棉花学报 ›› 2021, Vol. 33 ›› Issue (3): 224-234.doi: 10.11963/1002-7807.yxzlf.20210428
易翔1(),张立福1,3,*(
),吕新1,张泽1,田敏2,印彩霞1,马怡茹1,范向龙1
收稿日期:
2020-12-28
发布日期:
2021-06-02
通讯作者:
张立福
E-mail:20192012003@stu.shzu.edu.cn;zhanglf@radi.ac.cn
作者简介:
易翔(1996―),男,硕士研究生, 基金资助:
Yi Xiang1(),Zhang Lifu1,3,*(
),Lü Xin1,Zhang Ze1,Tian Min2,Yin Caixia1,Ma Yiru1,Fan Xianglong1
Received:
2020-12-28
Published:
2021-06-02
Contact:
Zhang Lifu
E-mail:20192012003@stu.shzu.edu.cn;zhanglf@radi.ac.cn
摘要:
【目的】地上部生物量是表征植物生命活动的重要参数。探索不同的光谱预处理方法和建模方法,实现对棉花地上部生物量快速、无损、准确的估算,对棉花长势监测和大田精准管理具有重要意义。【方法】以新陆早53号、新陆早45号为研究对象,设置不同施氮处理,于出苗后不同阶段获取棉花地上部生物量和无人机高光谱数据,通过连续投影算法(Successive projections algorithm,SPA)筛选不同预处理[一阶导数、二阶导数、Savitzky-Golay(SG平滑)、多元散射校正]后的特征波长,基于筛选出的不同波长组合使用偏最小二乘法回归(Partial least square regression,PLSR)和随机森林回归(Random forest regression,RFR)分别构建棉花地上部生物量估算模型,比较不同预处理后建立模型的精度,确定最优估算模型。【结果】(1)利用SPA算法对不同预处理后的光谱信息筛选出特征波长9~26个,可实现光谱信息降维。(2)基于SG平滑-SPA处理及PLSR方法建立的模型最佳,R2达到了0.63,均方根误差(Root mean square error,RMSE)为0.42,验证集的R2为0.67,RMSE为0.44。(3)一阶导数-SPA处理后,采用RFR构建的模型最佳,R2达到0.87,RMSE为0.45,验证集R2为0.81,RMSE为0.37。【结论】采用一阶导数预处理结合SPA筛选特征波长,经RFR构建的估算模型结果和验证效果均最佳,可用于棉花地上部生物量定量估算。
易翔,张立福,吕新,张泽,田敏,印彩霞,马怡茹,范向龙. 基于无人机高光谱融合连续投影算法估算棉花地上部生物量[J]. 棉花学报, 2021, 33(3): 224-234.
Yi Xiang,Zhang Lifu,Lü Xin,Zhang Ze,Tian Min,Yin Caixia,Ma Yiru,Fan Xianglong. Estimation of cotton above-ground biomass based on unmanned aerial vehicle hyperspectral and successive projections algorithm[J]. Cotton Science, 2021, 33(3): 224-234.
表1
不同处理下棉花AGB描述性统计"
棉花品种 Cotton variety | 处理 Treatment | 样本数量 Sample number | 最大值 Maximum | 最小值 Minimum | 平均值 Mean | 标准差 Standard deviation |
新陆早53号 | N0 | 15 | 2.73 | 0.32 | 1.30 | 0.74 |
Xinluzao 53 | N1 | 15 | 2.87 | 0.34 | 1.38 | 0.78 |
N2 | 15 | 3.53 | 0.34 | 1.43 | 0.96 | |
NC | 15 | 3.31 | 0.51 | 1.37 | 0.83 | |
N3 | 15 | 2.83 | 0.45 | 1.37 | 0.81 | |
N4 | 15 | 3.09 | 0.34 | 1.26 | 0.84 | |
新陆早45号 | N0 | 15 | 0.43 | 0.28 | 0.36 | 0.05 |
Xinluzao 45 | N1 | 15 | 0.76 | 0.33 | 0.58 | 0.13 |
N2 | 15 | 1.12 | 0.56 | 0.82 | 0.13 | |
NC | 15 | 1.41 | 0.73 | 0.97 | 0.16 | |
N3 | 15 | 1.71 | 0.68 | 1.08 | 0.27 | |
N4 | 15 | 2.19 | 0.87 | 1.35 | 0.41 |
表2
对光谱样本提取的特征波长"
预处理方法 Pretreatment | 特征波长数量 Number of characteristic wavelengths | 光谱样本提取的特征波长 Characteristic wavelength/nm |
原始光谱Original spectrum | 10 | 873、938、962、958、758、942、960、940、727、556 |
一阶导数First derivative | 9 | 940、931、942、947、971、511、976、936、949 |
二阶导数 Second derivative | 24 | 942、962、929、758、967、938、654、918、958、769、980、971、936、993、978、878、940、920、922、1 000、820、911、900、924 |
SG平滑 Savitzky-Golay smoothing | 25 | 916、951、900、676、907、993、820、876、982、973、953、962、765、991、958、938、987、1 000、889、931、971、942、447、922、700 |
多元散射校正Multiplicative scatter correction | 26 | 873、678、949、534、984、931、907、998、576、971、902、924、762、760、936、944、1 000、727、982、891、967、958、976、916、978、991 |
表3
棉花地上部生物量(AGB)估算模型"
预处理 Pretreatment | 偏最小二乘法回归(PLSR) Partial least squares regression | 随机森林回归(RFR) Random forest regression | |||
R2 | RMSE | R2 | RMSE | ||
原始光谱Original spectrum | 0.53 | 0.49 | 0.84 | 0.47 | |
一阶导数First derivative | 0.48 | 0.47 | 0.87 | 0.45 | |
二阶导数Second derivative | 0.52 | 0.46 | 0.84 | 0.45 | |
SG平滑Savitzky-Golay smoothing | 0.63 | 0.42 | 0.65 | 0.46 | |
多元散射校正Multiplicative scatter correction | 0.32 | 0.54 | 0.79 | 0.50 |
表4
棉花主要生育时期地上部生物量(AGB)估算模型验证"
预处理 Pretreatment | 偏最小二乘法回归(PLSR) Partial least squares regression | 随机森林回归(RFR) Random forest regression | |||
R2 | RMSE | R2 | RMSE | ||
原始光谱Original spectrum | 0.63 | 0.44 | 0.73 | 0.45 | |
一阶导数First derivative | 0.55 | 0.50 | 0.81 | 0.37 | |
二阶导数Second derivative | 0.57 | 0.49 | 0.75 | 0.40 | |
SG平滑Savitzky-Golay smoothing | 0.67 | 0.44 | 0.71 | 0.62 | |
多元散射校正Multiplicative scatter correction | 0.21 | 0.66 | 0.57 | 0.56 |
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