基于近地可见光成像传感器的棉花生长信息监测研究
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摘要
[目的]利用近地可见光成像传感器监测棉花生长发育过程中的主要生长信息。明确估测棉花冠层和叶片氮素状况的最佳光谱和颜色参数,建立图像透光率和图像截获的光合有效辐射分量估测叶面积指数和冠层截获和吸收的光合有效辐射分量的定量模型,为精准监测技术提供新的理论基础和技术支撑。
[方法]本研究用各种仪器和破坏性取样方法从不同年份不同处理的田间试验采集数据。(1)通过分析颜色参数、原始光谱及由两波段构建的比值、归一化差值和差值指数与氮素营养指标的相关关系,筛选出对氮素营养诊断敏感的颜色参数、波段和光谱指数,进而建立估测的定量模型,并根据决定系数和估计标准误对模型进行优选。(2)通过分析透光率和冠层截获的光合有效辐射分量的日变化判断图像测定适宜的时间段；比较分析不同仪器测量的透光率或冠层截获的光合有效辐射分量；用图像透光率和图像截获的光合有效辐射分量两个参数分别建立叶面积指数和冠层截获和吸收的光合有效辐射的估算模型。建立的所有模型均用独立的数据样本进行验证,选用决定系数、均方根误差和相对均方根误差等指标综合评定。
[结果]通过上述方法的研究和分析,获得以下几个方面的研究结果：
(1)棉花单叶氮素营长监测。不同传感器对叶绿素和氮素最敏感的波段分别为R710和R；光谱指数与叶绿素、氮素浓度和SPAD读数的相关性比原始光谱好,而且以蓝光和红光波段组成的差值指数(DI和R-B)的预测能力最佳；DI所建棉花叶片Chl a+b、Chla、Chlb、N和SPAD读数的预测模型的预测误差分别为0.0058、0.0050、0.0018和2.3002 mg g-1和4.9736(分别为均值的18.39%、19.47%、30.33%、11.69%和8.45%),预测精度R2分别为0.7965、0.7582、0.6608、0.7019和0.7338；R-B所建模型的预测性比DI差,对Chl a+b的预测精度最高(R2=0.7400),而预测Chl b的精度最低(R2=0.5653)。基于CIE 1976 L*a*b*颜色模型的颜色参数b*和HSI颜色模型的S是2种传感器与叶绿素、氮素浓度和叶色关系较好的颜色参数；b*对叶绿素、氮素浓度和SPAD读数的预测能力稍逊于DI,预测误差和精度都与DI的比较接近；而饱和度S值的预测RRMSE最大,整体预测精度小于0.62。
(2)棉花冠层叶片氮素营养估测。在可见光波段,冠层反射率随着冠层叶片氮素含量的增加而降低,且叶片含氮量的光谱敏感波段主要位于绿光和红光区域；与棉花冠层叶片含氮量的拟合效果最好的两种传感器的光谱指数为差值指数DI(R580,R680)和G-R,而颜色参数则分别为b*和H,同一传感器以光谱指数的拟合效果优于颜色参数,不同传感器以MSI200数据的拟合效果优于数码相机；利用独立试验资料检验所建模型的估测性能表明,差值指数对棉花冠层叶片氮素的预测能力优于比值指数和归一化差值指数,DI(R580,R680)和G-R所建模型的估测精度最高,分别为0.8131和0.7636。
(3)棉花叶面积指数估测。在太阳高度角最大且变化最小的正午时段,数码相机测量的图像透光率与线性光量子传感器测量的冠层透光率较一致且相对稳定；图像透光率能反映除吐絮期以外各时期的冠层透光状况,但是当LAI大于5时图像透光率出现饱和；综合分析2009和2010年数据,建立了图像透光率估测LAI的模型(R2=0.8438,SE=0.5605)；利用2007年独立试验资料检验估测模型的性能,模型检验的拟合度较高(R2=0.8767)且预测误差较小(RMSE=0.4305),当LAI>5时模型的预测能力降低；数字图像、LAI-2000和破坏性取样3种方法测量的LAI值之间均呈现显著的线性相关(R2>0.85),但是图像透光率的饱和性致使当LAI>5时明显低估叶面积指数。
(4)棉花冠层截获和吸收的光合有效辐射分量的估测。明确了正午时段的图像截获的光合有效辐射分量能够反映棉花冠层截获和吸收的光合有效辐射；利用2010年试验数据建立了fCover和fIPARimag参数估测截获和吸收的光合有效辐射分量的定量关系模型,决定系数大于0.93；并用2009年数据进行检验和评价,发现fIPARimag的预测性能优于fCover。
[结论]可以利用可见光成像传感器的光谱和颜色参数定量估测棉花冠层和单叶的氮素营养状况；用图像分量构建的图像透光率和图像截获的光合有效辐射分量参数能够有效估测叶面积指数和冠层截获和吸收的光合有效辐射分量。
[Object] The objectives of this paper were to monitor main growth information using ground-based visible imaging sensors in different growth stages, and to determine the spectral and color parameters for estimating canopy and leaf nitrogen nutrition status, and to develop quantitative model for estimating LAI and fIPAR and fAPAR with image transmittance and image fIPAR. Consequently, spectral and color parameters in visible region and image fractions index (transmittance and fIPAR) may provide theoretical basis and technical support of precise monitoring technique.
