基于多源遥感信息融合的小麦生长监测研究
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摘要
遥感作为现代信息技术的前沿技术,可以快速准确获取大面积作物营养与生长状态等实时信息,为实施精确农业提供重要的技术支撑,从而有助于实现作物生产的高产、高效、优质等目标。将多源遥感信息进行融合,可以获得较单一遥感数据更丰富、更精确的信息,进而提高遥感信息分析和提取的精度,增强作物生长监测的准确性与稳定性。本研究将多源遥感信息融合技术应用到小麦生长监测中,通过实施多年不同施氮水平的田间大区试验,利用不同尺度、不同时相遥感影像,以及地面高光谱辐射仪获取立体多平台的小麦冠层反射光谱信息,结合地面田间同步取样,综合运用遥感信息融合、光谱分析及数理统计分析等方法,研究建立了基于多源遥感信息融合的小麦生长监测预测模型,从而为区域性小麦生长状况的遥感监测提供了技术依据。
在不同小麦生态区,基于同步的SPOT-5多光谱遥感影像、地面高光谱数据和氮素营养参数,提出了一种基于波谱响应函数拟合和混合像元分解的纯净像元光谱提取方法,并对比分析了纯净像元光谱、模拟像元光谱和实测像元光谱与小麦叶片氮含量和氮积累量的定量关系。结果表明,模拟像元光谱对小麦叶片氮素营养状况的反演效果较好,纯净像元光谱反演效果次之,实测像元光谱最后；但基于模拟像元光谱的氮素状况监测模型不能直接外推至空间尺度,而模型检验结果显示,基于纯净像元光谱的氮素状况监测模型具有较好的精度和稳定性,该方法综合利用了地-空遥感的优点,具有较好的理论和适用性,可以推广应用到其他不同空间分辨率和光谱分辨率的遥感数据,从而为区域性小麦氮素营养状况的遥感监测提供了技术支撑。
基于地面高光谱数据、SPOT-5和HJ-CCD多光谱遥感影像数据,对比了不同尺度遥感信息估算小麦生长指标的精度,进一步研究了基于地面高光谱和SPOT-5耦合的生长指标遥感监测。结果显示,不同尺度遥感源相同波段的光谱反射率值存在差异,但它们的近红外波段均与LAI和叶干重具有良好相关性；基于地面高光谱构建的光谱指数对LAI和叶干重的监测效果最好,模拟像元光谱、纯净像元光谱次之,实测像元光谱最后,基于地面高光谱和SPOT-5耦合提纯的像元光谱对小麦LAI和叶干重仍然具有较稳定和准确的估测精度。基于SPOT-5和HJ-CCD遥感数据的LAI和叶干重空间填图趋势基本一致,前者监测精度高于后者。研究结果可为区域尺度小麦生长指标定量监测提供技术支撑。
综合利用混合像元线性分解与数据同化算法,以高空间分辨率SPOT-5数据反演的LAI修正高时间分辨率HJ-CCD数据反演的LAI序列,融合生成了覆盖冬小麦主要生育期的高空间和时间分辨率的LAI序列,并结合SPOT-5反演的LAI和实测LAI值分析了像元纯度、高空间分辨率遥感数据同化景数对融合效果的影响。结果表明,采用数据融合方法生成的LAI与检验LAI具有较高的一致性,像元纯度对融合效果影响较大；基于2景SPOT-5影像能够提高LAI序列估测精度,且优于基于1景SPOT-5影像的融合效果,显示适当增加高空间分辨率影像景数可提高融合精度。以类似方法,分别生成了高空间分辨率叶干重、叶片氮含量和氮积累量序列。
基于融合的高空间分辨率和高时间分辨率时序遥感数据,综合分析小麦关键生育期内的多时相遥感数据与小麦籽粒产量和蛋白质含量的定量关系,筛选出了小麦籽粒产量和蛋白质含量预测的最佳生育时期和适宜光谱参数,构建了小麦产量品质预测方法和模型。结果表明,小麦最佳估产时期为灌浆前期,其次为开花期,而基于拔节期～灌浆前期累积的光谱指数能更有效的预测小麦产量,比单时相光谱指数预测精度更高。小麦蛋白质含量预测最佳时期为开花期,而基于拔节期～开花期累积的光谱指数因包含了小麦主要生育期的生长动态信息,对小麦蛋白质含量预测结果更为可靠。
As the frontier modern information technology, the remote sensing can collect the nitrogen and growth status of crop in the field rapidly and accurately in large scale, which offers important technical support for implementation of precision farming and realizing high yield, good, quality and high efficiency in modern agricultural production. Multi-source remote sensing information can obtain more abundant and accurate information than single remote sensing data, and can improve the accuracy of analysis and extraction of remote sensing information in monitoring crop growth.
