基于3种空间预测方法的黄土区土壤颗粒组成空间分布研究—以宁夏海原县为例

doi:10.3864/j.issn.0578-1752.2020.18.008

Abstract

Abstract:

【Objective】 This study was oriented to explore for a model that was capable of predicting spatial distribution of soil particle size distribution in the region with complicated terrain and single parent material, like Haiyuan County, Ningxia. 【Method】 Empirical Bayesian Kriging with the symmetry logratio transform (SLR-EBK), regression Kriging with the symmetry logratio transform (SLR-RK) and random forest with the symmetry logratio transform (SLR-RF) were used to predict spatial distribution of soil particle size distribution in Haiyuan County based on the training set of 100 samples, combined with topographic factors, normalized difference vegetation index and soil types, and then the predictions were validated with a validating set of 24 validation points in the study area for comparison of prediction accuracy. 【Result】 (1) The auxiliary variables that came into the linear regression equator for prediction included elevation (Ele) and soil types. The auxiliary variables that came into the RF model included Ele, soil type, slope (Slo) and wind exposition index (WEI), of which Ele was the most important auxiliary variable, followed by soil types. Slo and wind exposition index (WEI) were less important. (2) Results predicted by three methods showed a spatial distribution trend that the sand content was lower in the southwest and higher in the northeast of the county, while the silt content and clay content were higher in the southwest and lower in the northeast. SLR-RK and SLR-RF could better describe local variation of different soil particle size contents. (3) The mean absolute error (MAE) and root-mean-square error (RMSE) of SLR-RF were lower than those of the other two methods. As for mean Aitchison distance (MAD), the sequences of MAD were obtained as following: SLR-RF (0.208)

Key words: soil particle size distribution, spatial prediction, empirical Bayesian Kriging, regression Kriging, random forest, symmetric log-ratio transform

SHEN Zhe,ZHANG RenLian,LONG HuaiYu,WANG Zhuan,ZHU GuoLong,SHI QianXiong,YU KeFan,XU AiGuo. Research on Spatial Distribution of Soil Particle Size Distribution in Loess Region Based on Three Spatial Prediction Methods—Taking Haiyuan County in Ningxia as an Example[J].Scientia Agricultura Sinica, 2020, 53(18): 3716-3728.

