本文選題：土壤有機(jī)質(zhì) + 輔助變量��； 參考：《長(zhǎng)江流域資源與環(huán)境》2017年08期

【摘要】：為快速準(zhǔn)確獲取省域尺度下土壤有機(jī)質(zhì)的空間分布狀況。以江西省2012年測(cè)土配方施肥項(xiàng)目采集的16 582個(gè)耕地表層(0~20 cm)土壤樣點(diǎn)數(shù)據(jù),借助四方位搜索法、地統(tǒng)計(jì)學(xué)和遙感影像分析技術(shù)提取環(huán)境因子和鄰近信息作為輔助變量,構(gòu)建基于地理坐標(biāo)與輔助變量的BP神經(jīng)網(wǎng)絡(luò)模型和普通克里金法結(jié)合的方法(BPNN_OK)、基于地理坐標(biāo)與輔助變量的RBF神經(jīng)網(wǎng)絡(luò)模型和普通克里金法結(jié)合的方法(RBFNN_OK)和普通克里金法(OK法)3種方法,模擬省域尺度下耕地表層(0~20 cm)土壤有機(jī)質(zhì)的空間分布。對(duì)2 416個(gè)驗(yàn)證樣點(diǎn)進(jìn)行獨(dú)立驗(yàn)證的研究結(jié)果顯示:基于輔助變量的神經(jīng)網(wǎng)絡(luò)模型較普通克里金法有較大提升。BPNN_OK法對(duì)土壤有機(jī)質(zhì)預(yù)測(cè)結(jié)果的均方根誤差、平均絕對(duì)誤差、平均相對(duì)誤差較OK法分別降低了2.76 g/kg、2.34 g/kg、9.83%,RBFNN_OK法較OK法分別降低了2.70 g/kg、2.29 g/kg、9.61%。研究顯示,基于輔助變量的神經(jīng)網(wǎng)絡(luò)模型與OK法結(jié)合的方法明顯地提高了土壤有機(jī)質(zhì)空間分布模擬精度,并且存在改進(jìn)和提高的空間。
[Abstract]:In order to obtain the spatial distribution of soil organic matter at provincial scale quickly and accurately.Based on the data of 16 582 topsoil samples collected from Jiangxi Province in 2012, environmental factors and adjacent information were extracted by means of four directions search method, geostatistics and remote sensing image analysis techniques.The method of combining BP neural network model based on geographical coordinates and auxiliary variables and ordinary Kriging method to build RBF neural network model based on geographical coordinates and auxiliary variables and ordinary Kerkin methodLijin method and OK method,The spatial distribution of soil organic matter was simulated on a provincial scale.The results of independent verification of 2 416 verification samples show that the neural network model based on auxiliary variables has a larger RMS error and an average absolute error than the ordinary Kriging method in predicting soil organic matter by the BPNNNOK method.The average relative error was 2.76 g / kg / kg 2.34 g / kg / kg 9.83% lower than that of OK method, and 2.70 g / kg / kg 2.29 g / kg / kg 9.61% lower than that of OK method respectively.The results show that the combination of neural network model based on auxiliary variables and OK method can obviously improve the simulation accuracy of soil organic matter spatial distribution, and there is room for improvement and improvement.
【作者單位】： 江西農(nóng)業(yè)大學(xué)江西省鄱陽(yáng)湖流域農(nóng)業(yè)資源與生態(tài)重點(diǎn)實(shí)驗(yàn)室/國(guó)土資源與環(huán)境學(xué)院;南方糧油作物協(xié)同創(chuàng)新中心;東華理工大學(xué)馬克思主義學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(41361049) 江西省自然科學(xué)基金項(xiàng)目(20122BAB204012) 江西省贛鄱英才“555”領(lǐng)軍人才項(xiàng)目(201295)~~
【分類號(hào)】：S153.6

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