
基于红边优化植被指数的寒地水稻叶片叶绿素含量遥感反演研究
Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI)
水稻叶片叶绿素含量遥感诊断是实现水稻精准施肥的核心要素。本研究通过分析寒地水稻关键生育期叶片高光谱反射率信息,同时结合PROSPECT模型叶绿素含量吸收系数,参考借鉴现有高光谱植被指数的构造方法和形式,利用相关性分析、连续投影法、遗传算法优化的粗糙集属性简约法进行高光谱特征选择,提出了仅含有695、507和465nm 3个高光谱特征波段的红边优化指数(ORVI)。与Index Data Base数据库中其他用于叶绿素含量反演植被指数,包括ND528,587、SR440,690、CARI、MCARI的反演结果进行了对比分析,结果表明:IDB数据库中的已有4种植被指数叶绿素含量反演模型的决定系数R2分别为0.672、0.630、0.595和0.574;ORVI植被所建立的叶绿素含量反演模型的决定系数R2为0.726,均方根误差RMSE为2.68,精度高于其他植被指数,说明了ORVI在实际的应用中,能够作为快速反演水稻叶绿素含量的高光谱植被指数。本研究能够为寒地水稻叶绿素含量高光谱遥感诊断及管理决策提供一定的客观数据支撑和模型参考。
Rice is one of the important staple crops in China, and the rice planted in Northeast China, such as in Liaoning, Jilin, and Heilongjiang regions, is called cold-region rice. The chlorophyll content in rice leaves is the most direct indicator of the rice growth period and can directly reflect on its nutritional value. Previous research demonstrates that when the chlorophyll content of rice changes, the reflectance of different bands changes at the spectral level. In addition, most of the research studies on the inversion of the rice’s chlorophyll content are based on the complex machine learning algorithms. Although the accuracy of the inversion of the constructed model has been improved, the structure of the model is relatively complex, and the model’s transplantation and universality are poor in the actual application process. Hence, in this study, the inversion of the chlorophyll content of rice leaves in the cold regions was assessed. An ASD ground object spectrometer was employed to procure the hyperspectral information of rice leaves in the critical growth period. On the basis of the feature selection method, the hyperspectral feature subset of the inversion of the chlorophyll content of rice was selected. The characteristic band vegetation index was constructed by combining the chlorophyll content absorption coefficients, and the chlorophyll content of rice was established through using regression analysis. Additionally, by combining the chlorophyll content absorption coefficients in the PROSPECT model, referring to the construction method and form of the existing hyperspectral vegetation index, and using correlation analysis, the continuous projection method and the genetic algorithm optimized the rough set attribute reduction, the hyperspectral features was selected, and the red edge optimization index (ORVI) with only 695, 507, and 465nm hyperspectral feature bands was proposed. Compared with the other vegetation indexes retrieved from the IDB database, namely, ND528,587, SR440,690, CARI, and MCARI, the results demonstrated that the determination coefficients of the abovementioned vegetation index inversion models were 0.672, 0.630, 0.595, and 0.574 respectively. The accuracy of the inversion model of chlorophyll content established by ORVI vegetation was higher than that of other vegetation indexes wherein the decision coefficients of the model were R2 =0.726 and RMSE = 2.68, revealing that ORVI can be used as a hyperspectral vegetation index for the rapid inversion of the rice’s chlorophyll content in practical applications. This research can thereby provide some objective data support and model reference for remote sensing diagnosis and management decision of the rice’s chlorophyll content in the cold regions.
