本文選題：土壤有機(jī)質(zhì) + 輔助變量��； 參考：《長江流域資源與環(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年測土配方施肥項目采集的16 582個耕地表層(0~20 cm)土壤樣點數(shù)據(jù),借助四方位搜索法、地統(tǒng)計學(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2 416個驗證樣點進(jìn)行獨立驗證的研究結(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法對土壤有機(jī)質(zhì)預(yù)測結(jié)果的均方根誤差、平均絕對誤差、平均相對誤差較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)江西省鄱陽湖流域農(nóng)業(yè)資源與生態(tài)重點實驗室/國土資源與環(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】：國家自然科學(xué)基金項目(41361049) 江西省自然科學(xué)基金項目(20122BAB204012) 江西省贛鄱英才“555”領(lǐng)軍人才項目(201295)~~
【分類號】：S153.6

【相似文獻(xiàn)】

相關(guān)期刊論文 前4條

1 魏周會;龔少紅;馮小明;;作物產(chǎn)量預(yù)測的時間序列神經(jīng)網(wǎng)絡(luò)模型[J];節(jié)水灌溉;2006年06期

2 米湘成,馬克平,鄒應(yīng)斌;人工神經(jīng)網(wǎng)絡(luò)模型及其在農(nóng)業(yè)和生態(tài)學(xué)研究中的應(yīng)用[J];植物生態(tài)學(xué)報;2005年05期

3 李容物,劉金福;七里街水文站實時洪水預(yù)報神經(jīng)網(wǎng)絡(luò)模型[J];福建林學(xué)院學(xué)報;1999年04期

4 任承輝,楊學(xué)震,洪雙旌,陳明華;利用神經(jīng)網(wǎng)絡(luò)理論實現(xiàn)對水土流失快速遙感監(jiān)測[J];中國水土保持;2000年02期

，

本文編號：1736163