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| 基于ASAR的冬小麦不同生育期土壤湿度反演 | |
| 引用本文： | 鲍艳松,刘 利,孔令寅,王纪华,刘良云.基于ASAR的冬小麦不同生育期土壤湿度反演[J].农业工程学报,2010,26(9):224-232. |
| 作者姓名： | 鲍艳松 刘 利 孔令寅 王纪华 刘良云 |
| 作者单位： | 1. 南京信息工程大学气象灾害省部共建教育部重点实验室,南京,210044 2. 国家农业信息化工程技术研究中心,北京,100089 3. 中国科学院对地观测与数字地球中心,北京,100190 |
| 基金项目： | 国家自然科学基金（40701130）；南京信息工程大学科研基金（S8107360001） |
| 摘 要： | 基于ASAR高级合成孔径雷达ASAR数据和地面调查数据，结合MIMICS模型分析方法，研究ASAR后向散射数据与土壤湿度及冬小麦结构参数之间的关系，构建冬小麦不同生育期土壤湿度反演模型。研究结果表明：冬小麦冠层散射影响ASAR信号探测土壤湿度的深度，冬小麦生长初期（起身期前）ASAR信号探测土壤湿度的最佳深度为0～20 cm，拔节期后ASAR信号探测土壤湿度的最佳深度为0～5 cm。冬小麦抽穗期前，ASAR IS2 VV模式后向散射系数与土壤湿度线性相关性较高，可以利用经验统计模型方法反演土壤湿度；冬小麦生长旺盛期（抽穗期），经验模型土壤湿度反演精度较差，多角度ASAR数据模型能够提高土壤湿度反演精度。利用该土壤湿度反演模型，起身期、拔节期和抽穗期土壤湿度反演的均方根误差分别为0.0125、0.0247和0.0298 g/g。 |
| 关 键 词： | 后向散射，土壤湿度，反演，冬小麦，ASAR |
| 收稿时间： | 2009/12/29 0:00:00 |
| 修稿时间： | 2010/2/28 0:00:00 |
Soil moisture estimation at various growth stages of winter wheat based on ASAR data |
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| Bao Yansong,Liu Li,Kong Lingyin,Wang Jihua and Liu Liangyun.Soil moisture estimation at various growth stages of winter wheat based on ASAR data[J].Transactions of the Chinese Society of Agricultural Engineering,2010,26(9):224-232. | |
| Authors: | Bao Yansong Liu Li Kong Lingyin Wang Jihua and Liu Liangyun |
| Abstract: | In this study, the relationship between Advanced Synthetic Aperture Radar (ASAR) data and soil moisture was studied using correlation analysis. The empirical and semi-empirical approaches were used to develop the soil moisture estimation models based on ASAR data. Some conclusions were drawn. Firstly, winter wheat crown scattering influenced the sounding depth of ASAR. The optional depths for monitoring the soil moisture by ASAR were 0-20 cm and 0-5 cm for before the erecting stage and after the jointing stage, respectively. Secondly, since ASAR data at VV polarization and low incidence angle had significant linear correlations with soil moisture, the empirical models were suitable to estimate soil moisture before the jointing stage; however, when winter wheat crown was dense (at the earing stage), the empirical model could not be applied to retrieve soil moisture with a high accuracy. Then, multi-angle ASAR data were used to develop a semi-empirical model to retrieve soil moisture with a higher accuracy. Finally, the soil moisture estimation models developed in this study were employed to estimate soil moisture, the Root Mean Squared Error of estimated soil moisture were 0.0125, 0.0247 and 0.0298 g/g, respectively. |
| Keywords: | backscattering coefficient soil moisture estimation winter wheat ASAR |
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