Sentinel-1 Synthetic Aperture Radar Images Research Articles

Synthetic aperture radar interferometry–based coseismic deformation and slip distribution of the 2022 Menyuan MS6.9 earthquake in Qinghai, China

On January 8, 2022, a 6.9 magnitude earthquake occurred in Menyuan County, Qinghai Province, with the epicenter located at the intersection of the Tuolaishan Fault and the Lenglongling Fault, which are part of the Qilian–Haiyuan fault zone. This study investigated the sliding characteristics and seismic mechanism of the earthquake to understand the activity and seismic risk of the fault on the northeastern margin of the Qinghai–Tibet Plateau. This paper analyzed Sentinel-1 synthetic aperture radar images to obtain the coseismic deformation field of the earthquake, which was then used to invert the slip distribution of the seismogenic fault and the coseismic Coulomb stress on the surrounding faults caused by the earthquake. It was found that the earthquake was primarily characterized by sinistral strike-slip movement. Along the satellite line of sight, the south wall of the fault had a maximum deformation of 0.62 m, and the north wall had a maximum deformation of 0.48 m. The coseismic slip distribution results indicated that the maximum slip of the earthquake was 4.51 m, and the moment magnitude was MW6.7. The Coulomb stress analysis showed that the 2016 Menyuan earthquake promoted the occurrence of the 2022 Menyuan earthquake.

Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

ABSTRACT Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification.

ReCuSum: A polyvalent method to monitor tropical forest disturbances

Change detection methods based on Earth Observations are increasingly used to monitor rainforest in the intertropical band. Until recently, deforestation monitoring was mainly based on remotely sensed optical images which often face limitations in humid tropical areas due to frequent cloud coverage. This leads to late detections of disturbance events. Since the launch of Sentinel-1 acquiring images with a revisit time of 12 days and a spatial resolution of 5 × 20 m in Brazil, Synthetic Aperture Radar (SAR) images have been increasingly used to monitor deforestation. In this study, we propose a multitemporal version of the change detection method we previously applied to timeseries of Sentinel-1 SAR images, to monitor deforestation/degradation in the Congo rainforest. Our approach is based on a Cumulative Sum (CuSum) method combined with a spatial recombination of Critical Thresholds (CuSum cross-Tc). The newly developed multitemporal CuSum method (ReCuSum) was applied to a time-series of 82 dual polarization (VH, VV) Ground Range Detected (GRD) Sentinel-1 images acquired in the Para State, in the Brazilian Amazonia, from 29/09/2016 to 01/07/2019. The ReCuSum method consists of iteratively applying the CuSum cross-Tc to monitor multiple changes in a time-series by splitting the time-series at each date of detected change and by independently iterating over the time periods resulting from the splits. The number of changes in the time-series was then analysed according to the vegetation type (Forest, non-forest vegetation) determined by visual inspection of optical Sentinel-2 image and PlanetScope monthly mosaic. This showed a difference between non-forest vegetation and forested areas. A threshold based on the number of changes (Tnbc) was then developed to differentiate forest from non-forest disturbances. The ability to monitor non-forest vegetation was analysed: the CuSum cross-Tc detected up to 90% of the total non-forest vegetation area over the study region in the past period. After removing past disturbances and past non-forest vegetation, then removing the pixels covered with non-forest vegetations based on Tnbc, the application of the ReCuSum led to a precision of 81%, a recall of 68%, a kappa coefficient of 0.72 and a F1- score of 0.74 in forest disturbance monitoring. According to these results, ReCuSum applied to Sentinel-1 time-series of images can be used for efficient forest disturbance monitoring and for generating a forest / non-forest map after the application of newly developed post-processing steps. Sentinel-1 imagery can be used for both Forest / Non-forest mapping and for forest disturbance detection. ReCuSum was released as an open-source GIT project available at: https://forgemia.inra.fr/bertrand.ygorra/cusum-deforestation_monitoring.

