基于电磁感应数据的南疆棉田土壤pH反演研究

doi:10.13866/j.azr.2022.04.29

摘要/Abstract

摘要：

土壤盐渍化是限制新疆农业发展的主要因素之一,准确获取土壤盐渍化信息,有利于调节农业灌溉用水量大与淡水资源相对匮乏的矛盾,对提高农业生产及环境可持续发展具有重要意义。以南疆阿拉尔垦区9块棉田土壤的pH为研究指标,以EM38-MK2大地电导率仪获取的棉田土壤表观电导率数据及室内测定土壤pH为数据源,采用全区、分区2种建模方法构建土壤剖面pH与土壤表观电导率间的线性回归模型,利用地统计软件对土壤剖面pH进行空间分析。结果表明：（1）分区条件下,土壤表观电导率与土壤剖面pH相关系数为0.60~0.95;全区条件下相关系数为0.28~0.46,均呈极显著负相关,表明EM38-MK2大地电导率仪可用于土壤pH的测定。（2）分区建模模型0.74≤R²≤0.93,2.00≤RPD≤3.50,RMSE较小,均优于全区模型,表明分区模型精度优于全区模型,且土壤含盐量较高的棉田,模型反演精度也较高。（3）空间分析结果表明受冬灌、棉花根系及棉花枯枝落叶等的影响,棉田深层土壤pH均高于表层土壤。研究结果可为土壤pH的快速测定提供思路,对土壤盐渍化的科学管理提供指导。

关键词: 土壤pH, 土壤表观电导率, 反演模型, 棉田, 空间分布, 南疆

Abstract:

Soil salinity aggregation will change soil acidity or alkalinity. As an important land resource reserve area in China, soil salinization and secondary salinization problems are frequent in Xinjiang, Therefore, accurately obtaining soil salinity information and effectively improving soil salinity is beneficial to regulating the contradiction between agricultural irrigation and freshwater resources. Such regulation is of considerable important to the sustainable development of agricultural production and ecological environment. In this study, the soil pH value of cotton fields in Alar Reclamation Area, southern Xinjiang, was taken as the research object, and the EM38-MK2 geodetic conductivity meter was used on the basis of electromagnetic induction technology to obtain soil apparent conductivity data of nine cotton fields in the Reclamation Area. In each cotton field, soil apparent conductivity thresholds were collected in accordance with cotton fields of 0-0.375, 0.375-0.75, 0.75-1.00 m depth, 54 profile samples were collected from three soil layers, and 486 soil profile samples were collected from nine cotton fields. The linear regression model between soil pH and soil apparent conductivity was constructed using two ideas of the global and different regions and combined with geostatistical software to quantify in cotton fields in the Reclamation Area. The two-tailed test results revealed that the correlation coefficients between apparent conductivity and soil pH were 0.60-0.95 for the idea of different regions under the condition of P < 0.01; while those for the global regions were 0.28-0.46. Both findings were highly significant negative correlations, indicating that EM38-MK2 could be used for the determination of soil pH. Linear regression models between apparent conductivity data and soil pH were constructed using the global region and different regions. The zonal models 0.74 ≤ R²≤ 0.93 and 2.00 ≤ RPD ≤ 3.50, which have smaller RMSE, showed high accuracy, indicating that the accuracy of the different region model is better than that of the global region models. Meanwhile, the accuracy of the soil pH inversion model is also higher in areas with higher salinity. The Kriging interpolation results showed that the soil pH in the deep layer of cotton fields in the Reclamation Area was higher than that in the surface layer due to the influence of winter irrigation, cotton root system and cotton dead leaves. This study aims to provide ideas for the rapid determination of soil pH. Therefore, soil acid-base risk prediction can be accurate, and effective risk countermeasures can be formulated rationally.

Key words: soil pH, soil apparent conductivity, inversion model, cotton fields, the spatial distribution, southern Xinjiang

王佳文,彭杰,纪文君,白建铎,冯春晖,李洪义. 基于电磁感应数据的南疆棉田土壤pH反演研究[J]. 干旱区研究, 2022, 39(4): 1293-1302.

WANG Jiawen,PENG Jie,JI Wenjun,BAI Jianduo,FENG Chunhui,LI Hongyi. Soil pH inversion based on electromagnetic induction data in cotton field of southern Xinjiang[J]. Arid Zone Research, 2022, 39(4): 1293-1302.

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土层/m	样本数/个	最大值	最小值	均值	标准差	变异系数/%
0~0.375	162	8.71	7.46	7.92	0.31	3.91
0.375~0.75	162	9.09	7.74	8.40	0.33	3.93
0.75~1.0	162	9.07	8.03	8.60	0.26	3.02
0~1.00	486	9.09	7.46	8.31	0.41	4.93

样地	样本数	最大值	最小值	均值	标准差	变异系数/%
1	18	9.02	7.96	8.58	0.38	4.43
2	18	8.80	8.03	8.47	0.18	2.16
3	18	8.72	7.46	8.09	0.39	4.83
4	18	8.93	7.55	8.31	0.43	5.20
5	18	8.94	7.83	8.43	0.34	3.99
6	18	8.59	7.55	8.20	0.32	3.94
7	18	8.99	7.89	8.41	0.35	4.17
8	18	8.66	7.66	8.29	0.31	3.68
9	18	9.09	7.81	8.34	0.48	5.75

	EC_h0.375	EC_h0.75	EC_v0.75	EC_v1.0	pH_0.375	pH_0.75	pH_1.0
EC_h0.375	1
EC_h0.75	0.96^**	1
EC_v0.75	0.94^**	0.97^**	1
EC_v1.0	0.79^**	0.91^**	0.92^**	1
pH_0.375	-0.30^**	-0.34^**	-0.28^**	-0.31^**	1
pH_0.75	-0.44^**	-0.45^**	-0.46^**	-0.45^**	0.50^**	1
pH_1.0	-0.42^**	-0.42^**	-0.43^**	-0.42^**	0.53^**	0.82^**	1

土层/m	建模因子
土层/m	EC_h0.375	（EC_h0.75+EC_v0.75）/2	EC_v1.0	EC_h0.375+（EC_h0.75+EC_v0.75）/2	（EC_h0.75+EC_v0.75）/2+EC_v1.0	EC_h0.375+（EC_h0.75+EC_v0.75）/2+EC_v1.0
0~0.375	0.80	0.76	0.72	0.87	0.83	0.88
0.375~0.75	0.67	0.62	0.56	0.84	0.78	0.84
0.75~1.00	0.81	0.80	0.79	0.82	0.80	0.84

土层/m	建模集			验证集
土层/m	R²	RMSE	RPD	R²	RMSE	RPD
0~0.375	0.21	0.26	0.87	0.13	0.31	0.82
0.375~0.75	0.38	0.26	1.06	0.33	0.26	1.05
0.75~1.00	0.29	0.22	0.95	0.21	0.22	0.99