C-band Radar Research Articles

Large-Scale Orthorectification of GF-3 SAR Images Without Ground Control Points for China’s Land Area

GaoFen-3 (GF-3) is a C-band multipolarization synthetic aperture radar (SAR) satellite with 12 imaging modes. However, its initial positioning accuracy remains unsatisfactory, thereby hindering its use for large-area surveying and mapping. This study proposes a block orthorectification method without ground control points (GCPs) using the GF-3 Fine strip II (FSII) mode. To address the challenges with the accuracy and efficiency of this method, an integrated block orthorectification method was developed to conduct integrated processing of large-scale GF-3 satellite images without GCPs. Geometric calibration was used to improve the absolute positioning accuracy of each SAR image. Then, several tie points (TPs) were extracted using the SAR scale-invariant feature transform (SIFT) operator. A parallel matching strategy was used in the block images registration. The block adjustment model was constructed to solve the orientation parameter of all SAR images. The experimental results of 1,468 GF-3 images of China’s entire land area show a TPs root-mean-square error of 0.724 pixel and 8.014 m for the independent checkpoint, suggesting that the proposed method can effectively improve the geometric accuracy of GF-3 satellite images and demonstrate the feasibility of large-scale SAR mapping without GCPs.

An Introduction to the Chinese High-Resolution Earth Observation System: Gaofen-1~7 Civilian Satellites

The Chinese High-resolution Earth Observation System (CHEOS) program has successfully launched 7 civilian satellites since 2010. These satellites are named by Gaofen (meaning high resolution in Chinese, hereafter noted as GF). To combine the advantages of high temporal and comparably high spatial resolution, diverse sensors are deployed to each satellite. GF-1 and GF-6 carry both high-resolution cameras (2 m resolution panchromatic and 8 m resolution multispectral camera), providing high spatial imaging for land use monitoring; GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter, mostly monitoring marine targets; GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera, dedicated to environmental remote sensing and climate research, such as aerosol, clouds, and greenhouse gas monitoring; and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying, facilitating the accumulation of basic geographic information. This study provides an overview of GF civilian series satellites, especially their missions, sensors, and applications.

Recognition of Polar Lows in Sentinel-1 SAR Images With Deep Learning

In this article, we explore the possibility of detecting polar lows in C-band synthetic aperture radar (SAR) images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absence of a maritime mesocyclone, respectively. The dataset is constructed using the ECMWF reanalysis version 5 (ERA5) dataset as baseline and it consists of 2004 annotated images. To our knowledge, this is the first dataset of its kind to be publicly released. The dataset is used to train a deep learning model to classify the labeled images. Evaluated on an independent test set, the model yields an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> score of 0.95, indicating that polar lows can be consistently detected from SAR images. Interpretability techniques applied to the deep learning model reveal that atmospheric fronts and cyclonic eyes are key features in the classification. Moreover, experimental results show that the model is accurate even if: 1) such features are significantly cropped due to the limited swath width of the SAR; 2) the features are partly covered by sea ice; and 3) land is covering significant parts of the images. By evaluating the model performance on multiple input image resolutions (pixel sizes of 500 m, 1 km, and 2 km), it is found that higher resolution yield the best performance. This emphasizes the potential of using high-resolution sensors like SAR for detecting polar lows, as compared to conventionally used sensors such as scatterometers.

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.

A Nonparametric Tropical Cyclone Wind Speed Estimation Model Based on Dual-Polarization SAR Observations

The C-band synthetic aperture radar (SAR) observation is one of the most popular sources for high-resolution tropical cyclone (TC) wind speed estimation. The scarcity of high wind speed data with good quality restricts the inversion accuracy of high wind speed. It is a challenge to obtain a high-precision wind speed inversion model with sparse data points. In this paper, the TC wind speed is successfully estimated from the dual-polarization SAR signal. Firstly, the training dataset with a total of 327 data points is formed using the Sentinel-1A EW/IW mode images and their temporal-spatial matched Stepped Frequency Microwave Radiometer (SFMR) measurements. Then, a novel nonparametric TC wind speed estimation model (hereafter NWSE model) is proposed with this dataset by using the Bayesian nonparametric general regression method. The wind speed is interpreted as a function of the cross-polarized normalized radar cross-sections (VH-NRCS) and incident angle for the NWSE model. Moreover, the wind speed obtained from the co-polarized signal is used to improve the accuracy of NWSE model under low wind speed. Finally, the validation results show the excellent overall consistency between the model retrieved wind speed and the collocated SFMR and SMAP measurements. Specifically, considering all the wind speeds, the overall root-mean-square error and absolute bias of NWSE model are 2.85 m/s and 2.26 m/s compared with the SMAP wind speed. When considering the wind speeds larger than 30 m/s, the RMSE and bias of NWSE model are 3.75 m/s and 2.78 m/s, respectively.

