| 基于时间稳定性和降维因子分析的土壤水分监测优化 |
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| 引用本文： | 刘玉娇,朱 青,吕立刚,廖凯华,徐 飞.基于时间稳定性和降维因子分析的土壤水分监测优化[J].土壤,2016,48(1):186-192. |
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| 作者姓名： | 刘玉娇 朱 青 吕立刚 廖凯华 徐 飞 |
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| 作者单位： | 1. 中国科学院南京地理与湖泊研究所,流域地理学重点实验室,南京 210008;中国科学院大学,北京 100049;2. 中国科学院南京地理与湖泊研究所,流域地理学重点实验室,南京 210008;3. 南京大学地理与海洋科学学院,南京,210046 |
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| 基金项目： | 国家自然科学基金项目(41271109；41301234)和中国科学院南京地理与湖泊研究所“一三五”重点项目(NIGLAS2012135005)资助 |
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| 摘 要： | 以南京市高淳区青山茶场中相邻的茶园和竹林坡地为研究区,对研究区土壤水分进行长期定点监测。基于土壤水分时间稳定性,结合因子分析选取典型样点组合,采用多元线性回归模型构建各监测点土壤水与典型样点间的数量关系。通过典型样点预测各监测点土壤水分,并检验预测结果,以期通过少数样点的监测来反映研究区的整体概况,优化研究区土壤水分监测。结果表明:在茶园仅监测7个点时,验证期RMSE小于1.5 cm3/cm3;竹林仅监测5个样点时,验证期RMSE小于1.7 cm3/cm3,模型能很好地预测研究区各样点土壤水分,为优化土壤水监测、减少野外工作量提供了理论依据。不同土地利用方式、不同深度处土壤水分分布特征存在显著差异;竹林土壤水分具有较强的时间稳定性,土壤水分的空间自相关性较茶园强;30 cm深处比10 cm深处土壤水分具有更稳定的空间分布结构。
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| 关 键 词： | 土壤水分 空间预测 时间稳定性 茶园 竹林 |
| 收稿时间： | 4/3/2015 12:00:00 AM |
| 修稿时间： | 7/4/2015 12:00:00 AM |
Optimizing Soil Moisture Monitoring Based on Temporal Stability and Factor Analysis |
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| LIU Yu-ji,ZHU Qing,LV Li-gang,LIAO Kai-hua and XU Fei.Optimizing Soil Moisture Monitoring Based on Temporal Stability and Factor Analysis[J].Soils,2016,48(1):186-192. |
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| Authors: | LIU Yu-ji ZHU Qing LV Li-gang LIAO Kai-hua and XU Fei |
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| Institution: | Key Laboratory of Watershed Geographic Sciences, Nanjing Instituted of Geographic and Limnology, Chinese Academy of Sciences,Key Laboratory of Watershed Geographic Sciences, Nanjing Instituted of Geographic and Limnology, Chinese Academy of Sciences,School of Geographic and Oceanographic Sciences, Nanjing University,Key Laboratory of Watershed Geographic Sciences, Nanjing Instituted of Geographic and Limnology, Chinese Academy of Sciences and Key Laboratory of Watershed Geographic Sciences, Nanjing Instituted of Geographic and Limnology, Chinese Academy of Sciences |
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| Abstract: | This paper armed to optimize soil moisture monitoring by taking hill slopes in a tea garden and a bamboo forest located in Gaochun district of Nanjing City as examples and monitoring soil moisture in long-term. Based on the temporal stability and factor analysis, representative sampling sites were selected to predict soil water contents for other sampling sites by building stepwise regression models, and then checked the predication accuracy. The results showed: when only monitoring soil water content at 7 representative sampling sites in tea garden,RMSE of prediction was≤1.5 cm3/cm3. In addition, while only monitoring soil water content at 5 representative sampling sites in bamboo forest,RMSE was≤1.7 cm3/cm3. This method can reduce the number of soil moisture monitoring sites in predicting soil water content on hill slopes with limit observations. In addition, land use type and soil depth can affect soil moisture characteristics. Bamboo forest had stronger temporal stability and higher spatial autocorrelation in soil moisture than tea garden, however, the performance of regression models for bamboo forest was worse than those for tea garden. It is noted that the spatial structure of soil moisture at 30 cm depth was more stable than that at 10 cm depth, implying the performance of regression models is better at 30 cm than at 10 cm depth. |
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| Keywords: | Soil water content Spatial prediction Temporal stability Tea garden Bamboo forest |
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