基于相对光谱变量的无人机遥感水稻估产及产量制图

doi:10.11873/j.issn.1004-0323.2020.2.0458

遥感技术与应用

2020, Vol. 35

Issue (2): 458-468 DOI: 10.11873/j.issn.1004-0323.2020.2.0458

遥感应用

基于相对光谱变量的无人机遥感水稻估产及产量制图

王飞龙¹(

),王福民²(

),胡景辉²,谢莉莉²,谢京凯¹

^1.浙江大学建筑工程学院，浙江杭州 310058
^2.浙江大学环境与资源学院，浙江杭州 310058

Estimating and Mapping Rice Yield Using UAV-Hyperspectral Imager based Relative Spectral Variates

Feilong Wang¹(

),Fumin Wang²(

),Jinghui Hu²,Lili Xie²,Jingkai Xie¹

^1.Institute of Hydrology and Water Resources, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
^2.Institute of Remote Sensing and Information Technology Application, College of Environment and Resource Science, Zhejiang University, Hangzhou 310058, China

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摘要：

及时准确地监测农作物产量信息对国家和区域的粮食生产、贸易及粮食安全预警具有重要意义。当前卫星遥感估产由于高时空分辨率难以同时满足、波段数量少等原因限制估产精度进一步提高，无人机成像高光谱技术以其高时空分辨率、丰富的波段数量和图谱结合的遥感影像等优势被广泛地应用到现代智慧农业与精准农业，使高精度的农作物估产成为了可能。常规无人机估产方法使用的不同时期植被指数在获取时具有不同的光照条件、大气条件和背景，这些外界条件的差异将会引起不同时期植被指数的误差，进而影响估产精度。针对该问题，提出“相对光谱变量”和“相对产量”的概念开展多时期相对变量水稻遥感估产。首先将高光谱成像仪获取的波段进行一对一的组合建立相对归一化光谱指数RNDSI集，并确定水稻不同生育期的最优RNDSI及其构成波段；然后建立不同生育期组合的水稻估产最优模型并做相应的验证。结果显示:使用分蘖期RNDSI_[784,635]、拔节期RNDSI_[807,744]、孕穗期RNDSI_[784,712]和抽穗期RNDSI_[816,736]组成的多元线性回归模型是多生育期估产的最优模型，R²和RMSE分别为0.74和248.97 kg/hm²，并对此结果进行验证，估产平均相对误差绝对值达到了4.31%，结果表明相对植被指数和相对产量的水稻遥感估产方法可较好地应用于像素级的水稻遥感估产。基于该模型绘制了水稻的田间产量分布图，可更加直观地表现不同区域的产量并进行精准地田间管理。关键词：无人机;成像高光谱;相对光谱变量;水稻;估产;空间分布

关键词： 无人机; 成像高光谱; 相对光谱变量; 水稻; 估产; 空间分布

Abstract:

Crop yield is important for national and regional food production, food trade and food security. Traditional yield estimation by satellite remote sensing is limited by many factors such as spatiotemporal resolution and number of bands. UAV imaging hyperspectral technology has been widely applied to modern intelligent agriculture and precision agriculture with its advantages of high spatial and temporal resolution, rich band number and the combination of image and spectrum It is possible to estimate crop yield accurately. The multi-temporal vegetation indices for yield estimation are obtained with different illumination conditions, atmospheric conditions and background values, the differences in these external conditions may result in errors in vegetation indices. Therefore, using these multi-temporal vegetation indices which containing these external conditions for yield estimation is likely to cause errors. To address this problem, this study proposes the concept of “relative spectral variables” and “relative yield” to estimate rice yield using multi- temporal relative variables. Firstly, the bands obtained from hyperspectral imager are combined to establish the Relative Normalized Difference Spectral Index(RNDSI) and the optimal RNDSI are selected for different growth stages. Then, the optimal models of rice yield estimation with different growth stage combinations are determined and validated. The results shows that multiple linear regression model consisting of tillering stage RNDSI_{[784, 635]}, jointing stage RNDSI_{[807, 744]}, booting stage RNDSI_{[784, 712]} and heading stage RNDSI_{[816, 736]} is the optimal models for rice yield estimation with R² of 0.74 and RMSE of 248.97 kg/ha. This model is validated and the result is acceptable with average relative error of 4.31%. In conclusions, the relative vegetation index and relative yield can be applied to the pixel-level yield estimation by remote sensing. Besides, the rice yield distribution map is drawn based on the model, which represents the differences of rice yield at different filed positions. The map may be used to carry out precise field management.

Key words: UAV Imagery hyperspectral Relative spectral variable Rice Yield estimation Spatial distribution

收稿日期: 2018-10-15 出版日期: 2020-07-10

ZTFLH:

TP79

基金资助: 国家重点研发计划(2016YFD0300601);国家自然科学基金项目(41871328);浙江省微波目标特性测量与遥感重点实验室开放基金(2018KF02)

通讯作者: 王福民 E-mail: wangfeilong@zju.edu.cn;wfm@zju.edu.cn

作者简介: 王飞龙（1994-），男，河南洛阳人，硕士研究生，主要从事水文与水资源、遥感应用方面的研究。E?mail：wangfeilong@zju.edu.cn

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引用本文:

王飞龙,王福民,胡景辉,谢莉莉,谢京凯. 基于相对光谱变量的无人机遥感水稻估产及产量制图[J]. 遥感技术与应用, 2020, 35(2): 458-468.

Feilong Wang,Fumin Wang,Jinghui Hu,Lili Xie,Jingkai Xie. Estimating and Mapping Rice Yield Using UAV-Hyperspectral Imager based Relative Spectral Variates. Remote Sensing Technology and Application, 2020, 35(2): 458-468.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.2.0458 或 http://www.rsta.ac.cn/CN/Y2020/V35/I2/458

图1 实验田位置

图2 研究区实验设计信息

图3 无人机高光谱平台

图4 无人机高光谱平台获取的高光谱数据立方体

图5 不同波段组合的相对光谱指数RNDSI与相对产量相关系数图

表1 水稻不同生育期RNDSI的最佳波段组合

图6 最佳RNDSI和相对产量的相关系数随生育期变化特征

表2 单一生育期估产模型

表3 两个生育期组合估产模型

表4 3个生育期组合估产模型

表5 4个生育期组合估产模型

表6 5个生育期组合估产模型

表7 模型评估参数

图7 模型验证实测产量与预测产量对比

图8 研究区水稻产量空间分布图

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