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Remote Sensing
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Active-Passive Microwave Sensing for Earth System Parameters
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Active-Passive Microwave Sensing for Earth System Parameters

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (1 August 2020) | Viewed by 5455

Special Issue Editors

Prof. Dr. Joel T. Johnson

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA
Interests: active microwave remote sensing, passive microwave remote sensing, microwave radiometry, electromagnetic theory, scattering from rough surfaces
Special Issues, Collections and Topics in MDPI journals
Dr. Narendra N. Das

E-Mail Website
Guest Editor
NASA Jet Propulsion laboratory, Radar Algorithms and Processing Group, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
Interests: microwave remote sensing, radar, radiometer, downscaling, soil moisture, root zone
Dr. Jian Peng

E-Mail Website
Guest Editor
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
Interests: remote sensing; water cycle; climate extremes; land–atmosphere interaction; land surface modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Thomas Jagdhuber

E-Mail Website
Guest Editor
German Aerospace Center, Microwaves and Radar Institute, Münchener Strasse 20, 82234 Wessling, Germany
Interests: multisensor data integration; data-to-model assimilation; surface and subsurface hydrology; active and passive remote sensing; radiometer; SAR; LiDAR; radiative transfer; polarimetry; parameter extraction; Earth system; soil; root zone; vegetation; plant ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Active and passive microwave signals from the Earth co-vary depending on the scattering and emission characteristics of natural media (e.g. soil, vegetation snow or ice). Based on such characteristic covariations, signals from different sensors can be combined for joint data analyses and retrieval of Earth system properties such as soil or plant moisture.

The currently operating fleet of Earth remote sensing satellites comprises various microwave radiometers (e.g. SMAP, SMOS & AMSR-2) and SAR sensors (e.g. SAOCOM-1, ALOS-2, Sentinel-1 & TanDEM-X). Moreover, new missions are planned for launch in the next years (e.g. RCM, WCOM, Tandem-L).

Their data can be integrated for joint estimation of parameters reflecting conditions of the Earth system. In this way, the advantages of different sensing techniques can be combined, and their individual drawbacks alleviated.

The special issue aims at presenting novel algorithms, application case studies and review discussions on active-passive microwave sensing for estimation of Earth system parameters (e.g. soil or plant moisture, surface roughness, vegetation or ice structure, snow water equivalent, …).

Contributions dealing with all components of the Earth System are welcome and may include in-situ measurements and/or non-microwave remote sensing data for parameter estimation at various spatial and temporal scales. Example potential focus areas include:

  • Advances in combining active and passive microwave sensing techniques to provide spatially distributed, high-resolution data,
  • Multi-sensor (active-passive) algorithms for estimation of Earth system parameters,
  • Space-borne, airborne or ground-based experiments to study active-passive estimation techniques of soil, vegetation, snow or ice parameters,
  • Case studies at global or local scale for dedicated estimation of single Earth system parameters with comparison of in-situ observations or modelling results.

Prof. Dr. Joel T. Johnson
Dr. Narendra N. Das
Dr. Jian Peng
Dr. Thomas Jagdhuber
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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

  • Active-Passive Sensing
  • Microwaves
  • Radiometer
  • SAR
  • Multi-Sensor
  • Data Fusion
  • Parameter Estimation
  • Earth System
  • Soil
  • Vegetation
  • Snow
  • Ice
  • Biomass
  • Forest

Published Papers (2 papers)

