumian12(salinity-sensitivity) and CCRI-44(salinity-tolerance) at Pailou experimental station of Nanjing Agricultural University, in order to provide the technical supports for real-time estimation and precision diagnose of plant water and salinity content in cotton under saline conditions.
The main results were as follows:
1. The construction of the cotton water stress index and changes of related physiological characteristics under salinity condition
Soil salinity significantly reduced the transpiration rate, water content and net photosynthetic rate of cotton functional leaves. The lower equation of the cotton water stress index was set up in1.25dS m-1salinity rate (well watered), and the cotton water stress index based on the above lower equation under different salinity rates was constructed. Comprehensive analysis the relationship between cotton water stress index and leaf water content and net photosynthetic rate revealed that the cotton water stress index is a good indicator to detect cotton water stress in salinity field.
2. Exploring hyperspectral bands and estimation indices for leaf water content of cotton (Gossypium hirsutum L.) in saline soil
The sensitive spectral bands for EWT and RWC occurred mainly within the near infrared (NIR) and short-wave infrared (SWIR) ranges. The best spectral indices for estimating leaf water content (RWC and EWT) in cotton were NDSI1(R1222, R2264), NDSI2(R1347, R2307), RSI1(R2264, R1321), RSI2(R2307, R1347) and1650/2220nm ratio, and the linear regression models based on the above spectral indices were identified as the best equations for the effective estimation of EWT and RWC in cotton. From testing of the derived equations, the model for EWT estimation based on the NDSI2(R1347, R2307) and RSI2(R2307, R1347) gave R2over0.85with more satisfactory performance than the spectral indices1650/2220nm ratio and the RWC models based on NDSI1(R1222, R2264), RSI1(R2264, R1321) in saline soil. The present spectral parameters of NDSI2(R1347, R2307) and RSI2(R2307, R1347) can be used for monitoring plant water stress in cotton cultivated in saline soil.
3. Monitoring cotton(Gossypium hirsutum L.) leaf ion content in saline soil with hyperspectral reflectance
The Na+, Cl-, and SO42-content in functional cotton leaves increased with the soil salinity rates increasing. In contrast, K+and Ca2+decreased at the same growth stage. The best spectral indices for estimating cotton leaf ion content were found to be NDSI (R1340, R2306) RSI(R2306, R1347); NDSI (R,346, R2276), RSI (R2276, R1343); NDSI (R1380, R2307), RSI (R2306, R1350); NDSI (R1200, R2211), RSI (R2202, R1361); NDSI (R1300, R2250), RSI (R2264, R1335); and NDSI (R1154, R2317), RSI (R2317, R1154) for K+, Na+, Ca2+, Mg2+, Cl-, and SO42-, respectively, and the linear, power and exponential regression models based on the above spectral indices were formulated. Among them, the linear equations based on RSI(R2306, R,347)、RSI(R2276, R1343)、RSI(R2306, R1350), NDSI(R1340,R2306)、NDSI(R1346,R2276)、 NDSI(R1380,R2307)、NDSI(R1200,R2211)、NDSI(R1154、R2317), the power equations based on RSI(R2202, R1361)、RSI(R2317, R1154), NDSI(R1300,R2250) and exponential equations based on RSI(R2264, R1335), for K+, Na+, Ca2+, Mg2+, Cl-, and SO42-, respectively, can well estimated the ion content of cotton under different levels of salinity, After testing of the derived equations, the high fit between the measured and estimated values indicate that the present models based on RSI is better than the models based on NDSI, and could be used for the reliable estimation of leaf salinity in cotton plants with R2greater than0.69under different saline conditions.
4. Monitoring simulation of soil electrical conductivity based on hyperspectral parameter of cotton(Gossypium hirsutum L.) functional leaves.
