Synthetic Aperture Radar Instrument Research Articles

InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m.

Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

Upland crop-rice cropping systems (UCR) facilitate sustainable agricultural intensification. Accurate UCR cultivation mapping is needed to ensure food security, sustainable water management, and rural revitalization. However, datasets describing cropping systems are limited in spatial coverage and crop types. Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples, which limits its applications over large regions. We describe a novel algorithm (RRSS) for automatic mapping of upland crop–rice cropping systems using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) data. One indicator, the VV backscatter range, was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR. The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions. This study developed 10-m UCR maps of a major rice bowl in South China, the Xiang-Gan-Min (XGM) region. The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites, with an overall accuracy of 91.92%. There were 7348 km2 areas of UCR, roughly one-half of them located in plains. The UCR was represented mainly by oilseed-UCR and tobacco-UCR, which contributed respectively 69% and 15% of UCR area. UCR patterns accounted for only one-tenth of rice production, which can be tripled by intensification from single rice cropping. Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm, which could be further applied to generate 10-m UCR datasets for application at national or global scales.

Spatial Variability in Salt Marsh Drainage Controlled by Small Scale Topography

AbstractWater movement in coastal wetlands is affected by spatial differences in topography and vegetation characteristics as well as by complex hydrological processes operating at different time scales. Traditionally, numerical models have been used to explore the hydrodynamics of these valuable ecosystems. However, we still do not know how well such models simulate water‐level fluctuations beneath the vegetation canopy since we lack extensive field data to test the model results against observations. This study utilizes remotely sensed images of sub‐canopy water‐level change to understand how marshes drain water during falling tides. We employ rapid repeat interferometric observations from the NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument to analyze the spatial variability in water‐level change within a complex of marshes in Terrebonne Bay, Louisiana. We also used maps of herbaceous aboveground biomass derived from the Airborne Visible/Infrared Imaging Spectrometer‐Next Generation to evaluate vegetation contribution to such variability. This study reveals that the distribution of water‐level change under salt marsh canopies is strongly influenced by the presence of small geomorphic features (<10 m) in the marsh landscape (i.e., levees, tidal channels), whereas vegetation plays a minor role in retaining water on the platform. This new type of high‐resolution remote sensing data offers the opportunity to study the feedback between hydrodynamics, topography and biology throughout wetlands at an unprecedented spatial resolution and test the capability of numerical models to reproduce such patterns. Our results are essential for predicting the vulnerability of these delicate environments to climate change.

Semi-Supervised Urban Change Detection Using Multi-Modal Sentinel-1 SAR and Sentinel-2 MSI Data

Urbanization is progressing at an unprecedented rate in many places around the world. The Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions, combined with deep learning, offer new opportunities to accurately monitor urbanization at a global scale. Although the joint use of SAR and optical data has recently been investigated for urban change detection, existing data fusion methods rely heavily on the availability of sufficient training labels. Meanwhile, change detection methods addressing label scarcity are typically designed for single-sensor optical data. To overcome these limitations, we propose a semi-supervised urban change detection method that exploits unlabeled Sentinel-1 SAR and Sentinel-2 MSI data. Using bitemporal SAR and optical image pairs as inputs, the proposed multi-modal Siamese network predicts urban changes and performs built-up area segmentation for both timestamps. Additionally, we introduce a consistency loss, which penalizes inconsistent built-up area segmentation across sensor modalities on unlabeled data, leading to more robust features. To demonstrate the effectiveness of the proposed method, the SpaceNet 7 dataset, comprising multi-temporal building annotations from rapidly urbanizing areas across the globe, was enriched with Sentinel-1 SAR and Sentinel-2 MSI data. Subsequently, network performance was analyzed under label-scarce conditions by training the network on different fractions of the labeled training set. The proposed method achieved an F1 score of 0.555 when using all available training labels, and produced reasonable change detection results (F1 score of 0.491) even with as little as 10% of the labeled training data. In contrast, multi-modal supervised methods and semi-supervised methods using optical data failed to exceed an F1 score of 0.402 under this condition. Code and data are made publicly available.

