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Downscaling algorithm using active-passive microwave observations with variable incident angle

  • role： First author第一作者
  • Affiliation：

    College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China

  • Email：guopeng@sdau.edu.cn
  • Introduction：郭鹏，1985年生，男，副教授，研究方向为主被动微波联合降尺度土壤水分反演。E-mail: guopeng@sdau.edu.cn
GUO Peng1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

  • Email：zhaotj@aircas.ac.cn
  • Introduction：赵天杰，1985年生，男，副研究员，研究方向为微波遥感土壤水分及其冻融态。E-mail: zhaotj@aircas.ac.cn
ZHAO Tianjie2*,  
  • Affiliation：

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

SHI Jiancheng3,  
  • Affiliation：

    Shanghai Aerospace Electronic Tecnology Institute, Shanghai 201109 China

SUN Yanlong4,  
  • Affiliation：

    Shanghai Aerospace Electronic Tecnology Institute, Shanghai 201109 China

HUANG Shuo4,  
  • Affiliation：

    Shanghai Satellite Engineering Institute, Shanghai 201109, China

NIU Shengda5

résumé

Soil moisture derived from passive microwave remote sensing has a low spatial resolution, which cannot meet the requirement for the application of regional hydrological and agricultural activities, such as meteorological forecast, flood forecast, and irrigation management. The integrated microwave load of radar and radiometer loaded on “land-water resource satellite,” which is planned and demonstrated by Chinese civil space infrastructure, will obtain high-resolution soil moisture with the combination of active and passive microwave observation by using a one-dimensional synthetic aperture imaging observation mode. However, the incidence angle changes during observation. Present downscaling algorithms with combination of active and passive microwave observations are developed under the condition of fixed incident angles of radar and radiometer. In this study, downscaling algorithms based on time series regression analysis and spectral analysis are tested using the air flight experimental data of a lightning river basin to demonstrate that the feasibility of each for application to the incidence angles of radiometer and radar differs.The downscaling method based on time series regression analysis is the basic algorithm for Soil Moisture Active and Passive. The principal theoretical basis for this method is the linear relationship between the brightness temperature observation of radiometer and the backscatter coefficient observation of radar, and the linear relationship is related to surface roughness, vegetation, and incident angle. The spectral analysis downscaling method was first proposed and applied to remote sensing water vapor downscaling. Its theoretical basis is to obtain a high-resolution image by correctly simulating the spatial characteristics of the image spectral domain.The downscaling results based on active and passive observation regression analysis can reproduce spatial details, but the root-mean-square error (RMSE) is large. The minimum RMSE of V polarization is 7.57 K, and the minimum RMSE of H polarization is 7.46 K. The downscaling results based on spectral analysis can basically reflect the spatial distribution of the original observation. Nevertheless, evident plate phenomena occur in some areas, the spatial transition is not smooth, and the traces of downscaling are obvious. The minimum RMSE of H polarization is 7.13 K, and that of V polarization is 6.61 K. In accordance with the RMSE, the overall accuracy of the spectral downscaling method is higher than that of the time series regression analysis.The downscaling method based on the regression analysis of active and passive observation depends on the time series observation of active and passive microwave observation. The method of spectrum analysis does not need to regress to determine the relationship between active and passive microwave. It directly uses low-resolution passive radiometer observation and high-resolution radar copolarization (vv) observation for downscaling and does not need to rely on long-term time observation. The experimental results indicate that the time series regression analysis downscaling method can obtain the best results when the incident angle of radiometer is 27.5° and that of radar is 52.5° or 55°. The minimum RMSEs of V and H polarizations are 7.57 K and 7.46 K, respectively. The minimum RMSEs of V and H polarizations are 7.13 K and 6.61 K, respectively, for the spectral analysis downscaling method, which are 0.44k and 0.85 K, respectively, lower than those of the time series regression analysis downscaling method.

mots-clés

microwave remote sensing;radiometer;radar;downscaling;soil water

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