During near-infrared and middle-infrared spectral bands, with soil salinity rate increased, the spectral reflectance of cotton functional leaves increased, and spectral parameter Normalized difference spectrum index (NDSI) based on1350nm and2307nm correlated to soil electrical conductivity well, soil EC monitoring model was constructed as EC=-42.899NDSI (R1350, R2307)+27.338, with vegetation index NDSI (R1350, R2307) as independent variable. Vegetation index Thematic Mapper5(TM5-SWIR) was most correlation to soil EC during all derivative spectral parameters, so soil EC monitoring model was constructed as EC=0.0574(TM5-SWIR)2-2.5928(TM5-SWIR)+30.021, with vegetation index TM5-SWIR as independent variable. Take2009experiment data to test soil conductivity models, and show that with the predicted values of soil EC by the two models were very consistent with the observed values, with determination coefficient of0.887,0.8136,and root mean square error (RMSE) of1.09ds-m-1,1.29ds-m-1. The experiment shows that soil EC in saline cotton field can be effectively monitored by two spectral parameters of NDSI (R1350, R2307) and TM5-SWIR.
5. Monitoring simulation of soil electrical conductivity based on hyperspectral parameter of cotton (Gossypium hirsutum L.) functional leaves.
As the soil salinity increasing, the Na+、K+、Ca2+、Mg2+、Cl-、SO42-and HCO3-content increased. At the same salinity levels, the Na、K+、Ca2+、Mg2+、Cl-、SO42-and HCO3-content declined as the postpone of the growth stage. The imaginary part of soil dielectric constant model was developed through the relation of imaginary part of dielectric constant (ε"), soil bulk conductivity, conductivity of soil solution, and soil ion content in mixed-salinity soil to retrieve soil ion content. After testing with the data of2009, the results showed that the real part of dielectric constant model based on Dobson had high values of R2, low values of RMSE, and can be used to retrieve soil water status well. The inversed values of total concentration of salt (Sc), Cl-, Ca2+by using the model were very consistent with the observed values, with high R2values of0.9417,0.6852,0.7965, and root mean square error (RMSE) lower than0.34g/kg,0.09g/kg and0.13g/kg, respectively. However, the RMSE of total concentration of salt (Sc), Cl-, Na+were small at high frequencies (C-band), with R2values of0.8655,0.8408,0.8117,0.8217, and RMSE value of0.25g.kg-1,0.15g.kg-1,0.02g.kg-1,0.21g.kg-1respectively. The high fit between the measured and invered values indicate that the present models could be used for the reliable estimation of soil salinity in cotton field under different saline conditions.
主要研究结果如下：
1.盐分条件下棉花水分胁迫指数模型的构建及相关生理特性的变化
随盐分水平的升高,棉花功能叶的蒸腾速率、含水量和净光合速率呈明显的下降趋势,以1.25dS m-1盐分处理作为充分灌水(即无水分胁迫)建立了棉花水分胁迫指数模型下基线方程,以此构建了不同土壤盐分下棉花水分胁迫指数模型。综合分析棉花水分胁迫指数与叶片含水量和净光合速率的关系,认为水分胁迫指数模型可以很好的反映棉花的受胁迫程度。