[Methods] In this study, we used various kinds methods to odserve data in different growth stages from 2006 to 2010 in cotton field. (1) A systematic analysis was undertaken on quantitative relationships of nitrogen nutrition indices to color parameters, sensitive wavebands and major spectral indices, such as the ratio index (RI), normalized difference index (ND) and difference index (DI), which composed of any two wavelengths with original reflectance. Futhermore, the quantitative models were developed and the optimum models werw selected which with the maximum determination coefficients (R2) and the minimum RMSE. (2) Analysis of the diurnal pattern of transmittance and fIPAR of the cotton canopy showed that the best time for observe data was around solar noon, because at this time the solar elevation angle is high and remains relatively constant during measurements. Additional, by analyzing the relationships among various transmittance or fIPAR, we determined that Timag and fIPARimag could be used to estimate light attenuation and light interception in the cotton canopy. Hence, the estimated models of LAI and fIPAR and fAPAR were established using Timag and fIPARimag parameters. Overall, the ability of all estimated models in this paper were validated using an independent dataset, accepted statistics indices included determination coefficients (R2), RMSE and RRMSE.
[Results] The main results of this paper as follows:
(1) The results indicated that the maximum sensitivity of reflectance to variation in chlorophyll, nitrogen contents and SPAD readings was found in the far-red wavelength region at 710 nm and in the red wavelength region (R) for two sensors, respectively. Furthermore, spectral indices could improve the prediction ability obviously, and difference indices (DI and R-B) of different sensors composed of blue and red wavelengths gave a better prediction performance. The models to retrieve chlorophyll, nitrogen contents and SPAD readings using DI were the most feasible models with the maximum determination coefficients (R2) and the minimum RMSE, especially, DI(R440, R710), DI(R440, R710), DI(R420, R710), DI(R420, R720) and DI(R49o, R710) were the optimum indices for the models of chlorophyll a+b, chlorophyll a, chlorophyll b and N, and SPAD readings, respectively. R-B was the optimum index of digital camera but its prediction performances were lower than these of DI. Additional, b* (CIE 1976 L*a*b* color model) and S (HSI color model) were the optimum color parameters, and the prediction ability of b* was lower than that of DI. However, the prediction performance of S was relative weak with the highest RRMSE and the lowest R2.
(2) The results showed that canopy spectral reflectance decreased with increasing leaf nitrogen content, and the bands sensitive to leaf nitrogen content occurred the green and red regions mainly. Furthermore, the models to retrieve canopy leaf nitrogen contents using DI(R580, R680) and G-R were most feasible with the maximum determination coefficients (R2) and the minimum standard error (SE) for two visible sensors, respectively. Additional, b* (CIE 1976 L*a*b* color model) and H (HSI color model) were the optimum color parameters. On the whole, for the fitting effects, the spectral index was superior to color parameters for the same sensor, and MSI200 superior to digital camera. Then, the prediction performances of the spectral indices of digital camera were validated by using independent dataset. We found that difference indices DI(Rs8o, R680) and G-R were the optimum indicators of canopy leaf nitrogen content with the highest predictive precision (0.8131 and 0.7636, respectively)and accuracy (1.0149 and 0.9661) and the lowest RMSE (2.3313 and 2.7406 mg g-1, approximately 6.52% and 8.24% of the mean).
