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        <title>Frontiers in Remote Sensing | Microwave Remote Sensing section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/remote-sensing/sections/microwave-remote-sensing</link>
        <description>RSS Feed for Microwave Remote Sensing section in the Frontiers in Remote Sensing journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-13T06:10:09.137+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1728399</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1728399</link>
        <title><![CDATA[On demand machine learning-driven surface freeze-thaw retrieval across Canadian agricultural regions using Sentinel-1 SAR data]]></title>
        <pubdate>2026-01-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shahabeddin Taghipourjavi</author><author>Christophe Kinnard</author><author>Alexandre Roy</author>
        <description><![CDATA[This study explores the prediction of freeze-thaw (FT) states across agricultural fields in four Canadian provinces-Alberta, Manitoba, Saskatchewan, and Québec-using Random Forest classification and regression models. Soil temperature data at a 5 cm depth were gathered from 174 agricultural weather stations from 2016 to 2023. Sentinel-1 SAR VH radar backscatter indicators were processed using Google Earth Engine (GEE). Two modeling approaches were evaluated: a classification model trained on in situ data, where soil states were rigidly classified as either frozen or thawed, and a regression model trained against in situ soil freezing probabilities. Additionally, other site-specific ancillary variables such as latitude, altitude, crop type, and soil type were tested as potential predictors. The regression model using the Exponential Freeze-Thaw Algorithm (EFTA) derived from VH radar backscatter (VHEFTA) demonstrated strong discrimination between frozen and thawed states, and emerged as the most influential factor, accounting for over 90% of the model’s predictive ability. Models using VHEFTA alone achieved up to 81.4% accuracy for classifying FT state, with only a slight improvement to 82.1% when combined with other predictors. Spatial and temporal validation showed stable accuracy (0.79–0.83) and F1-scores (0.75–0.88) across regions and years. Evaluation of model sensitivity to seasonal and temperature variability revealed that although uncertainties were not fully eliminated during transitional periods for both binary and probability-based FT models, binary-based models consistently showed lower error rates and stronger performance. The final FT model was implemented within an interactive web-based tool that generates on-demand FT maps for user-supplied regions of interest across Canadian agricultural and open-land areas.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1718353</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1718353</link>
        <title><![CDATA[ASCAT soil moisture retrieval using deep learning: a focus on localization strategy]]></title>
        <pubdate>2026-01-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lan Anh Dinh</author>
        <description><![CDATA[This study investigates the intercomparison of daily soil moisture (SM) retrieval from ASCAT (Advanced SCATterometer) observations using machine learning. The exploitation of spatial structure through convolutional neural networks (CNNs) is shown to significantly enhance retrieval performance compared to a standard multilayer perceptron (MLP), with spatial correlation with the target ERA5 SM increasing from 0.55 to 0.91 and temporal correlation from 0.61 to 0.73. Incorporating “localization” (i.e., a strategy to adjust the neural network (NN) behavior to local conditions) into the model is a key factor for improving retrieval quality, resulting in more accurate SM estimates, reduced regional biases, improved temporal dynamics, and more realistic representations of extreme SM events. Our NN-based retrievals show strong agreement with in situ SM measurements, achieving temporal correlations of 0.60 and 0.68 for the MLP and CNN models, respectively, in the contiguous United States (CONUS) during 2019. These findings underscore the critical role of spatial learning and localization in SM retrieval from remote sensing data such as ASCAT.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1680450</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1680450</link>
        <title><![CDATA[ConvAttentionNet: a high-performance model for efficient and accurate PolSAR data classification]]></title>
        <pubdate>2025-09-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohammed Q. Alkhatib</author><author>Ali Jamali</author><author>Avik Bhattacharya</author>