2.基于高光谱参数的盐土棉田棉花功能叶含水量监测模型研究
棉花功能叶含水量的敏感波段主要位于近红外和中红外波段,基于上述敏感波段,构建了最佳的(ratio spectral indices) RSI和(Normalized difference spectrum index)NDSI,以此建立了棉花功能叶含盐量的线性函数、幂函数和指数函数监测方程,比较发现,以1650/2220nm ratio, NDSI1(R1222, R2264)、NDSI2(R1347, R2307)、RSI1(R2264, R1321)和RSI2(R2307, R1347)为自变量构建的RWC和EWT线性回归方程的决定系数最大,标准误较小。利用2009年试验数据对所建立的模型进行检验发现：以NDSI2(R1347,R2307)和RSI2(R2307, R1347)为自变量构建的EWT监测模型的决定系数均较大,根均方差较小,拟合度均优于RWC监测模型,因此,以NDSI2(R1347, R2307)和RSI2(R2307, R1347)为自变量构建的EWT监测模型可以用来实时监测盐碱条件下棉花功能叶的水分状况。
3.基于高光谱参数的盐土棉田棉花功能叶含盐量监测模型研究
随土壤盐分水平的升高,棉花功能叶Na+,Cl-和SO42-含量逐渐上升,而K+和Ca2+呈相反的趋势。基于敏感波段,构建了最佳的RVI和NDSI,以此建立了棉花功能叶含盐量的线性函数、幂函数和指数函数监测方程,比较而言,以RSI(R2306,R1347)、RSI(R2276, R1343)、RSI(R2306,R1350)为自变量构建的K+,Na+,Ca2+线性回归方程、以RSI(R2202, R1361)、 RSI(R2317, R1154)为自变量构建的Mg2+, SO42幂函数方程、以RSI(R2264, R,335)为自变量构建的C1-指数函数方程、以NDSI(R1340, R2306)、NDSI(R1346, R2276)、NDSI(R1380,R2307)、 NDSI(R1200, R2211)、NDSI(R1154, R2317)为自变量构建的K+, Na+, Ca2+, Mg2+, SO42线性回归方程和以NDSI(R1300, R2250)为自变量构建的C1-幂函数方程的决定系数最大,标准误较小。利用2009年试验数据对所建立的模型进行检验发现：以RSI(R2306,R1347)、 RSI(R2276, R1343)、 RSI(R2306, R1350)为自变量构建的K+,Na+,Ca2+线性方程的监测模型、以RSI(R2202,R1361)、RSI(R2317, R1154)为自变量构建的Mg2+, SO42幂函数方程的监测模型和以RSI(R2264, R1335)为自变量构建的Cl-指数方程的监测模型的决定系数均大于0.69,根均方差较小,拟合度均优于以NDSI为自变量构建的的监测模型,因此,以RSI为自变量的监测模型可以用来实时监测盐碱条件下棉花功能叶的盐分状况。
4.基于棉花功能叶高光谱参数的土壤电导率监测模拟
棉花功能叶光谱反射率在近红外和中红外区域均随土壤盐分水平的升高而升高；以敏感波段1350nm和2307nm构建的归一化光谱指数NDSI(R1350, R2307)与土壤电导率的决定系数最高,基于此构建了基于NDSI(R1350, R2307)勺棉田土壤EC监测模型：EC=-42.899NDSI(R1350, R2307)+27.338;在光谱微分参数中,以TM影像的第5个波段(TM5-SWIR)与棉田土壤EC的决定系数最高,构建了基于TM5-SWIR的棉田土壤EC监测模型：EC=0.0574TM5-SWIR2-2.5928TM5-SWIR+30.021。模型检验的结果表明：分别以NDSI(R,350, R2307)和TM5-SWIR为自变量的监测模型的预测精度均较高,分别为0.887和0.8136,根均方差均较小,分别为1.09和1.29ds·m-1,表明利用棉花功能叶NDSI(R1350, R2307)和TM5-SWIR这两个高光谱参数均能较好地监测棉田土壤电导率。
5.基于土壤介电常数的盐土棉田土壤含水量、含盐量变化的反演研究
随土壤盐分水平的升高,土壤中可溶性Na+、K+、Ca2+、Mg2+、Cl-、SO42-和HCO3-含量上升,随生育期的推迟,同一盐分水平下土壤中可溶性Na+、K+、Ca2+、Mg2+、 C-、SO42-和HCO3含量呈下降趋势,综合分析土壤介电常数虚部与土壤体电导率土壤体电导率与土壤溶液电导率、土壤溶液电导率与土壤的离子浓度的一系列关系,最终建立了土壤介电常数的虚部模型。进一步利用2009年试验数据对土壤含水量、含盐量进行反演发现,Dobson的实部模型的R2较高(0.7428)、根均方差(RMSE)较小(1.16%),可以很好的反演土壤的含水量状况。在对含盐量进行反演时,频率较低(f<3Ghz)时,土壤Sc、Cl-、Ca2+的R2较高,分别为0.9417,0.6852,0.7965,RMSE较小分别为0.13g.kg-1,0.34g.kg-1,0.09g.kg-1;频率较高(f≥3Ghz)时,土壤Sc、Cl-、Ca2+、Na+的反演结果可以看出,Sc的检测结果最好,Na+次之,而Cl-、Ca2+的检验全结果较差,其R2及RMSE分别为0.8655,0.8408,0.8117,0.8217及0.25g.kg-1,0.15g.kg-1,0.02g.kg-1,0.21g.kg-1.总体上,模型检验结果较好,表明模型可以很好的对土壤水分、盐离子状况进行反演。