E-Agriculture Planning Tool for Supporting Smallholder Cocoa Intensification Using Remotely Sensed Data

Remote sensing approaches are often used to monitor land cover change. However, the small physical size (about 1–2 hectare area) of smallholder orchards and the cultivation of cocoa (Theobroma cocoa L.) under shade trees make the use of many popular satellite sensors inefficient to distinguish cocoa orchards from forest areas. Nevertheless, high-resolution satellite imagery combined with novel signal extraction methods facilitates the differentiation of coconut palms (Cocos nucifera L.) from forests. Cocoa grows well under established coconut shade, and underplanting provides a viable opportunity to intensify production and meet demand and government targets. In this study, we combined grey-level co-occurrence matrix (GLCM) textural features and vegetation indices from Sentinel datasets to evaluate the sustainability of cocoa expansion given land suitability for agriculture and soil capability classes. Additionally, it sheds light on underexploited areas with agricultural potential. The mapping of areas where cocoa smallholder orchards already exist or can be grown involved three main components. Firstly, the use of the fine-resolution C-band synthetic aperture radar and multispectral instruments from Sentinel-1 and Sentinel-2 satellites, respectively. Secondly, the processing of imagery (Sentinel-1 and Sentinel-2) for feature extraction using 22 variables. Lastly, fitting a random forest (RF) model to detect and distinguish potential cocoa orchards from non-cocoa areas. The RF classification scheme differentiated cocoa (for consistency, the coconut–cocoa areas in this manuscript will be referred to as cocoa regions or orchards) and non-cocoa regions with 97 percent overall accuracy and over 90 percent producer’s and user’s accuracies for the cocoa regions when trained on a combination of spectral indices and GLCM textural feature sets. The top five variables that contributed the most to the model were the red band (B4), red edge curve index (RECI), blue band (B2), near-infrared (NIR) entropy, and enhanced vegetation index (EVI), indicating the importance of vegetation indices and entropy values. By comparing the classified map created in this study with the soil and land capability legacy information of Bougainville, we observed that potential cocoa regions are already rated as highly suitable. This implies that cocoa expansion has reached one of many intersecting limits, including land suitability, political, social, economic, educational, health, labour, and infrastructure. Understanding how these interactions limit cocoa productivity at present will inform further sustainable growth. The tool provides inexpensive and rapid monitoring of land use, suitable for a sustainable planning framework that supports responsible agricultural land use management. The study developed a heuristic tool for monitoring land cover changes for cocoa production, informing sustainable development that balances the needs and aspirations of the government and farming communities with the protection of the environment.

Challenges in the Description of Sea Ice for a Kilometer-Scale Weather Forecasting System

Abstract Simulation of atmosphere–ocean–ice interactions in coupled Earth modeling systems with kilometer-scale resolution is a new challenge in operational numerical weather prediction. This study presents an assessment of sensitivity experiments performed with different sea ice products in a convective-scale weather forecasting system for the European Arctic. On kilometer-scale resolution sea ice products are challenged by the large footprint of passive microwave satellite observations and issues with spurious sea ice detection of the higher-resolution retrievals based on synthetic aperture radar instruments. We perform sensitivity experiments with sea ice concentration fields of 1) the global ECMWF-IFS forecast system, 2) a newly developed multisensor product processed through a coupled sea ice–ocean forecasting system, and 3) the AMSR2 product based on passive microwave observations. There are significant differences between the products on O(100) km scales in the northern Barents Sea and along the Marginal Ice Zone north of the Svalbard archipelago and toward the Fram Strait. These differences have a direct impact on the modeled surface skin temperature over ocean and sea ice, the turbulent heat flux, and 2-m air temperature (T2M). An assessment of Arctic weather stations shows a significant improvement of forecasted T2M in the north and east of Svalbard when using the new multisensor product; however, south of Svalbard this product has a negative impact. The different sea ice products are resulting in changes of the surface turbulent heat flux of up to 400 W m−2, which in turn results in T2M variations of up to 5°C. Over a 2-day forecast lead time this can lead to uncertainties in weather forecasts of about 1°C even hundreds of kilometers away from the sea ice. Significance Statement Weather forecasting in polar regions requires an accurate description of sea ice properties due to the very important atmosphere–ocean–ice interactions. With the increasing resolution of weather forecasting systems, there is also a need to advance the resolution of the sea ice characteristics in the models. This is, however, not straightforward due to various issues in the sea ice satellite products. This study explores new products and approaches to integrate high-resolution sea ice in a weather prediction system. We find that the model is sensitive to the choice of the sea ice product and that it is still challenging to provide an accurate sea ice field on a kilometer-scale resolution.