        <description><![CDATA[This paper presents ConvAttentionNet, a lightweight and high performing deep learning model developed for accurate and efficient classification of Polarimetric Synthetic Aperture Radar (PolSAR) imagery. The proposed architecture combines multiscale convolutional mixer blocks with a directional convolution based attention mechanism to effectively capture spatial features and suppress background noise. Designed to address the challenges of limited labeled data and computational constraints, ConvAttentionNet achieves superior performance while maintaining a compact model size. Experimental results on three benchmark datasets (Flevoland, San Francisco, and Oberpfaffenhofen) demonstrate that ConvAttentionNet consistently outperforms state of the art CNN based, transformer based, and wavelet based models. It achieves an overall accuracy (OA) of 97.24% and a Kappa coefficient of 96.98 on the Flevoland dataset using only 1% of the training data. These results confirm the model’s robustness, label efficiency, and generalization capabilities, making it a practical solution for operational remote sensing scenarios with limited computational resources. The source code for this work will be publicly available at: https://github.com/aj1365/ConvAttentionNet.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1610005</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1610005</link>
        <title><![CDATA[Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level]]></title>
        <pubdate>2025-08-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Johannes Löw</author><author>Steven Hill</author><author>Insa Otte</author><author>Christoph Friedrich</author><author>Michael Thiel</author><author>Tobias Ullmann</author><author>Christopher Conrad</author>
        <description><![CDATA[Climate change and increasing weather and seasonal dynamics challenge agricultural landscapes. To cope with this challenge information on crop performance is key. This study presents a novel framework for bridging landscape-scale vegetation dynamics with field-level crop phenology using Sentinel-1 radar time series. Unlike previous approaches that focus on local algorithm optimisation or SAR feature selection, this work integrates two scales: (1) landscape patterns derived from annual distributions of time series metrics (TSMs) and (2) field-level phenology, both linked to growing degree days (GDD). TSMs were generated through breakpoint analyses over different smoothing intensities for Sentinel-1 polarisation (PolSAR) and interferometric coherence (InSAR) features, capturing crop, orbit and sensor-specific responses. The framework quantifies uncertainties inherent in both remote sensing and ground observations, and evaluates trackable progress (phenological stage detectability) and tracking range (GDD variance around stages) to assess accuracy under variable acquisition geometries, weather and smoothing parameters. Applied to the DEMMIN site (Germany), the analysis revealed consistent TSM-GDD relationships for wheat, rape, and sugar beet, with descriptors such as soil fertility and water availability explaining spatial patterns (R2 ≈ 0.8). Key novelties include the identification of low tracking ranges in drought years, the demonstration of the impact of orbit-specific incidence angles on monitoring fidelity, and the highlighting of Sentinel-1’s ability to resolve phenological variance across fragmented landscapes. By harmonising multi-scale SAR time series with agro-meteorological data, this approach advances transferable methods for operational crop monitoring, supporting precision agriculture and regional yield assessment beyond localised models.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1657576</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1657576</link>
        <title><![CDATA[Evaluation of the effect of satellite motion on GNSS-R wind speed retrieval: insights from TRITON]]></title>
        <pubdate>2025-07-30T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Ming-Yi Chen</author><author>Hwa Chien</author><author>Wen-Hao Yeh</author><author>Li-Ching Lin</author><author>Yu-Fu Liou</author>
        <description><![CDATA[TRITON is a newly launched GNSS-Reflectometry (GNSS-R) satellite mission by Taiwan, designed to enhance global sea surface wind monitoring. Among its scientific objectives, TRITON delivers high-resolution Delay Doppler Map (DDM) observations that enable novel investigations into the physical mechanisms shaping GNSS-R signal structures. In this study, we highlight the critical yet often overlooked role of transmitter–receiver relative velocity (Vrel) in influencing DDM morphology within the bistatic measurement geometry. Traditional geophysical model function (GMF) retrieval methods, which rely primarily on surface scattering assumptions, often neglect this orbital dynamic factor. Leveraging a deep learning-based framework, we empirically demonstrate that unaccounted-for Vrel can introduce systematic misinterpretations of surface roughness, likely due to DDM distortion. By explicitly incorporating Vrel as an input feature, our retrieval model achieves improved wind speed estimation accuracy from TRITON data, reducing root-mean-square error (RMSE) by over 11%. These results underscore the importance of orbital dynamics in GNSS-R applications and position TRITON as a valuable platform for advancing ocean remote sensing capabilities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1613748</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1613748</link>
        <title><![CDATA[Bare surface soil moisture and surface roughness estimation using multi-band multi-polarization NISAR-like SAR data]]></title>
        <pubdate>2025-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sami Najem</author><author>Nicolas Baghdadi</author><author>Henri Bazzi</author><author>Mehrez Zribi</author><author>Dharmendra Kumar Pandey</author><author>Muddu Sekhar</author>
