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Advances in Microwave Remote Sensing for Earth Observation (EO)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 11663

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Special Issue Editor

Dr. Arnab Muhuri

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Guest Editor

Hydrology and Climatology, Institute of Geography, Universität Heidelberg, Im Neuenheimer Feld 348, D-69120 Heidelberg, Germany
Interests: satellite-based (SAR & optical) Earth observation algorithms; performance assessment of operational snow cover monitoring algorithms in forested landscapes; microwave remote sensing of soil parameters

Special Issue Information

Dear Colleagues,

Satellite remote sensing is a crucial tool for large-scale monitoring applications due to its high spatial coverage, temporal repetivity, and diverse imaging configurations available among the existing and future state-of-the-art missions. Such characteristics can play a vital role in the remote retrieval of land cover parameters for key geopolitical decision-making processes. With the advent of active and passive microwave remote sensing platforms, spatially detailed landcover parameters can be acquired in a timely manner due to their weather-independent observational capabilities and enhanced repetivity offered by constellation missions such as the RADARSAT Constellation Mission (RCM) and Sentinel. Moreover, high-resolution active microwave satellite systems have contributed to increased spatial sampling and a consequent increase in the data volume, which in turn has resulted in the natural amalgamation of the domain of machine learning and Earth observation.

The last few decades of microwave remote sensing have seen a successful implementation of crucial applications, from soil moisture monitoring to oil spill detection, from assessment of the impact of the Fukushima Daiichi nuclear power plant accident to real-time detection of deforestation in the Amazon, from detection of presence of UNESCO heritage archaeological underground structures to observation of seasonal variation of snow cover over the Himalayas. The focus of this Special Issue is to highlight such unique advances in microwave remote sensing for Earth observation. I invite you all to contribute to this issue and look forward to your insightful submissions.

Dr. Arnab Muhuri
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 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

Earth observation
active microwave remote sensing
passive microwave remote sensing
polarimetric SAR: full, dual, compact
machine learning
image classification
target decomposition techniques
interferometric SAR

Published Papers (7 papers)

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Research

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16 pages, 3840 KiB

Open AccessArticle

A Frequency–Azimuth Spectrum Estimation Method for Uniform Linear Array Based on Deconvolution

by Daiqiang Lu, Zhiming Cai, Wei Guo, Zhixiang Yao and Huanzhi Cao

Remote Sens. 2024, 16(3), 518; https://doi.org/10.3390/rs16030518 - 29 Jan 2024

Viewed by 642

Abstract

The frequency–azimuth (FRAZ) spectrum is a critical characteristic in passive target detection and tracking, as it encapsulates information regarding the signal’s frequency and azimuth. However, due to the inherent limitations in the sonar array’s physical aperture and the analysis time of the system, the signal often suffers from undersampling in both spatial and temporal dimensions. This undersampling leads to energy leakage across the azimuth and frequency domains, adversely affecting the resolution of the FRAZ spectrum. Such a reduction in resolution hampers multitarget resolution and feature extraction. To address these challenges, this study introduces a deconvolution-based FRAZ spectrum estimation method tailored for uniform linear arrays. The proposed method initiates by decoupling the azimuth and frequency in the FRAZ spectrum, forming a two-dimensional point scattering function that possesses shift-invariance. Subsequent to this, the power spectrum and the two-dimensional point scattering function undergo deconvolution using the Richardson–Lucy (R–L) iterative algorithm. The final stage involves calculating the signal azimuths and frequencies based on the deconvolution results from the preceding step. Comparative analyses involving simulations and sea test results reveal that the proposed method achieves a narrower main lobe width and diminished background noise in contrast to traditional FRAZ spectrum estimation techniques. This improvement is instrumental in minimizing the target’s energy leakage in both the azimuth and frequency domains. Full article

(This article belongs to the Special Issue Advances in Microwave Remote Sensing for Earth Observation (EO))

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18 pages, 3610 KiB

Open AccessArticle

A Deep-Learning Scheme for Hydrometeor Type Classification Using Passive Microwave Observations

by Ruiyao Chen and Ralf Bennartz

Remote Sens. 2023, 15(10), 2670; https://doi.org/10.3390/rs15102670 - 20 May 2023