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References 53

[1]	吴克宁, 赵瑞. 土壤质地分类及其在我国应用探讨. 土壤学报, 2019,56(1):227-241.
	WU K N, ZHAO R. Soil texture classification and its application in China. Acta Pedologica Sinica, 2019,56(1):227-241. (in Chinese)
[2]	李宗善, 杨磊, 王国梁, 侯建, 信忠保, 刘国华, 傅伯杰. 黄土高原水土流失治理现状、问题及对策. 生态学报, 2019,39(20):7398-7409.
	LI Z S, YANG L, WANG G L, HOU J, XIN Z B, LIU G H, FU B J. The management of soil and water conservation in the Loess Plateau of China: Present situations, problems, and counter-solutions. Acta Ecologica Sinica, 2019,39(20):7398-7409. (in Chinese)
[3]	LIU T L, JUANG K W, LEE D Y. Interpolating soil properties using kriging combined with categorical information of soil maps. Soil Science Society of America Journal, 2006,70(4):1200-1209. doi: 10.2136/sssaj2005.0126
[4]	徐剑波, 宋立生, 彭磊, 张桥. 土壤养分空间估测方法研究综述. 生态环境学报, 2011,20(8/9):1379-1386.
	XU J B, SONG L S, PENG L, ZHANG Q. Research review on methods of spatial prediction of soil nutrients. Ecology and Environmental Sciences, 2011, 20(8/9):1379-1386. (in Chinese)
[5]	HEBA E, MOHAMED A, ADEL A A, GAD A A. Spatial variation of soil carbon and nitrogen pools by using ordinary Kriging method in an area of north Nile Delta, Egypt. Catena, 2014,113:70-78. doi: 10.1016/j.catena.2013.09.008
[6]	WANG Z, SHI W J. Mapping soil particle-size fractions: A comparison of compositional kriging and log-ratio kriging. Journal of Hydrology, 2017,546(3):526-541. doi: 10.1016/j.jhydrol.2017.01.029
[7]	何甜辉, 蔡建楠, 罗杰敏, 何冠星, 邓依婷. 基于普通克里格的中山市农田土壤砷的空间变异特性. 四川化工, 2018,122(2):55-57.
	HE T H, CAI J N, LUO J M, HE G X, DENG Y T. Spatial variation characteristics of arsenic in farmland soil based on ordinary kriging in Zhongshan city. Sichuan Chemical Industry, 2018, 122(2):55-57. (in Chinese)
[8]	宋丰骥, 常庆瑞, 钟德燕. 黄土高原沟壑区土壤养分空间变异及其与地形因子的相关性. 西北农林科技大学学报(自然科学版), 2011,39(12):174-180.
	SONG F J, CHANG Q R, ZHONG D Y. Spatial variability of soil nutrients and its relations to topographical factors in hilly and gully area of Loess Plateau. Journal of Northwest A & F University (Natural Science Edition), 2011,39(12):174-180. (in Chinese)
[9]	解文艳, 周怀平, 杨振兴, 冯悦晨, 白雪, 杜燕玲. 黄土高原东部潇河流域农田土壤有机质时空变异及影响因素. 农业资源与环境学报, 2019,36(1):96-104.
	XIE W Y, ZHOU H P, YANG Z X, FENG Y C, BAI X, DU Y L. The spatial-temporal variation of soil organic matter and its influencing factors in Xiaohe River basin in eastern Loess Plateau, China. Journal of Agricultural Resources and Environment, 2019,36(1):96-104. (in Chinese)
[10]	文雯, 周宝同, 汪亚峰, 黄勇. 基于辅助环境变量的土壤有机碳空间插值—以黄土丘陵区小流域为例. 生态学报, 2013,33(19):6389-6397. doi: 10.5846/stxb201305030916
	WEN W, ZHOU B T, WANG Y F, HUANG Y. Soil organic carbon interpolation based on auxiliary environmental covariates: a case study at small watershed scale in Loess Hilly region. Acta Ecologica Sinica, 2013,33(19):6389-6397. (in Chinese) doi: 10.5846/stxb201305030916
[11]	连纲, 郭旭东, 傅伯杰, 虎陈霞. 黄土高原县域土壤养分空间变异特征及预测—以陕西省横山县为例. 土壤学报, 2008,45(4):3-10.
	LIAN G, GUO X D, FU B J, HU C X. Spatial variability and prediction of soil nutrients on a county scale on the Loess Plateau-a case study of Hengshan county, Shaanxi province. Acta Pedologica Sinica, 2008,45(4):3-10. (in Chinese)
[12]	LIU Z P, SHAO M A, WANG Y Q. Estimating soil organic carbon across a large-scale region: A state-space modeling approach. Soil Science, 2012,177:607-618. doi: 10.1097/SS.0b013e318272f822