        <description><![CDATA[The upcoming NISAR Earth-observation satellite will utilize dual frequencies simultaneously, providing synthetic aperture radar (SAR) remote sensing data in both the L-band and S-band. With its ability to operate single-polarization, dual-polarization, and quad-polarization modes, NISAR will offer significant capabilities for land surface observation applications, particularly for estimating surface soil moisture (SSM) and surface roughness (Hrms). This study aims to demonstrate NISAR’s future potential in SSM and Hrms estimation by evaluating the single (SP), double (DP) and quad (QP) polarization configurations. Noisy synthetic NISAR-like data was generated using the Dubois-B model for both S- and L-bands. The use of a priori information on the soil moisture was also examined for SSM and Hrms estimations. Various neural networks (NNs) were trained using the noisy synthetic dataset. Validation was performed on noisy synthetic data, as real NISAR data is not yet available. Out of the NISAR configurations tested, the QP configuration was shown to be the most performant, with RMSE on SSM estimation of 4.2 vol.%, for QP configuration compared to 5.1 and 8.2 vol.% for SP and DP configurations when not using a priori knowledge of soil moisture conditions. RMSE on Hrms was 0.3 cm for QP configuration, compared to 0.7 and 0.6 cm for SP and DP configurations. The QP was also shown to be more capable of mitigating the effect of the incidence angle on the estimation of SSM and Hrms compared to the two other configurations. Moreover, simultaneous use of S- and L-bands enhances SSM and Hrms estimation compared to using either of these frequency bands alone in single-, dual-, or quad-polarization configurations. Furthermore, using a priori knowledge of soil moisture conditions was successful in improving the estimation precision for SSM for all NISAR configurations. Notably, for QP configuration, RMSE on SSM estimation was 3.9 vol.% and 3.2 vol.% when a priori information on SSM was considered respectively in dry to slightly wet and very wet conditions. These findings demonstrate the high potential of the future NISAR sensor for estimating SSM and Hrms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1579261</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1579261</link>
        <title><![CDATA[Using spaceborne SAR and ground-based measurements to identify spatial patterns in soil moisture and seasonal thaw timing in permafrost environments of Alaska]]></title>
        <pubdate>2025-05-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>W. Brad Baxter</author><author>Zachary Hoppinen</author><author>Kristofer Lasko</author><author>Tate Meehan</author><author>David Brodylo</author><author>Taylor D. Sullivan</author><author>Amanda J. Barker</author><author>Thomas A. Douglas</author>
        <description><![CDATA[Spatiotemporal patterns in soil moisture play a critical role in the near-surface energy balance in permafrost regions, yet soil moisture detection in periglacial environments is complicated by highly heterogeneous terrain conditions. We integrate ground-based and spaceborne microwave methods to investigate patterns and controls on surface soil moisture (SSM) in boreal and arctic permafrost environments of Alaska. Soil sampling, geophysics, and probing revealed heterogeneous SSM with significant fine-scale (1 m) variability by topographic setting (p < 0.001) and pedological characteristics (p = 0.01) in arctic tundra, and by land cover type (p < 0.001) in low-relief boreal forest. SSM spatial autocorrelation was greatest below 20 m thresholds demonstrating the adequate spatial resolution for capturing natural SSM heterogeneity at these sites. SMAP L-band was tested for coarse (9 km) soil moisture detection in boreal forest but demonstrated low representativeness from limited ground-based measurements. Finer resolution (∼20 m) relative SSM derived from Sentinel-1 C-band time series in arctic tundra more closely represents the noted SSM autocorrelation length and is explored for visualizing SSM landscape variability. Satellite detection biases created by high-profile tussocks and thick organic soil horizons identified with probe-SSM reveal the need for site-specific soil information in satellite-SSM interpretations. Lastly, time-series of C-band backscatter distributions in boreal forest demonstrated potential for tracking soil thaw onset beneath residual spring snowpack. These results illustrate the complexity of SSM monitoring in periglacial environments and the potential for C-band backscatter and L-band SMAP for large-scale tracking of SSM in these environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1574072</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1574072</link>
        <title><![CDATA[Analyzing satellite and airborne Ka-band passive microwave observations over land for temperature and vegetation monitoring]]></title>
        <pubdate>2025-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Richard de Jeu</author><author>Yoann Malbeteau</author><author>Ruxandra-Maria Zotta</author><author>Wouter Dorigo</author><author>Xiaoling Wu</author><author>Jeffrey Walker</author><author>Diego G. Miralles</author>