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Research

25 pages, 29919 KiB  
Article
Spatiotemporal Variation of Snow Depth in the Northern Hemisphere from 1992 to 2016
by Xiongxin Xiao, Tingjun Zhang, Xinyue Zhong and Xiaodong Li
Remote Sens. 2020, 12(17), 2728; https://doi.org/10.3390/rs12172728 - 24 Aug 2020
Cited by 26 | Viewed by 2721
Abstract
A comprehensive and hemispheric-scale snow cover and snow depth analysis is a prerequisite for all related processes and interactions investigation on regional and global surface energy and water balance, weather and climate, hydrological processes, and water resources. However, such studies were limited by [...] Read more.
A comprehensive and hemispheric-scale snow cover and snow depth analysis is a prerequisite for all related processes and interactions investigation on regional and global surface energy and water balance, weather and climate, hydrological processes, and water resources. However, such studies were limited by the lack of data products and/or valid snow retrieval algorithms. The overall objective of this study is to investigate the variation characteristics of snow depth across the Northern Hemisphere from 1992 to 2016. We developed long-term Northern Hemisphere daily snow depth (NHSnow) datasets from passive microwave remote sensing data using the support vector regression (SVR) snow depth retrieval algorithm. NHSnow is evaluated, along with GlobSnow and ERA-Interim/Land, for its accuracy across the Northern Hemisphere against meteorological station snow depth measurements. The results show that NHSnow performs comparably well with a relatively high accuracy for snow depth with a bias of −0.6 cm, mean absolute error of 16 cm, and root mean square error of 20 cm when benchmarked against the station snow depth measurements. The analysis results show that annual average snow depth decreased by 0.06 cm per year from 1992 to 2016. In the three seasons (autumn, winter, and spring), the areas with a significant decreasing trend of seasonal maximum snow depth are larger than those with a significant increasing trend. Additionally, snow cover days decreased at the rate of 0.99 day per year during 1992–2016. This study presents that the variation trends of snow cover days are, in part, not consistent with the variation trends of the annual average snow depth, of which approximately 20% of the snow cover areas show the completely opposite variation trends for these two indexes over the study period. This study provides a new perspective in snow depth variation analysis, and shows that rapid changes in snow depth have been occurring since the beginning of the 21st century, accompanied by dramatic climate warming. Full article
(This article belongs to the Special Issue Active-Passive Microwave Sensing for Earth System Parameters)
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18 pages, 3722 KiB  
Article
Assessment and Improvement of Sea Surface Microwave Emission Models for Salinity Retrieval in the East China Sea
by Xuchen Jin, Xianqiang He, Yan Bai, Palanisamy Shanmugam, Jianyun Ying, Fang Gong and Qiankun Zhu
Remote Sens. 2019, 11(21), 2486; https://doi.org/10.3390/rs11212486 - 24 Oct 2019
Cited by 2 | Viewed by 2000
Abstract
Accurate prediction of sea surface emission is the key for sea surface salinity retrieval from satellite microwave radiometer. In order to retrieve salinity from satellite observation, several sea surface microwave emission models have been developed based on theoretical or empirical methods and validated [...] Read more.
Accurate prediction of sea surface emission is the key for sea surface salinity retrieval from satellite microwave radiometer. In order to retrieve salinity from satellite observation, several sea surface microwave emission models have been developed based on theoretical or empirical methods and validated by in-situ measurements in different regions. However, their performances are still unclear in the Chinese coastal waters. In this study, based on two cruises measurements in the East China Sea (ECS), including the brightness temperature measured by a shipborne microwave radiometer and other auxiliary data (sea surface salinity, sea surface temperature and wind speed), the performances of different sea surface emission models are tested. The results showed that the developed models provide fairly good accuracy in predicting brightness temperature; for example, the accuracy of small perturbance/small scale approximation model (SPM/SSA), two-scale model (TSM) and empirical model is in the range from 0.6 K to 3 K. Moreover, the TSM and empirical models are further improved by optimizing the model parameters in the ECS. Finally, the sea surface salinity were retrieved from shipborne measured data based on the improved models, and the results show that the root mean square (rms) differences between retrieved and in-situ sea surface salinity is about 0.4 psu, indicating the significant improvement by the regional model parameters. Full article
(This article belongs to the Special Issue Active-Passive Microwave Sensing for Earth System Parameters)
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