基于光譜分類的土壤鹽分含量預(yù)測(cè)
發(fā)布時(shí)間：2019-02-18 19:47
【摘要】：基于相似土壤組分和光譜特征,利用土壤光譜反射率數(shù)據(jù)和曲線特征來(lái)進(jìn)行土壤光譜分類,同時(shí)充分挖掘有效信息是光譜分析的重要應(yīng)用方向之一。借助模糊k-均值聚類方法將土壤光譜數(shù)據(jù)分成四個(gè)類別(分類前先將原始光譜進(jìn)行范圍歸一化處理),比較分析了不同類型土壤在光譜分類前后的高光譜特征,然后利用Kennard-Stone法將各類別樣本劃分為建模集和預(yù)測(cè)集,將預(yù)處理后的建模集光譜數(shù)據(jù)作為輸入量,采用偏最小二乘回歸法(PLSR)方法建立全局和各自類別的鹽分預(yù)測(cè)模型。結(jié)果表明:光譜分類建模較按土壤系統(tǒng)分類建模和全局建模的精度有明顯提高,其預(yù)測(cè)模型總體的預(yù)測(cè)決定系數(shù)RP2、預(yù)測(cè)均方根誤差RMSEP、相對(duì)分析誤差RPD和RPIQ(樣本觀測(cè)值三四分位數(shù)Q3與一四分位數(shù)Q1之差與RMSEP的比值)四個(gè)指標(biāo)分別從0.664、1.219、1.733和1.461提高至0.818、1.132、2.356和2.422,其中RPD提高幅度達(dá)23.13%,四個(gè)類別所建模型RPD均大于2.0,可以對(duì)土壤含鹽量進(jìn)行較為精確的定量研究。研究結(jié)果為利用大樣本光譜數(shù)據(jù)建立大尺度區(qū)域的鹽分等土壤屬性預(yù)測(cè)模型提供一種新的思路和方法。
[Abstract]:Based on similar soil components and spectral characteristics, soil spectral classification based on soil spectral reflectance data and curve features, while fully mining effective information is one of the important applications of spectral analysis. The soil spectral data were classified into four categories by using fuzzy k-means clustering method. The hyperspectral characteristics of different types of soils before and after spectral classification were compared and analyzed. Then the samples of each class are divided into modeling set and prediction set by using Kennard-Stone method. The spectral data of pre-processed modeling set is taken as input quantity, and the global and their salt prediction models are established by partial least square regression (PLSR) method. The results show that the precision of spectral classification modeling is significantly higher than that of soil system classification modeling and global modeling, and the prediction model total prediction decision coefficient RP2, is used to predict root mean square error (RMSEP,). The relative analysis errors RPD and RPIQ (the difference between quartile Q3 and quartile Q1 and RMSEP) increased from 0.664, 1.219, 1.733 and 1.461 to 0.818, 1.132, 2.356 and 2.422, respectively, and the difference between Q3 and Q1 increased from 0.664, 1.219, 1.733 and 1.461 to 0.818, 1.132, 2.356 and 2.422, respectively. The increase of RPD was 23.13, and the RPD of the four models was more than 2.0, so the salt content of soil could be studied accurately and quantitatively. The results provide a new idea and method for the prediction model of soil properties such as salinity in large scale region using large sample spectral data.
【作者單位】： 塔里木大學(xué)機(jī)械電氣化工程學(xué)院;塔里木大學(xué)現(xiàn)代農(nóng)業(yè)工程重點(diǎn)實(shí)驗(yàn)室;塔里木大學(xué)植物科學(xué)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(41261083,41361048,11464039)資助~~
【分類號(hào)】：S151.93
，
本文編號(hào)：2426144
[Abstract]:Based on similar soil components and spectral characteristics, soil spectral classification based on soil spectral reflectance data and curve features, while fully mining effective information is one of the important applications of spectral analysis. The soil spectral data were classified into four categories by using fuzzy k-means clustering method. The hyperspectral characteristics of different types of soils before and after spectral classification were compared and analyzed. Then the samples of each class are divided into modeling set and prediction set by using Kennard-Stone method. The spectral data of pre-processed modeling set is taken as input quantity, and the global and their salt prediction models are established by partial least square regression (PLSR) method. The results show that the precision of spectral classification modeling is significantly higher than that of soil system classification modeling and global modeling, and the prediction model total prediction decision coefficient RP2, is used to predict root mean square error (RMSEP,). The relative analysis errors RPD and RPIQ (the difference between quartile Q3 and quartile Q1 and RMSEP) increased from 0.664, 1.219, 1.733 and 1.461 to 0.818, 1.132, 2.356 and 2.422, respectively, and the difference between Q3 and Q1 increased from 0.664, 1.219, 1.733 and 1.461 to 0.818, 1.132, 2.356 and 2.422, respectively. The increase of RPD was 23.13, and the RPD of the four models was more than 2.0, so the salt content of soil could be studied accurately and quantitatively. The results provide a new idea and method for the prediction model of soil properties such as salinity in large scale region using large sample spectral data.
【作者單位】： 塔里木大學(xué)機(jī)械電氣化工程學(xué)院;塔里木大學(xué)現(xiàn)代農(nóng)業(yè)工程重點(diǎn)實(shí)驗(yàn)室;塔里木大學(xué)植物科學(xué)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(41261083,41361048,11464039)資助~~
【分類號(hào)】：S151.93
，
本文編號(hào)：2426144
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