        <description><![CDATA[Passive microwave observations at Ka-band (36‐37 GHz) have been widely available for decades, but their full potential for land applications has been hardly exploited. This study analyzed Ka‐band observations at different spatial scales. Between September and October 2019, a series of airborne flights carrying L‐ and Ka‐band instruments were conducted at the Yanco study area in southeastern Australia. Complementary satellite‐based passive microwave data, including Ka‐band observations from the Advanced Microwave Scanning Radiometer 2 (AMSR2), were also collected. These data were compared against LST from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8, as well as vegetation indices such as the Normalized Difference Vegetation Index (NDVI). A strong correlation (R2 = 0.98) was found between Ka‐band vertically polarized brightness temperature from AMSR2 and MODIS LST over a 12‐year period. Airborne Ka‐band observations similarly showed a strong spatial correlation with Landsat 8 LST (R2 = 0.70), but only for areas with dense vegetation (NDVI ≥ 0.6). At lower NDVI values, the observations became more sensitive to soil surface characteristics, particularly soil wetness (soil moisture > 0.3 m3 m−3), causing up to 20 K drops in brightness temperature. The Ka‐band Vegetation Optical Depth (VOD) was derived using a radiative transfer model for both satellite and airborne data. The satellite‐derived Ka-band VOD closely matched published VOD products from other frequencies, and the aircraft‐based VOD provided realistic spatial patterns over different landscapes. At the satellite scale, a clear relationship between VOD and NDVI was observed. The aircraft-based VOD signal was noisier and had a weak spatial correlation with NDVI, although it demonstrated similar trends as at the satellite scale. These results highlight the promising capability of Ka‐band observations for land applications, and its varying sensitivity across scales, with local variability being more pronounced at higher spatial resolutions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1554084</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1554084</link>
        <title><![CDATA[Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis]]></title>
        <pubdate>2025-03-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mahboubeh Boueshagh</author><author>Joan M. Ramage</author><author>Mary J. Brodzik</author><author>David G. Long</author><author>Molly Hardman</author><author>Hans-Peter Marshall</author>
        <description><![CDATA[Seasonal snowpack is a crucial water resource, making accurate Snow Water Equivalent (SWE) estimation essential for water management and environmental assessment. This study introduces a novel approach to Passive Microwave (PMW) SWE estimation, leveraging the strong, unexpected correlation between SWE and the Spatial Standard Deviation (SSD) of PMW Calibrated Enhanced-Resolution Brightness Temperatures (CETB). By integrating spatial statistics, linear correlation, machine learning (Linear Regression, Random Forest, GBoost, and XGBoost), and SHapley Additive exPlanations (SHAP) analysis, this research evaluates CETB SSD as a key feature to improve SWE estimations or other environmental retrievals by investigating environmental drivers of CETB SSD. Analysis at three sites—Monument Creek, AK; Mud Flat, ID; and Jones Pass, CO—reveals site-specific SSD variability, showing correlations of 0.64, 0.82, and 0.72 with SNOTEL SWE, and 0.67, 0.89, and 0.67 with PMW-derived SWE, respectively. Among the sites, Monument Creek exhibits the highest ML model accuracy, with Random Forest and XGBoost achieving test R2 values of 0.89 and RMSEs ranging from 0.37 to 0.39 [K] when predicting CETB SSD. SHAP analysis highlights SWE as the driver of CETB SSD at Monument Creek and Mud Flat, while soil moisture plays a larger role at Jones Pass. In snow-dominated regions with less surface heterogeneity, such as Monument Creek, SSDs can improve SWE estimation by capturing snow spatial variability. In complex environments like Jones Pass, SSDs aid SWE retrievals by accounting for factors such as soil moisture that impact snowpack dynamics. PMW SSDs can enhance remote sensing capabilities for snow and environmental research across diverse environments, benefiting hydrological modeling and water resource management.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1481848</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1481848</link>
        <title><![CDATA[Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data]]></title>
        <pubdate>2025-01-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ibrahim Olalekan Alabi</author><author>Hans-Peter Marshall</author><author>Jodi Mead</author><author>Ernesto Trujillo</author>