[13]	SAMSONOVA V P, BLAGOVESHCHENSKII Y N, MESHALKINA Y L. Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands. Eurasian Soil Science, 2017,50(3):305-311. doi: 10.1134/S1064229317030103
[14]	杨文静, 王一博, 刘鑫, 孙哲. 基于BP神经网络的青藏高原土壤养分评价. 冰川冻土, 2019,41(1):215-226.
	YANG W J, WANG Y B, LIU X, SUN Z. Nutrient evaluation of the soil in the Qinghai-Tibet Plateau based on BP neural network. Journal of Glaciology and Geocryology, 2019,41(1):215-226. (in Chinese)
[15]	周斌, 许红卫, 王人潮. 基于分类树方法的土壤有机质空间制图研究. 土壤学报, 2003,40(6):801-808.
	ZHOU B, XU H W, WANG R C. Soil Organic matter mapping based on classification tree modeling. Acta Pedologica Sinica, 2003,40(6):801-808. (in Chinese)
[16]	ITO E, ONO K, ITO Y M, ARAKI M. A neural network approach to simple prediction of soil nitrification potential: A case study in Japanese temperate forests. Ecological Modelling, 2008,219(1/2):200-211. doi: 10.1016/j.ecolmodel.2008.08.011
[17]	郑伟, 马楠. 一种改进的决策树后剪枝算法. 计算机与数字工程, 2015,43(6):26-32.
	ZHENG W, MA N. An improved post-pruning algorithm for decision tree. Computer and Digital Engineering, 2015,43(6):26-32. (in Chinese)
[18]	BREIMAN L. Random Forests. Machine Learning, 2001,45(1):5-32. doi: 10.1023/A:1010933404324
[19]	LIU F, ROSSITER D, SONG X D, ZHANG G L, WU H Y, ZHAO Y G. An approach for broad-scal epredictive soil properties mapping in low-relief areas based on responses to solar radiation. Soil Science Society of America Journal, 2020, 84(1):1-19. doi: 10.1002/saj2.v84.1
[20]	郭澎涛, 李茂芬, 罗微, 林清火, 唐群锋, 刘志崴. 基于多源环境变量和随机森林的橡胶园土壤全氮含量预测. 农业工程学报, 2015,31(5):194-200.
	GUO P T, LI M F, LUO W, LIN Q H, TANG Q F, LIU Z W. Prediction of soil total nitrogen for rubber plantation at regional scale based on environmental variables and random forest approach. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(5):194-200. (in Chinese)
[21]	MAREIKE L, BRUNO G, BERND H. Uncertainty in the spatial prediction of soil texture: comparison of regression tree and random forest models. Geoderma, 2012,170(3):70-79. doi: 10.1016/j.geoderma.2011.10.010
[22]	GERALD F, OZIAS K L, HOUNKPATIN, GERHARD W, THIEL M. High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: A comparison of machine learning and multiple linear regression models. Plos One, 2017, 12(1):e0170478. doi: 10.1371/journal.pone.0170478 pmid: 28114334
[23]	SILA A, SHEPHERD K D, POKHARIYAL G P, TOWETT E, WEULLOW E, NYAMBURA M. Using spectral subspaces to improve infrared spectroscopy prediction of soil properties. Isij International, 2015,37(2):188-193. doi: 10.2355/isijinternational.37.188
[24]	刘艳芳, 宋玉玲, 郭龙, 陈奕云, 卢延年, 刘以. 结合高光谱信息的土壤有机碳密度地统计模型. 农业工程学报, 2017,33(2):191-199.
	LIU Y F, SONG Y L, GUO L, CHEN Y Y, LU Y N, LIU Y. Geostatistical models of soil organic carbon density prediction based on soil hyperspectral reflectance. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(2):191-199. (in Chinese)
[25]	ZHANG S W, SHEN C Y, CHEN X Y, YE H C, HUANG Y F, LAI S. Spatial interpolation of soil texture using compositional kriging and regression kriging with consideration of the characteristics of compositional data and environment variables. Journal of Integrative Agriculture, 2013,12(9):1673-1683. doi: 10.1016/S2095-3119(13)60395-0
[26]	张甘霖, 龚子同. 土壤调查实验室分析方法, 北京: 科学出版社, 2012: 8-16.
