基于范畴型变量和贝叶斯最大熵的土壤有机质空间预测
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摘要
选取湖北省沙洋县为研究区域,以土壤质地与土壤有机质定量关系为辅助信息,利用贝叶斯最大熵(BME)方法对沙洋县土壤有机质含量进行空间预测,并与以土壤质地和土壤全氮为辅助变量的协同克里格方法预测结果精度作对照,探讨两种方法的预测效果。结果表明,协同克里格方法和BME方法均能较好反映研究区有机质空间分布特征。在辅助变量与土壤养分存在显著相关性条件下,BME方法能更好地利用范畴型变量等多种类型辅助信息。比较极值误差范围、平均绝对误差、均方根误差等方面,BME方法在土壤属性空间预测方面具有更高精度,且能有效降低数据获取成本和难度,在县域尺度土壤属性空间预测上具有更大优势。
In this paper, spatial dis tribution of soil organic matter(SOM) was predicted based on the Bayesian Maximum Entropy method(BME) with the auxiliary information generated by quantitative relationship between soil texture and SOM. The results were compared with those obtained by Cokriging methods(CK) using soil texture and soil total nitrogen as auxiliary variables. The prediction accuracy of BME and CK was analyzed by mean absolute error(MAE) and root-mean-square error(RMSE). Results indicated that BME could describe the spatial distribution characteristics of SOM of the study area as well as CK. In the condition of significant correlation between auxiliary variables and SOM, BME could effectively narrow the range of errors, reduce the MAE and RMSE, improve the prediction accuracy and reduce the cost and difficulty of data acquisition. Therefore, BME is a better method in the spatial prediction of soil properties in the county scale.
In this paper, spatial dis tribution of soil organic matter(SOM) was predicted based on the Bayesian Maximum Entropy method(BME) with the auxiliary information generated by quantitative relationship between soil texture and SOM. The results were compared with those obtained by Cokriging methods(CK) using soil texture and soil total nitrogen as auxiliary variables. The prediction accuracy of BME and CK was analyzed by mean absolute error(MAE) and root-mean-square error(RMSE). Results indicated that BME could describe the spatial distribution characteristics of SOM of the study area as well as CK. In the condition of significant correlation between auxiliary variables and SOM, BME could effectively narrow the range of errors, reduce the MAE and RMSE, improve the prediction accuracy and reduce the cost and difficulty of data acquisition. Therefore, BME is a better method in the spatial prediction of soil properties in the county scale.
引文
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[32]庞夙,李廷轩,王永东,等.县域农田土壤铜含量的协同克里格插值及采样数量优化[J].中国农业科学,2009,42(8):2828-2836.
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[35]石淑芹,陈佑启,李正国,等.基于土壤类型和微量元素辅助信息的土壤属性空间模拟[J].农业工程学报,2010,26(12):199-205.
[2]GESSLER P E,CHADWICK OA,CHAMRAN F,et al.Modeling soil-landscape and ecosystem properties using terrain attributes[J].Soil Science Society of American Journal.2000,64(6):2045-2056.
[3]MOORE ID,GESSLER PE,NIELSEN GA,et al.Soil attribute prediction using terrain analysis[J].Soil Science Society of American Journal,1993;57(2):443-452.
[4]ZHU A X,BAND L,VERTESSY R,et al.Derivation of soil properties using a soil land inference model(So LIM)[J].Soil Science Society of American Journal.1997,61(2):523-533.
[5]WU J,NORVELL W A,HOPKINS DG,et al.Improved prediction and mapping of soil copper by kriging with auxiliary data for cation-exchange capacity[J].Soil Science Society of America Journal,2003,67(3):919-927.
[6]郭龙,张海涛,陈家赢,等.基于协同克里格插值和地理加权回归模型的土壤属性空间预测比较[J].土壤学报,2012,49(5):1037-1042.
[7]罗明,裴韬.空间软数据及其插值方法研究进展[J].地理科学进展,2009,28(5):663-672.
[8]GOOVAERTS P.Geostatistics for Natural Resources Evaluation[M].New York:Oxford University Press,1997.
[9]JUANG K W,LEE D Y.A Comparison of Three Kriging Methods Using Auxiliary Variables in Heavy-Metal Contaminated Soils[J].Journal of Environmental Quality,1998,27(2):355-363.
[10]姜勇,李琪,张晓珂,等.利用辅助变量对污染土壤锌分布的克里格估值[J].应用生态学报,2006,17(1):97-101.