        <description><![CDATA[Current terrestrial snow depth mapping from space faces challenges in spatial coverage, revisit frequency, and cost. Airborne lidar, although precise, incurs high costs and has limited geographical coverage, thereby necessitating the exploration of alternative, cost-effective methodologies for snow depth estimation. The forthcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, with its 12-day global revisit cycle and 1.25 GHz L-band frequency, introduces a promising avenue for cost-effective, large-scale snow depth and snow water equivalent (SWE) estimation using L-band Interferometric SAR (InSAR) capabilities. This study demonstrates InSAR’s potential for snow depth estimation via machine learning. Using 3 m resolution L-band InSAR products over Grand Mesa, Colorado, we compared the performance of three machine learning approaches (XGBoost, ExtraTrees, and Neural Networks) across open, vegetated, and the combined (open + vegetated) datasets using Root Mean Square Error (RMSE), Mean Bias Error (MBE), and R2 metrics. XGBoost emerged as the superior model, with RMSE values of 9.85 cm, 10.46 cm, and 9.88 cm for open, vegetated, and combined regions, respectively. Validation against in situ snow depth measurements resulted in an RMSE of approximately 16 cm, similar to in situ validation of the airborne lidar. Our findings indicate that L-band InSAR, with its ability to penetrate clouds and cover extensive areas, coupled with machine learning, holds promise for enhancing snow depth estimation. This approach, especially with the upcoming NISAR launch, may enable high-resolution (∼10 m) snow depth mapping over extensive areas, provided suitable training data are available, offering a cost-effective approach for snow monitoring. The code and data used in this work are available at https://github.com/cryogars/uavsar-lidar-ml-project.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1417417</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1417417</link>
        <title><![CDATA[ARISGAN: Extreme super-resolution of arctic surface imagery using generative adversarial networks]]></title>
        <pubdate>2024-08-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Christian Au</author><author>Michel Tsamados</author><author>Petru Manescu</author><author>So Takao</author>
        <description><![CDATA[Introduction: This research explores the application of generative artificial intelligence, specifically the novel ARISGAN framework, for generating high-resolution synthetic satellite imagery in the challenging arctic environment. Realistic and high-resolution surface imagery in the Arctic is crucial for applications ranging from satellite retrieval systems to the wellbeing and safety of Inuit populations relying on detailed surface observations.Methods: The ARISGAN framework was designed by combining dense block, multireceptive field, and Pix2Pix architecture. This innovative combination aims to address the need for high-quality imagery and improve upon existing state-of-the-art models. Various tasks and metrics were employed to evaluate the performance of ARISGAN, with particular attention to land-based and sea ice-based imagery.Results: The results demonstrate that the ARISGAN framework surpasses existing state-of-the-art models across diverse tasks and metrics. Specifically, land-based imagery super-resolution exhibits superior metrics compared to sea ice-based imagery when evaluated across multiple models. These findings confirm the ARISGAN framework’s effectiveness in generating perceptually valid high-resolution arctic surface imagery.Discussion: This study contributes to the advancement of Earth Observation in polar regions by introducing a framework that combines advanced image processing techniques with a well-designed architecture. The ARISGAN framework’s ability to outperform existing models underscores its potential. Identified limitations include challenges in temporal synchronicity, multi-spectral image analysis, preprocessing, and quality metrics. The discussion also highlights potential avenues for future research, encouraging further refinement of the ARISGAN framework to enhance the quality and availability of high-resolution satellite imagery in the Arctic.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1399839</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1399839</link>
        <title><![CDATA[Sea surface barometry with an O2 differential absorption radar: retrieval algorithm development and simulation]]></title>
        <pubdate>2024-07-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bing Lin</author><author>Matthew Walker Mclinden</author><author>Xia Cai</author><author>Gerald M. Heymsfield</author><author>Nikki Privé</author><author>Steven Harrah</author><author>Lihua Li</author>
        <description><![CDATA[Sea surface air pressure observations are a significant gap in the current Earth observing systems. This study addresses retrieval algorithm development and the evaluation of the potential impact of instrumental and environmental uncertainties on sea level pressure retrievals for the measurements of O2 differential absorption radar systems operating at three spectrally evenly spaced close-frequency bands (65.5, 67.75, and 70.0 GHz). A simulated northern hemispheric summer case is used to simulate retrieval uncertainties. To avoid high attenuation and a low signal-to-noise ratio, radar measurements from weather conditions with a rain rate ≥1 mm/h are not used in the retrieval. This study finds that a retrieval algorithm combining all three channels, i.e., the 3-channel approach, can effectively mitigate major atmospheric and sea surface influences on sea surface air pressure retrieval. The major uncertainty of sea surface pressure retrieval is due to the standard deviation in radar power returns. Analysis and simulation demonstrate the potential of global sea surface pressure observations with errors of about 1∼2 mb, which is urgently needed for the improvement of numerical weather prediction models. Future work will emphasize instrument development and field experiments. It is anticipated that an O2 differential absorption radar system will be available for meteorological applications in a few years.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1401653</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1401653</link>
