基于GF-1卫星数据的冬小麦叶片氮含量遥感估算

李粉玲; 常庆瑞; 申健; 王力

doi:10.11975/j.issn.1002-6819.2016.09.022

基于GF-1卫星数据的冬小麦叶片氮含量遥感估算

李粉玲^1,2,
常庆瑞^1,2,
申健¹,
王力¹

1.
西北农林科技大学资源环境学院，杨凌712100
2.
农业部西北植物营养与农业环境重点实验室，杨凌 712100

基金项目: 国家863计划项目（2013AA102401）。

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出版历程
- 收稿日期: 2015-11-05
- 修回日期: 2016-03-01
- 发布日期: 2016-04-30

Remote sensing estimation of winter wheat leaf nitrogen content based on GF-1 satellite data

1.
College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
2.
Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Yangling 712100, China

摘要

摘要: 以陕西关中地区大田和小区试验下的冬小麦为研究对象，探讨基于国产高分辨率卫星GF-1号多光谱数据的冬小麦叶片氮含量估算方法和空间分布格局。基于GF-1号光谱响应函数对地面实测冬小麦冠层高光谱进行重采样，获取GF-1号卫星可见光-近红外波段的模拟反射率，并构建光谱指数，利用与叶片氮含量在0.01水平下显著相关的8类光谱指数，分别建立叶片氮含量的一元线性、一元二次多项式和指数回归模型。通过光谱指数与叶片氮含量的敏感性分析，以及所建模型的综合对比分析，获取适合冬小麦叶片氮含量估算的最佳模型。结果表明：模拟卫星宽波段光谱反射率和卫星实测光谱反射率间的相关系数高于0.95，具有一致性；改进型的敏感性指数综合考虑了模型的稳定性、敏感性和变量的动态范围，敏感性分析表明比值植被指数对叶片氮含量的变化响应能力最强；综合模拟方程决定系数、模型敏感性分析、精度检验和遥感制图的结果，认为基于比值植被指数建立的叶片氮含量估算模型适用性最强，模拟结果与实际空间分布格局最为接近，为基于GF-1卫星数据的区域性小麦氮素营养监测提供了理论依据和技术支持。
- 卫星 /
- 氮 /
- 敏感性分析 /
- GF-1 /
- 冬小麦
Abstract: Abstract: Nitrogen is a major element for plant growth and yield formation in agronomic crops. Crop nitrogen content estimation by remote sensing technique has been being a topic research in remote sensing monitoring of agricultural parameters. Hyper-spectral remote sensing with wealth of spectral information has been widely used in crop physiological and biochemical information extraction. It provides theoretical basis for estimating crop biochemical parameters based on multi-spectral satellite data. In terms of multi-spectral satellite remote sensing, spectral reflectances and spectral indices are effective ways to establish estimation models of biochemical parameters, but which bands and spectral indices are more effective and reliable for leaf nitrogen concentration monitoring in winter wheat is still debatable. In this article, ground-based canopy spectral reflectance and leaf nitrogen content (LNC) of winter wheat were measured from field and plot experiments including varied nitrogen fertilization levels and winter wheat varieties across the whole growth stages. Multi-spectral broadband reflectance was simulated by using the measured hyper-spectral reflectance and spectral response functions of multi-spectral camera of GF-1 satellite with a spatial resolution of 8 m, and then, they were used for the establishment of spectral index (SI). Eight spectral indices significantly correlated with LNC at the 0.01 probability level were used to construct the LNC estimation models in a linear, quadratic polynomial and exponential regression model respectively. Considering the influence factors in evaluating the efficiency of the SI–LNC model, i.e., the stability of the SI to other perturbing factors, the sensitivity of the SI to a unit change of LNC, and the dynamic range of the SI, the improved sensitivity index was proposed based on the NE and TVI index models. The optimal LNC estimation model was given according to the sensitivity and accuracy analysis, and the model was used to inverse the LNC in greenup growth period based on the GF-1 satellite image. The results showed that: 1) The simulated multi-spectral reflectance was highly correlated with the spectral reflectance from remote sensing images in visible and near infrared bands. They were consistent with each other keeping a correlation coefficient of greater than 0.95. It was concluded that the simulated broadband SI considering the spectral response function could be used to analyze the quantitative relationship with leaf nitrogen in both different growth periods and whole growth stage. 2) The SI based on the simulated spectral reflectance was significantly related with the LNC at 0.01 probability level with the correlation coefficient of greater than 0.6. A different pattern of the best combinations was found for 6 two-band spectral indices. The selection of 610-690 nm paired with 750-900 nm was the most effective two-band combination in RVI index, which was also the center wavelengths of the red and near infrared bands for GF-1 satellite data. 3) The sensitivity analysis indicated that all the regression models of selected SI passed the significance test at 0.01 probability level. The TCARI/OSAVI and RVI indices linearly related with LNC implied a stable response to the LNC changes. The first-order differentials of RVI and TCARI/OSAVI with respect to LNC were 9.44 and 3.08, and the sensitivity indices were 0.0671 and 0.1979 respectively. The RVI index was regarded as the most suitable index for LNC estimation. 4) The TCARI/OSAVI and RVI indices performed well in accuracy test, and the RVI index was more excellent in remote sensing mapping based on the GF-1 satellite image. Taking all factors into consideration, we believed the model based on the RVI index was optimal for LNC estimation with the determination coefficient of 0.6.
- satellites /
- nitrogen /
- sensitivity analysis /
- GF-1 /
- winter wheat

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