	ZHANG G L, GONG Z T. Soil Survey Laboratory Methods. Beijing: Science Press, 2012: 8-16. (in Chinese)
[27]	韩光中, 王德彩, 谢贤健. 中国主要土壤类型的土壤容重传递函数研究. 土壤学报, 2016,53(1):93-102.
	HAN G Z, WANG D C, XIE X J. Pedotransfer functions for prediction of soil bulk density for major types of soils in China. Acta Pedologica Sinica, 2016,53(1):93-102. (in Chinese)
[28]	姜赛平, 张怀志, 张认连, 李兆君, 谢良商, 徐爱国. 基于三种空间预测模型的海南岛土壤有机质空间分布研究. 土壤学报, 2018,55(4):227-237.
	JIANG S P, ZHANG H Z, ZHANG R L, LI Z J, XIE L S, XU A G. Research on spatial distribution of soil organic matter in Hainan Island based on three spatial prediction models. Acta Pedologica Sinica, 2018,55(4):227-237. (in Chinese)
[29]	JURGEN B, OLEG A. Land-surface parameters specific to topo- climatology. Developments in Soil Science, 2009,33(11):195-226.
[30]	胡良平. 提高回归模型拟合优度的策略(Ⅱ)-算术均值变换与其他变量变换. 四川精神卫生, 2019,32(1):9-15.
	HU L P. Strategy of improving the goodness of fit of the regression model(Ⅱ)-the transformation of the arithmetic mean and the other variable transformations. Sichuan Mental Health, 2019,32(1):9-15. (in Chinese)
[31]	张晋昕, 李河. 回归分析中定性变量的赋值. 循证医学, 2005,5(3):169-171. doi: 10.3969/j.issn.1671-5144.2005.03.010
	ZHANG J X, LI H. The way to evaluate qualitative variables in regression analysis. The Journal of Evidence-Based Medicine, 2005,5(3):169-171. (in Chinese) doi: 10.3969/j.issn.1671-5144.2005.03.010
[32]	AITCHISON J. On criteria for measures of compositional difference. Mathematical Geology, 1992,24(4):365-379. doi: 10.1007/BF00891269
[33]	GUPTA A, KAMBLE T, MACHIWAL D. Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India. Environmental Earth Sciences, 2017,76(15):512. doi: 10.1007/s12665-017-6814-3
[34]	鲁民颉, 董有福. EBK算法中半变异函数模型对DEM插值精度差异性研究. 测绘技术装备, 2018(1):22-24.
	LU M J, DONG Y F. Study on the difference in accuracy of DEM interpolation based on the semi-variant function model in EBK. Geomatics Technology and Equipment, 2018(1):22-24. (in Chinese)
[35]	李欣海. 随机森林模型在分类与回归分析中的应用. 应用昆虫学报, 2013,50(4):314-321.
	LI X H. Using “random forest”for classification and regression. Chinese Journal of Applied Entomology, 2013,50(4):314-321. (in Chinese)
[36]	GENUER R, POGGI J M, TULEAU-MALOT C. Variable selection using random forests. Pattern Recognition Letters, 2010,31(14):2225-2236. doi: 10.1016/j.patrec.2010.03.014
[37]	冯盼峰, 温永仙. 基于随机森林算法的两阶段变量选择研究. 系统科学与数学, 2018,38(1):119-130.
	FENG P F, WEN Y X. Two-Stage stepwise variable selection based on random forests. Journal of Systems Science and Mathematical Sciences, 2018,38(1):119-130. (in Chinese)
[38]	赵北庚. 基于R语言randomForest包的随机森林建模研究. 计算机光盘软件与应用, 2015,18(2):152-153.
	ZHAO B G. Research on randomForest modeling based on R language random forest package. Computer CD Software and Applications, 2015,18(2):152-153. (in Chinese)
[39]	LIU Q, GU Y L, WANG S H, WANG C C, MA Z H. Canopy spectral characterization of wheat stripe rust in latent period. Journal of Spectroscopy, 2015,2015(1):1-11.
[40]	周敏, 史振威, 丁火平. 遥感图像飞机目标分类的卷积神经网络方法. 中国图象图形学报, 2017,22(5):702-708.
	ZHOU M, SHI Z W, DING H P. Aircraft classification in remotesensing images using convolutional neural networks. Journal of Image and Graphics, 2017, 22(5):702-708. (in Chinese)
[41]	AITCHISON J. The statistical analysis of compositional data. Technometrics, 1982,30(1):120-121. doi: 10.1080/00401706.1988.10488337