(3) Analysis of the diurnal pattern of transmittance of the cotton canopy showed that the best time for measuring Timag was around solar noon, because at this time the solar elevation angle is high and remains relatively constant during measurements. Around solar noon, Timag was in good agreement with Tquan (transmittance measured with a linear quantum sensor). By analyzing the relationships among Timag, Tquan, and diffuse non-interceptance (DIFN), we determined that Timag could be used to estimate light attenuation in the cotton canopy at different stages, except for the boll opening stage. In addition, Timag was saturated at LAI>5. We analyzed the relationship between LAIdest (LAI measured destructively) and Timag using data from 2009 and 2010. The R2 and SE of the calibration model were 0.8438 and 0.5605, respectively. The ability of Timag to predict LAI was validated using an independent dataset (2007 data). The determination coefficient and RMSE of the validation model were 0.8767 and 0.4305, respectively. However, the model underestimated LAI as the LAI exceeded 5. The Timag saturation, which was largely because of errors in image recognition and segmentation, resulted in underestimation of LAI. Intercomparisons of LAI estimates showed that there were small discrepancies and significant correlations among data obtained from digital images, the LAI-2000, and destructive sampling methods. Data from the LAI-2000 was highly consistent with that obtained by destructive sampling.
(4) The results indicated that the fIPARimag which calculated with canopy images taken around solar noon can model the fIPAR and fAPAR of cotton canopy. Then, regression analysis were made on the relationships between fCover and fIPARimag and flPAR and fAPAR, with the determination of coefficient (R2) exceeded 0.93. Tests with independent dataset (2009 data) showed that the prediction performance of fIPARimag is superior to fCover.
[Conclusions] Hence, spectral and color parameters in visible region may provide an effective and feasible means of estimating canopy and individual leaf nitrogen nutrition status quantitatively in cotton field. Moreover, Timag and fIPARimag which derived from four image fractions can provide a new and accurate means of estimating LAI and fAPAR and fIPAR. Consequently, the digital camera could be mounted on a tractor or farm vehicle for real-time, non-destructive monitoring of LAI to support field management.
[方法]本研究用各种仪器和破坏性取样方法从不同年份不同处理的田间试验采集数据。(1)通过分析颜色参数、原始光谱及由两波段构建的比值、归一化差值和差值指数与氮素营养指标的相关关系,筛选出对氮素营养诊断敏感的颜色参数、波段和光谱指数,进而建立估测的定量模型,并根据决定系数和估计标准误对模型进行优选。(2)通过分析透光率和冠层截获的光合有效辐射分量的日变化判断图像测定适宜的时间段；比较分析不同仪器测量的透光率或冠层截获的光合有效辐射分量；用图像透光率和图像截获的光合有效辐射分量两个参数分别建立叶面积指数和冠层截获和吸收的光合有效辐射的估算模型。建立的所有模型均用独立的数据样本进行验证,选用决定系数、均方根误差和相对均方根误差等指标综合评定。
[结果]通过上述方法的研究和分析,获得以下几个方面的研究结果：
(1)棉花单叶氮素营长监测。不同传感器对叶绿素和氮素最敏感的波段分别为R710和R；光谱指数与叶绿素、氮素浓度和SPAD读数的相关性比原始光谱好,而且以蓝光和红光波段组成的差值指数(DI和R-B)的预测能力最佳；DI所建棉花叶片Chl a+b、Chla、Chlb、N和SPAD读数的预测模型的预测误差分别为0.0058、0.0050、0.0018和2.3002 mg g-1和4.9736(分别为均值的18.39%、19.47%、30.33%、11.69%和8.45%),预测精度R2分别为0.7965、0.7582、0.6608、0.7019和0.7338；R-B所建模型的预测性比DI差,对Chl a+b的预测精度最高(R2=0.7400),而预测Chl b的精度最低(R2=0.5653)。基于CIE 1976 L*a*b*颜色模型的颜色参数b*和HSI颜色模型的S是2种传感器与叶绿素、氮素浓度和叶色关系较好的颜色参数；b*对叶绿素、氮素浓度和SPAD读数的预测能力稍逊于DI,预测误差和精度都与DI的比较接近；而饱和度S值的预测RRMSE最大,整体预测精度小于0.62。
