基于改进OK模型的土壤有机质空间分布预测——以宜都市红花套镇为例
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摘要
选择合适的土壤有机质(SOM)预测模型是提高区域化空间分布模拟精度的前提,也是监测土壤碳库动态变化和指导农田土壤肥力投入的基础。以湖北宜都红花套镇柑橘区为例,设置普通克里格(OK)插值的SOM结果作对照,分别建立SOM及其最显著相关辅助变量碱解氮间的建模协同克里格(COK_1)、全局协同克里格(COK_2)和两个融合辅助变量协同相关性的改进OK模型(CCOK_1、CCOK_2),探讨纳入辅助变量、改变辅助信息插值数量以及结合辅助变量协同相关性对SOM含量预测的影响。结果表明:1)OK、CCOK_1和CCOK_2的块基比为25%～75%,表现出中等空间自相关性,而COK_1和COK_2的块基比小于25%,具有强烈的空间自相关,SOM的空间异质性受结构性因素影响的比重更大。2)SOM的预测含量范围为7.38～29.03 g?kg~(-1),使用COK_1和COK_2模型插值获得的有机质空间分布较OK更为破碎,CCOK_1和CCOK_2的插值结果则呈连续片状分布,更符合研究区土地利用类型分布的实际情况。3)SOM的空间分布预测精度由高到低依次为CCOK_1≈CCOK_2>COK_2>COK_1≈OK,OK和COK_1两者精度指标相近,COK_2的拟合效果有一定改进,但CCOK_1和CCOK_2的相关系数(r)分别从0.10升高到0.70和0.69,均方根误差(RMSE)分别降低了15.40%和14.78%,预测精度最高。因此,本研究提出的融合辅助变量协同相关性的改进OK模型的估算效果最优且在最大程度上提高辅助信息的参与度,可为SOM预测提供参考。
Choosing a suitable prediction model to estimate soil organic matter(SOM)content is not only a prerequisite to improve the accuracy of spatial distribution simulation,but also the basis for monitoring dynamic changes in soil carbon pool and for guiding soil fertility input in farming.In order to achieve this,a research was set up to investigate the advantages of combined traditional Ordinary Kriging(OK)interpolation and Co-Kriging(COK)interpolation in constructing a new model that integrates Cooperative Correlation of auxiliary variables with OK model(CCOK).The following three aspects were thus discussed:1)whether the inclusion of auxiliary variables had an impact on SOM prediction result;2)what were the differences in SOM prediction results caused by changes in the number of auxiliary information interpolations;and 3)how improved SOM prediction accuracy by cooperative correlation of auxiliary variables.To address these research questions,we collected 329 soil samples from a citrus plantation in Honghuatao Town located in the north Yidu City,Hubei Province.Through physical and chemical analysis,14 soil properties were extracted.The correlation between SOM and other soil properties were discussed based on Pearson correlation coefficient(r)and available nitrogen was chosen as model auxiliary variable with the most significant correlation with SOM.With reference of OK(the control),we constructed modeling COK(COK_1),global COK(COK_2)and two improved OK models(CCOK_1 and CCOK_2).Among the models,COK_1 was a COK model which used modeling set auxiliary variables to participate in modeling.Based on COK_1,COK_2changed the modeling set auxiliary variables to global auxiliary variables.CCOK_1 and CCOK_2 represented the OK interpolation models of two forms of functions constructed by the target variables and its auxiliary variables.Some of the results obtained were as follows:1)the range of the nugget/sill proportions of OK,CCOK_1 and CCOK_2 were 25%-75%,which belonged to medium spatial autocorrelation.However,the nugget/sill proportions of COK_1 and COK_2 were less than 25%,belonging to strong spatial autocorrelation.It then showed that the spatial variability of SOM as cross-variance function with auxiliary variables was more easily recognized by semi-variogram models.2)The predicted SOM in the study area was within 7.38–29.03 g?kg~(-1).Compared with OK interpolation,the strong spatial autocorrelation of COK_1 and COK_2 meant that the spatial distribution of SOM was even more fragmented.Furthermore,plots of CCOK_1 and CCOK_2 predictions were flaky,with digital mapping results of SOM with higher or lower values,which was more consistent with the actual distribution of land use in the study area.3)The accuracies of COK_1 and OK were similar,but that of COK_2 was higher than the above two.Nevertheless,the correlation coefficients(r)of CCOK_1 and CCOK_2 increased from0.10 to 0.70 and 0.69,with root mean square errors(RMSE)decreasing by 15.40%and 14.78%,respectively.Finally,the overall accuracy of SOM digital soil mapping was CCOK_1≈CCOK_2>COK_2>COK_1≈OK.This indicated that CCOK model minimized error between measured and predicted values in SOM prediction.Thus,the synergy of combined SOM estimation and auxiliary variables was a better correlation than the addition of only auxiliary variables or changing the amount of auxiliary variables.The improved OK model proposed in this study improved the maximum participation of auxiliary information,thereby providing a reliable reference for SOM prediction.