The 2023 Mw 7.8 and 7.6 Earthquake Doublet in Southeast Türkiye: Coseismic and Early Postseismic Deformation, Faulting Model, and Potential Seismic Hazard

Abstract On 6 February 2023, Mw 7.8 and 7.6 earthquakes struck southeast Türkiye and northwest Syria. They are the largest earthquakes in Türkiye in over 80 yr, causing significant damage and fatalities. We used Advanced Land Observation Satellite-2 and Sentinel-1 Synthetic Aperture Radar images to obtain near-field coseismic displacements by Differential Interferometric Synthetic Aperture Radar (DInSAR) and pixel offset tracking (POT). Discontinuities of the surface deformation suggest that the Mw 7.8 event ruptured ∼320 km along the East Anatolian fault, and the Mw 7.6 event ruptured ∼150 km near Elbistan, southern Türkiye. We inverted these earthquakes' fault geometry and slip distribution based on Global Navigation Satellite Systems, DInSAR, and POT displacements. The estimated fault slip model shows that the first Mw 7.8 event ruptured a steeply southeast-dipping fault, and the seismogenic fault of the second Mw 7.6 event is north-dipping with complex geometry. The dip angle of subfaults of the Mw 7.6 earthquake decreases from east to west. Faults responsible for the two earthquakes are dominated by left-lateral strike-slip motion, with the maximum slip of ∼9.1 m. Early postseismic deformation within two months exhibits displacement discontinuities in the Amanos and Pazarcık segments and the Çardak fault, suggesting that the afterslip partially compensated the coseismic slip deficit at the shallow depths. Furthermore, static Coulomb failure stress changes induced by the two earthquakes indicate that the southwestern Pütürge segment of the East Anatolian fault has a high risk of future rupture.

Extracting water body data based on SDWI and threshold segmentation: A case study in permafrost area surrounding Salt Lake in Hoh Xil, Qinghai-Tibet Plateau, China

There are a large number of lakes, rivers, and other natural water bodies distributed in the permafrost area of the Qinghai-Tibet Plateau (QTP). The changes in water bodies will affect the distribution of water resources in surrounding areas and downstream areas, resulting in environmental impact and bringing potential flood disasters, which will induce more serious issues and problems in alpine and high-altitude areas with a fragile habitat (such as the QTP in China). Generally, effective, reasonable, and scientific monitoring of large-scale water bodies can not only document the changes in water bodies intuitively, but also provide important theoretical reference for subsequent environmental impact prediction, and disaster prevention and mitigation in due course of time. The large-scale water extraction technology derived from the optical remote sensing (RS) image is seriously affected by clouds, bringing about large differences among the extracted water result products. Synthetic aperture radar (SAR) RS technology has the unique advantage characteristics of all-weather, all-day, strong penetration, and not being affected by clouds, which is hopeful in extracting water body data, especially for days with cloudy weather. The data extraction of large-scale water bodies based on SAR images can effectively avoid the errors caused by clouds that become prevalent at present. In this paper, the Hoh Xil Salt Lake on the QTP and its surrounding five lakes are taken as the research objects. The 2-scene Sentinel-1 SAR image data covering the whole area on 22 August 2022 was used to verify the feasibility of extracting water body data in permafrost zones. Furthermore, on 22 August 2022, the wealth here was cloudy, which made the optical RS images, e.g., Sentinel-2 images full of clouds. The results show that: using the Sentinel-1 image and threshold segmentation method to extract water body data is efficient and effective with excellent results in permafrost areas. Concretely, the Sentinel-1 dual-polarized water index (SDWI), calculated by combining dual vertical–vertical (VV) polarized and vertical–horizontal (VH) polarized data is a useful index for water extraction and the result is better than each of the VV or VH polarized images.

Revealing the Land Subsidence Deceleration in Beijing (China) by Gaofen-3 Time Series Interferometry

