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作物学报 ›› 2020, Vol. 46 ›› Issue (8): 1248-1257.doi: 10.3724/SP.J.1006.2020.01004

• 耕作栽培·生理生化 • 上一篇    下一篇

MDBPSO算法优化的全波段光谱数据协同冠层SIF监测小麦条锈病

白宗璠1,竞霞1,*(),张腾1,董莹莹2   

  1. 1西安科技大学测绘科学与技术学院, 陕西西安 710054
    2中国科学院遥感与数字地球研究所数字地球重点实验室, 北京 100094
  • 收稿日期:2020-01-09 接受日期:2020-03-24 出版日期:2020-08-12 网络出版日期:2020-04-17
  • 通讯作者: 竞霞
  • 作者简介:E-mail: bzf1529@163.com
  • 基金资助:
    国家自然科学基金项目(41601467)

Canopy SIF synergize with total spectral reflectance optimized by the MDBPSO algorithm to monitor wheat stripe rust

BAI Zong-Fan1,JING Xia1,*(),ZHANG Teng1,DONG Ying-Ying2   

  1. 1College of Geometrics, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China
    2Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100094, China
  • Received:2020-01-09 Accepted:2020-03-24 Published:2020-08-12 Published online:2020-04-17
  • Contact: Xia JING
  • Supported by:
    National Natural Science Foundation of China (41601467)(41601467)

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摘要：

为了从全波段光谱数据中提取对小麦条锈病敏感的特征参量, 提高小麦条锈病遥感探测模型的运行效率和精度, 本文首先从惯性权重和粒子更新方式两个方面对传统离散粒子群算法(discrete binary particle swarm optimization, DBPSO)进行改进, 利用改进离散粒子群算法(modified discrete binary particle swarm optimization, MDBPSO)从全波段光谱数据中优选遥感探测小麦条锈病严重度的特征变量, 然后与冠层日光诱导叶绿素荧光(solar-induced chlorophyll fluorescence, SIF)数据相结合作为自变量分别利用随机森林(random forest, RF)和后向传播(back propagation, BP)神经网络算法构建小麦条锈病遥感探测模型, 并将其与相关系数(correlation coefficient, CC)分析法和DBPSO算法提取特征参量构建模型的精度进行对比分析。结果表明: (1) MDBPSO算法比传统DBPSO算法具有更快的收敛速度和更高的寻优精度, 改进前后其迭代次数从395次减少到156次, 最优适应度函数(optimum fitness value, OFV)值从0.145减小到0.127。(2)采用MDBPSO算法选择特征变量时, RF和BP神经网络两种方法构建的模型精度均高于CC分析法和DBPSO算法, 其中RF算法预测病情指数(disease index, DI)值和实测DI值间的检验集决定系数(validation set determination coefficient, R2V)比CC分析法和DBPSO算法分别提高了9%和3%, 均方根误差(validation set root mean square error, RMSEV)分别降低了28%和11%, BP神经网络算法预测DI值和实测DI值间的R2V比CC分析法和DBPSO算法分别提高了13%和6%, RMSEV分别降低了21%和10%, 利用MDBPSO算法优选特征参量能够提高小麦条锈病的遥感探测精度。(3)在MDBPSO、DBPSO和CC分析法3种特征选择算法中, RF算法构建的模型精度均高于BP神经网络算法, 其中RF模型预测DI值和实测DI值间的R2V比BP神经网络算法至少提高了7%, 平均提高了9%, RMSEV至少降低了15%, 平均降低了20%。以MDBPSO算法优选的特征参量为自变量利用RF方法构建的小麦条锈病遥感探测的MDBPSO-RF模型是小麦条锈病遥感探测适宜模型, 该研究结果为进一步实现作物健康状况大面积高精度遥感监测提供了新的思路。

关键词: 全波段反射光谱, 改进离散粒子群, 日光诱导叶绿素荧光, 小麦条锈病, 特征波段

Abstract:

In order to extract the characteristic parameters sensitive to wheat stripe rust from total spectral reflectance and improve the operation efficiency and estimation accuracy of the wheat stripe rust remote sensing detection model, this paper improved the traditional discrete binary particle swarm optimization (DBPSO) algorithm from two aspects: inertia weight and particle update method. The modified discrete binary particle swarm optimization (MDBPSO) algorithm was used to select the characteristic parameters for the severity of wheat stripe rust from the total spectral reflectance. The selected characteristic variables were combined with canopy solar-induced chlorophyll fluorescence (SIF) data as independent variables to construct wheat stripe rust estimation model. Random forest (RF) algorithm and back propagation (BP) neural network algorithm were used as model construction method to compare and analyze the accuracy of the feature parameter construction model extracted by the correlation coefficient (CC) analysis method and the DBPSO algorithm. The MDBPSO algorithm had faster convergence speed and higher optimization accuracy than the DBPSO algorithm. The number of iterations before and after the improvement was reduced from 395 to 156. The optimal fitness function (OFV) value decreased from 0.145 to 0.127. When the MDBPSO algorithm was used to select feature variables, the accuracy of the models constructed by the two methods of RF and BP neural networks was higher than that by CC analysis and DBPSO. The validation set determination coefficient (R2V) between the predicted disease index (DI) value and the measured DI value of the RF algorithm was 9% and 3% higher than that of the CC analysis method and the DBPSO algorithm, the validation set root mean square error (RMSEV) was reduced by 28% and 11%, respectively. The R2V between the predicted disease index (DI) value and the measured DI value of the BP neural network algorithm was 13% and 6% higher than that of the CC analysis method and DBPSO algorithm, respectively, and the RMSEV was reduced by 21% and 10% respectively. The MDBPSO algorithm can improve the remote sensing detection accuracy of wheat stripe rust. Among the three feature selection algorithms of MDBPSO, DBPSO, and CC analysis, the accuracy of the model constructed by the RF algorithm was higher than that by the BP neural network algorithm. The R2V between the predicted DI value and the measured DI value of the RF model was at least 7% higher than that of BP neural network algorithm, with an average increase of 9%; RMSEV had reduced by at least 15%, with an average reduction of 20%. The MDBPSO-RF model for wheat stripe rust remote sensing detection constructed by RF method using the characteristic parameters selected by the MDBPSO algorithm as independent variables is an appropriate model for wheat stripe rust remote sensing detection. The research results provide new ideas for further realizing large-area high-precision remote sensing monitoring of crop health.

Key words: total spectral reflectance, modified particle swarm optimization, solar-induced chlorophyll fluorescence, stripe rust of wheat, feature band

图1

SNV变换后反射率-病情指数相关系数曲线图(n = 52) =图中虚线表示0.1%显著水平, R0.001[52] = 0.443。"

图2

DBPSO和MDBPSO算法的适应度函数收敛曲线"

图3

DBPSO和MDBPSO算法选择的特征波段"

图4

小麦条锈病病情指数实测值与预测值分布 — 1:1关系线; ■ 训练集数据; △ 检验集数据。"

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