Choosing a suitable prediction model to estimate soil organic matter(SOM)content is not only a prerequisite to improve the accuracy of spatial distribution simulation,but also the basis for monitoring dynamic changes in soil carbon pool and for guiding soil fertility input in farming.In order to achieve this,a research was set up to investigate the advantages of combined traditional Ordinary Kriging(OK)interpolation and Co-Kriging(COK)interpolation in constructing a new model that integrates Cooperative Correlation of auxiliary variables with OK model(CCOK).The following three aspects were thus discussed:1)whether the inclusion of auxiliary variables had an impact on SOM prediction result;2)what were the differences in SOM prediction results caused by changes in the number of auxiliary information interpolations;and 3)how improved SOM prediction accuracy by cooperative correlation of auxiliary variables.To address these research questions,we collected 329 soil samples from a citrus plantation in Honghuatao Town located in the north Yidu City,Hubei Province.Through physical and chemical analysis,14 soil properties were extracted.The correlation between SOM and other soil properties were discussed based on Pearson correlation coefficient(r)and available nitrogen was chosen as model auxiliary variable with the most significant correlation with SOM.With reference of OK(the control),we constructed modeling COK(COK_1),global COK(COK_2)and two improved OK models(CCOK_1 and CCOK_2).Among the models,COK_1 was a COK model which used modeling set auxiliary variables to participate in modeling.Based on COK_1,COK_2changed the modeling set auxiliary variables to global auxiliary variables.CCOK_1 and CCOK_2 represented the OK interpolation models of two forms of functions constructed by the target variables and its auxiliary variables.Some of the results obtained were as follows:1)the range of the nugget/sill proportions of OK,CCOK_1 and CCOK_2 were 25%-75%,which belonged to medium spatial autocorrelation.However,the nugget/sill proportions of COK_1 and COK_2 were less than 25%,belonging to strong spatial autocorrelation.It then showed that the spatial variability of SOM as cross-variance function with auxiliary variables was more easily recognized by semi-variogram models.2)The predicted SOM in the study area was within 7.38–29.03 g?kg~(-1).Compared with OK interpolation,the strong spatial autocorrelation of COK_1 and COK_2 meant that the spatial distribution of SOM was even more fragmented.Furthermore,plots of CCOK_1 and CCOK_2 predictions were flaky,with digital mapping results of SOM with higher or lower values,which was more consistent with the actual distribution of land use in the study area.3)The accuracies of COK_1 and OK were similar,but that of COK_2 was higher than the above two.Nevertheless,the correlation coefficients(r)of CCOK_1 and CCOK_2 increased from0.10 to 0.70 and 0.69,with root mean square errors(RMSE)decreasing by 15.40%and 14.78%,respectively.Finally,the overall accuracy of SOM digital soil mapping was CCOK_1≈CCOK_2>COK_2>COK_1≈OK.This indicated that CCOK model minimized error between measured and predicted values in SOM prediction.Thus,the synergy of combined SOM estimation and auxiliary variables was a better correlation than the addition of only auxiliary variables or changing the amount of auxiliary variables.The improved OK model proposed in this study improved the maximum participation of auxiliary information,thereby providing a reliable reference for SOM prediction.