Tracking changes in coastal land cover in the Yellow Sea, East Asia, using Sentinel-1 and Sentinel-2 time-series images and Google Earth Engine

Coastal zones are essential ecosystems due to their provision of invaluable ecosystem services. However, the geomorphologic characteristics of coastal zones are becoming more complex and changeable due to global warming, sea-level rise (SLR), and the intensification of anthropogenic activities. Therefore, accurate and timely knowledge of coastal land cover types (including tidal flats, coastal vegetation, and year-long water cover) is needed for coastal research and sustainable management. To date, land cover products for coastal areas are mainly derived from moderate resolution imaging spectroradiometer images, but few studies have used Sentinel-1 synthetic aperture radar (S1) and Sentinel-2 Multispectral Instrument (S2) images, which can provide more detailed maps. We developed a Rule-based Time Series Classification (RTSC) approach to map coastal land cover types at a 10 m resolution, combining S1/S2 time-series images (2015–2019) and Google Earth Engine (GEE). These products were developed for the coastal zone of the Yellow Sea (YS), East Asia, which is an essential ecosystem protecting a coastal population of 60 million people from storms and SLR effects. Accuracy assessment showed that the annual maps of coastal land cover had high overall accuracy. The coastal land cover types for the YS in 2019 comprised 3593.42 km2 of tidal flats, 28,506.98 km2 of coastal vegetation, and 5436.92 km2 of coastal year-long water. The interannual dynamics of the coastal land cover area in the YS during 2015–2019 were smaller. This study provides a promising method that combines S1/2 time series, a RTSC approach, and GEE to map coastal land cover areas at large scales. The 10 m resolution maps generated in this study are the most current dataset of coastal land cover types for the YS, and they potentially provide a basis for the sustainable management and conservation of this important coastal zone.

Monitoring Coastal Landslides along the Northeast Black Sea of Bulgaria Using SAR data

The geological conditions along the Northeast Black sea coast of Bulgaria are favorable for landslides emergence and development. Those processes have been rigorously documented in the last fifty years by Earth science researchers. Their efforts contributed in a broader context to the landscape conservation and to mitigate the risks for the population caused by the mentioned surface disruptions. In this paper the obtained results record the temporal behavior of several landslides located in stated region during the last five years. The main data source to monitor the mentioned landslides are data from synthetic aperture radar (SAR) instrument processed by the differential interferometric SAR (DInSAR) method. The produced results were validated by means of global navigation satellite system (GNSS) measurements carried out at purposely created geodetic networks, satellite images with high spatial resolution and digital images obtained by unmanned aerial systems (UASs). The results presented in this paper unambiguously confirm the usability of the produced information for the local authorities and other stakeholders thus contributing to the improvement of landslide risk management practices and to the better planning of the territory where the researched landslides are located.

