田安紅, 付承彪, 熊黑鋼, 趙俊三

田安紅1,2, 付承彪1**, 熊黑鋼3,4, 趙俊三2

(1. 曲靖師范學(xué)院信息工程學(xué)院 曲靖 655011; 2. 昆明理工大學(xué)國(guó)土資源工程學(xué)院 昆明 650093; 3. 北京聯(lián)合大學(xué)應(yīng)用文理學(xué)院 北京 100083; 4. 新疆大學(xué)資源與環(huán)境科學(xué)學(xué)院 烏魯木齊 830046)

傳統(tǒng)對(duì)土壤元素的反演模型經(jīng)常采用線性的偏最小二乘模型(PLSR), 例如, 夏芳等[14]以浙江省36個(gè)縣市的農(nóng)田土壤為研究對(duì)象, 分析有機(jī)質(zhì)與8種重金屬的相關(guān)性, 并采用PLSR預(yù)測(cè)8種重金屬的含量, 仿真結(jié)果表明PLSR對(duì)重金屬Ni和Cr的預(yù)測(cè)效果較好, 其相對(duì)預(yù)測(cè)性能(RPD)值為1.8~2.0, 而剩余6種重金屬預(yù)測(cè)模型的RPD值均為1.0~1.4。王文俊等[15]以山西的褐土為研究對(duì)象, 利用PLSR對(duì)20種高光譜變換后的預(yù)處理方法進(jìn)行建模估算總氮含量, 仿真結(jié)果表明一階導(dǎo)數(shù)預(yù)處理后建模能得到更好的預(yù)測(cè)結(jié)果, 且最佳的預(yù)處理方法為平均光譜曲線與標(biāo)準(zhǔn)差曲線的乘積, 其次為平均光譜曲線與平均光譜曲線的一階導(dǎo)數(shù)、與標(biāo)準(zhǔn)差曲線的乘積, PLSR模型能對(duì)總氮進(jìn)行有效的預(yù)測(cè)。然而, 土壤高光譜與土壤某元素間的關(guān)系表現(xiàn)為非線性, 傳統(tǒng)線性PLSR對(duì)土壤元素的反演精度有限, 因此需要探索非線性的預(yù)測(cè)方法。

1 材料與方法

1.1 土壤樣本采集

研究區(qū)位于新疆維吾爾自治區(qū)昌吉回族自治州境內(nèi), 87°44￠~88°46￠E, 43°29￠~45°45￠N, 距烏魯木齊約70 km。該區(qū)域土壤鹽漬化嚴(yán)重, 土壤表層的鹽分含量為5.34~44.45 g×kg-1 [1], 夏季非常炎熱, 降水稀少, 蒸發(fā)強(qiáng)烈, 年蒸發(fā)量高達(dá)2 000 mm。

圖1 無(wú)人為干擾區(qū)(A)和人為干擾區(qū)(B)鹽漬土采樣點(diǎn)示意圖

藍(lán)色方框?yàn)樗恢? 紅色圓圈為農(nóng)場(chǎng)位置, 黃色方框?yàn)闊o(wú)人為干擾區(qū)(A區(qū)), 綠色方框?yàn)槿藶楦蓴_區(qū)(B區(qū))。The blue box is the location of the canal, the red circle is the location of the farm, the yellow box is the undisturbed area (area A), and the green box is the human disturbing area (area B).

1.2 野外高光譜測(cè)量

55個(gè)樣本點(diǎn)的野外高光譜采用FieldSpec?3 Hi- Res高精度地物光譜儀測(cè)量, 該儀器的波段范圍300~2 500 nm。350~1 000 nm波段的采樣間隔為1.4 nm, 1 000~2 500 nm波段的采樣間隔為1 nm。野外測(cè)量時(shí)選擇當(dāng)?shù)貢r(shí)間13:00—15:00, 且晴朗無(wú)風(fēng)的天氣進(jìn)行。每次測(cè)量之前用白板進(jìn)行光譜校正處理, 每個(gè)土壤樣本點(diǎn)采用梅花樁采樣法于5個(gè)方向重復(fù)采集10次高光譜, 測(cè)定高度為距離土壤表面15 cm。計(jì)算平均值為該樣點(diǎn)的原始高光譜數(shù)據(jù)。同時(shí), 因邊緣波段(350~390 nm和2 401~2 500 nm)信噪比低及存在水分吸收帶(1 355~1 410 nm和1 820~1 942 nm)的干擾, 刪除這些波段范圍的高光譜數(shù)據(jù)。

1.3 建立BP神經(jīng)網(wǎng)絡(luò)模型

1.4 模型精度評(píng)價(jià)方法

2 結(jié)果與分析

2.1 研究區(qū)鹽漬土4種主要陰離子含量統(tǒng)計(jì)

