典型柑橘种植区土壤有机质空间分布与含量预测
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摘要
以湖北省宜都市红花套镇典型柑橘种植区采集到的329个土壤样本为研究对象,设置土壤有机质(SOM)进行普通克里格(OK)插值的结果为参照,借助地理探测器选取与SOM相关性最大的前5种主要影响因子,分别建立全局模型多元线性回归、偏最小二乘回归和局部模型地理加权回归(GWR),再深入分析模型残差的结构性,构造GWR扩展模型GWRMLR、GWRPLSR,讨论几种SOM预测模型的差异。结果表明:使用GWRPLSR模型预测研究区SOM含量的均方误差和均方根误差可分别降低到9.834和3.136,相对分析误差提高到1.468,实测值与预测值间的相关系数(r)达0.743,具有最高的预测精度,GWRMLR其次,说明除SOM与主要影响因子间存在空间相关性,分析模型残差可进一步消除预测的不平稳性。因此,将模型残差项纳入考虑的局部扩展模型更适宜进行区域化SOM空间分布预测与数字土壤制图。
329 soil samples were collected from the citrus growing areas in Honghuatao Town,Yidu City,Hubei Province.Based on the principle of spatial stratified heterogeneity,the top five major impact factors having the greatest correlation with soil organic matter(SOM)were selected with the GeoDetector software.Using the interpolation results of ordinary Kriging as control,the global model multiple linear regression(MLR),partial least squares regression(PLSR)and local model geographical weighted regression(GWR)were established by the soil organic matter and its main environmental factors.After analyzing the structure of the model residuals,GWRMLRand GWRPLSR were constructed as the extensions of GWR model.The results showed that the mean square error(MSE),root mean square error(RMSE),relative analysis error(RPD)and the correlation coefficient(r)between measured and predicted values of GWRPLSR were 9.834,3.136,1.468,0.743,respectively.The GWRPLSRmodel had the highest prediction accuracy,followed by GWRMLR.In summary,except for the spatial correlation between SOM and its major impact factors,analyzing model residuals can further eliminate the predicted instability.Therefore,taking the model residual terms into consideration is more suitable to predict the regional SOM spatial distribution and digital soil mapping.
329 soil samples were collected from the citrus growing areas in Honghuatao Town,Yidu City,Hubei Province.Based on the principle of spatial stratified heterogeneity,the top five major impact factors having the greatest correlation with soil organic matter(SOM)were selected with the GeoDetector software.Using the interpolation results of ordinary Kriging as control,the global model multiple linear regression(MLR),partial least squares regression(PLSR)and local model geographical weighted regression(GWR)were established by the soil organic matter and its main environmental factors.After analyzing the structure of the model residuals,GWRMLRand GWRPLSR were constructed as the extensions of GWR model.The results showed that the mean square error(MSE),root mean square error(RMSE),relative analysis error(RPD)and the correlation coefficient(r)between measured and predicted values of GWRPLSR were 9.834,3.136,1.468,0.743,respectively.The GWRPLSRmodel had the highest prediction accuracy,followed by GWRMLR.In summary,except for the spatial correlation between SOM and its major impact factors,analyzing model residuals can further eliminate the predicted instability.Therefore,taking the model residual terms into consideration is more suitable to predict the regional SOM spatial distribution and digital soil mapping.
引文
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[29]张巧萍,张玉亭,聂胜委,等.信阳毛尖茶园土壤有机质和硫、镁含量研究[J].土壤通报,2015,46(1):153-156.
[30]张水清,黄绍敏,郭斗斗.河南三种土壤阳离子交换量相关性及预测模型研究[J].土壤通报,2011,42(3):627-631.
[31]连纲,郭旭东,傅伯杰,等.基于环境相关法和地统计学的土壤属性空间分布预测[J].农业工程学报,2009,25(7):237-242.
[2]赵业婷,齐雁冰,常庆瑞,等.渭河平原县域农田土壤有机质时空变化特征[J].土壤学报,2013,50(5):1048-1053.
[3]刘艳芳,卢延年,郭龙,等.基于地类分层的土壤有机质光谱反演校正样本集的构建[J].土壤学报,2016,53(2):332-341.
[4]唐梦迎,丁建丽,夏楠,等.干旱区典型绿洲土壤有机质含量分布特征及其影响因素[J].土壤学报,2017,54(3):759-766.