Viewed by 1054

Abstract

This paper proposes a novel approach for hydrometeor classification using passive microwave observations. The use of passive measurements for this purpose has not been extensively explored, despite being available for over four decades. We utilize the Micro-Wave Humidity Sounder-2 (MWHS-2) to relate microwave brightness temperatures to hydrometeor types derived from the global precipitation measurement’s (GPM) dual-frequency precipitation radar (DPR), which are classified into liquid, mixed, and ice phases. To achieve this, we utilize a convolutional neural network model with an attention mechanism that learns feature representations of MWHS-2 observations from spatial and temporal dimensions. The proposed algorithm classified hydrometeors with 84.7% accuracy using testing data and captured the geographical characteristics of hydrometeor types well in most areas, especially for frozen precipitation. We then evaluated our results by comparing predictions from a different year against DPR retrievals seasonally and globally. Our global annual cycles of precipitation occurrences largely agreed with DPR retrievals with biases being 8.4%, −11.8%, and 3.4%, respectively. Our approach provides a promising direction for utilizing passive microwave observations and deep-learning techniques in hydrometeor classification, with potential applications in the time-resolved observations of precipitation structure and storm intensity with a constellation of smallsats (TROPICS) algorithm development. Full article

(This article belongs to the Special Issue Advances in Microwave Remote Sensing for Earth Observation (EO))

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19 pages, 57018 KiB

Open AccessArticle

Feature Selection for Edge Detection in PolSAR Images

by Anderson A. De Borba, Arnab Muhuri, Mauricio Marengoni and Alejandro C. Frery

Remote Sens. 2023, 15(9), 2479; https://doi.org/10.3390/rs15092479 - 08 May 2023

Cited by 2 | Viewed by 1423

Abstract

Edge detection is one of the most critical operations for moving from data to information. Finding edges between objects is relevant for image understanding, classification, segmentation, and change detection, among other applications. The Gambini Algorithm is a good choice for finding evidence of edges. It finds the point at which a function of the difference of properties is maximized. This algorithm is very general and accepts many types of objective functions. We use an objective function built with likelihoods. Imaging with active microwave sensors has a revolutionary role in remote sensing. This technology has the potential to provide high-resolution images regardless of the Sun’s illumination and almost independently of the atmospheric conditions. Images from PolSAR sensors are sensitive to the target’s dielectric properties and structures in several polarization states of the electromagnetic waves. Edge detection in polarimetric synthetic-aperture radar (PolSAR) imagery is challenging because of the low signal-to-noise ratio and the data format (complex matrices). There are several known marginal models stemming from the complex Wishart model for the full complex format. Each of these models renders a different likelihood. This work generalizes previous studies by incorporating the ratio of intensities as evidence for edge detection. We discuss solutions for the often challenging problem of parameter estimation. We propose a technique which rejects edge estimates built with thin evidence. Using this idea of discarding potentially irrelevant evidence, we propose a technique for fusing edge pieces of evidence from different channels that only incorporate those likely to contribute positively. We use this approach for both edge and change detection in single- and multilook images from three different sensors. Full article

(This article belongs to the Special Issue Advances in Microwave Remote Sensing for Earth Observation (EO))

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24 pages, 21041 KiB

Open AccessArticle

PolSAR Models with Multimodal Intensities

by Jodavid A. Ferreira, Abraão D. C. Nascimento and Alejandro C. Frery

Remote Sens. 2022, 14(20), 5083; https://doi.org/10.3390/rs14205083 - 11 Oct 2022

Cited by 2 | Viewed by 1877

Abstract

Polarimetric synthetic aperture radar (PolSAR) systems are an important remote sensing tool. Such systems can provide high spacial resolution images, but they are contaminated by an interference pattern called multidimensional speckle. This fact requires that PolSAR images receive specialised treatment; particularly, tailored models which are close to PolSAR physical formation are sought. In this paper, we propose two new matrix models which arise from applying the stochastic summation approach to PolSAR, called compound truncated Poisson complex Wishart (CTPCW) and compound geometric complex Wishart (CGCW) distributions. These models offer the unique ability to express multimodal data. Some of their mathematical properties are derived and discussed—characteristic function and Mellin-kind log-cumulants (MLCs). Moreover, maximum likelihood (ML) estimation procedures via expectation maximisation algorithm for CTPCW and CGCW parameters are furnished as well as MLC-based goodness-of-fit graphical tools. Monte Carlo experiment results indicate ML estimates perform at what is asymptotically expected (small bias and mean square error) even for small sample sizes. Finally, our proposals are employed to describe actual PolSAR images, presenting evidence that they can outperform other well-known distributions, such as

