| 集成学习支持的复杂地貌类型区土壤全钾含量自适应曲面建模 |
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| 引用本文： | 刘永坤,刘伟,王昌阳,陈嘉明,马进.集成学习支持的复杂地貌类型区土壤全钾含量自适应曲面建模[J].测绘通报,2019,0(2):63-70. |
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| 作者姓名： | 刘永坤 刘伟 王昌阳 陈嘉明 马进 |
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| 作者单位： | 江苏师范大学地理测绘与城乡规划学院,江苏 徐州,221116;中国矿业大学环境与测绘学院,江苏 徐州,221116;武汉大学遥感信息工程学院,湖北 武汉,430079 |
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| 基金项目： | 国家自然科学青年基金（41601405）；全国大学生创新创业训练重点项目（201710320025Z）；全国大学生创新创业训练省级指导项目（201610320099X） |
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| 摘 要： | 土壤属性空间分布受复杂地学环境要素影响，空间分异特征十分明显，用单一全局插值模型进行土壤属性模拟，难以实现高精度模拟。对于空间不连续、全局插值模型精度有限及适应性差的特点，本文提出了一种集成学习支持的、融合地学环境变量的土壤属性自适应曲面建模方法（ASM-SP）。利用2013年采集的110个样点数据，使用回归克里金（RK）、贝叶斯克里金（BK）、普通克里金插值法（OK）、反距离加权法（IDW）、ASM-SP，分别对青海湖复杂地貌类型区进行土壤全钾含量的插补。本文采用逐点交叉验证（LOOCV）插值方法模拟精度。结果表明，ASM-SP不仅考虑了地学环境变量与土壤属性的非线性关系，而且融合了多个模型的适应性优势，是实现复杂地貌类型区土壤全钾含量的高精度模拟的一种新方法。
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| 关 键 词： | 空间插值 自适应曲面建模 环境变量 线性扫描算法 土壤全钾含量 逐点交叉验证 |
| 收稿时间： | 2018-04-28 |
| 修稿时间： | 2018-10-30 |
Adaptive surface modeling of soil total potassium content based on integrated learning support in complex geomorphological regions |
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| LIU Yongkun,LIU Wei,WANG Changyang,CHEN Jiaming,MA Jin.Adaptive surface modeling of soil total potassium content based on integrated learning support in complex geomorphological regions[J].Bulletin of Surveying and Mapping,2019,0(2):63-70. |
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| Authors: | LIU Yongkun LIU Wei WANG Changyang CHEN Jiaming MA Jin |
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| Institution: | 1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China;
2. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China |
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| Abstract: | The spatial distribution of soil attributes is influenced by the elements of complex geoscience environment, and the spatial differentiation characteristics are very obvious. It is difficult to simulate soil attributes with a single global interpolation model. For the characteristics of spatial discontinuity, limited precision of global interpolation model and poor adaptability, a soil attribute adaptive surface modeling method(ASM-SP) based on integrated learning support and integrated geoscience environment variables was proposed. Based on 110 samples collected in 2013, regression Kriging(RK), Bayesian Kriging (BK), ordinary Kriging(OK) and inverse distance weighting method(IDW), ASM-SP were used to interpolate the soil total potassium content in the complex geomorphological area of Qinghai Lake, respectively. In this paper, the simulation accuracy of different interpolation methods was evaluated by Leave One Out Validation. ASM-SP not only considers the nonlinear relationship between geo-environmental variables and soil attributes, but also combines the adaptive advantages of multiple models. It is a new method to simulate soil total potassium content in complex geomorphological regions with high precision. |
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| Keywords: | spatial interpolation adaptive surface modeling environmental variables linear scanning algorithm soil total potassium content leave one out validati |
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