Revealing the land subsidence in Beijing, China, induced by the massive groundwater extraction in the past three decades, is important to mitigate the hazards and protect the residences and infrastructure. Many SAR (Synthetic Aperture Radar) datasets have been successfully applied to reveal the land subsidence over Beijing in previous research, while few works were achieved on land subsidence revealed by time-series InSAR (Interferometric Synthetic Aperture Radar) with Gaofen-3 SAR images. In this study, we successfully perform the time-series InSAR analysis with Gaofen-3 SAR images to extract the land subsidence in Beijing from 2020 to 2021. The Sentinel-1 SAR images were used to assess the accuracy of Gaofen-3 images. The subsidence scale and extent are consistent in detected major subsidence bowls between the two datasets. The spatial–temporal evolution and the deceleration of Beijing land subsidence were revealed by comparing with the Sentinel-1 results from 2017 to 2020. Moreover, we evaluated the interferometric performance of Gaofen-3 satellite SAR imagery and analyzed the main factors that mostly influence the coherence and quality of interferograms. Our results proved that the long perpendicular baselines decrease the coherence seriously over the study area, and the artifacts induced by inaccurate orbit information reduce the quality of the Gaofen-3 interferograms. Refining and removing the two main artifacts could improve the quality of interferograms formed by Gaofen-3 SAR images.

The assessment of shoreline changes along the Johor Strait using Sentinel-1 synthetic aperture radar imagery and GIS

ABSTRACT This paper presents a robust approach for shoreline detection using the K-Means clustering and the Digital Shoreline Analysis System developed by the United States Geological Survey. The shoreline changes along the Johor Straits of Malaysia are assessed by using the multi-temporal Sentinel-1 Synthetic Aperture Radar images from 2018 to 2022. This area is known to be important for both socioeconomic activities. The retrieved shorelines are found reliable with a good accuracy of 1.4 m when validating against the Global Positioning System rapid static survey. This merits finding a robust approach that goes beyond the normal practice in Malaysia where manual digitization is usually exercised. The rate of change is calculated by using the end point rate (EPR) for providing a short-term (annual) rate of change while using the linear regression rate (LRR) for providing a long-term (5 year) rate of change. Based on the EPR, the mean annual changes are approximately 10 m/yr for erosion and up to 9.83 m/yr for accretion; meanwhile, the mean of 5-year changes based on the LRR is 62% and 37% for erosion and accretion, respectively. This study reveals that 10 out of 12 sites along Johor Strait are categorized as ‘high’ erosion, two locations are categorized as ‘medium’ erosion and none are as ‘low’ erosion.

DynIceData: a gridded ice–water classification dataset at short-time intervals based on observations from multiple satellites over the marginal ice zone

ABSTRACT High-resolution observations of short-term changes in sea ice are critical to understanding ice dynamics and also provide important information used in advice to shipping, especially in the Arctic. Although individual satellite sensors provide periodic sea ice observations with spatial resolutions of tens of meters, information regarding changes that occur over short time intervals of minutes or hours is limited. In this study, a gridded ice–water classification dataset with a high temporal resolution was developed based on observations acquired by multiple satellite sensors in the Marginal Ice Zone (MIZ). This dataset – DynIceData – which combines Sentinel-1 Synthetic Aperture Radar (SAR) data with Gaofen-3 (GF-3) SAR and SDGSAT-1 thermal infrared imagery was used to obtain observations of the MIZ with a range of temporal resolutions ranging from minutes to tens of hours. The areas of the Arctic covered include the Kara Sea, Beaufort Sea, and Greenland Sea during the period from August 2021 to August 2022. Object-oriented segmentation and thresholding were used to obtain the ice–water classification map from Sentinel-1 and GF-3 SAR image pairs and Sentinel-1 SAR and SDGSAT-1 thermal image pairs. The time interval between the images in each pair ranged from 1 minute to 68 hours. Ten-kilometer grid sample granules with a spatial resolution of 25 m for the GF-3 SAR data and 30 m for the SDGSAT-1 thermal data were used. The classification was verified as having an overall accuracy of at least 95.58%. The DynIceData dataset consists of 7338 samples, which could be used as reference data for further research on rapid changes in sea ice patterns at different short time scales and provide support for research on thermodynamic and dynamic models of sea ice in combination with other environmental data, thus potentially improving the accuracy of sea ice forecasting using Artificial Intelligence. The dataset can be accessed at https://doi.org/10.57760/sciencedb.j00001.00784.

Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection

Monitoring variations in the extent of wet snow over space and time is essential for many applications, such as hydrology, mountain ecosystems, meteorology and avalanche forecasting. The Synthetic Aperture Radar (SAR) measurements from the Sentinel-1 satellite help detect wet snow in almost all weather conditions. Most detection methods use a fixed threshold to a winter image ratio with one or two reference images (with no snow or dry snow). This study aimed to explore the potential of image segmentation methods from different families applied to Sentinel-1 SAR images to improve the detection of wet snow over the French Alps. Several segmentation methods were selected and tested on a large alpine area of 100 × 100 km2. The segmentation methods were evaluated over one season using total snow masks from Sentinel-2 optical measurements and outputs from forecasters’ bulletins combining model and in-situ observations. Different metrics were used (such as snow probability, correlations, Hamming distance, and structure similarity scores). The standard scores illustrated that filtering globally improved the segmentation results. Using a probabilistic score as a function of altitude highlights the interest in some segmentation methods, and we show that these scores could be relevant to calibrate the parameters of these methods better.

Ice Velocity Variations of the Cook Ice Shelf, East Antarctica, from 2017 to 2022 from Sentinel-1 SAR Time-Series Offset Tracking

The Cook Glacier drains a significant portion of the Wilkes Subglacial Basin, the largest subglacial basin in East Antarctica—which feeds the Cook Ice Shelf. The ice velocity of the Cook Ice Shelf needs to be monitored precisely and accurately, as it plays a critical role in determining the ice discharge from the Wilkes Subglacial Basin. In this study, we measured the annual ice velocities of the Cook Ice Shelf using the offset tracking technique on Sentinel-1 synthetic aperture radar images obtained from 2017 to 2022. Time-series offsets in the range and azimuth directions were determined from the offset tracking pairs with a temporal baseline of 36 days obtained from January to December of each year. Statistical evaluations of the spatiotemporal variations of the time-series offsets effectively eliminated the erroneous offsets in the original offset fields; the remaining offsets were then used to produce two-dimensional annual ice velocities. The direction of the ice flow of the Cook Ice Shelf was almost constant during the period 2017–2022, and the variations in the magnitude of annual ice velocities were investigated. The annual ice velocities of the Cook East Ice Shelf (CEIS) stayed constant and showed a gradual increase from the grounding line to the ice front, except in the western part. Ice velocities of the western part of the CEIS have not changed much at the grounding line during the 6-year period, while in the dynamic shelf ice zone, ice velocities accelerated by up to 22% because of the development of numerous crevasses and fractures. The ice velocities of the Cook West Ice Shelf (CWIS) were about two times higher than those of the CEIS and tended to increase rapidly from the grounding line to the ice front. The annual ice velocities at the grounding line of CWIS increased rapidly from 1330 to 1450 m/a over 6 years, with 70% of this acceleration observed after 2021. This was attributed to a reduction in the ice shelf volume because of the evolution of surface crevasses and rifts, leading to a decrease in the ice shelf’s buttressing potential. In particular, the loss of a portion of the dynamic shelf ice zone due to a series of ice front collapses in February 2022 likely caused the rapid speed-up of the ice shelf. The results of this study indicate that the buttressing potential of the CWIS and the western part of the CEIS has been significantly reduced, which could mean serious instability of the marine ice sheet in this region.

Evaluation of optical and synthetic aperture radar image fusion methods: a case study applied to Sentinel imagery

<p>This paper evaluates different optical and synthetic aperture radar (SAR) image fusion methods applied to open-access Sentinel images with global coverage. The objective of this research was to evaluate the potential of image fusion methods to get a greater visual difference in land cover, especially in oil palm crops with natural forest areas that are difficult to differentiate visually. The application of the image fusion methods: Brovey (BR), high-frequency modulation (HFM), Gram-Schmidt (GS), and principal components (PC) was evaluated on Sentinel-2 optical and Sentinel-1 SAR images using a cloud computing environment. The results show that the application of the implemented optical/SAR image fusion methods allows the creation of a synthetic image with the characteristics of both data sources. The multispectral information provided by the optical image and information associated with the geometry and texture/roughness of the land covers, provided by the SAR image, allows a greater differentiation in the visualization of the various land covers, achieving a better understanding of the study area. The fusion methods that visually presented greater characteristics associated with the SAR image were the BR and GS methods. The HFM method reached the best statistical indicators; however, this method did not present significant visual changes in the SAR contribution.</p>