In this study, the technology based on fusing multi-source remote sensing information was applied to growth monitoring in wheat. A series of field experiments with different nitrogen levels were carried out, multi-plat wheat spectral reflectance were obtained with different scales and temporal images and ground hyperspectral spectroradiometer, field sampling and testing synchronously implemented. Then, based on the technology of fusing multi-source remote sensing information and analysis of spectral reflectance and mathematical statistic, the models that monitoring wheat nitrogen status and growth characters based on fusing multi-source remote sensing information were established in this paper. The result can offer a technological support for monitoring wheat growth status with remote sensing in large scale.
A pure pixel spectrum extraction method was proposed based on spectral response function and pixel unmixing by coupling SPOT-5, ground-spectrum and field measured data of different wheat ecological zones in this paper. The quantitative relationships between leaf nitrogen concentration (LNC) and leaf nitrogen accumulation (LNA) of wheat and simulated, measured and pure pixel spectra have been developed. The results showed that the sequence of estimation accuracy was simulated, pure and measured pixel spectra respectively. But leaf nitrogen status monitoring model based on simulated pixel spectra couldn't be extrapolated directly to regional level. In addition, the model testing results based on independent data indicated that the monitoring model based on pure pixel spectra performed well in different wheat ecological areas. Which maybe contribute to pixel unmixing based on integrating ground-and space-remotely sensed data. Therefore, the pure pixel spectrum extraction method can be applied to remotely sensed data with different spatial and spectral resolutions and estimate wheat nitrogen status in regional scale.
The performance of different scale remote sensing data was evaluated in terms of the accuracy of monitoring model based on coupling ground-spectrum, SPOT-5, HJ-CCD and field measured data of different wheat ecological zones, years and nitrogen level in this paper. The results showed there are differences in the reflectance of same bands based on different scale remote sensing data, but their near-infrared band were linearly related significantly to LAI and leaf dry weight. The sequence of estimation accuracy was ground-spectrum, simulated, pure and measured pixel spectra respectively. In addition, SPOT-5and HJ-CCD images have a highly uniform spatial distribution pattern of wheat LAI and leaf dry weight, but the former was higher than the latter in the estimation accuracy. Nevertheless, these results offer a technological support for regional quantitative monitoring of wheat.
By combining the techniques of linear pixel unmixing and data assimilation, the LAI based on SPOT-5image with high spatial resolution was used to adjust the time-series LAI based on HJ-CCD image with high temporal resolution, and LAI series covering the whole winter wheat growth period and with high spatial and temporal resolutions were generated. The effects of pixel purity and the number of high spatial image on the performance of fusing method were analyzed by comparing the LAI with fusing method and LAI from SPOT-5image or observed LAI. The results showed that the estimated LAI with fusing method has high consistency with observed LAI and the pixel purity is main obstacle factor. The fusion results based on two scenes of SPOT-5images are better than that based on one image. Meanwhile, leaf dry weight, leaf nitrogen concentration and leaf nitrogen accumulation series with high spatial and temporal resolutions were generated based on the data fusion.