Multi-disciplinary optimization of the low-thrust orbit raising for the hydroterra Earth Explorer mission

Hydroterra was selected in September 2018 as one of the three candidates for the ESA's Earth Explorer-10 mission. Hydroterra aims at observing and improving the understanding of fundamental processes of daily water cycle, enhancing prediction capability of extreme rainfall and related flooding. To achieve this goal, the mission concept envisages placing a satellite embarking a synthetic aperture radar instrument in geosynchronous orbit. The satellite is designed to be launched with VEGA-C into low Earth orbit and to be transferred to the operational orbit by means of electric propulsion. In this paper, the multidisciplinary optimization process carried out to obtain the baseline electric orbit raising transfer for Hydroterra will be presented. The computed trajectory will meet the mission requirements, including targeting to the operational orbit, the launcher performance model, and the target on-station mass. Additionally, system-level constraints related to the satellite design that have an influence on the ideally optimized trajectory will be derived and considered in the optimization. In particular, the impact of the solar cell degradation on the thrust and specific impulse levels of the thruster due to the passage through the Van-Allen belts, the need to re-charge the batteries before entering the next eclipse, and the agility requirements will be accounted for. The obtained results show that a feasible electric orbit raising trajectory can be successfully obtained for the Hydroterra mission.

Unsupervised domain adaptation for global urban extraction using Sentinel-1 SAR and Sentinel-2 MSI data

Accurate and up-to-date maps of built-up areas are crucial to support sustainable urban development. Earth Observation (EO) is a valuable data source to cover this demand. In particular, Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions offer new opportunities to map built-up areas on a global scale. Using Sentinel-2 images, recent urban mapping efforts achieved promising results by training Convolutional Neural Networks (CNNs) on available built-up data. However, these results strongly depend on the availability of local reference data for fully supervised training or assume that the application of CNNs to unseen areas (i.e. across-region generalization) produces satisfactory results. To alleviate these shortcomings, it is desirable to leverage Semi-Supervised Learning (SSL) algorithms that can take advantage of unlabeled data, especially because satellite data is plentiful. In this paper, we propose a novel Domain Adaptation (DA) approach using SSL that jointly exploits Sentinel-1 SAR and Sentinel-2 MSI to improve across-region generalization for built-up area mapping. Specifically, two identical sub-networks are incorporated into the proposed model to perform built-up area segmentation from SAR and optical images separately. Assuming that consistent built-up area segmentation should be obtained across data modality, we design an unsupervised loss for unlabeled data that penalizes inconsistent segmentation from the two sub-networks. Therefore, we propose to use complementary data modalities as real-world perturbations for consistency regularization. For the final prediction, the model takes both data modalities into account. Experiments conducted on a test set comprised of sixty representative sites across the world showed that the proposed DA approach achieves strong improvements (F1 score 0.694) over fully supervised learning from Sentinel-1 SAR data (F1 score 0.574), Sentinel-2 MSI data (F1 score 0.580) and their input-level fusion (F1 score 0.651). To demonstrate the effectiveness of DA, we also performed a comparison with two state-of-the-art products, namely GHS-BUILT-S2 and WSF 2019, on the test set. The comparison showed that our model is capable of producing built-up area maps with comparable or even better quality than the state-of-the-art global human settlement maps. Therefore, the multi-modal DA offers great potential to be adapted to produce easily updateable human settlements maps at a global scale.

Monitoring of Coastal Aquaculture Sites in the Philippines through Automated Time Series Analysis of Sentinel-1 SAR Images