[42]	王库. 回归克里格在土壤全氮空间预测上的应用. 中国农学通报, 2013,29(20):142-147.
	WANG K. Application of regression kriging on the spatial prediction of total soil nitrogen. Chinese Agricultural Science Bulletin, 2013,29(20):142-147. (in Chinese)
[43]	MOORE I D, GESSSLER P E, NIELSEN G A, PETERSON G A. Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 1993,57(2):443-452. doi: 10.2136/sssaj1993.03615995005700020026x
[44]	杨其坡, 武伟, 刘洪斌. 基于地形因子和随机森林的丘陵区农田土壤有效铁空间分布预测. 中国生态农业学报, 2018,26(3):422-431.
	YANG Q P, WU W, LIU H B. Prediction of spatial distribution of soil available iron in a typical hilly farmland using terrain attributes and random forest model. Chinese Journal of Eco-Agriculture, 2018,26(3):422-431. (in Chinese)
[45]	张丽萍, 王小云, 张赫斯. 沙盖黄土丘陵坡地土壤理化特性随地形变化规律研究. 地理科学, 2011,31(2):178-183.
	ZHANG L P, WANG X Y, ZHANG H S. Evolution of physical and chemical characteristics of loess with different landforms in slope field under sand cover. Scientia Geographica Sinica, 2011,31(2):178-183. (in Chinese)
[46]	张世文, 黄元仿, 苑小勇, 王睿, 叶回春, 段增强, 龚关. 县域尺度表层土壤质地空间变异与因素分析. 中国农业科学, 2011,44(6):1154-1164.
	ZHANG S W, HUANG Y F, YUAN X Y, WANG R, YE H C, DUAN Z Q, GONG G. The spatial variability and factor analysis of topsoil texture on a county scale. Scientia Agricultura Sinica, 2011,44(6):1154-1164. (in Chinese)
[47]	马冉, 刘洪斌, 武伟. 流域尺度下地形属性对土壤质地类型变异的影响—以重庆市彭水县一小流域为例. 农业资源与环境学报, 2019,36(3):279-286.
	MA R, LIU H B, WU W. Effect of topographic attributes on soil texture class variations at a watershed scale: A case study of a basin in Pengshui County of Chongqing, China. Journal of Agricultural Resources and Environment, 2019,36(3):279-286. (in Chinese)
[48]	LI A D, GUO P T, WU W, LIU H B. Impacts of terrain attributes and human activities on soil texture class variations in hilly areas, south-west China. Environmental Monitoring and Assessment, 2017,189(6):281. doi: 10.1007/s10661-017-5997-0 pmid: 28534308
[49]	BACIS C M, ROSA V S OLIVEIRA V A L, DOS SANTOS M L, DOS ANJOS L H C, DE CARVALHO D F. Topography and spatial variability of soil physical properties. Scientia Agricola, 2009,66(3):338-352. doi: 10.1590/S0103-90162009000300009
[50]	王丽, 王力, 王全九. 不同坡度坡耕地土壤氮磷的流失与迁移过程. 水土保持学报, 2015,29(2):69-75.
	WANG L, WANG L, WANG Q J. The processes of nitrogen and phosphorus loss and migration in slope cropland under different slopes. Journal of Soil and Water Conservation, 2015,29(2):69-75. (in Chinese)
[51]	郑子成, 秦凤, 李廷轩. 不同坡度下紫色土地表微地形变化及其对土壤侵蚀的影响. 农业工程学报, 2015,31(8):168-175.
	ZHENG Z C, QIN F, LI T X. Changes in soil surface microrelief of purple soil under different slope gradients and its effects on soil erosion. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(8):168-175. (in Chinese)
[52]	邹心雨, 张卓栋, 吴梦瑶, 万缘强. 河北坝上地区坡面尺度土壤机械组成的空间变异. 中国水土保持科学, 2019,17(5):44-53.
	ZOU X Y, ZHANG Z D, WU M Y, WAN Y Q. Spatial variability of particle size distribution at slope scale in Bashang region of Hebei province. Science of Soil and Water Conservation, 2019, 17(5):44-53. (in Chinese)
[53]	杨艳丽, 史学正, 于东升, 王洪杰, 徐茂, 王果. 区域尺度土壤养分空间变异及其影响因素研究. 地理科学, 2008,28(6):788-792.
	YANG Y L, SHI X Z, YU D S, WANG H J, XU M, WANG G. Spatial heterogeneity of soil nutrients and their affecting factors at regional scale. Scientia Geographica Sinica, 2008,28(6):788-792. (in Chinese)