Quantitative relations between multi-temporal remote sensing data in main growth period of wheat and yield and protein content were analyzed based on the time series data fused by fusing high spatial and temporal resolution remote sensing data. On this basis, the optimum period predicting yield and protein content was selected and predicting models were constructed. The results showed the optimum period predicting wheat yield was initial filling, secondly anthesis. Cumulative value of spectral parameters from jointing to initial filling was highly correlated with grain yield and the predicting accuracy was higher than mono temporal. Meanwhile, the results also showed the optimum period predicting wheat grain protein content was anthesis. Cumulative value of spectral parameters from jointing to anthesis could reflect dynamic growth information of wheat main growth stages, which predicting result was more stable than mono temporal spectral parameters.
在不同小麦生态区,基于同步的SPOT-5多光谱遥感影像、地面高光谱数据和氮素营养参数,提出了一种基于波谱响应函数拟合和混合像元分解的纯净像元光谱提取方法,并对比分析了纯净像元光谱、模拟像元光谱和实测像元光谱与小麦叶片氮含量和氮积累量的定量关系。结果表明,模拟像元光谱对小麦叶片氮素营养状况的反演效果较好,纯净像元光谱反演效果次之,实测像元光谱最后；但基于模拟像元光谱的氮素状况监测模型不能直接外推至空间尺度,而模型检验结果显示,基于纯净像元光谱的氮素状况监测模型具有较好的精度和稳定性,该方法综合利用了地-空遥感的优点,具有较好的理论和适用性,可以推广应用到其他不同空间分辨率和光谱分辨率的遥感数据,从而为区域性小麦氮素营养状况的遥感监测提供了技术支撑。
基于地面高光谱数据、SPOT-5和HJ-CCD多光谱遥感影像数据,对比了不同尺度遥感信息估算小麦生长指标的精度,进一步研究了基于地面高光谱和SPOT-5耦合的生长指标遥感监测。结果显示,不同尺度遥感源相同波段的光谱反射率值存在差异,但它们的近红外波段均与LAI和叶干重具有良好相关性；基于地面高光谱构建的光谱指数对LAI和叶干重的监测效果最好,模拟像元光谱、纯净像元光谱次之,实测像元光谱最后,基于地面高光谱和SPOT-5耦合提纯的像元光谱对小麦LAI和叶干重仍然具有较稳定和准确的估测精度。基于SPOT-5和HJ-CCD遥感数据的LAI和叶干重空间填图趋势基本一致,前者监测精度高于后者。研究结果可为区域尺度小麦生长指标定量监测提供技术支撑。
综合利用混合像元线性分解与数据同化算法,以高空间分辨率SPOT-5数据反演的LAI修正高时间分辨率HJ-CCD数据反演的LAI序列,融合生成了覆盖冬小麦主要生育期的高空间和时间分辨率的LAI序列,并结合SPOT-5反演的LAI和实测LAI值分析了像元纯度、高空间分辨率遥感数据同化景数对融合效果的影响。结果表明,采用数据融合方法生成的LAI与检验LAI具有较高的一致性,像元纯度对融合效果影响较大；基于2景SPOT-5影像能够提高LAI序列估测精度,且优于基于1景SPOT-5影像的融合效果,显示适当增加高空间分辨率影像景数可提高融合精度。以类似方法,分别生成了高空间分辨率叶干重、叶片氮含量和氮积累量序列。
基于融合的高空间分辨率和高时间分辨率时序遥感数据,综合分析小麦关键生育期内的多时相遥感数据与小麦籽粒产量和蛋白质含量的定量关系,筛选出了小麦籽粒产量和蛋白质含量预测的最佳生育时期和适宜光谱参数,构建了小麦产量品质预测方法和模型。结果表明,小麦最佳估产时期为灌浆前期,其次为开花期,而基于拔节期～灌浆前期累积的光谱指数能更有效的预测小麦产量,比单时相光谱指数预测精度更高。小麦蛋白质含量预测最佳时期为开花期,而基于拔节期～开花期累积的光谱指数因包含了小麦主要生育期的生长动态信息,对小麦蛋白质含量预测结果更为可靠。
As the frontier modern information technology, the remote sensing can collect the nitrogen and growth status of crop in the field rapidly and accurately in large scale, which offers important technical support for implementation of precision farming and realizing high yield, good, quality and high efficiency in modern agricultural production. Multi-source remote sensing information can obtain more abundant and accurate information than single remote sensing data, and can improve the accuracy of analysis and extraction of remote sensing information in monitoring crop growth.