        <title><![CDATA[Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced Arctic spring sea ice surface classification]]></title>
        <pubdate>2024-07-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Weibin Chen</author><author>Michel Tsamados</author><author>Rosemary Willatt</author><author>So Takao</author><author>David Brockley</author><author>Claude de Rijke-Thomas</author><author>Alistair Francis</author><author>Thomas Johnson</author><author>Jack Landy</author><author>Isobel R. Lawrence</author><author>Sanggyun Lee</author><author>Dorsa Nasrollahi Shirazi</author><author>Wenxuan Liu</author><author>Connor Nelson</author><author>Julienne C. Stroeve</author><author>Len Hirata</author><author>Marc Peter Deisenroth</author>
        <description><![CDATA[The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution Ku-band radar altimetry data over the polar regions up to 81° North. The combination of synthetic aperture radar (SAR) mode altimetry (SRAL instrument) from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise this synergy between altimetry and imagery to demonstrate a novel application of deep learning to distinguish sea ice from leads in spring. We use SRAL classified leads as training input for pan-Arctic lead detection from OLCI imagery. This surface classification is an important step for estimating sea ice thickness and to predict future sea ice changes in the Arctic and Antarctic regions. We propose the use of Vision Transformers (ViT), an approach adapting the popular deep learning algorithm Transformer, for this task. Their effectiveness, in terms of both quantitative metric including accuracy and qualitative metric including model roll-out, on several entire OLCI images is demonstrated and we show improved skill compared to previous machine learning and empirical approaches. We show the potential for this method to provide lead fraction retrievals at improved accuracy and spatial resolution for sunlit periods before melt onset.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2023.1148328</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2023.1148328</link>
        <title><![CDATA[Automatic wide area land cover mapping using Sentinel-1 multitemporal data]]></title>
        <pubdate>2023-12-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>David Marzi</author><author>Antonietta Sorriso</author><author>Paolo Gamba</author>
        <description><![CDATA[This study introduces a methodology for land cover mapping across extensive areas, utilizing multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) data. The objective is to effectively process SAR data to extract spatio-temporal features that encapsulate temporal patterns within various land cover classes. The paper outlines the approach for processing multitemporal SAR data and presents an innovative technique for the selection of training points from an existing Medium Resolution Land Cover (MRLC) map. The methodology was tested across four distinct regions of interest, each spanning 100 × 100 km2, located in Siberia, Italy, Brazil, and Africa. These regions were chosen to evaluate the methodology’s applicability in diverse climate environments. The study reports both qualitative and quantitative results, showcasing the validity of the proposed procedure and the potential of SAR data for land cover mapping. The experimental outcomes demonstrate an average increase of 16% in overall accuracy compared to existing global products. The results suggest that the presented approach holds promise for enhancing land cover mapping accuracy, particularly when applied to extensive areas with varying land cover classes and environmental conditions. The ability to leverage multitemporal SAR data for this purpose opens new possibilities for improving global land cover maps and their applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2023.1073765</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2023.1073765</link>
        <title><![CDATA[Evaluating the effective resolution of enhanced resolution SMAP brightness temperature image products]]></title>
        <pubdate>2023-03-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>David G. Long</author><author>Mary J. Brodzik</author><author>Molly Hardman</author>