Salinity is considered to be one of the major limiting factors for plant growth and agricultural productivity. Cotton is one of the most important economic crops in China, which has been reported to be salt tolerant. With the reduction of field area, more and more cotton was planted in saline soil. Thus, study the effect and monitoring of soil salinity on water and salinity content is one of important research in development of agriculture. The objective of this study was to determine the crop water stress index, cotton water, salinity content, soil electrical conductivity monitoring model based on hyperspectral reflectance, ascertain the suitable frequency for monitoring soil water and salinity content and establish the imaginary part of dielectric constant models through comprehensive use of the infrared temperature measurement technology, spectral radiation technology and electromagnetic spectrum technology, on the basis of multiple pot experiments under varied soil salinity levels with Sumian12(salinity-sensitivity) and CCRI-44(salinity-tolerance) at Pailou experimental station of Nanjing Agricultural University, in order to provide the technical supports for real-time estimation and precision diagnose of plant water and salinity content in cotton under saline conditions.
The main results were as follows:
1. The construction of the cotton water stress index and changes of related physiological characteristics under salinity condition
Soil salinity significantly reduced the transpiration rate, water content and net photosynthetic rate of cotton functional leaves. The lower equation of the cotton water stress index was set up in1.25dS m-1salinity rate (well watered), and the cotton water stress index based on the above lower equation under different salinity rates was constructed. Comprehensive analysis the relationship between cotton water stress index and leaf water content and net photosynthetic rate revealed that the cotton water stress index is a good indicator to detect cotton water stress in salinity field.
2. Exploring hyperspectral bands and estimation indices for leaf water content of cotton (Gossypium hirsutum L.) in saline soil
The sensitive spectral bands for EWT and RWC occurred mainly within the near infrared (NIR) and short-wave infrared (SWIR) ranges. The best spectral indices for estimating leaf water content (RWC and EWT) in cotton were NDSI1(R1222, R2264), NDSI2(R1347, R2307), RSI1(R2264, R1321), RSI2(R2307, R1347) and1650/2220nm ratio, and the linear regression models based on the above spectral indices were identified as the best equations for the effective estimation of EWT and RWC in cotton. From testing of the derived equations, the model for EWT estimation based on the NDSI2(R1347, R2307) and RSI2(R2307, R1347) gave R2over0.85with more satisfactory performance than the spectral indices1650/2220nm ratio and the RWC models based on NDSI1(R1222, R2264), RSI1(R2264, R1321) in saline soil. The present spectral parameters of NDSI2(R1347, R2307) and RSI2(R2307, R1347) can be used for monitoring plant water stress in cotton cultivated in saline soil.