The performance of speckle filters on Copernicus Sentinel-1 SAR images containing natural oil slicks

Synthetic aperture radar (SAR) is traditionally used in the identification, mapping and analysis of petroleum slicks, regardless of their origin. On SAR images, oil slicks appear as dark patches that contrast with the brightness of the surrounding sea surface. This distinction allows for automated detection algorithms to be designed using computer vision methods for objective oil slick identification. Nevertheless, efficient interpretation of the SAR imagery by statistical analysis can be diminished due to the speckle effect present on SAR images, a granular artefact associated with the coherent nature of SAR that visually degrades the image quality. In this study, a quantitative and qualitative assessment of common SAR image despeckling methods is presented, analysing their performance when applied to images containing natural oil slicks. The assessment is performed on Copernicus Sentinel-1 images acquired with various temporal and environmental conditions. The assessment covers a diverse array of filters that employ Bayesian and non-linear statistics in the spatial, transform and wavelet domains, focussing on their demonstrated performance and capabilities for edge and texture retention. In summary, the results reveal that filters using local statistics in the spatial domain produce consistent desired effects. The novel SAR-BM3D algorithm can be used effectively, albeit with a higher computational demand. Supplementary material: Implementations of the speckle filters used in this paper are made available at https://github.com/cavrinceanu/specklefilters under an MIT license. Image statistics data are available in the supplementary table at https://doi.org/10.6084/m9.figshare.13010405 Thematic collection: This article is part of the Remote sensing for site investigations on Earth and other planets collection available at: https://www.lyellcollection.org/cc/remote-sensing-for-site-investigations-on-earth-and-other-planets

High-temporal-resolution monitoring of reservoir water storage of the Lancang-Mekong River

The Lancang-Mekong River (LMR) is an important transboundary river in Southeast Asia shared by China, Myanmar, Laos, Thailand, Cambodia, and Vietnam from upstream to downstream. Construction and operation of dams in the LMR basin has profoundly affected its natural streamflow regime. It is therefore important to monitor changes in reservoir water storage and to quantify the impact of reservoir operation on the redistribution of surface water resources over this basin. Given the difficulty in obtaining in-situ measurements of reservoirs on the LMR, we integrated multisource remote sensing, including satellite altimetry and optical and synthetic aperture radar (SAR) images, to generate weekly water levels and water storages of nine largest reservoirs on the main stem of the LMR from 2017 to 2021. Specifically, partial surface water extent (SWE) of reservoirs was extracted from Sentinel-1 SAR images and digital elevation models (DEMs), using Random Forest algorithms trained by partial SWE derived from Sentinel-2 optical images, showing an overall accuracy higher than 95%. Based on the partial SWE and water level estimates from ICESat-2 and Global Ecosystem Dynamics Investigation (GEDI, International Space Station-based) data, the relationships between water levels and partial SWE were derived to convert partial SWE into water level time series. Furthermore, water storage time series of the nine reservoirs were obtained from water level time series and hypsometric functions derived from SRTM DEMs that were corrected by ICESat-2 data to remove systematic errors. For the Xiaowan Reservoir on the Lancang River, there is close agreement between remote sensing-derived water levels and in-situ water levels in terms of a normalized RMSE lower than 5%. Results indicate that multisource remote sensing has large potential for high-temporal-resolution monitoring of reservoir water levels and water storage. This could more precisely evaluate impacts of cascade reservoirs on the streamflow of the LMR and facilitate drought and flood mitigation for riverine countries.

Urban Impervious Surface Extraction Based on Deep Convolutional Networks Using Intensity, Polarimetric Scattering and Interferometric Coherence Information from Sentinel-1 SAR Images

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas.

