摘要
叶面积指数(LAI)是评价作物长势和作物产量的重要参数。为有效利用高光谱信息,优选出最佳波段进而构建新型双波段指数来提高LAI估测精度,以冬小麦为研究对象,获取冬小麦孕穗期无人机高光谱数据和实测地面LAI数据,开展冬小麦LAI反演研究。首先采用连续投影算法(SPA)、最佳指数法(OIF)以及逐波段组合法(E)分别进行无人机高光谱数据最佳波段筛选,进而将所选最佳波段构建新型双波段指数(VI_OIF,VI_SPA,VI_E);然后将构建的新型双波段指数和常规双波段指数(VI_F)与LAI进行相关性对比分析,最后结合支持向量回归(SVR)、偏最小二乘回归(PLSR)和随机森林回归模型(RFR)进行LAI估算,并对比分析常规双波段指数的估算精度,验证最佳波段选择方法构建新型双波段指数的最佳回归模型反演LAI的可行性。结果表明:(1)新构建双波段指数VI_OIF,VI_SPA,VI_E和VI_F与冬小麦LAI的相关性均达到0.05的显著水平,其中VI_SPA和VI_E与LAI的相关系数高于0.65,且RSI_SPA和RSI_E与LAI的相关性较高(r>0.71);(2)对比分析VI_OIF、VI_SPA、VI_E和VI_F构建的SVR模型、PLSR模型和RFR模型的冬小麦LAI估测精度,VI_SPA_PLSR模型估测精度最高,R^(2)和RMSE分别为0.75和0.90。该方法可为无人机高光谱数据波段选择以及冬小麦LAI反演提供技术支持和理论参考。
Leaf area index(LAI)is an important parameter to evaluate crop condition and crop yield.In order to effectively utilize hyperspectral information and improve the estimation accuracy of LAI,the best band was selected,and the new two-band vegetation indexes were constructed.In this study,winter wheat was taken as the research object,the UAV hyperspectral data and ground LAI data were obtained at the booting stage.First,the successive projection algorithm(SPA),optimum index factor(OIF),and each band combination method(E)were used to screen the best band of UAV hyperspectral data,and then the selected best bands were constructed into the new two-band vegetation indexes(VI_OIF,VI_SPA,VI_E).Then,the new two-band vegetation indexes and the conventional two-band vegetation indexes(VI_F)constructed were compared and analyzed for correlation with LAI.Finally,support vector regression(SVR),partial least square(PLSR)and random forest for regression(RFR)were used to construct LAI estimation models.Meanwhile,comparing with the estimation accuracy of the conventional two-band vegetation indexes,the feasibility of LAI estimation was verified by the optimal regression model of the best new two-band vegetation indexes.The results were as follows:(1)The newly constructed two-band vegetation indexes VI_OIF,VI_SPA,VI_E and VI_F correlated with LAI were all at the significant level of 0.05,VI_SPA and VI_E correlated(r>0.65),among which RSI_SPA and RSI_E had the highest correlation coefficient with LAI(r>0.71);(2)The accuracy of LAI estimation of winter wheat based on SVR model,PLSR model and RFR model constructed by VI_OIF,VI_SPA,VI_E and VI_F were compared and analyzed.It was found that the VI_SPA_PLSR model had the highest accuracy and the best predictive ability,whose coefficient of determination(R^(2))and root mean square error(RMSE)were 0.75 and 0.90,respectively.The research results can provide technical support and theoretical reference for the band selection of UAV hyperspectral data and winter wheat LAI estimation.
作者
孔钰如
王李娟
冯海宽
徐艺
梁亮
徐璐
杨小冬
张青琪
KONG Yu-ru;WANG Li-juan;FENG Hai-kuan;XU Yi;LIANG Liang;XU Lu;YANG Xiao-dong;ZHANG Qing-qi(School of Geography,Geomatics and Planning,Jiangsu Normal University,Xuzhou 221116,China;Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture,Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2022年第3期933-939,共7页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(41401397,41971305,41771469)
江苏省自然科学基金项目(BK20140237)
江苏省研究生科研与实践创新计划项目(KYCX20_2370,XSJCX11015)资助。
关键词
无人机
高光谱影像
波段选择
冬小麦
叶面积指数
Unmanned aerial vehicle(UAV)
Hyperspectral image
Band selection
Winter wheat
Leaf area index