3. Monitoring cotton(Gossypium hirsutum L.) leaf ion content in saline soil with hyperspectral reflectance
The Na+, Cl-, and SO42-content in functional cotton leaves increased with the soil salinity rates increasing. In contrast, K+and Ca2+decreased at the same growth stage. The best spectral indices for estimating cotton leaf ion content were found to be NDSI (R1340, R2306) RSI(R2306, R1347); NDSI (R,346, R2276), RSI (R2276, R1343); NDSI (R1380, R2307), RSI (R2306, R1350); NDSI (R1200, R2211), RSI (R2202, R1361); NDSI (R1300, R2250), RSI (R2264, R1335); and NDSI (R1154, R2317), RSI (R2317, R1154) for K+, Na+, Ca2+, Mg2+, Cl-, and SO42-, respectively, and the linear, power and exponential regression models based on the above spectral indices were formulated. Among them, the linear equations based on RSI(R2306, R,347)、RSI(R2276, R1343)、RSI(R2306, R1350), NDSI(R1340,R2306)、NDSI(R1346,R2276)、 NDSI(R1380,R2307)、NDSI(R1200,R2211)、NDSI(R1154、R2317), the power equations based on RSI(R2202, R1361)、RSI(R2317, R1154), NDSI(R1300,R2250) and exponential equations based on RSI(R2264, R1335), for K+, Na+, Ca2+, Mg2+, Cl-, and SO42-, respectively, can well estimated the ion content of cotton under different levels of salinity, After testing of the derived equations, the high fit between the measured and estimated values indicate that the present models based on RSI is better than the models based on NDSI, and could be used for the reliable estimation of leaf salinity in cotton plants with R2greater than0.69under different saline conditions.
4. Monitoring simulation of soil electrical conductivity based on hyperspectral parameter of cotton(Gossypium hirsutum L.) functional leaves.
During near-infrared and middle-infrared spectral bands, with soil salinity rate increased, the spectral reflectance of cotton functional leaves increased, and spectral parameter Normalized difference spectrum index (NDSI) based on1350nm and2307nm correlated to soil electrical conductivity well, soil EC monitoring model was constructed as EC=-42.899NDSI (R1350, R2307)+27.338, with vegetation index NDSI (R1350, R2307) as independent variable. Vegetation index Thematic Mapper5(TM5-SWIR) was most correlation to soil EC during all derivative spectral parameters, so soil EC monitoring model was constructed as EC=0.0574(TM5-SWIR)2-2.5928(TM5-SWIR)+30.021, with vegetation index TM5-SWIR as independent variable. Take2009experiment data to test soil conductivity models, and show that with the predicted values of soil EC by the two models were very consistent with the observed values, with determination coefficient of0.887,0.8136,and root mean square error (RMSE) of1.09ds-m-1,1.29ds-m-1. The experiment shows that soil EC in saline cotton field can be effectively monitored by two spectral parameters of NDSI (R1350, R2307) and TM5-SWIR.
5. Monitoring simulation of soil electrical conductivity based on hyperspectral parameter of cotton (Gossypium hirsutum L.) functional leaves.
As the soil salinity increasing, the Na+、K+、Ca2+、Mg2+、Cl-、SO42-and HCO3-content increased. At the same salinity levels, the Na、K+、Ca2+、Mg2+、Cl-、SO42-and HCO3-content declined as the postpone of the growth stage. The imaginary part of soil dielectric constant model was developed through the relation of imaginary part of dielectric constant (ε"), soil bulk conductivity, conductivity of soil solution, and soil ion content in mixed-salinity soil to retrieve soil ion content. After testing with the data of2009, the results showed that the real part of dielectric constant model based on Dobson had high values of R2, low values of RMSE, and can be used to retrieve soil water status well. The inversed values of total concentration of salt (Sc), Cl-, Ca2+by using the model were very consistent with the observed values, with high R2values of0.9417,0.6852,0.7965, and root mean square error (RMSE) lower than0.34g/kg,0.09g/kg and0.13g/kg, respectively. However, the RMSE of total concentration of salt (Sc), Cl-, Na+were small at high frequencies (C-band), with R2values of0.8655,0.8408,0.8117,0.8217, and RMSE value of0.25g.kg-1,0.15g.kg-1,0.02g.kg-1,0.21g.kg-1respectively. The high fit between the measured and invered values indicate that the present models could be used for the reliable estimation of soil salinity in cotton field under different saline conditions.
引文
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