With the unprecedented expansion of aquaculture around the world, there is a critical need to monitor its progress. In Palawan, Philippines, coastal aquaculture is gaining momentum towards increasing fish production, a pressure that presents a challenge to the sustainability of these areas. In this paper, we explore the application of Earth observation methods to map coastal aquaculture development in Palawan and evaluate the extent of its change. The European Space Agency Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral instrument sensor data were applied in fully automatic mode to build maps of Palawan coastal aquaculture. The maps were validated using Google Earth high-resolution optical images and in situ observations in Malampaya Sound, and demonstrated a successful detection rate of 72%, while the false alarm rate was less than 7.5%. Objects only 5 m across, four times smaller than the spatial resolution of the Sentinel-1 sensors, were successfully detected using the developed methodology, thus exceeding the capabilities of other published methods that are limited to detecting large groups of aquaculture structures. The maps revealed aquaculture structures in high quantities in Malampaya Sound, Taytay Bay, and other locations in the coastal waters of Palawan, Philippines. A significant change of aquaculture spatial distribution was identified by comparing aquaculture maps generated with an interval of three years. This new automated methodology was validated as robust for mapping aquaculture objects in Palawan, Philippines, and can be applied to aquaculture studies in other regions worldwide.

A Synthetic Aperture Radar Imaging Mode Utilizing Frequency Scan for Time-of-Echo Compression

The synthetic aperture radar (SAR) imaging mode described in this paper utilizes the available signal bandwidth to form a narrow frequency-scanning transmit antenna beam illuminating the swath of interest from far to near range. The imaging technique is named frequency Scan for Time-of-Echo Compression (f-STEC), because, for a proper choice of mode parameters, the radar echo duration is reduced, i.e., compressed. The paper provides a detailed analysis of the f-STEC imaging technique; derives the operational and performance parameters as well as — to the authors knowledge — for the first time, analytic expressions for the f-STEC timing constraints. Further, the performance and trade-space is reported and compared to both conventional and modern, i.e., digital beamforming imaging modes. The f-STEC imaging technique is shown to be specifically advantageous for SAR systems operating at higher carrier frequencies or an attractive add-on for state-of-the-art SAR instruments.

Atmospheric Phase Screen Compensation on Wrapped Ground-Based SAR Interferograms

This work proposes a method for estimating and compensating the atmospheric phase screen (APS) in sets of synthetic aperture radar (SAR) interferograms generated with a ground-based SAR (GB-SAR) instrument. We address the presented approach’s physical, statistical, and mathematical framework by discussing its potential and limitations. In contrast with other existing algorithms that estimate the APS from the unwrapped phase signals, our methodology is based on the straightforward analysis of the wrapped phases, directly. Therefore, the method is not affected by any potential phase unwrapping mistake, and it is suitable for multitemporal interferometric SAR (InSAR) applications. The effects of the local topography, the decorrelation noise, and the ground deformation on the APS estimates are deeply studied. Experiments performed on simulated and real GB-SAR InSAR data corroborate the validity of the theory. In particular, the simulated results show that the method is beneficial in zones with medium-to-high topographic slopes (e.g., for Alpine and mountainous regions).

A machine learning approach for accurate crop type mapping using combined SAR and optical time series data

A country’s food needs mainly depend upon its agriculture resources and require reliable information related to crop health, distribution, and acreage estimation to manage and monitor resources to implement a sustainable agricultural system. Different methodologies have been used to collect this information. However, the availability of earth resource satellite data with improved spatial, spectral, and temporal resolutions, such as the European Space Agency's (ESA) Copernicus program satellites Sentinel-1 (S1) and Sentinel-2 (S2), are creating more practicability to generate crop type maps. S1 and S2, both operating in a constellation of twin satellites, carry a C-band Synthetic Aperture Radar (SAR) and Multispectral Instrument (MSI). Vertical transmit and horizontal receive (VH), and vertical transmit and vertical receive (VV) channels of S1 were used to exploit the temporal backscatter of crops present in the study area. In this research, a machine learning random forest classification algorithm is used for accurate crop type mapping through the combination of SAR (S1) and optical (S2) time-series data. Random forest classifier has produced considerable improved accuracies of crop type mapping in previous studies as it uses ensemble decision trees trained on sample data which permit the vote in favor of the most popular land use/ land cover class. The key objectives of this study are to investigate the classification accuracies for different data combinations. Three plots of data are tested (i) S1 (ii) S2 (iii) S1 & S2. A combination of SAR and optical data turn out with the best overall accuracy of 97% and a kappa coefficient of 0.97. Space-borne SAR and optical data add a new aspect of crop type mapping, which increases the classification accuracy by including valuable parameters and beating the drawbacks of each other. By comparing the results, it can be concluded that combining all-weather accessible SAR and spectrally rich optical data accomplished more accurate outcomes. It would be an imperative advance for the future endeavor to estimate crop biomass and biophysical parameters.