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土壤类型 Soil type	采样个数 Sampling point	本县面积占比 Proportion of area (%)
黄绵土 Cultivated loessial soil	65	53.33
黑垆土 Dark loessial soil	21	15.31
灰钙土 Sierozem	19	14.87
灰褐土 Gray cinnamonic soil	9	6.87
新积土 Alluvial soil	5	4.71
粗骨土 Skeletal soil	3	3.75
红黏土 Red clay	2	1.16

辅助变量 Auxiliary variable	粒级 Soil particle
辅助变量 Auxiliary variable	砂粒 Slr-sand	粉粒 Slr-silt	黏粒 Slr-clay
高程Ele	-0.387^**	0.341^**	0.239^**
坡度Slo	0.044	0.061	-0.111
平面曲率 HC	-0.129	0.043	0.107
剖面曲率 PC	-0.018	0.019	-0.011
地形湿度指数 TWI	0.130	-0.132	0.065
风力作用指数 WEI	0.129	-0.045	-0.111
归一化植被指数 NDVI	-0.296^**	0.235^**	0.204^**
土壤类型 ST1	0.576^**	--	--
土壤类型 ST2	--	0.435^**	--
土壤类型 ST3	--	--	0.466^**

样本组 Sample group	粒级 Soil particle	样本数 Sample size	最大值 Max (%)	最小值 Min (%)	均值 Mean (%)	标准差 Std. deviation (%)	变异系数 CV (%)	偏度 Skewness
训练集 Calibration sample	砂粒Sand	100	79.18	43.15	61.82	8.67	14.02	-0.04
	粉粒 Silt	100	40.40	13.21	23.62	5.66	23.96	0.16
	黏粒Clay	100	23.07	7.59	14.56	4.00	27.47	0.24
验证集 Validation sample	砂粒Sand	24	77.49	48.68	63.96	7.23	11.30	-0.34
	粉粒 Silt	24	34.06	13.76	22.68	4.97	21.91	0.48
	黏粒Clay	24	22.61	8.10	13.36	3.40	25.45	0.72

粒级Soil particle	方程Equator	调整决定系数Adjusted R²	概率Probability
砂粒 Slr-sand	slr-sand=0.160-0.140Ele+0.875ST1	0.339	<0.001
粉粒 Slr-silt	slr-silt=-0.070+0.097Ele+0.811ST2	0.208	<0.001
黏粒 Slr-clay	slr-clay=-0.054+0.042Ele+0.945ST3	0.205	<0.001

粒级Soil particle	辅助变量Auxiliary variable	Ntree	Mtry
砂粒 Slr-sand	Ele、ST1、Slo、WEI	300	2
粉粒 Slr-silt	Ele、ST2、Slo、WEI	300	2
黏粒 Slr-clay	Ele、ST3、Slo、WEI	300	2

Research on Spatial Distribution of Soil Particle Size Distribution in Loess Region Based on Three Spatial Prediction Methods—Taking Haiyuan County in Ningxia as an Example

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方法 Method	砂粒 Sand		粉粒 Silt		黏粒 Clay		MAD
方法 Method	MAE (%)	RMSE (%)	MAE (%)	RMSE (%)	MAE (%)	RMSE (%)	MAD
SLR-EBK	5.706	6.741	3.515	4.308	3.113	4.069	0.274
SLR-RK	4.404	5.447	2.756	3.408	2.839	3.677	0.235
SLR-RF	3.809	4.868	2.623	3.317	2.189	2.914	0.208

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