(2)棉花冠层叶片氮素营养估测。在可见光波段,冠层反射率随着冠层叶片氮素含量的增加而降低,且叶片含氮量的光谱敏感波段主要位于绿光和红光区域；与棉花冠层叶片含氮量的拟合效果最好的两种传感器的光谱指数为差值指数DI(R580,R680)和G-R,而颜色参数则分别为b*和H,同一传感器以光谱指数的拟合效果优于颜色参数,不同传感器以MSI200数据的拟合效果优于数码相机；利用独立试验资料检验所建模型的估测性能表明,差值指数对棉花冠层叶片氮素的预测能力优于比值指数和归一化差值指数,DI(R580,R680)和G-R所建模型的估测精度最高,分别为0.8131和0.7636。
(3)棉花叶面积指数估测。在太阳高度角最大且变化最小的正午时段,数码相机测量的图像透光率与线性光量子传感器测量的冠层透光率较一致且相对稳定；图像透光率能反映除吐絮期以外各时期的冠层透光状况,但是当LAI大于5时图像透光率出现饱和；综合分析2009和2010年数据,建立了图像透光率估测LAI的模型(R2=0.8438,SE=0.5605)；利用2007年独立试验资料检验估测模型的性能,模型检验的拟合度较高(R2=0.8767)且预测误差较小(RMSE=0.4305),当LAI>5时模型的预测能力降低；数字图像、LAI-2000和破坏性取样3种方法测量的LAI值之间均呈现显著的线性相关(R2>0.85),但是图像透光率的饱和性致使当LAI>5时明显低估叶面积指数。
(4)棉花冠层截获和吸收的光合有效辐射分量的估测。明确了正午时段的图像截获的光合有效辐射分量能够反映棉花冠层截获和吸收的光合有效辐射；利用2010年试验数据建立了fCover和fIPARimag参数估测截获和吸收的光合有效辐射分量的定量关系模型,决定系数大于0.93；并用2009年数据进行检验和评价,发现fIPARimag的预测性能优于fCover。
[结论]可以利用可见光成像传感器的光谱和颜色参数定量估测棉花冠层和单叶的氮素营养状况；用图像分量构建的图像透光率和图像截获的光合有效辐射分量参数能够有效估测叶面积指数和冠层截获和吸收的光合有效辐射分量。
[Object] The objectives of this paper were to monitor main growth information using ground-based visible imaging sensors in different growth stages, and to determine the spectral and color parameters for estimating canopy and leaf nitrogen nutrition status, and to develop quantitative model for estimating LAI and fIPAR and fAPAR with image transmittance and image fIPAR. Consequently, spectral and color parameters in visible region and image fractions index (transmittance and fIPAR) may provide theoretical basis and technical support of precise monitoring technique.
[Methods] In this study, we used various kinds methods to odserve data in different growth stages from 2006 to 2010 in cotton field. (1) A systematic analysis was undertaken on quantitative relationships of nitrogen nutrition indices to color parameters, sensitive wavebands and major spectral indices, such as the ratio index (RI), normalized difference index (ND) and difference index (DI), which composed of any two wavelengths with original reflectance. Futhermore, the quantitative models were developed and the optimum models werw selected which with the maximum determination coefficients (R2) and the minimum RMSE. (2) Analysis of the diurnal pattern of transmittance and fIPAR of the cotton canopy showed that the best time for observe data was around solar noon, because at this time the solar elevation angle is high and remains relatively constant during measurements. Additional, by analyzing the relationships among various transmittance or fIPAR, we determined that Timag and fIPARimag could be used to estimate light attenuation and light interception in the cotton canopy. Hence, the estimated models of LAI and fIPAR and fAPAR were established using Timag and fIPARimag parameters. Overall, the ability of all estimated models in this paper were validated using an independent dataset, accepted statistics indices included determination coefficients (R2), RMSE and RRMSE.