[11]张贝,李卫东,杨勇,等.贝叶斯最大熵地统计学方法及其在土壤和环境科学上的应用[J].土壤学报,2011,48(4):831-839.
[12]LEE SJ,BALLING R,GOBER P.Bayesian Maximum Entropy Mapping and the Soft Data Problem in Urban Climate Research[J].Annals of the Association of American Geographers.2008,98(2):309-322.
[13]李亮亮,依艳丽,凌国鑫,等.地统计学在土壤空间变异研究中的应用[J].土壤通报,2005,36(2):265-268.
[14]孙英君,王劲峰,柏延臣.地统计学方法进展研究[J].地球科学进展,2004,19(2):268-274.
[15]王军,邱扬.土地质量的空间变异与尺度效应研究进展[J].地理科学进展,2005,24(4):28-35.
[16]THOMAS J S,DAVID W M.The Spatial variation of plant available phosphorus in pastures with contrasting management[J].Soil Science Society of America Journal,2003,67(3):826-836.
[17]范胜龙,黄炎和,林金石.表征土壤有机碳区域分布的优化空间插值模型研究[J].水土保持研究,2011,18(6):1-5.
[18]ORTON TG,LARK RM.Account for the uncertainty in the local mean in spatial prediction by Bayesian Maximum Entropy[J].Stochastic Environmental Research and Risk Assessment.2007,21(6):773-784.
[19]史舟,李艳,程街亮.水稻土重金属空间分布的随机模拟和不确定评价[J].环境科学,2007,28(1):209-214.
[20]WANG G,GERTNER G,PARYSOW P.Spatial prediction and uncertainty analysis of topographic factors for the revised soil loss equation(RUSEL)[J].Soil Water Conservation,2000,55(3):374-384.
[21]BRUS DJ,BOGAERT P,HEUVELINK GBM.Bayesian maximum entropy prediction of soilcategories using a traditional soil map as soft information[J].European Journal of Soil Science,2007,59(2):166-177.
[22]DOUAIK A,VAN MEIRVENNE M,TóTH T.Soil salinity mapping using spatio-temporal kriging and Bayesian maximum entropy with interval soft data[J].Geoderma,2005,128(3):234-248.
[23]李明阳,张晓利,刘方,等.基于贝叶斯最大熵模型的紫金山松材线虫危害程度时空分析[J].西北农林科技大学学报(自然科学版),2012,(7):99-105.
[24]D’OR,D,P.BOGAERT,G.CHRISTAKOS.Application of the BME approach to soil texture mapping[J].Stochastic Environmental Research and Risk Assessment,2001,15(1):87-100.
[25]D’OR,D,BOGAERT,P.2004A.Spatial prediction of categorical variableswith the Bayesian Maximum Entropy approach:the Ooyplodercase study[J].European Journal of Soil Science,2004,55(4),763-775.
[26]李爱华,柏延臣.基于贝叶斯最大熵的甘肃省多年平均降水空间化研究[J].中国沙漠,2012,32(5):1408-1416.
[27]孙永厚,周洪彪,黄美发.基于最大熵的测量不确定度的贝叶斯评估方法[J].统计与决策,2008,(12):143-145.
[28]鲍士旦.土壤农化分析[M].北京:中国农业出版社,2005.
[29]曾强,吴平,陈星峰,等.南平植烟土壤质地状况及其与土壤养分的关系[J].安徽农业科学,2012,40(5):2763-2765.
[30]张爽,潘伟,孙振生,等.苹果梨园土壤质地与土壤养分间的相关分析[J].黑龙江农业科学,2010,(2):31-32.
[31]邹松兵.基于GIS的甘肃中部地区气温降水分布模式研究[D].兰州:兰州大学,2002.
[32]庞夙,李廷轩,王永东,等.县域农田土壤铜含量的协同克里格插值及采样数量优化[J].中国农业科学,2009,42(8):2828-2836.
[33]CHRISTAKOS G.A Bayesian Maximum-Entropy view to the spatial estimation problem[J].Mathematical Geology,1990,22(7):763-777.
[34]杨勇,李卫东,贺立源.基于贝叶斯最大熵与历史数据的土壤属性空间预测[J].中国科技论文在线,2012,5(19):1864-1870.
[35]石淑芹,陈佑启,李正国,等.基于土壤类型和微量元素辅助信息的土壤属性空间模拟[J].农业工程学报,2010,26(12):199-205.