[5]赵明松,张甘霖,吴运金,等.江苏省土壤有机质含量时空变异特征及驱动力研究[J].土壤学报,2014,51(3):448-458.
[6]鲁剑巍,陈防,王富华,等.湖北省柑橘园土壤养分分级研究[J].植物营养与肥料学报,2002(4):390-394.
[7]黄安,杨联安,杜挺,等.基于多元成土因素的土壤有机质空间分布分析[J].干旱区地理,2015,38(5):994-1003.
[8]KUMAR S.Estimating spatial distribution of soil organic carbon for the Midwestern United States using historical database[J].Chemosphere,2015,127:49-57.
[9]ZENG C Y,YANG L,ZHU A X,et al.Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method[J].Geoderma,2016,281:69-82.
[10]付苗苗,刘梅英,牛智有,等.基于近红外光谱法的水稻秸秆可溶性糖快速检测[J].华中农业大学学报,2016,35(2):115-121.
[11]于雷,洪永胜,耿雷,等.基于偏最小二乘回归的土壤有机质含量高光谱估算[J].农业工程学报,2015,31(14):103-109.
[12]侯艳军,塔西甫拉提·特依拜,买买提·沙吾提,等.荒漠土壤有机质含量高光谱估算模型[J].农业工程学报,2014,30(16):113-120.
[13]GUO L,ZHAO C,ZHANG H T,et al.Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology[J].Geoderma,2017,285:280-292.
[14]宁秀红,郭龙,张海涛.基于空间自回归和地理加权回归模型的不同尺度下土地利用程度研究[J].华中农业大学学报,2013,32(4):48-54.
[15]KUMAR S,LAL R,LIU D S.A geographically weighted regression kriging approach for mapping soil organic carbon stock[J].Geoderma,2012,189:627-634.
[16]瞿明凯,李卫东,张传荣,等.地理加权回归及其在土壤和环境科学上的应用前景[J].土壤,2014,46(1):15-22.
[17]杨顺华,张海涛,陈家赢,等.平原丘陵过渡带土壤有机碳空间分布及环境影响[J].中国环境科学,2015,35(12):3728-3736.
[18]KUMAR S,LAL R,LIU D S,et al.Estimating the spatial distribution of organic carbon density for the soils of Ohio,USA[J].Journal of geographical sciences,2013,23(2):280-296.
[19]HARRIS P,FOTHERINGHAM A S,CRESPO R,et al.The use of geographically weighted regression for spatial prediction:an evaluation of models using simulated data sets[J].Mathematical geosciences,2010,42(6):657-680.
[20]鲍士旦.土壤农化分析[M].北京:中国农业出版社,2000.
[21]杨顺华,张海涛,郭龙,等.基于回归和地理加权回归Kriging的土壤有机质空间插值[J].应用生态学报,2015,26(6):1649-1656.
[22]王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.
[23]WANG J F,LI X H,CHRISTAKOS G,et al.Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region,China[J].International journal of geographical information science,2010,24(1):107-127.
[24]湛东升,张文忠,余建辉,等.基于地理探测器的北京市居民宜居满意度影响机理[J].地理科学进展,2015,34(8):966-975.
[25]HU Y,WANG J F,LI X H,et al.Geographical detector-based risk assessment of the under-five mortality in the 2008 Wenchuan earthquake,China[J].PLoS One,2011,6(6):e21427.
[26]覃文忠.地理加权回归基本理论与应用研究[D].上海:同济大学,2007.
[27]于婧.基于GIS和地统计学方法的土壤养分空间变异及应用研究[D].武汉:华中农业大学,2007.
[28]徐小逊,张世熔,余妮娜,等.沱江中游土壤有效铁空间分布及其影响因素分析[J].西南农业学报,2012,25(3):977-981.
[29]张巧萍,张玉亭,聂胜委,等.信阳毛尖茶园土壤有机质和硫、镁含量研究[J].土壤通报,2015,46(1):153-156.
[30]张水清,黄绍敏,郭斗斗.河南三种土壤阳离子交换量相关性及预测模型研究[J].土壤通报,2011,42(3):627-631.
[31]连纲,郭旭东,傅伯杰,等.基于环境相关法和地统计学的土壤属性空间分布预测[J].农业工程学报,2009,25(7):237-242.