[1]张骁,王雪,孙华,等.基于光谱数据连续小波分析的多花黑麦草产草量估测方法[J].江苏农业学报,2026,42(07):1399-1407.[doi:doi:10.3969/j.issn.1000-4440.2026.07.011]
　ZHANG Xiao,WANG Xue,SUN Hua,et al.Estimation method of Lolium multiflorum Lamk. yield based on continuous wavelet analysis of spectral data[J].,2026,42(07):1399-1407.[doi:doi:10.3969/j.issn.1000-4440.2026.07.011]
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基于光谱数据连续小波分析的多花黑麦草产草量估测方法()

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
42
期数:
2026年07期
页码:
1399-1407
栏目:
农业信息工程
出版日期:
2026-07-31

文章信息/Info

Title:
Estimation method of Lolium multiflorum Lamk. yield based on continuous wavelet analysis of spectral data
作者:
张骁1; 王雪2; 孙华1; 李文涛1; 沈宇1; 孙毓蔓1; 施庆华1; 顾克余1; 杨智青1
(1.江苏沿海地区农业科学研究所,江苏盐城224002；2.南京农业大学智慧农业学院<人工智能学院>,江苏南京210095)
Author(s):
ZHANG Xiao1; WANG Xue2; SUN Hua1; LI Wentao1; SHEN Yu1; SUN Yuman1; SHI Qinghua1; GU Keyu1; YANG Zhiqing1
(1.Institute of Agricultural Sciences in the Coastal District of Jiangsu Province, Yancheng 224002, China；2.College of Smart Agriculture （College of Artificial Intelligence）, Nanjing Agricultural University, Nanjing 210095, China)
关键词:
多花黑麦草; 产草量; 高光谱; 连续小波分析; 估测模型
Keywords:
ryegrass; grass yield; hyperspectral; continuous wavelet analysis; estimation model
分类号:
S127；S431.14
DOI:
doi:10.3969/j.issn.1000-4440.2026.07.011
文献标志码:
A
摘要:
为提高多花黑麦草产草量的估测精度,本研究以不同氮肥类型及施氮量处理的多花黑麦草单播及其与毛叶苕子混播试验为基础,通过获取拔节期高光谱数据及抽穗期产草量,以传统光谱指数、波段优化的归一化差值植被指数及连续小波分析(Continuous wavelet analysis, CWA)的光谱数据分别进行多花黑麦草单播及混播模式下多花黑麦草产草量估测。结果表明，基于CWA的光谱数据构建的模型可有效克服基于传统植被指数及波段优化的归一化差值植被指数构建的模型在高生物量条件下易饱和的局限,提升多花黑麦草产草量的估测精度；基于CWA的小波系数WF(885,4)构建的产草量估测模型决定系数(R2)高达0.84,其对验证集多花黑麦草产草量的估测值与实测值的偏移值(Bias)、均方根误差(RMSE)、相对均方根误差(RRMSE)和平均绝对百分比误差(MAPE)分别为1 958.54 kg/hm2、3 269.29 kg/hm2、18.45%和17.71%,其对多花黑麦草-毛叶苕子混播模式下的产草量估测虽存在一定的系统性低估,但估测值与实测值仍达到极显著相关,说明该模型对混播牧草产草量亦具有较好的估测能力。本研究结果为多花黑麦草区域生产力评估、栽培措施优化提供了技术支撑,同时亦为其他禾本科牧草产草量及品质遥感估测提供了基础。
Abstract:
To improve the estimation accuracy of ryegrass (Lolium Multiflorum Lamk.) yield, a ryegrass monoculture test and a mixed sowing test with hairy vetch under different nitrogen fertilizer types and application rates were conducted in this study. By obtaining hyperspectral data at jointing stage and grass yield at heading stage, the yield of ryegrass under monoculture and mixed sowing modes was estimated by traditional spectral indices, a band-optimized normalized difference vegetation index and spectral data based on continuous wavelet analysis (CWA). The results showed that the model constructed based on CWA spectral data could effectively overcome the limitations of models constructed based on traditional vegetation indices and the band-optimized normalized difference vegetation index, which were prone to saturation under high biomass conditions, and improve the estimation accuracy of ryegrass yield. The determination coefficient (R2) of the grass yield estimation model based on the wavelet coefficient WF(885,4) was as high as 0.84. For the validation set, the bias value (Bias), root mean square error (RMSE), relative root mean square error (RRMSE) and mean absolute percentage error (MAPE) between the estimated and measured ryegrass yields were 1 958.54 kg/hm2, 3 269.29 kg/hm2, 18.45% and 17.71%, respectively. Although there was a certain systematic underestimation of the grass yield under the mixed sowing mode, the estimated value was still significantly correlated with the measured value. It showed that the model also had good predictive capability for mixed pasture yield. The results of this study provide technical support for regional productivity assessment and cultivation practice optimization of ryegrass, and also provide a basis for remote sensing estimation of yield and quality of other gramineous forages.

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备注/Memo

备注/Memo:
收稿日期：2025-09-27基金项目：农业农村部沿海盐碱地农业科学观测实验站开放课题暨沿海所科研基金项目(YHS202110)作者简介：张骁(1995-),男,内蒙古赤峰人,硕士，助理研究员,主要从事智慧农业、高光谱遥感、牧草智慧栽培等研究。(E-mail)17851602710@163.com。通讯作者：杨智青,(E-mail)yangzhiq88@126.com
更新日期/Last Update: 2026-08-21