农业工程学报
農業工程學報
농업공정학보
2013年
11期
139-146
,共8页
林卉%梁亮*%张连蓬%杜培军
林卉%樑亮*%張連蓬%杜培軍
림훼%량량*%장련봉%두배군
遥感%支持向量机%回归分析%叶面积指数(LAI)%反演%小麦
遙感%支持嚮量機%迴歸分析%葉麵積指數(LAI)%反縯%小麥
요감%지지향량궤%회귀분석%협면적지수(LAI)%반연%소맥
remote sensing%support vector machines%regression analysis%LAI%inversion%wheat
为给小麦田间管理提供基础数据,利用高光谱指数实现了小麦冠层叶面积指数(LAI)值的估测.在21种高光谱指数中筛选出了与 LAI 值相关性最强的指数 OSAVI,建立了小麦 LAI 值反演的最小二乘支持向量回归(LS-SVR)模型.分析表明,模型校正集决定系数(C-R2)与预测集决定系数(P-R2)分别达0.851与0.848,可实现小麦LAI值的精确反演,且对LAI值较高与较低的样本均具备良好的预测能力,可有效避免冠层郁闭度等因素对估测结果的影响.利用LS-SVR模型与OMIS影像实现了小麦LAI遥感专题图的制作,其填图结果与地面实测值拟合模型R2达0.774,RMSE仅为0.476,2组数据具有较高的相似度.结果表明:可利用高光谱指数实现小麦冠层LAI值信息的准确获取,且OSAVI系反演建模的优选指数,LS-SVR为建模的优选算法.该研究可为小麦等农作物的长势评估提供参考.
為給小麥田間管理提供基礎數據,利用高光譜指數實現瞭小麥冠層葉麵積指數(LAI)值的估測.在21種高光譜指數中篩選齣瞭與 LAI 值相關性最彊的指數 OSAVI,建立瞭小麥 LAI 值反縯的最小二乘支持嚮量迴歸(LS-SVR)模型.分析錶明,模型校正集決定繫數(C-R2)與預測集決定繫數(P-R2)分彆達0.851與0.848,可實現小麥LAI值的精確反縯,且對LAI值較高與較低的樣本均具備良好的預測能力,可有效避免冠層鬱閉度等因素對估測結果的影響.利用LS-SVR模型與OMIS影像實現瞭小麥LAI遙感專題圖的製作,其填圖結果與地麵實測值擬閤模型R2達0.774,RMSE僅為0.476,2組數據具有較高的相似度.結果錶明:可利用高光譜指數實現小麥冠層LAI值信息的準確穫取,且OSAVI繫反縯建模的優選指數,LS-SVR為建模的優選算法.該研究可為小麥等農作物的長勢評估提供參攷.
위급소맥전간관리제공기출수거,이용고광보지수실현료소맥관층협면적지수(LAI)치적고측.재21충고광보지수중사선출료여 LAI 치상관성최강적지수 OSAVI,건립료소맥 LAI 치반연적최소이승지지향량회귀(LS-SVR)모형.분석표명,모형교정집결정계수(C-R2)여예측집결정계수(P-R2)분별체0.851여0.848,가실현소맥LAI치적정학반연,차대LAI치교고여교저적양본균구비량호적예측능력,가유효피면관층욱폐도등인소대고측결과적영향.이용LS-SVR모형여OMIS영상실현료소맥LAI요감전제도적제작,기전도결과여지면실측치의합모형R2체0.774,RMSE부위0.476,2조수거구유교고적상사도.결과표명:가이용고광보지수실현소맥관층LAI치신식적준학획취,차OSAVI계반연건모적우선지수,LS-SVR위건모적우선산법.해연구가위소맥등농작물적장세평고제공삼고.
Determination of crops' leaf area index (LAI) is of great significance for growth monitoring, water-fertilizer regulation and yield assessment. For the sake of providing basic data for wheat field management, estimation of LAI value of wheat canopy was conducted by using hyperspectral indices. The optimization of Soil-adjusted Vegetation Index(OSAVI)which is the strongest correlation with LAI was selected from 16 kinds of existing hyperspectral indices like GREEN-NDVI and 5 kinds of newest established hyperspectral indices like FD730, and linear model for wheat LAI inversion was established by adopting the Least Squares Method algorithm. The analysis results showed that the calibration set decision coefficient (C-R2) and prediction set decision coefficient (P-R2) of the model reached 0.832 and 0.825 respectively, the Root Mean Square Error of Calibration set (RMSEC) and the Root Mean Square Error of Prediction set (RMSEP) were 0.478 and 0.461 correspondingly, so the accurate inversion of wheat LAI could been realized. To further improve inversion precision, the model was optimized by using the Least Squares Support Vector Regression (LS-SVR). In comparison with linear model, the coefficients of C-R2 and P-R2 reached 0.851 and 0.848 respectively, obviously, higher than the ones of linear model. In the meantime, RMSEC and RMSEP were 0.467 and 0.441 correspondingly, lower than the ones of linear model. The facts also demonstrated that the LS-SVR model was better than linear model for inversion. In order to analyze prediction ability of OSVAI with regard to different LAI samples, comparative analysis was implemented between OSVAL index and the indices such as GREEN-NDVI. The results indicated that OSVAI model built had good prediction ability for the higher LAI value samples and the lower LAI value samples, and meanwhile it could also avoid influencing the result of estimation by the canopy density effectively. Finally, remote sensing thematic map of wheat LAI was achieved by using the LS-SVR model with the OMIS images. By comparing the map result with the ground measurement, the R2 value of fitting model was 0.774, the RMSE was only 0.476, which proved that higher similarity existed in the two sets of data. The results indicated that wheat canopy LAI information could be acquired accurately by using hyperspectral indices, and OSAVI was optimal index for inversion modeling, LS-SVR was the optimization algorithm for modeling. The study can provide a reference for crops growth assessment such as wheat.