W_{m}^{C}

G_{m}^{0}

, and

K_{m}

. Full article

(This article belongs to the Special Issue Advances in Microwave Remote Sensing for Earth Observation (EO))

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Review

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21 pages, 17022 KiB

Open AccessReview

A Review of Remote Sensing of Atmospheric Profiles and Cloud Properties by Ground-Based Microwave Radiometers in Central China

by Guirong Xu

Remote Sens. 2024, 16(6), 966; https://doi.org/10.3390/rs16060966 - 10 Mar 2024

Viewed by 534

Abstract

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds. Full article

(This article belongs to the Special Issue Advances in Microwave Remote Sensing for Earth Observation (EO))

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39 pages, 1483 KiB

Open AccessReview

Flood Detection with SAR: A Review of Techniques and Datasets

by Donato Amitrano, Gerardo Di Martino, Alessio Di Simone and Pasquale Imperatore

Remote Sens. 2024, 16(4), 656; https://doi.org/10.3390/rs16040656 - 10 Feb 2024

Viewed by 2199

Abstract

Floods are among the most severe and impacting natural disasters. Their occurrence rate and intensity have been significantly increasing worldwide in the last years due to climate change and urbanization, bringing unprecedented effects on human lives and activities. Hence, providing a prompt response to flooding events is of crucial relevance for humanitarian, social and economic reasons. Satellite remote sensing using synthetic aperture radar (SAR) offers a great deal of support in facing flood events and mitigating their effects on a global scale. As opposed to multi-spectral sensors, SAR offers important advantages, as it enables Earth’s surface imaging regardless of weather and sunlight illumination conditions. In the last decade, the increasing availability of SAR data, even at no cost, thanks to the efforts of international and national space agencies, has been deeply stimulating research activities in every Earth observation field, including flood mapping and monitoring, where advanced processing paradigms, e.g., fuzzy logic, machine learning, data fusion, have been applied, demonstrating their superiority with respect to traditional classification strategies. However, a fair assessment of the performance and reliability of flood mapping techniques is of key importance for an efficient disasters response and, hence, should be addressed carefully and on a quantitative basis trough synthetic quality metrics and high-quality reference data. To this end, the recent development of open SAR datasets specifically covering flood events with related ground-truth reference data can support thorough and objective validation as well as reproducibility of results. Notwithstanding, SAR-based flood monitoring still suffers from severe limitations, especially in vegetated and urban areas, where complex scattering mechanisms can impair an accurate extraction of water regions. All such aspects, including classification methodologies, SAR datasets, validation strategies, challenges and future perspectives for SAR-based flood mapping are described and discussed. Full article

(This article belongs to the Special Issue Advances in Microwave Remote Sensing for Earth Observation (EO))

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33 pages, 1696 KiB

Open AccessReview

Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions

by Srinivasarao Tanniru and RAAJ Ramsankaran

Remote Sens. 2023, 15(4), 1052; https://doi.org/10.3390/rs15041052 - 15 Feb 2023

Cited by 5 | Viewed by 2983

Abstract

Monitoring snowpack depth is essential in many applications at regional and global scales. Space-borne passive microwave (PMW) remote sensing observations have been widely used to estimate snow depth (SD) information for over four decades due to their responsiveness to snowpack characteristics. Many approaches comprised of static and dynamic empirical models, non-linear, machine-learning-based models, and assimilation approaches have been developed using spaceborne PMW observations. These models cannot be applied uniformly over all regions due to inherent limitations in the modelling approaches. Further, the global PMW SD products have masked out in their coverage critical regions such as the Himalayas, as well as very high SD regions, due to constraints triggered by prevailing topographical and snow conditions. Therefore, the current review article discusses different models for SD estimation, along with their merits and limitations. Here in the review, various SD models are grouped into four types, i.e., static, dynamic, assimilation-based, and machine-learning-based models. To demonstrate the rationale behind these drawbacks, this review also details various causes of uncertainty, and the challenges present in the estimation of PMW SD. Finally, based on the status of the available PMW SD datasets, and SD estimation techniques, recommendations for future research are included in this article. Full article

(This article belongs to the Special Issue Advances in Microwave Remote Sensing for Earth Observation (EO))

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