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Precise Parameter Estimation in Remote Sensing Observations: From Satellite Corrections,...
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Precise Parameter Estimation in Remote Sensing Observations: From Satellite Corrections, Atmospheric Delay to Phase Unwrapping

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 767

Editor

Prof. Dr. Baocheng Zhang

E-Mail Website
Guest Editor
Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
Interests: data processing; quality control; precise point positioning; atmospheric inversion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate parameter estimation, which is crucial for Earth observation, permeates the entire remote sensing process and directly determines the usability of satellite data. This Special Issue focuses on key parameter estimation in remote sensing observation, covering the latest advancements in important stages such as satellite calibration, atmospheric delay compensation, and phase unwrapping. Satellite calibration is as an important step in ensuring the accuracy of source data, focusing on analyzing the radiometric calibration of satellite payloads to establish a reliable observation baseline and reduce parameter deviations caused by inherent errors in the observation system. Second, atmospheric delay compensation methods, including the modeling and calibration of the ionosphere and troposphere, aim to improve the interference mechanism of the atmosphere on signal transmission. Finally, phase unwrapping, a key issue in remote sensing, analyzes high-precision algorithms, error suppression strategies, and uncertainty quantification in the inversion steps, particularly in synthetic aperture radar interferometry (InSAR); it provides strong support for high-precision terrain mapping, surface deformation monitoring, and other applications.

We are pleased to announce the launch of a new Special Issue in Remote Sensing that aims to compile studies exploring the latest research results on parameter estimation techniques. We welcome research findings that cover topics ranging from upstream satellite calibration and midstream atmospheric delay correction to downstream phase unwrapping. Additionally, we seek contributions on the application progress of these technologies in high-precision terrain mapping, millimeter-scale surface deformation monitoring, glacier change tracking, urban infrastructure safety, geological disaster warning, and hydrological parameter inversion. Potential topics may include, but are not limited to, the following:

  • Novel satellite/sensor calibration theories and techniques;
  • Innovative methods for the precise modeling and correction of atmospheric delay;
  • Advanced algorithms for phase unwrapping and deformation information extraction;
  • Fusion framework for parameter estimation using multi-source remote sensing data;
  • Applications of machine learning and artificial intelligence in precise parameter estimation.

Prof. Dr. Baocheng Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • parameter estimation 
  • Earth observation 
  • satellite correction 
  • calibration (radiographic calibration/geometric calibration) 
  • atmospheric delay compensation 
  • phase unwrapping 
  • InSAR

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Published Papers (1 paper)

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Research

30 pages, 13578 KB  
Article
A Semi-Supervised Topographic Inversion Algorithm for Small-Scale Tidal Flats Based on Multi-Source Data Fusion Under Spatially Clustered ICESat-2 Label Distributions
by Hao Chen, Xiaowen Luo, Feng Gui, Jiaxin Cui, Jiayang Chen and Qi Li
Remote Sens. 2026, 18(12), 2017; https://doi.org/10.3390/rs18122017 - 17 Jun 2026
Viewed by 410
Abstract
High-precision topography of tidal flats is essential for coastal monitoring, geomorphic change analysis, and ecological assessment. Although satellite remote sensing supports repeated and large-area observation, topographic inversion over small-scale tidal flats—here defined as localized intertidal patches with limited areal extent, represented in this [...] Read more.
High-precision topography of tidal flats is essential for coastal monitoring, geomorphic change analysis, and ecological assessment. Although satellite remote sensing supports repeated and large-area observation, topographic inversion over small-scale tidal flats—here defined as localized intertidal patches with limited areal extent, represented in this study by a 1.11 km2 tidal flat near Dafeng Port—remains challenging, because ICESat-2 laser altimetry tracks across such areas are typically sparse and spatially clustered within narrow sub-regions, leaving extensive observation-blind zones without direct elevation labels. This label-clustering problem constrains the applicability of traditional empirical models and tends to cause deep learning models to generalize poorly beyond the spatial distribution of training samples. To address this issue, this study proposes a Residual Attention Physical-constraint Semi-supervised U-Net (RAPS-UNet) that fuses ICESat-2 ATL03/ATL08 elevation labels with Sentinel-1 SAR and Sentinel-2 optical features. The preprocessing pipeline comprises refined ICESat-2 photon filtering, adaptive inundation-frequency extraction, multi-source feature selection, and baseline DEM construction. RAPS-UNet integrates residual learning, attention-based multi-source fusion, physics-constrained loss, and confidence-weighted pseudo-label augmentation to improve extrapolation under clustered-label conditions. A four-level validation protocol—in-distribution validation, spatial holdout testing, and field-based assessment over both interpolation and extrapolation zones—was designed to evaluate spatial generalization. Against a field-surveyed DEM, RAPS-UNet achieved an overall RMSE of 0.20 m, an MAE of 0.16 m, and an R2 of 0.91; the field-based interpolation and extrapolation zones yielded RMSEs of 0.17 m and 0.22 m, respectively, while the spatial holdout test reached an RMSE of 0.23 m and an R2 of 0.81. Relative to the traditional inundation frequency–elevation linear model (RMSE = 0.35 m), RAPS-UNet reduced the field-validation RMSE by approximately 43%. The proposed framework therefore offers a practical approach for fine-scale coastal-zone topographic mapping under sparse and spatially clustered altimetry conditions. Full article
(This article belongs to the Special Issue Precise Parameter Estimation in Remote Sensing Observations: From Satellite Corrections, Atmospheric Delay to Phase Unwrapping)
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