| 基于车载近地遥感系统的冬小麦生育早期产量估测方法 |
| |
| 引用本文： | 李树强,李民赞.基于车载近地遥感系统的冬小麦生育早期产量估测方法[J].农业工程学报,2014,30(3):120-127. |
| |
| 作者姓名： | 李树强 李民赞 |
| |
| 作者单位： | 1. 河南科技大学农业工程学院，洛阳 471003; 2. 中国农业大学现代精细农业系统集成研究教育部重点实验室，北京 100083;;2. 中国农业大学现代精细农业系统集成研究教育部重点实验室，北京 100083; |
| |
| 基金项目： | 国家科技支撑计划（2012BAH29B04）；863计划（2012AA101901）联合资助 |
| |
| 摘 要： | 冬小麦生育早期的产量估测对于制定冬小麦整个生长期的精准管理策略具有重要的参考意义。该文利用车载近地遥感估产系统对冬小麦生育早期冠层叶片光谱信息进行动态获取,提出了一种基于冠层光谱信息的动态光化学植被指数MPRI(mobile photochemical reflectance index),构建了基于MPRI的冬小麦产量车载近地遥感估产模型,分析了估测效果,结合GIS手段对估产数据进行了空间分析。研究结果表明:冬小麦生育早期冠层指数MPRI对冬小麦的产量单点估测具有一定的效果,决定系数R2约为0.78。车载近地遥感估产系统动态测量时,MPRI表现出良好的数据识别能力。通过设置阈值能够剔除动态测量中的土壤背景干扰信息,说明MPRI对于冬小麦生育早期产量具有较好的估测效果。对动态估产结果进行空间分析,能够掌握小区域内小麦生育早期产量的空间分布情况,为冬小麦生育早期产量估测提供了新的思路和方法。
|
| 关 键 词： | 农作物 遥感 模型 生育早期 产量预测 MPRI GIS |
| 收稿时间： | 2013/8/21 0:00:00 |
| 修稿时间： | 2013/12/27 0:00:00 |
Yield estimation of winter wheat in early growth periods by vehicle-borne ground-based remote sensing system |
| |
| Li Shuqiang and Li Minzan.Yield estimation of winter wheat in early growth periods by vehicle-borne ground-based remote sensing system[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(3):120-127. |
| |
| Authors: | Li Shuqiang and Li Minzan |
| |
| Institution: | 1. College of Agricultural Engineering, Henan University of Science and Technology, Luoyang 471003, China; 2. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China;;2. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; |
| |
| Abstract: | Abstract: The index of crop growth monitoring has a close relationship with crop yield. It could forecast a large-scale food state that indicates a possibility of either a missing or surplus yield as early as possible, and it is therefore important for the macro control of food. Using near-ground remote sensing is significant to understanding the growth of crops and providing accurate and scientific data for precision agriculture. For the small area growers, the vehicle-borne system shows the good prospects and has gradually become the first choice method. This paper discusses a method that is one of the most important tools for yield prediction for winter wheat in the jointing stage. It is an efficient, flexible, and economical operation for a small region. Usually the vehicle-borne growth monitoring system cannot maintain steady operations due to the row spacing of winter wheat in the jointing stage. The background interference on the reflectance will not be suppressed effectively, which will result in a deviation in the growth monitoring and yield prediction. In order to overcome this problem, a new vegetation index named MPRI (mobile photochemical reflectance index) was applied for the yield prediction in this paper. The MPRI derives from the PRI (photochemical reflectance index),which is defined as a normalized difference index using two narrow reflectance bands at 531 and 570 nm that are closely related to xanthophyll cycle pigment content. It has been successfully used to estimate leaf photosynthetic light use efficiency (LUE) across species which vary in water content and nitrogen concentration. Previous research studies demonstrated that a consistent relationship could be established between PRI and LUE calculated from gas exchange measurements at the leaf, small canopy, and full forestor crop canopy scales. It also showed some relationship between the PRI, LUE and wheat yield. The MPRI, which is proposed by this article, was constructed from the two reflectance bands which is a similar principle to PRI, and is constantly obtained by the vehicle-borne system sensors. The tests were carried out by the vehicle-borne system on the winter wheat field. The vehicle-borne system collected the reflectance data of the wheat canopy with the sensors at a sampling rate of 1 point per second. The GPS receiver obtained the location information at the same rate. The indexes of NDVI, TCARI, and MPRI were separately used for the diagnosis and analysis of the yield of wheat canopy, and finally their diagnosis results were contrasted. The results indicated that: It has satisfactory forecasting accuracy on the wheat yield by using the MPRI on the moving monitoring, and the R2 was about 0.76, which was same effect as much higher as by using NDVI. However, the MPRI has a better effect on removing the background interference. This is mainly because the canopy and the soil show the significant difference color. The MPRI of soil and winter wheat canopy are easily distinguishable by the threshold. By focusing on the yield spatial distribution, it was proposed that wheat yields, which were predicted by MPRI, were proven to be transformed with inverse distance weighted (IDW). It was proved that this method showed a positive effect on the yield prediction with the canopy reflectance in the jointing stage of wheat. |
| |
| Keywords: | crops remote sensing models early growth period yield Estimation MPRI GIS |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
