| 地表粗糙度影响下的GNSS-R土壤湿度反演仿真分析 |
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| 引用本文： | 梁勇,杨磊,吴秋兰,洪学宝,汉牟田,杨东凯.地表粗糙度影响下的GNSS-R土壤湿度反演仿真分析[J].武汉大学学报(信息科学版),2018,43(10):1546-1552. |
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| 作者姓名： | 梁勇 杨磊 吴秋兰 洪学宝 汉牟田 杨东凯 |
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| 作者单位： | 1.山东农业大学信息科学与工程学院, 山东 泰安, 271018 |
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| 基金项目： | 国家十三五重点研发计划2016YFC0803104国家高技术研究发展计划（863计划）2013AA102301国家农业信息化工程技术研究中心开放课题KF2015W003北航北斗技术成果转化及产业化资金资助项目BARI1709 |
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| 摘 要： | 基于双天线全球导航卫星系统反射技术（global navigation satellite system reflectometry，GNSS-R），建立了两个修正地表粗糙度影响的土壤湿度反演模型——解析模型和人工神经网络模型，并以GPS L1 C/A码为例建立了GNSS-R土壤湿度仿真平台，仿真分析了地表粗糙度对两个模型反演精确度的影响。结果表明，当地表均方根高度大于0.010 m时，必须对解析模型进行粗糙度修正。粗糙度影响修正结果显示，小粗糙度情况下修正的解析模型取得了良好的结果，但对于大粗糙度有一定局限性。在均方根高度大于0.025 m时，进行土壤粗糙度修正前，人工神经网络模型精度比解析模型提高了36.83%~72.36%。进行修正后，人工神经网络模型的精度比解析模型提高了42.86%~54.40%。人工神经网络模型在修正前后取得了相近的精度，无修正的人工神经网络模型精度比有修正的解析模型精度仍提高了35.83%~53.48%。
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| 关 键 词： | GNSS-R 双天线 土壤湿度 土壤粗糙度 人工神经网络 |
| 收稿时间： | 2017-05-19 |
Simulation of Soil Roughness Impact in GNSS-R Soil Moisture Retrieval |
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| Affiliation: | 1.School of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China2.School of Electronic and Information Engineering, Beihang University, Beijing 100191, China3.Beihang BeiDou Application Research Institute of Jinhua, Jinhua 321000, China |
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| Abstract: | This paper presents two dual antenna GNSS-R(global navigation satellite system reflectometry) soil moisture retrieval models with soil roughness compensation-an analytic model and an artificial neural network (ANN) model. Then a simulator for GNSS-R soil moisture retrieval is built in consideration of GPS L1 C/A code modulation. After that the impact of soil roughness is elaborated. The simulation results show that the roughness compensation is necessary for the analytic model when the RMSH(root mean square high) is larger than 0.010 m. The roughness compensation works well for small roughness, but there are some limitations for large roughness. Under the situation where RMSH is greater than 0.025 m, the accuracy of ANN model is 36.83%-72.36% higher than the analytic model without roughness compensation, and the accuracy of ANN model is 42.86%-54.40% higher than the analytic model with roughness compensation. The ANN model achieves similar accuracy regardless of roughness compensation, and the accuracy of ANN model without compensation is still 35.83%-53.48% higher than the analytic model with compensation. |
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