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何连,秦其明,任华忠,都骏,孟晋杰,杜宸.利用多时相Sentinel-1 SAR数据反演农田地表土壤水分[J].农业工程学报,2016,32(3):142-148.DOI:10.11975/j.issn.1002-6819.2016.03.020
利用多时相Sentinel-1 SAR数据反演农田地表土壤水分
投稿时间:2015-09-06  修订日期:2015-12-22
中文关键词:  土壤水分  遥感  合成孔径雷达  反演  多时相  农田地表
基金项目:国家自然科学基金重点项目(41230747);中国博士后科学基金特别资助项目(2015T80012)
作者单位
何连 北京大学遥感与地理信息系统研究所北京 100871 
秦其明 北京大学遥感与地理信息系统研究所北京 100871 
任华忠 北京大学遥感与地理信息系统研究所北京 100871 
都骏 北京大学遥感与地理信息系统研究所北京 100871 
孟晋杰 北京大学遥感与地理信息系统研究所北京 100871 
杜宸 北京大学遥感与地理信息系统研究所北京 100871 
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中文摘要:土壤水分是陆面生态系统水分和能量循环的重要变量,在农田干旱监测、作物长势监测和作物估产等应用研究中具有重要的作用。该文结合基于变化检测的Alpha近似模型,利用Sentinel-1卫星获取的多时相C波段合成孔径雷达(synthetic aperture radar,SAR)数据,实现了农田地表土壤水分的反演。该文首先利用微波辐射传输模型验证了Alpha近似模型在土壤水分反演中的合理性。研究发现,对于土壤散射占主导的区域,Alpha近似模型对辐射传输模型有较好的近似,能够有效地消除地表粗糙度和植被对雷达后向散射系数的影响。在此基础上,结合怀来研究区多时相Sentinel-1 SAR数据,利用Alpha近似模型构建了土壤水分观测方程组,通过求解方程组得到了农田地表土壤水分。地面验证结果表明,土壤水分反演的均方根误差(root mean square error,RMSE)为0.06 cm3/cm3,平均偏差为0.01 cm3/cm3,精度较好。该文研究为利用高重访周期、多时相的Sentinel-1 SAR数据获取农田地表土壤水分提供了参考。
He Lian,Qin Qiming,Ren Huazhong,Du Jun,Meng Jinjie,Du Chen.Soil moisture retrieval using multi-temporal Sentinel-1 SAR data in agricultural areas[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2016,32(3):142-148.DOI:10.11975/j.issn.1002-6819.2016.03.020
Soil moisture retrieval using multi-temporal Sentinel-1 SAR data in agricultural areas
Author NameAffiliation
He Lian Institution of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China 
Qin Qiming Institution of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China 
Ren Huazhong Institution of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China 
Du Jun Institution of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China 
Meng Jinjie Institution of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China 
Du Chen Institution of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, China 
Key words:soil moisture  remote sensing  synthetic aperture radar  retrieval  multi-temporal  agricultural fields
Abstract: Soil moisture is a key variable that links the water and energy cycles. Its information is also essential for many applications, such as agricultural drought monitoring, crop status monitoring and crop yield prediction. Sentinel-1 of the European Space Agency (ESA) is composed of 2 satellites, Sentinel-1A and Sentinel-1B, which share the same orbital plane with a 180° orbital phasing difference. The Sentinel-1 mission can provide C-band synthetic aperture radar (SAR) data with a global revisit time of just 6 days and high spatial resolution of about tens of meters, thus showing a strong potential for global soil moisture monitoring at high/moderate spatial resolutions. The aim of this study was to investigate the capability of multi-temporal Sentinel-1 C-band SAR data with a short repeating cycle in soil moisture estimation over agricultural fields. In order to retrieve soil moisture, an algorithm based on the change detection technique was utilized. This algorithm (referred to as alpha approximation approach) relies on the assumptions that the contributions of vegetation and surface roughness to the radar backscattered signal are multiplicative. Therefore, the effects of vegetation and surface roughness on radar backscattering coefficients can be decoupled from the effects of soil moisture changes by rationing multi-temporal like-polarized (HH and VV) intensities between two close acquisition dates. The ratio is expected to track changes in soil moisture only since the changes of surface roughness, canopy structure and vegetation biomass take place at longer temporal scales than soil moisture changes. The alpha approximation approach was firstly evaluated by comparing with data sets simulated by a theoretical radiative transfer (RT) scattering model. It was found that the alpha approximation approach was overall in good agreement with the RT scattering model without introducing significant errors for bare surface and low vegetation area, which confirmed that the alpha approximation approach was a simple and effective way to reduce the influences of vegetation and surface roughness. Furthermore, under the assumption of alpha approximation, the ratio of 2 consecutive backscatter measurements could be approximately represented as the squared ratio of corresponding Bragg scattering coefficients. For Sentinel-1 SAR data with only one like-polarized channel (i.e. VV), N SAR acquisitions would result in N - 1 linear equations in N unknown Bragg scattering coefficients. To solve this underdetermined system of equations, a bounded linear least-squares optimization was applied. Once the unknown Bragg scattering coefficients were retrieved, the relative dielectric constant could be analytically derived with the soil moisture being estimated by the inversion of microwave dielectric model. The alpha approximation approach was then applied to 4 consecutive Sentinel-1 SAR images acquired over Huailai experiment field. Soil moisture maps were successfully obtained for each date. The results were validated using ground measurements on one acquisition date, with root mean squared error (RMSE) value of 0.06 cm3/cm3 and mean bias value of 0.01 cm3/cm3. The results demonstrated the overall good performance of the alpha approximation approach. These results imply that multi-temporal Sentinel-1 SAR data show great potential in achieving high resolution and accurate soil moisture retrievals over agricultural fields.
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