InSAR Phase Unwrapping Error Correction for Rapid Repeat Measurements of Water Level Change in Wetlands

Here, we present an enhanced algorithm to correct interferometric synthetic aperture radar (InSAR) phase unwrapping errors by incorporating iterative spatial bridging between islands and phase closure among interferograms. We use rapid repeat airborne synthetic aperture radar acquisitions from NASA’s airborne uninhabited aerial vehicle synthetic aperture radar (UAVSAR) instrument to estimate short-term changes in water level within coastal wetlands from a stack of consecutive interferograms acquired with very short temporal separation (~30 min). The algorithm is applied to six consecutive UAVSAR images collected in tidal wetlands of the Wax Lake Delta, Louisiana, USA. Validation of our water level change retrievals with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> field observations was conclusive with high correlation and an RMSE generally smaller than 3 cm. Comparison of our algorithm with other phase unwrapping error correction methods shows significant improvement (30%–35% increase in the number of correctly unwrapped pixels) when applied to rapid changes in water level. The set of corrections presented in this work enables measurement of water level change in deltas and other areas where tides drive highly dynamic flooding of inland vegetated areas. Although demonstrated for water level change, the method is applicable to other InSAR datasets with large spatial gradients or observed discontinuities between coherent but spatially isolated areas.

Random Forest Classification of Inundation Following Hurricane Florence (2018) via L-Band Synthetic Aperture Radar and Ancillary Datasets

In response to Hurricane Florence of 2018, NASA JPL collected quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument, observing record-setting river stages across North and South Carolina. Fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging, stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study used random forest classification to generate maps of inundation from L-band UAVSAR imagery processed using the Freeman–Durden decomposition method. An average overall classification accuracy of 87% is achieved with this methodology, with areas of both under- and overprediction for the focus classes of open water and inundated forest. Fuzzy logic operations using hydrologic variables are used to reduce the number of small noise-like features and false detections in areas unlikely to retain water. Following postclassification refinement, estimated flood extents were combined to an event maximum for societal impact assessments. Results from the Hurricane Florence case study are discussed in addition to the limitations of available validation data for accuracy assessments.

High-Resolution Polar Low Winds Obtained from Unsupervised SAR Wind Retrieval

High-resolution sea surface observations by spaceborne synthetic aperture radar (SAR) instruments are sorely neglected resources for meteorological applications in polar regions. Such radar observations provide information about wind speed and direction based on wind-induced roughness of the sea surface. The increasing coverage of SAR observations in polar regions calls for the development of SAR-specific applications that make use of the full information content of this valuable resource. Here we provide examples of the potential of SAR observations to provide details of the complex, mesoscale wind structure during polar low events, and examine the performance of two current wind retrieval methods. Furthermore, we suggest a new approach towards accurate wind vector retrieval of complex wind fields from SAR observations that does not require a priori wind direction input that the most common retrieval methods are dependent on. This approach has the potential to be particularly beneficial for numerical forecasting of weather systems with strong wind gradients, such as polar lows.