        <description><![CDATA[The MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) includes conventional- and enhanced-resolution radiometer brightness temperature (TB) images on standard, compatible grids from calibrated satellite radiometer measurements collected over a multi-decade period. Recently, the CETB team processed the first 4 years of enhanced resolution Soil Moisture Active Passive (SMAP) L-band (1.41 GHz) radiometer TB images. The CETB processing employs the radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm to create enhanced resolution images, which are posted on fine resolution grids. In this paper, we evaluate the effective resolution of the SMAP TB image products using coastline and island crossings. We similarly evaluate the effective resolution of the SMAP L1C_TB_E enhanced resolution product that is based on Backus-Gilbert processing. We present a comparison of the spatial resolution of the rSIR and L1C_TB_E enhanced resolution products with conventionally-processed (gridded) SMAP data. We find that the effective resolution of daily CETB rSIR SMAP TB images is slightly finer than that of L1C_TB_E and about 30% finer than conventionally processed data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2023.1105627</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2023.1105627</link>
        <title><![CDATA[Simulations of sea surface reflection for V-band O2 differential absorption radar barometry]]></title>
        <pubdate>2023-02-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bing Lin</author><author>Matthew Walker Mclinden</author><author>Gerald M. Heymsfield</author><author>Yongxiang Hu</author><author>Nikki Privé</author><author>Lihua Li</author><author>Steven Harrah</author><author>Kevin Horgan</author><author>Xia Cai</author><author>Jim Carswell</author>
        <description><![CDATA[This study simulates V-band sea surface reflectance and normalized radar cross-section (NRCS) for sea surface air pressure barometry using a differential absorption radar operating at three spectrally even spaced close frequency bands (65.5, 67.75 and 70.0 GHz) with ± 15° cross-track scanning angle. The reflectance ratios of two neighboring frequency pairs and the ratio of the two ratios or three-channel approach are the focus of this study. Impacts of major sea surface geophysical variables such as sea surface temperature, wind, salinity, whitecap, and incidence angle on these reflection properties are analyzed. The reflection simulation is essentially based on geometric optics of rough sea surface. Simulation shows that NRCS values are sufficiently strong within the scanning angle and sea surface salinity would only introduce minimal variations in the surface reflection. The impact of sea surface reflection variations with sea surface temperature, wind, and whitecaps on sea surface barometry are mitigated when the ratios of frequency-paired radar signals are used. Furthermore, the ratios of a three-channel approach are very close to unity and calibration or compensation for the reflectance ratios may not be needed for sea level pressure retrievals. These results improve our understanding of sea surface reflection variations and would help the system design and development.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2022.1060144</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2022.1060144</link>
        <title><![CDATA[Deep attentive fusion network for flood detection on uni-temporal Sentinel-1 data]]></title>
        <pubdate>2022-12-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ritu Yadav</author><author>Andrea Nascetti </author><author>Yifang Ban </author>
        <description><![CDATA[Floods are occurring across the globe, and due to climate change, flood events are expected to increase in the coming years. Current situations urge more focus on efficient monitoring of floods and detecting impacted areas. In this study, we propose two segmentation networks for flood detection on uni-temporal Sentinel-1 Synthetic Aperture Radar data. The first network is “Attentive U-Net”. It takes VV, VH, and the ratio VV/VH as input. The network uses spatial and channel-wise attention to enhance feature maps which help in learning better segmentation. “Attentive U-Net” yields 67% Intersection Over Union (IoU) on the Sen1Floods11 dataset, which is 3% better than the benchmark IoU. The second proposed network is a dual-stream “Fusion network”, where we fuse global low-resolution elevation data and permanent water masks with Sentinel-1 (VV, VH) data. Compared to the previous benchmark on the Sen1Floods11 dataset, our fusion network gave a 4.5% better IoU score. Quantitatively, the performance improvement of both proposed methods is considerable. The quantitative comparison with the benchmark method demonstrates the potential of our proposed flood detection networks. The results are further validated by qualitative analysis, in which we demonstrate that the addition of a low-resolution elevation and a permanent water mask enhances the flood detection results. Through ablation experiments and analysis we also demonstrate the effectiveness of various design choices in proposed networks. Our code is available on Github at https://github.com/RituYadav92/UNI_TEMP_FLOOD_DETECTION for reuse.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2022.1021781</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2022.1021781</link>
        <title><![CDATA[Arctic sea ice coverage from 43 years of satellite passive-microwave observations]]></title>
        <pubdate>2022-10-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Claire L. Parkinson</author>