Estimasi Simpanan Karbon Hutan Mangrove Menggunakan Citra (SAR) Synthetic Aperture Radar Sentinel -1 di Taman Hutan Raya Ngurah Rai,Bali


 
 
 
 Taman Hutan Raya (Tahura) Ngurah Rai, Bali, is recorded to have a mangrove forest covering an area of 1,373.5 ha, which plays a role in absorbing carbon from the atmosphere. Carbon is stored in the form of biomass. The bigger the biomass, the bigger the carbon stock in it. Direct measurement of biomass in the field requires an expensive method, a relatively long time, and narrow area coverage. Nowadays, the weakness of measuring carbon stocks directly in the field can be overcome by remote sensing approaches, especially estimations from satellite imagery combined with geographic information systems (SIG). One of the images that can be used to estimate carbon stock is the Sentinel-1 Synthetic Aperture Radar (SAR) image. The aim of this research is to estimate biomass and carbon stocks based on SAR backscatter distribution. The method used is the analysis of satellite imagery combined with field data. Field data collection and above-ground biomass (AGB) calculations were carried out by taking 30 plots with a plot size of 10 x 10 m. The results showed that there was a correlation between the backscatter value of SAR images and AGB from field measurements (r = 0.59 and the AGB estimation model y = 57.42x + 1223.8, where y is biomass and x is backscatter), and the coefficient of determination R2 = 0.36. The estimation model developed next is used to calculate the overall AGB, which is then used to estimate the overall aboveground carbon storage.
 
 
 

Monitoring of Paddy and Maize Fields Using Sentinel-1 SAR Data and NGB Images: A Case Study in Papua, Indonesia

This study addresses the question of how to evaluate the growth stage of food crops, for instance, paddy (Oryza sativa) and maize (Zea mays), from two different sensors in selected developed areas of Papua Province of Indonesia. Level-1 Ground Range Detected (L1 GRD) images from Sentinel-1 Synthetic Aperture Radar (SAR) data were used to investigate the growth of paddy and maize crops. An NGB camera was then used to obtain the Green Normalized Difference Vegetation Index (GNDVI), and the Enhanced Normalized Difference Vegetation Index (ENDVI) as in situ measurement. Afterwards, the results were analyzed based on the Radar Vegetation Index (RVI) and the Vertical-Vertical (VV) and Vertical Horizontal (VH) band backscatters at incidence angles of 30.55°–45.88°, and 30.59°–46.16° in 2021 and 2022, respectively. The findings showed that Sigma0_VV_db and sigma0_VH_db had a strong correlation (R2 above 0.900); however, polarization modification is required, specifically in the maize field. The RVI calculated and backscatter changes in this study were comparable to the in situ measurements, specifically those of paddy fields, in 2022. Even though the results of this study were not able to prove the RVI values from the two relative orbits (orbit31 and orbit155) due to the different angle incidences and the availability of the Sentinel-1 SAR data set over the study area, the division of SAR image data based on each relative orbit adequately represents the development of crops in our study areas. The significance of this study is expected to support food crop security and the implementation of development plans that contribute to the local government’s goals and settings.

Deep Learning-Based Virtual Optical Image Generation and Its Application to Early Crop Mapping

This paper investigates the potential of cloud-free virtual optical imagery generated using synthetic-aperture radar (SAR) images and conditional generative adversarial networks (CGANs) for early crop mapping, which requires cloud-free optical imagery at the optimal date for classification. A two-stage CGAN approach, including representation and generation stages, is presented to generate virtual Sentinel-2 spectral bands using all available information from Sentinel-1 SAR and Sentinel-2 optical images. The dual-polarization-based radar vegetation index and all available multi-spectral bands of Sentinel-2 imagery are particularly considered for feature extraction in the representation stage. A crop classification experiment using Sentinel-1 and -2 images in Illinois, USA, demonstrated that the use of all available scattering and spectral features achieved the best prediction performance for all spectral bands, including visible, near-infrared, red-edge, and shortwave infrared bands, compared with the cases that only used dual-polarization backscattering coefficients and partial input spectral bands. Early crop mapping with an image time series, including the virtual Sentinel-2 image, yielded satisfactory classification accuracy comparable to the case of using an actual time-series image set, regardless of the different combinations of spectral bands. Therefore, the generation of virtual optical images using the proposed model can be effectively applied to early crop mapping when the availability of cloud-free optical images is limited.