Flood damage assessment with Sentinel-1 and Sentinel-2 data after Sardoba dam break with GLCM features and Random Forest method

Accurate mapping and monitoring of flooded areas are immensely required for disaster management purposes, such as for damage assessment and mitigation. In this study, the flood damage mapping performances of two satellite Earth Observation sensors, i.e., European Space Agency's Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) multispectral optical instruments, were evaluated using the Random Forest (RF) supervised classification method and various feature types. The study area was Sardoba Reservoir (Uzbekistan) and its surroundings, in which a disastrous dam failure occurred on May 1, 2020. After the failure of a part of the earthfill dam, a large region with settlements and agricultural areas in Uzbekistan and Kazakhstan was flooded. S1 and S2 cloudless data with a short temporal interval acquired soon after the event were available for the area. Four different data availability scenarios, such as (i) only S1 pre- and post-flood data; (ii) only S2 pre- and post-flood data; (iii) S1 pre- and post-flood and S2 pre-flood data; and (iv) S1 and S2 pre- and post-flood data were evaluated in terms of classification accuracy. In addition to the polarization information of S1 and the intensity values of S2 bands, feature maps produced from these datasets, such as vegetation and water indices, textural information obtained from gray level co-occurrence matrix (GLCM), and the principal component analysis (PCA) bands were employed in the RF method. The results show that the fusion of S1 and S2 data exhibit very high classification accuracy for the flooded areas and can separate the inundated vegetation as well. The use of S2 pre-event data together with the S1 pre- and post-event data is recommended for obtaining high accuracy even when post-event optical data is not available.

Hybrid Analog-Digital SAR Instrument With Reflector Antenna and Overlapped Subarray Feed for Earth Observation

As part of the European Space Agency (ESA) studies of new instrument for Earth observation, the design of a synthetic aperture radar (SAR) architecture based on overlapped array antennas was launched last year. The objective of the activity is to develop a test unit of a reflector based SAR antenna fed by an array organized in overlapped subarrays, supporting novel multichannel wide-swath SAR architectures, and exploiting multiple beams in both azimuth and range/elevation directions. In the letter, a hybrid analog/digital beamforming on reception with analog beamforming approach on transmission is analyzed. The antenna system consists of a large oversize reflector with a feedarray capable of generating multiple beams on receive for digital processing. In the letter, a description of the hybrid SAR Instrument is presented, describing the characteristics of the system, the antenna, the considered feedarray options and the main characteristics and system performances.

A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery

Cotton is an important cash crop in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely monitoring of the cotton distribution is crucial for cotton cultivation management and international trade. However, most of the previous researches on cotton identification using remotely sensed images are highly dependent on training samples, and the collection of samples is time-consuming and expensive. To overcome this limitation, a new index, termed as Cotton Mapping Index (CMI), was developed in this study for automatic cotton mapping using time series of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) satellite data. Four sites in the United States (U.S.) and four sites in China were selected to develop and assess the performance of the CMI. The spectral characteristics derived from Sentinel-2 and backscattering coefficients derived from Sentinel-1 for cotton and non-cotton crops during the cotton growth period were analyzed. Considering the phenology differences of crops in different regions, the features at an adaptive window were adopted to construct the CMI. The results showed that at the peak greenness period, the multiplication of red-edge 1 and red-edge 2 band for cotton samples were much larger than those for non-cotton samples, whereas the spectral angle at the red band as well as the absolute values of backscattering coefficients in vertical transmit and vertical receive (VV) polarization for cotton samples were much smaller than those for non-cotton samples. Based on these findings, the CMI was developed to identify cotton cultivated area within the cropland area. The overall accuracy of classification results for the sites in the U.S. was higher than 81.20%, and the mean relative error for the sites in Xinjiang of China was 26.69%. The CMI, which incorporated optical and radar features, had a better performance than the indices using optical features solely. The advantage of the CMI over supervised classifiers (i.e., k-nearest neighbors, support vector machine and random forest) is that no training samples are required. Moreover, the cotton distribution map can be obtained before the harvest using the CMI. These results indicated the potential of the CMI for cotton mapping. The applicability of CMI in other regions with different cropping systems and crop types needs to be further assessed in the future study.