植被指数 / 叶绿素反演 / 水稻叶片 / 高光谱遥感 / 红边优化指数ORVI {{custom_keyword}} /
vegetation index / chlorophyll inversion / rice leaf / hyperspectral remote sensing / optimizing red-edge vegetation index (ORVI) {{custom_keyword}} /
1 |
常俊彦, 宋明阳, 于晓曼, 等. 沈阳地区水稻生产的生态环境影响研究[J]. 农业环境科学学报, 2018, 37(8): 249-257.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
2 |
凌启鸿, 王绍华, 丁艳锋, 等. 关于用水稻“顶3顶4叶叶色差”作为高产群体叶色诊断统一指标的再论证[J]. 中国农业科学, 2017, 50(24): 42-50.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
3 |
裴信彪, 吴和龙, 马萍, 等. 基于无人机遥感的不同施氮水稻光谱与植被指数分析[J]. 中国光学, 2018, 11(5): 144-152.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
4 |
何勇, 彭继宇, 刘飞, 等. 基于光谱和成像技术的作物养分生理信息快速检测研究进展[J]. 农业工程学报, 2015, 31(3): 182-197.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
5 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
6 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
7 |
谢立勇, 孙雪, 赵洪亮, 等. FACE条件下水稻生育后期剑叶光合色素含量及产量构成的响应研究[J]. 中国生态农业学报, 2015, 23(4): 47-53.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
8 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
9 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
10 |
刘桃菊, 胡雯君, 张笑东, 等. 水稻冠层高光谱特征变量与叶片叶绿素含量的相关性研究[J]. 激光生物学报, 2015, 24(5): 428-435.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
11 |
曹英丽, 邹焕成, 郑伟, 等. 水稻叶片高光谱数据降维与叶绿素含量反演方法研究[J]. 沈阳农业大学学报, 2019, 50(1): 107-113.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
12 |
谢凯, 蒋蘋, 罗亚辉. 稻瘟病胁迫下水稻叶片叶绿素含量与光谱特征参数的相关性研究[J]. 中国农学通报, 2017, 33(17): 123-128.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
13 |
张建, 孟晋, 赵必权, 等. 消费级近红外相机的水稻叶片叶绿素(SPAD)分布预测[J]. 光谱学与光谱分析, 2018, 38(3): 79-86.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
14 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
15 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
16 |
李苑溪, 陈锡云, 罗达, 等. 铜胁迫下玉米叶片反射光谱的红边位置变化及其与叶绿素的关系[J]. 光谱学与光谱分析, 2018, 38(2): 220-225.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
17 |
冯海宽, 杨福芹, 杨贵军, 等. 基于特征光谱参数的苹果叶片叶绿素含量估算[J]. 农业工程学报, 2018, 34(6): 190-196.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
18 |
兰玉彬, 邓小玲, 曾国亮. 无人机农业遥感在农作物病虫草害诊断应用研究进展[J]. 智慧农业, 2019, 1(2): 1-19.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
19 |
裴信彪, 吴和龙, 马萍, 等. 基于无人机遥感的不同施氮水稻光谱与植被指数分析[J]. 中国光学, 2018, 11(05): 144-152.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
20 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
21 |
武旭梅, 常庆瑞, 落莉莉, 等. 水稻冠层叶绿素含量高光谱估算模型[J]. 干旱地区农业研究, 2019, 37(3): 238-243.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
22 |
曹英丽, 邹焕成, 郑伟, 等. 水稻叶片高光谱数据降维与叶绿素含量反演方法研究[J]. 沈阳农业大学学报, 2019, 50(01): 107-113.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
23 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
24 |
秦占飞, 常庆瑞, 申健, 等. 引黄灌区水稻红边特征及SPAD高光谱预测模型[J]. 武汉大学学报(信息科学版), 2016, (8): 1168-1175.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
25 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
26 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
27 |
王洋, 肖文, 邹焕成, 等. 基于PROSPECT模型的植物叶片干物质估测建模研究[J]. 沈阳农业大学学报, 2018, 49(1): 121-127.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
28 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
29 |
刘明博, 唐延林, 李晓利, 等. 水稻叶片氮含量光谱监测中使用连续投影算法的可行性[J]. 红外与激光工程, 2014, 43(4): 1265-1271.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
30 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
31 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
32 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
33 |
周扬帆, 陈佑启, 何英彬. 基于高光谱曲线的马铃薯与其他主要作物光谱差异性分析[J]. 中国农业资源与区划, 2017, 38(11): 10-16.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
34 |
陈桂芬, 赵姗, 曹丽英, 等. 基于迁移学习与卷积神经网络的玉米植株病害识别[J]. 智慧农业, 2019, 1(2): 34-44.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_ref.label}} |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
/
〈 |
|
〉 |