[Results] The main results of this paper as follows:
(1) The results indicated that the maximum sensitivity of reflectance to variation in chlorophyll, nitrogen contents and SPAD readings was found in the far-red wavelength region at 710 nm and in the red wavelength region (R) for two sensors, respectively. Furthermore, spectral indices could improve the prediction ability obviously, and difference indices (DI and R-B) of different sensors composed of blue and red wavelengths gave a better prediction performance. The models to retrieve chlorophyll, nitrogen contents and SPAD readings using DI were the most feasible models with the maximum determination coefficients (R2) and the minimum RMSE, especially, DI(R440, R710), DI(R440, R710), DI(R420, R710), DI(R420, R720) and DI(R49o, R710) were the optimum indices for the models of chlorophyll a+b, chlorophyll a, chlorophyll b and N, and SPAD readings, respectively. R-B was the optimum index of digital camera but its prediction performances were lower than these of DI. Additional, b* (CIE 1976 L*a*b* color model) and S (HSI color model) were the optimum color parameters, and the prediction ability of b* was lower than that of DI. However, the prediction performance of S was relative weak with the highest RRMSE and the lowest R2.
(2) The results showed that canopy spectral reflectance decreased with increasing leaf nitrogen content, and the bands sensitive to leaf nitrogen content occurred the green and red regions mainly. Furthermore, the models to retrieve canopy leaf nitrogen contents using DI(R580, R680) and G-R were most feasible with the maximum determination coefficients (R2) and the minimum standard error (SE) for two visible sensors, respectively. Additional, b* (CIE 1976 L*a*b* color model) and H (HSI color model) were the optimum color parameters. On the whole, for the fitting effects, the spectral index was superior to color parameters for the same sensor, and MSI200 superior to digital camera. Then, the prediction performances of the spectral indices of digital camera were validated by using independent dataset. We found that difference indices DI(Rs8o, R680) and G-R were the optimum indicators of canopy leaf nitrogen content with the highest predictive precision (0.8131 and 0.7636, respectively)and accuracy (1.0149 and 0.9661) and the lowest RMSE (2.3313 and 2.7406 mg g-1, approximately 6.52% and 8.24% of the mean).
(3) Analysis of the diurnal pattern of transmittance of the cotton canopy showed that the best time for measuring Timag was around solar noon, because at this time the solar elevation angle is high and remains relatively constant during measurements. Around solar noon, Timag was in good agreement with Tquan (transmittance measured with a linear quantum sensor). By analyzing the relationships among Timag, Tquan, and diffuse non-interceptance (DIFN), we determined that Timag could be used to estimate light attenuation in the cotton canopy at different stages, except for the boll opening stage. In addition, Timag was saturated at LAI>5. We analyzed the relationship between LAIdest (LAI measured destructively) and Timag using data from 2009 and 2010. The R2 and SE of the calibration model were 0.8438 and 0.5605, respectively. The ability of Timag to predict LAI was validated using an independent dataset (2007 data). The determination coefficient and RMSE of the validation model were 0.8767 and 0.4305, respectively. However, the model underestimated LAI as the LAI exceeded 5. The Timag saturation, which was largely because of errors in image recognition and segmentation, resulted in underestimation of LAI. Intercomparisons of LAI estimates showed that there were small discrepancies and significant correlations among data obtained from digital images, the LAI-2000, and destructive sampling methods. Data from the LAI-2000 was highly consistent with that obtained by destructive sampling.
(4) The results indicated that the fIPARimag which calculated with canopy images taken around solar noon can model the fIPAR and fAPAR of cotton canopy. Then, regression analysis were made on the relationships between fCover and fIPARimag and flPAR and fAPAR, with the determination of coefficient (R2) exceeded 0.93. Tests with independent dataset (2009 data) showed that the prediction performance of fIPARimag is superior to fCover.
[Conclusions] Hence, spectral and color parameters in visible region may provide an effective and feasible means of estimating canopy and individual leaf nitrogen nutrition status quantitatively in cotton field. Moreover, Timag and fIPARimag which derived from four image fractions can provide a new and accurate means of estimating LAI and fAPAR and fIPAR. Consequently, the digital camera could be mounted on a tractor or farm vehicle for real-time, non-destructive monitoring of LAI to support field management.
引文
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