表1 無(wú)人為干擾區(qū)和人為干擾區(qū)鹽漬土4種陰離子含量描述性統(tǒng)計(jì)

2.2 不同含量鹽漬土的高光譜曲線特征

圖2 無(wú)人為干擾區(qū)(A區(qū))和人為干擾區(qū)(B區(qū))不同含量鹽漬土壤樣本的高光譜曲線圖

2.3 不同人為干擾區(qū)鹽漬土含量反演特征波段的選擇

因此, 本研究將兩種光譜變換在0階、一階和二階微分中通過(guò)0.05檢驗(yàn)的波段選擇為特征波段, 研究區(qū)通過(guò)0.05顯著性檢驗(yàn)的波段數(shù)量個(gè)數(shù)如表2所示, 特征波段對(duì)應(yīng)的高光譜值作為后續(xù)BP神經(jīng)網(wǎng)絡(luò)模型的輸入變量。

圖3 無(wú)人為干擾區(qū)(A區(qū))和人為干擾區(qū)(B區(qū))鹽漬土高光譜與含量的相關(guān)系數(shù)

<|0.05|表示顯著相關(guān)。<|0.05| indicates significant correlation.

表2 無(wú)人為干擾區(qū)和人為干擾區(qū)通過(guò)0.05顯著性檢驗(yàn)的鹽漬土高光譜波段數(shù)量個(gè)數(shù)

R表示原始高光譜, LogR表示對(duì)數(shù)變換后的光譜。R is the original hyperspectral, LogR is logarithmic transformation of R.

2.4 不同人為干擾區(qū)鹽漬土含量高光譜反演模型

表3 無(wú)人為干擾區(qū)和人為干擾區(qū)鹽漬土含量高光譜反演模型的精度

RPD: 相對(duì)預(yù)測(cè)性能。RPD: relative prediction performance.

圖4 無(wú)人為干擾區(qū)(a)和人為干擾區(qū)(b)鹽漬土含量實(shí)測(cè)值和BP模型預(yù)測(cè)值的散點(diǎn)圖

圖中預(yù)測(cè)數(shù)據(jù)為高光譜對(duì)數(shù)二階微分(LogR)的BP模型預(yù)測(cè)值。The predicted values are prediction results of BP model with spectral logarithmic transformation.

圖5 無(wú)人為干擾區(qū)(a)和人為干擾區(qū)(b)鹽漬土含量實(shí)測(cè)值與BP模型預(yù)測(cè)值的擬合效果

圖中預(yù)測(cè)數(shù)據(jù)為高光譜對(duì)數(shù)二階微分(LogR)的BP模型預(yù)測(cè)值。The predicted values are prediction results of BP model with spectral Logarithmic transformation.

3 結(jié)論

圖6 無(wú)人為干擾區(qū)(a)和人為干擾區(qū)(b)鹽漬土含量BP模型的訓(xùn)練過(guò)程

3)統(tǒng)計(jì)相關(guān)系數(shù)在0階、一階和二階微分中通過(guò)0.05檢驗(yàn)的波段數(shù)量, R變換在無(wú)人為干擾區(qū)分別為0個(gè)、38個(gè)和77個(gè), 在人為干擾區(qū)分別為0個(gè)、39個(gè)和74個(gè); LogR變換在無(wú)人為干擾區(qū)分別為1 822個(gè)、264個(gè)和121個(gè), 在人為干擾區(qū)分別為1 659個(gè)、121個(gè)和86個(gè)。

4)無(wú)人為干擾區(qū)的最佳反演模型為二階微分的LogR光譜變換對(duì)應(yīng)的BP模型, 其RPD為3.309, 表明該模型的預(yù)測(cè)能力非常強(qiáng)。人為干擾區(qū)的最佳反演模型為一階微分的LogR光譜變換對(duì)應(yīng)的BP模型, 其RPD為2.234, 表明該模型的預(yù)測(cè)能力很好。

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Tian Anhong1,2, FU Chengbiao1**, XIONG Heigang3,4, ZHAO Junsan2

(1.College of Information Engineering, Qujing Normal University, Qujing 655011, China; 2. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China; 3. College of Applied Arts and Science, Beijing Union University, Beijing 100083, China; 4. College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, China)

S151.9

10.13930/j.cnki.cjea.190700

* 國(guó)家自然科學(xué)基金項(xiàng)目(41901065, 41671198, 41761081)資助

付承彪, 主要從事遙感與地理信息系統(tǒng)的研究。E-mail: fucb305@163.com

田安紅, 主要從事干旱區(qū)鹽漬土的高光譜研究。E-mail: tianfucb@163.com

2019-09-26

2019-12-10

* This study was supported by the National Natural Science Foundation of China (41901065, 41671198, 41761081).

, E-mail: fucb305@163.com

Dec. 10, 2019

Sep. 26, 2019;