引文
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[2]WALLACE A.Soil organic matter is essential to solving soil and environmental problems[J].Communications in Soil Science and Plant Analysis,2008,25(1/2):15-28
[3]黄昌勇,徐建明.土壤学[M].第3版.北京:中国农业出版社,2010:29-30HUANG C Y,XU J M.Soil Science[M].3rd ed.Beijing:China Agriculture Press,2010:29-30
[4]马泉来,高凤杰,张志民,等.我国东北黑土丘陵区小流域土壤有机质空间分布模拟[J].环境科学研究,2016,29(3):382-390MA Q L,GAO F J,ZHANG Z M,et al.Simulation of spatial distribution of soil organic matter in a mollisol watershed in Northeastern China[J].Research of Environmental Sciences,2016,29(3):382-390
[5]WANG K,ZHANG C R,LI W D,et al.Mapping soil organic matter with limited sample data using geographically weighted regression[J].Journal of Spatial Science,2014,59(1):91-106
[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,2002,8(4):390-394LU J W,CHEN F,WANG F H,et al.Study of classification of the soil nutrient status of citrus orchard in Hubei Province[J].Plant Nutrition and Fertilizer Science,2002,8(4):390-394
[7]叶荣生.有机肥对柑橘营养及生长的影响[D].重庆:西南大学,2013:16-18YE R S.Effect of organic fertilizer on nutrition and growth of citrus[D].Chongqing:Southwest University,2013:16-18
[8]SCULL P,FRANKLIN J,CHADWICK O A,et al.Predictive soil mapping:A review[J].Progress in Physical Geography:Earth and Environment,2003,27(2):171-197
[9]LI Y.Can the spatial prediction of soil organic matter contents at various sampling scales be improved by using regression kriging with auxiliary information?[J].Geoderma,2010,159(1/2):63-75
[10]LI S C,ZHAO Z Q,XIE M M,et al.Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression[J].Environmental Modelling&Software,2010,25(12):1789-1800
[11]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
[12]XIAO Y,GU X M,YIN S Y,et al.Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains,Northwest China[J].Springerplus,2016,5(1):425
[13]SINGH A,SANTRA P,KUMAR M,et al.Spatial assessment of soil organic carbon and physicochemical properties in a horticultural orchard at arid zone of India using geostatistical approaches[J].Environmental Monitoring and Assessment,2016,188(9):529
[14]李增兵,赵庚星,赵倩倩,等.县域耕地地力评价中土壤养分空间插值方法的比较研究[J].中国农学通报,2012,28(20):230-236LI Z B,ZHAO G X,ZHAO Q Q,et al.Comparison of spatial interpolation methods for soil nutrients in cultivated land fertility evaluation[J].Chinese Agricultural Science Bulletin,2012,28(20):230-236
[15]LIU Y L,GUO L,JIANG Q H,et al.Comparing geospatial techniques to predict SOC stocks[J].Soil and Tillage Research,2015,148:46-58
[16]刘爱利,王培法,丁园圆.地统计学概论[M].北京:科学出版社,2012:154-155LIU A L,WANG P F,DING Y Y.Introduction to Geostatistics[M].Beijing:Science Press,2012:154-155
[17]侯景儒,黄竞先.地质统计学的理论与方法[M].北京:地质出版社,1990:59-60HOU J R,HUANG J X.Theory and Methods of Geostatistical[M].Beijing:Geological Publishing House,1990:59-60
[18]史舟,李艳.地统计学在土壤学中的应用[M].北京:中国农业出版社,2006:85-86SHI Z,LI Y.Geostatistics and its Application in Soil Science[M].Beijing:China Agriculture Press,2006:85-86
[19]苏晓燕,赵永存,杨浩,等.不同采样点数量下土壤有机质含量空间预测方法对比[J].地学前缘,2011,18(6):34-40SU X Y,ZHAO Y C,YANG H,et al.A comparison of predictive methods for mapping the spatial distribution of soil organic matter content with different sampling densities[J].Earth Science Frontiers,2011,18(6):34-40
[20]YANG Q Y,LUO W Q,JIANG Z C,et al.Improve the prediction of soil bulk density by cokriging with predicted soil water content as auxiliary variable[J].Journal of Soils and Sediments,2016,16(1):77-84
[21]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):3728-3736YANG S H,ZHANG H T,CHEN J Y,et al.The spatial variability of soil organic carbon in plain-hills transition belt and its environmental impact[J].China Environmental Science,2015,35(12):3728-3736
[22]鲍士旦.土壤农化分析[M].北京:中国农业出版社,2000:30-237BAO S D.Soil and Agricultural Chemistry Analysis[M].Beijing:China Agriculture Press,2000:30-237
[23]司涵,张展羽,吕梦醒,等.小流域土壤氮磷空间变异特征分析[J].农业机械学报,2014,45(3):90-96SI H,ZHANG Z Y,LU M X,et al.Spatial variability of soil nitrogen and phosphorus in small watershed[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(3):90-96
[24]张晗,赵小敏,欧阳真程,等.多尺度下的南方山地丘陵区耕地质量空间自相关分析--以江西省黎川县为例[J].中国生态农业学报,2018,26(2):263-273ZHANG H,ZHAO X M,OUYANG Z C,et al.Multi-scale spatial autocorrelation analysis of cultivated land quality in China’s southern hillside areas:A case study of Lichuan County,Jiangxi Province[J].Chinese Journal of Eco-Agriculture,2018,26(2):263-273
[25]KUMAR S.Estimating spatial distribution of soil organic carbon for the Midwestern United States using historical database[J].Chemosphere,2015,127:49-57
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