In this study, the technology based on fusing multi-source remote sensing information was applied to growth monitoring in wheat. A series of field experiments with different nitrogen levels were carried out, multi-plat wheat spectral reflectance were obtained with different scales and temporal images and ground hyperspectral spectroradiometer, field sampling and testing synchronously implemented. Then, based on the technology of fusing multi-source remote sensing information and analysis of spectral reflectance and mathematical statistic, the models that monitoring wheat nitrogen status and growth characters based on fusing multi-source remote sensing information were established in this paper. The result can offer a technological support for monitoring wheat growth status with remote sensing in large scale.
A pure pixel spectrum extraction method was proposed based on spectral response function and pixel unmixing by coupling SPOT-5, ground-spectrum and field measured data of different wheat ecological zones in this paper. The quantitative relationships between leaf nitrogen concentration (LNC) and leaf nitrogen accumulation (LNA) of wheat and simulated, measured and pure pixel spectra have been developed. The results showed that the sequence of estimation accuracy was simulated, pure and measured pixel spectra respectively. But leaf nitrogen status monitoring model based on simulated pixel spectra couldn't be extrapolated directly to regional level. In addition, the model testing results based on independent data indicated that the monitoring model based on pure pixel spectra performed well in different wheat ecological areas. Which maybe contribute to pixel unmixing based on integrating ground-and space-remotely sensed data. Therefore, the pure pixel spectrum extraction method can be applied to remotely sensed data with different spatial and spectral resolutions and estimate wheat nitrogen status in regional scale.
The performance of different scale remote sensing data was evaluated in terms of the accuracy of monitoring model based on coupling ground-spectrum, SPOT-5, HJ-CCD and field measured data of different wheat ecological zones, years and nitrogen level in this paper. The results showed there are differences in the reflectance of same bands based on different scale remote sensing data, but their near-infrared band were linearly related significantly to LAI and leaf dry weight. The sequence of estimation accuracy was ground-spectrum, simulated, pure and measured pixel spectra respectively. In addition, SPOT-5and HJ-CCD images have a highly uniform spatial distribution pattern of wheat LAI and leaf dry weight, but the former was higher than the latter in the estimation accuracy. Nevertheless, these results offer a technological support for regional quantitative monitoring of wheat.
By combining the techniques of linear pixel unmixing and data assimilation, the LAI based on SPOT-5image with high spatial resolution was used to adjust the time-series LAI based on HJ-CCD image with high temporal resolution, and LAI series covering the whole winter wheat growth period and with high spatial and temporal resolutions were generated. The effects of pixel purity and the number of high spatial image on the performance of fusing method were analyzed by comparing the LAI with fusing method and LAI from SPOT-5image or observed LAI. The results showed that the estimated LAI with fusing method has high consistency with observed LAI and the pixel purity is main obstacle factor. The fusion results based on two scenes of SPOT-5images are better than that based on one image. Meanwhile, leaf dry weight, leaf nitrogen concentration and leaf nitrogen accumulation series with high spatial and temporal resolutions were generated based on the data fusion.
Quantitative relations between multi-temporal remote sensing data in main growth period of wheat and yield and protein content were analyzed based on the time series data fused by fusing high spatial and temporal resolution remote sensing data. On this basis, the optimum period predicting yield and protein content was selected and predicting models were constructed. The results showed the optimum period predicting wheat yield was initial filling, secondly anthesis. Cumulative value of spectral parameters from jointing to initial filling was highly correlated with grain yield and the predicting accuracy was higher than mono temporal. Meanwhile, the results also showed the optimum period predicting wheat grain protein content was anthesis. Cumulative value of spectral parameters from jointing to anthesis could reflect dynamic growth information of wheat main growth stages, which predicting result was more stable than mono temporal spectral parameters.
引文
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