| 基于CyGNSS数据的土壤水分与植被光学厚度反演研究 |
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| 引用本文： | 严清赟,金双根,黄为民,贾燕,魏思远.基于CyGNSS数据的土壤水分与植被光学厚度反演研究[J].南京气象学院学报,2021,13(2):194-203. |
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| 作者姓名： | 严清赟 金双根 黄为民 贾燕 魏思远 |
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| 作者单位： | 南京信息工程大学 遥感与测绘工程学院, 南京, 210044;江苏省协同精密导航定位与智能应用工程研究中心, 南京, 210044,南京信息工程大学 遥感与测绘工程学院, 南京, 210044;江苏省协同精密导航定位与智能应用工程研究中心, 南京, 210044;中国科学院上海天文台, 上海, 200030,纽芬兰纪念大学(加拿大) 应用科学与工程学院, 圣约翰斯, A1B3X,南京邮电大学 地理与生物信息学院, 南京, 210023,南京信息工程大学 遥感与测绘工程学院, 南京, 210044 |
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| 基金项目： | 国家自然科学基金（42001362，42001375） |
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| 摘 要： | 本文提出了一种仅基于CyGNSS数据，能够同时反演土壤水分与植被光学厚度的方案，该方案使用了神经网络与暴力穷举算法.首先考察了2018年以及2020年的数据，并对结果进行了验证.通过分析发现反演结果与参考数据展现了良好的一致性.土壤水分的反演结果与2018年和2020年的测试数据比较，其相关系数分别高达0.86和0.84，均方根误差分别为0.064和0.071 cm3/cm3；对于植被光学厚度，2018年与2020年的相关系数均为0.98，均方根误差分别为0.079和0.084.研究结果表明，CyGNSS可作为一种新型且独立的泛热带土壤水分与植被光学厚度反演手段.
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| 关 键 词： | 全球导航卫星系统反射测量 土壤水分 神经网络 植被光学厚度 |
| 收稿时间： | 2021/1/15 0:00:00 |
Retrievals of soil moisture and vegetation optical depth using CyGNSS data |
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| YAN Qingyun,JIN Shuanggen,HUANG Weimin,JIA Yan and WEI Siyuan.Retrievals of soil moisture and vegetation optical depth using CyGNSS data[J].Journal of Nanjing Institute of Meteorology,2021,13(2):194-203. |
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| Authors: | YAN Qingyun JIN Shuanggen HUANG Weimin JIA Yan and WEI Siyuan |
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| Institution: | School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044;Jiangsu Engineering Center for Collaborative Navigation/Positioning and Smart Applications, Nanjing University of Information Science & Technology, Nanjing 210044,School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044;Jiangsu Engineering Center for Collaborative Navigation/Positioning and Smart Applications, Nanjing University of Information Science & Technology, Nanjing 210044;Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030,Faculty of Engineering and Applied Science, Memorial University, St. John''s, NL A1B3X5,School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023 and School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044 |
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| Abstract: | In this paper,a new scheme is proposed for simultaneously retrieving Soil Moisture (SM) and Vegetation Optical Depth (VOD),solely from the Cyclone Global Navigation Satellite System (CyGNSS) data.This work is accomplished by employing two pre-trained neural networks (NNs),including one for computing SM from the CyGNSS data and Soil Moisture Active Passive (SMAP) VOD as well as the other for calculating VOD from the CyGNSS data and SMAP SM product,through a brute-force searching.By adopting the proposed method,the posterior SM/VOD can be estimated merely using the CyGNSS data,free from other auxiliary data.The attained results are validated against SMAP products for two separate periods:the whole year of 2018 and a recent course in 2020.Satisfactory agreements between the retrieved and referred SM/VOD are achieved,with correlation coefficients (r) of 0.86 and 0.84,along with root-mean-square errors (RMSEs) of 0.064 and 0.071 cm3/cm3 for SM in the years of 2018 and 2020,respectively;and for the verification of VOD,r=0.98 and RMSE=0.079 are acquired for 2018,and r=0.98 and RMSE=0.084 for 2020,respectively.The good consistency obtained in this work illustrates the capability of CyGNSS as a new independent source for estimating pan-tropical SM and VOD. |
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| Keywords: | Global Navigation Satellite System-Reflectometry (GNSS-R) Soil Moisture (SM) Neural Network (NN) Vegetation Optical Depth (VOD) |
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