Trade Wind Boundary Layer Turbulence and Shallow Precipitating Convection: New Insights Combining SAR Images, Satellite Brightness Temperature, and Airborne In Situ Measurements

AbstractThe imprint of marine atmospheric boundary layer (MABL) dynamical structures on sea surface roughness, as seen from Sentinel‐1 synthetic aperture radar (SAR) acquisitions, is investigated. We focus on 13 February 2020, a case study of the elucidating the role of clouds‐circulation coupling in climate field campaign. For suppressed conditions, convective rolls imprint on sea surface roughness is confirmed through the intercomparison with MABL turbulent organization deduced from airborne measurements. A discretization of the SAR wide swath into 25 × 25 km2 tiles then allows us to capture the spatial variability of the turbulence organization varying from rolls to cells. Second, we objectively detect cold pools within the SAR image and combine them with geostationary brightness temperature. The geometrical or physically‐based metrics of cold pools are correlated to cloud properties. This provides a promising methodology to analyze the dynamics of convective systems as seen from below and above.

Assessment of Machine Learning Methods for Urban Types Classification Using Integrated SAR and Optical Images in Nonthaburi, Thailand

Urbanization and expansion in each city of emerging countries have become an essential function of Earth’s surface, with the majority of people migrating from rural to urban regions. The various urban category characteristics have emphasized the great importance of understanding and creating suitable land evaluations in the future. The overall objective of this study is to classify the urban zone utilizing building height which is estimated using Sentinel-1 synthetic aperture radar (SAR) and various satellite-based indexes of Sentinel-2A. The first objective of this research is to estimate the building height of the Sentinel-1 SAR in Nonthaburi, Thailand. A new indicator, vertical-vertical-horizontal polarization (VVH), which can provide a better performance, is produced from the dual-polarization information, vertical-vertical (VV), and vertical-horizontal (VH). Then, the building height model was developed using indicator VVH and the reference building height data. The root means square error (RMSE) between the estimated and reference height is 1.413 m. Then, the second objective is to classify three classes of urban types, which are composed of residential buildings, commercial buildings, and other buildings, including vegetation, waterbodies, car parks, and so on. Spectral indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built up the index (NDBI) are extracted from the Sentinel-2A data. To classify the urban types, a three-machine learning classifier, support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were developed. The classification uses randomly trained data from each 500 m focus study which are divided into a 100 × 100 m grid. Different models are examined using different variables, for example, classification using only building height and only spectral indices. The indices and estimated building height were used to classify the urban types. Not only the average of various satellite-based indices and building height of 100 × 100 m grid was used, but also the minimum, maximum, mean, and standard deviation were calculated from NDVI, NDWI, NDBI, and building height. There are a total of 16 variables used in the model. Eventually, the principal components analysis (PCA) was used to reduce the variables and get better performance of the models. SVM showed better accuracy than the other two, RF and KNN. The accuracies of SVM, RF, and KNN are 0.86, 0.75, and 0.76, respectively.

Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

The purpose of this study was to develop a method for retrieving the rain rate from C-band (∼5.3 GHz) synthetic aperture radar (SAR) images during tropical cyclones (TCs). Seven dual-polarized (vertical-vertical [VV] and vertical-horizontal [VH]) Sentinel-1 (S-1) SAR images were acquired in the interferometric-wide (IW) swath mode during the Satellite Hurricane Observation Campaign. These images were collocated with rain rates measured by the Stepped-Frequency Microwave Radiometers onboard National Oceanic and Atmospheric Administration aircraft. Wind speeds were retrieved from the VH-polarized SAR images using the geophysical model function (GMF) S1IW.NR. We determined the difference between the measured normalized radar cross section (NRCS) based on VV-polarized SAR and the predicted NRCS derived using the geophysical model function CMOD5.N forced with wind speeds retrieved from VH-polarized SAR images. Rain cells were identified as regions in the images where the NRCS difference was greater than 0.5 dB or smaller than −0.5 dB. We found that the difference in the NRCS decreased and the VH-polarized wind speed increased with increasing rain rate. Based on these findings, we developed an empirical function for S-1 SAR rain retrieval in a TC, naming it CRAIN_S1. The validation of the CRAIN_S1 results with Tropical Rainfall Measuring Mission data resulted in a root mean square error of 0.58 mm/hr and a correlation of 0.89. This study provides an alternate method for rain monitoring utilizing SAR data with a fine spatial resolution.