InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

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Publicado en:Remote Sensing vol. 16, no. 10 (2024), p. 1764
Autor principal: Wu, Kefu
Otros Autores: Fu, Haiqiang, Zhu, Jianjun, Hu, Huacan, Li, Yi, Liu, Zhiwei, Wan, Afang, Wang, Feng
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100 1 |a Wu, Kefu  |u School of Geoscience and Info-Physics, Central South University, Changsha 410083, China; <email>kefuwu@csu.edu.cn</email> (K.W.); <email>jjz@csu.edu.cn</email> (J.Z.); <email>csuhuacan@csu.edu.cn</email> (H.H.); <email>fysxjsxw@csu.edu.cn</email> (Y.L.); <email>liuzhiwei@csu.edu.cn</email> (Z.L.) 
245 1 |a InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects 
260 |b MDPI AG  |c 2024 
513 |a Journal Article 
520 3 |a The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m. 
653 |a Accuracy 
653 |a Coastal zone 
653 |a Atmospheric conditions 
653 |a Calibration 
653 |a Topography 
653 |a Biomass 
653 |a Correlation analysis 
653 |a Stochastic models 
653 |a Decomposition 
653 |a Ionosphere 
653 |a Interferometry 
653 |a Interferometric synthetic aperture radar 
653 |a Propagation 
653 |a Systematic errors 
653 |a Digital Elevation Models 
653 |a Parameter estimation 
653 |a Root-mean-square errors 
653 |a Atmospheric effects 
653 |a Satellite observation 
653 |a Satellites 
700 1 |a Fu, Haiqiang  |u School of Geoscience and Info-Physics, Central South University, Changsha 410083, China; <email>kefuwu@csu.edu.cn</email> (K.W.); <email>jjz@csu.edu.cn</email> (J.Z.); <email>csuhuacan@csu.edu.cn</email> (H.H.); <email>fysxjsxw@csu.edu.cn</email> (Y.L.); <email>liuzhiwei@csu.edu.cn</email> (Z.L.) 
700 1 |a Zhu, Jianjun  |u School of Geoscience and Info-Physics, Central South University, Changsha 410083, China; <email>kefuwu@csu.edu.cn</email> (K.W.); <email>jjz@csu.edu.cn</email> (J.Z.); <email>csuhuacan@csu.edu.cn</email> (H.H.); <email>fysxjsxw@csu.edu.cn</email> (Y.L.); <email>liuzhiwei@csu.edu.cn</email> (Z.L.) 
700 1 |a Hu, Huacan  |u School of Geoscience and Info-Physics, Central South University, Changsha 410083, China; <email>kefuwu@csu.edu.cn</email> (K.W.); <email>jjz@csu.edu.cn</email> (J.Z.); <email>csuhuacan@csu.edu.cn</email> (H.H.); <email>fysxjsxw@csu.edu.cn</email> (Y.L.); <email>liuzhiwei@csu.edu.cn</email> (Z.L.) 
700 1 |a Li, Yi  |u School of Geoscience and Info-Physics, Central South University, Changsha 410083, China; <email>kefuwu@csu.edu.cn</email> (K.W.); <email>jjz@csu.edu.cn</email> (J.Z.); <email>csuhuacan@csu.edu.cn</email> (H.H.); <email>fysxjsxw@csu.edu.cn</email> (Y.L.); <email>liuzhiwei@csu.edu.cn</email> (Z.L.) 
700 1 |a Liu, Zhiwei  |u School of Geoscience and Info-Physics, Central South University, Changsha 410083, China; <email>kefuwu@csu.edu.cn</email> (K.W.); <email>jjz@csu.edu.cn</email> (J.Z.); <email>csuhuacan@csu.edu.cn</email> (H.H.); <email>fysxjsxw@csu.edu.cn</email> (Y.L.); <email>liuzhiwei@csu.edu.cn</email> (Z.L.) 
700 1 |a Wan, Afang  |u The First Institute of Surveying and Mapping of Hunan Province, Changsha 410002, China; <email>wanafang@xcyy.net.cn</email> (A.W.); <email>wangfeng@xcyy.net.cn</email> (F.W.) 
700 1 |a Wang, Feng  |u The First Institute of Surveying and Mapping of Hunan Province, Changsha 410002, China; <email>wanafang@xcyy.net.cn</email> (A.W.); <email>wangfeng@xcyy.net.cn</email> (F.W.) 
773 0 |t Remote Sensing  |g vol. 16, no. 10 (2024), p. 1764 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
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