Soil moisture model of Inner Mongolia based on GNSS ZTD and meteorological elements
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摘要: 土壤水含量是农牧业衡量干旱的重要指标，对气候生态具有重要影响，土壤水的变化趋势对于区域的水土流失和气候变化研究等工作具有重要意义，而我国对于土壤水含量的监测起步较晚，因此有必要利用其他已有数据开展土壤水含量模型研究. 利用内蒙古已有全球导航卫星系统(GNSS)天顶对流层延迟(ZTD)数据和湿度、日照以及蒸发量数据进行土壤水含量反演模型研究. 首先将各要素与土壤水含量进行相关性分析，因土壤水含量与GNSS ZTD数据均存在观测噪声，所以应对数据进行去噪处理. 利用小波变换方法剔除噪声，去噪后土壤水含量数据与各要素相关性均有所提高，土壤水含量与湿度相关性最好，两者各实验点的平均相关性为0.645；土壤水含量与日照和蒸发量呈负相关，其平均相关性分别为−0.561、−0.547；而土壤水含量与GNSS ZTD数据相关性最小，其平均相关性为0.271. 根据各要素与土壤水含量的相关性，进行土壤水含量模型构建并进行可靠性验证. 经验证误差统计发现：实验区域NMWJ站模型精度最高，其精度为90.1%；HLAR站点模型精度最低，其精度为69.1%；各站点的平均精度为81.35%. 基于多变量要素的土壤水含量模型可为土壤水含量的趋势变化研究提供参考，通过研究土壤水含量的变化趋势，对区域进行水资源的合理分配利用从而达到节约水资源目的.
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关键词:
- 土壤水含量 /
- 反演 /
- 全球导航卫星系统(GNSS) /
- 天顶对流层延迟(ZTD) /
- 气象要素 /
- 内蒙古
Abstract: Soil water content is an important indicator of drought in agriculture and animal husbandry, and has an important impact on climate and ecology. The change trend of soil water is of great significance for regional soil erosion and climate change research. But monitoring of soil water content in China is lagging. Thus, soil water content should be investigated using other existing data. In this study, the existing Global Navigation Satellite System (GNSS) zenith tropospheric delay (ZTD) and humidity, sunshine, and evaporation data in Inner Mongolia was used to investigate soil water content inversion. The correlation between each element and soil water content was analyzed. Noises was observed in the soil water content and GNSS ZTD data. Wavelet transform was used to eliminate the noises. After denoising, the correlation between soil water content data and each element was improved, and the correlation between soil water content and humidity is the best. The average correlation between the two experimental points is 0.645. Negative correlations are observed between soil water content and sunshine and evaporation, and their average correlations are −0.561 and −0.547, respectively. The correlation between soil water content and GNSS ZTD data is the smallest, with an average correlation of 0.271. Then, a soil water content model was constructed on the basis of the correlation between each element and soil water content, and its reliability was verified. The verified error statistics show that the NMWJ station model in the experimental area has the highest accuracy, with the accuracy of 90.1%, whereas the HLAR site model has the lowest accuracy, with 69.1%. The average accuracy of each station in the study area is 81.35%. The soil water content model based on multivariable elements can provide reference for the research on the change trend of soil water content. Reasonable distribution and utilization of water resources in the region can be conducted through the research on the change trend of soil water content to conserve water resources. -
图 8 气象要素反演土壤水含量
注：反演值1为仅气象要素反演土壤水含量，反演值2为日照、蒸发量和GNSS ZTD反演土壤水含量，反演值3为气象要素融合GNSS ZTD土壤水含量反演.
表 1 土壤水含量与各要素去噪前后相关性对比
站点 原数据相关性 小波分解后相关性 平均相对湿度 日照 蒸发量 GNSS ZTD 0 cm地表温度 平均相对湿度 日照 蒸发量 GNSS ZTD NMDW 0.698 –0.398 –0.511 0.143/0.063 –0.339 0.822 –0.532 –0.691 0.315 NMZL 0.590 –0.401 –0.369 0.386 –0.086/0.279 0.687 –0.515 –0.500 0.389 HLAR 0.593 –0.448 –0.575 0.113/0.163 –0.413 0.650 –0.580 –0.672 –0.192 NMTK 0.498 –0.484 –0.267 0.310 0.175/0.004 0.596 –0.681 –0.420 0.230 NMAG 0.533 –0.496 –0.492 0.329 –0.112/0.101 0.623 –0.594 –0.644 0.375 NMWJ 0.473 –0.431 –0.268 0.198 0.091/0.141 0.493 –0.464 –0.356 0.127/0.040 注：红色字体为显著性值，未标注则其显著性为0.001. 
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表 2 相对湿度与GNSS ZTD相关性
站点 NMAG NMDW HLAR NMTK NMWJ NMZL 相关性 0.754 –0.150 –0.303 0.562 0.335 0.361 注：表2相关性均通过显著性检验.
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表 3 气象要素土壤水含量模型
站点 常数项 湿度 日照 蒸发量 NMDW –0.012 0.207 0.008 –0.003 HLAR 0.208 0.105 –0.053 –0.015 NMAG 0.120 0.020 –0.017 –0.009 NMTK 0.142 0.086 –0.102 0.041 NMWJ 0.132 0.062 –0.032 –0.001 NMZL 0.087 0.163 –0.049 –0.002
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表 4 日照、蒸发量和GNSS ZTD构建土壤水含量模型
站点 常数项 日照 蒸发量 GNSS ZTD NMDW –0.053 –0.034 –0.013 0.101 0 HLAR 0.465 –0.073 –0.018 –0.073 0 NMAG 0.111 –0.019 –0.010 0.011 0 NMTK 0.124 –0.117 0.004 0.043 0 NMWJ 0.096 –0.040 –0.030 0.038 6 NMZL 0.154 –0.067 –0.012 0.041 8
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表 5 气象要素和GNSS ZTD土壤水含量模型
站点 常数项 湿度 日照 蒸发量 GNSS ZTD/m NMDW 0.170 0.232 0.011 –0.002 –0.092 HLAR 0.698 0.202 –0.039 –0.009 –0.261 NMAG 0.312 0.051 –0.016 –0.008 –0.091 NMTK 0.360 0.129 –0.096 0.051 –0.116 NMWJ 0.344 0.103 –0.034 0.001 –0.112 NMZL 0.648 0.234 –0.049 –0.002 –0.291
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表 6 土壤水含量模型误差统计
m 站点 湿度、日照和蒸发量反演误差 日照、蒸发量和GNSS ZTD反演误差 湿度、日照、蒸发量和GNSS ZTD反演误差 平均偏差 RMSE 平均偏差 RMSE 平均偏差 RMSE NMDW 0.012 9 0.022 0 0.010 0 0.032 4 0.013 7 0.020 9 HLAR 0.023 0 0.051 3 0.026 3 0.056 1 0.022 3 0.050 8 NMAG 0.000 7 0.027 3 0.006 8 0.029 7 0.009 0 0.015 1 NMTK 0.013 7 0.024 4 0.014 4 0.031 4 0.013 4 0.023 3 NMWJ 0.005 2 0.017 7 0.002 0 0.370 0 0.006 5 0.013 3 NMZL 0.018 6 0.026 1 0.021 8 0.039 3 0.016 4 0.025 5
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