        <description><![CDATA[Satellite passive-microwave instrumentation has allowed the monitoring of Arctic sea ice over the past 43 years, and this monitoring has revealed and quantified major changes occurring in Arctic sea ice coverage. The 43-year 1979–2021 record shows considerable interannual variability but also a long-term downward trend in Arctic sea ice that is clear from many vantage points: A linear-least-square trend of −54,300 ± 2,700 km2/year for yearly average sea ice extents; statistically significant negative trends for each of the 12 calendar months; negative trends for each of nine regions into which the Arctic sea ice cover is divided; the fact that for all 12 calendar months the highest monthly average sea ice extent came in the first 8 years of the record and the lowest monthly average sea ice extent came in the last 10 years of the record; and a prominent shortening of the sea ice season throughout much of the marginal ice zone, with the length of the sea ice season in some locations decreasing by over 100 days and some locations previously experiencing months-long sea ice seasons now typically no longer having a sea ice season at all. The overall, Arctic-wide trend value of the yearly average sea ice extents since 1979 has consistently had a negative magnitude exceeding two standard deviations of the trend line slope since 1990 and has remained in the narrow range of −53,000 km2/yr to −55,500 km2/yr since 2011.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2022.1028587</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2022.1028587</link>
        <title><![CDATA[Characteristics of a pre-monsoon dryline atmospheric boundary layer over the rain shadow region: A case study]]></title>
        <pubdate>2022-10-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Raman Solanki</author><author>Neelam Malap</author><author>K. Gayatri</author><author>Y. Jaya Rao</author><author>Thara Prabhakaran</author>
        <description><![CDATA[A dryline is the zone of distinct moisture gradient separating warm, moist, and hot, dry air masses. It is usually associated with mesoscale phenomena and plays a significant role in atmospheric boundary layer (ABL) dynamics including initiation of convection/thunderstorms. In the tropical Indian region, these dryline conditions are normally associated with the pre-monsoon season. In the present study, dryline characteristics over a rain shadow region in the Indian subcontinent were investigated utilizing observations and Weather Research and Forecasting (WRF) model from 28 to 30 May 2019. Based upon Wind Profiler Radar and MicroWave Radiometer Profiler measurements, the ABL characteristics were investigated. Interestingly, the ABL height was found to evolve up to 5 km with the horizontal wind vectors oscillating between north-westerly and north-easterly flow. During the intense ABL deepening, stronger downdraft cores were observed in comparison with the updraft cores. The stronger downdrafts entrained free-tropospheric dry air thereby further deepening the ABL. Based upon the entrainment velocity estimates at the ABL top and the variations in potential temperature, the dynamic entrainment fluxes were estimated and further implemented for evaluating two slab models to recreate the ABL growth. With this analysis, we demonstrate the significant contribution of entrainment fluxes on ABL growth during dryline conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2021.778691</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2021.778691</link>
        <title><![CDATA[Observing Sucrose Accumulation With Sentinel-1 Backscatter]]></title>
        <pubdate>2021-12-20T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Nadja den Besten</author><author>Susan Steele-Dunne</author><author>Benjamin Aouizerats</author><author>Ariel Zajdband</author><author>Richard de Jeu</author><author>Pieter van der Zaag</author>
        <description><![CDATA[In this study the impact of sucrose accumulation in Sentinel-1 backscatter observations is presented and compared to Planet optical observations. Sugarcane yield data from a sugarcane plantation in Xinavane, Mozambique are used for this study. The database contains sugarcane yield of 387 fields over two seasons (2018-2019 and 2019-2020). The relation between sugarcane yield and Sentinel-1 VV and VH backscatter observation is analyzed by using the Normalized Difference Vegetation Index (NDVI) data as derived from Planet Scope optical imagery as a benchmark. The different satellite observations were compared over time to sugarcane yield to understand how the relation between the observations and yield evolves during the growing season. A negative correlation between yield and Cross Ratio (CR) from Sentinel-1 backscatter was found while a positive correlation between yield and Planet NDVI was observed. An additional modeling study on the dielectric properties of the crop revealed how the CR could be affected by sucrose accumulation during the growing season and supported the opposite correlations. The results shows CR contains information on sucrose content in the sugarcane plant. This sets a basis for further development of sucrose monitoring and prediction using a combination of radar and optical imagery.]]></description>
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