Two-Dimensional Spectral Analysis Filter for Removal of LFM Radar Interference in Spaceborne SAR Imagery

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel&#x2014;2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method.

Estimation of Flood Inundation and Depth During Hurricane Florence Using Sentinel-1 and UAVSAR Data

We studied the temporal and spatial changes in flood water elevation and variation in the surface extent due to flooding resulting from Hurricane Florence (September 2018) using the L-band observation from an unmanned aerial vehicle synthetic aperture radar (UAVSAR) and C-band synthetic aperture radar (SAR) sensors on Sentinel-1. The novelty of this study lies in the estimation of the changes in the flood depth during the hurricane and investigating the best method. Overall, flood depths from SAR were observed to be well-correlated with the spatially distributed ground-based observations ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2} = 0.79$ </tex-math></inline-formula> –0.96). The corresponding change in water level ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\partial \text{h}/\partial \text{t}$ </tex-math></inline-formula> ) also compared well between the remote sensing approach and the ground observations ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2} = 0.90$ </tex-math></inline-formula> ). This study highlights the potential use of SAR remote sensing for inundated landscapes (and locations with scarce ground observations), and it emphasizes the need for more frequent SAR observations during flood inundation to provide spatially distributed and high temporal repeat observations of inundation to characterize flood dynamics.

First Quasi-Synchronous Hurricane Quad-Polarization Observations by C-Band Radar Constellation Mission and RADARSAT-2

This is the first presentation of quasi-synchronous spaceborne synthetic aperture radar (SAR) high-resolution images acquired from C-band Radar Constellation Mission (RCM) and RADARSAT-2 consisting of quad-polarization (HH &#x002B; HV &#x002B; VH &#x002B; VV) wide-swath observations of Hurricane Epsilon. These measurements clearly show that the denoised HV- and VH-polarized normalized radar cross sections (NRCSs) have great consistency. NRCS values at HV- and VH-polarizations are more sensitive to wind speeds and less sensitive to incidence angles or wind directions than those at HH and VV for hurricane force winds. For large incidence angles and high wind speeds, the sensitivity of HH-polarized NRCS to wind speed is higher than that of VV. HH- and VV-polarized NRCSs gradually lose wind direction dependency at high winds. It is notable that the time interval between the two SAR acquisitions is only 3 min. This allows for a direct comparison of HV- and VH-polarized images to investigate the variations in high-resolution backscattering within the hurricane vortex, thereby revealing the most dynamical areas. An asymmetric dynamic is observed around the eye of Hurricane Epsilon, based on positive and negative differences (VH&#x2013;HV) in the western and eastern parts of the eye. The impacts of rain on quad-polarized NRCS are also examined using collocated rain rates from the Global Precipitation Mission (GPM) and wind speeds from the Soil Moisture Active Passive (SMAP). Significant rain-induced NRCS attenuations are about 1.7 dB for HH and VV, and 2.2 dB for HV and VH, when the rain rate is 20 mm/h. These attenuations are associated with rain-induced turbulence and atmospheric absorption. This work shows that the collocated RCM and RADARSAT-2 hurricane observations provide a unique analysis of synoptic and joint C-band measurements of the ocean surface in quad-polarization; this is noteworthy in view of preparations for the next generation of dual-polarization scatterometer (SCA) onboard second-generation meteorological operational satellite program (MetOp-SG).

Assimilating C-Band Radar Data for High-Resolution Simulations of Precipitation: Case Studies over Western Sumatra

Accurate high-resolution precipitation forecasts are critical yet challenging for weather prediction under complex topography or severe synoptic forcing. Data fusion and assimilation aimed at improving model forecasts, as one possible approach, has gained increasing attention in past decades. This study investigates the influence of the observations from a C-band Doppler radar over the west coast of Sumatra on high-resolution numerical simulations of precipitation around its vicinity under the Madden–Julian oscillation (MJO) in January and February 2018. Cases during various MJO phases were selected for simulations with an advanced research version of the weather research and forecasting (WRF) model at a cloud-permitting scale (~3 km). A 3-dimensional variational (3DVAR) data assimilation method and a hybrid three-dimensional ensemble–variational data assimilation (3DEnVAR) method, based on the NCEP Gridpoint Statistical Interpolation (GSI) assimilation system, were used to assimilate the radar reflectivity and the radial velocity data. The WRF-simulated precipitation was validated with the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data, and the fractions skill score (FSS) was calculated in order to evaluate the radar data impacts objectively. The results show improvements in the simulated precipitation with hourly radar data assimilation 6 h prior to the simulations. The modifications with assimilation were validated through the observation departure and moist convection. It was found that forecast improvements are relatively significant when precipitation is more related to local-scale convection but rather small when the background westerly wind is strong under the MJO active phase. The additional simulation experiments, under a 1- or 2-day assimilation cycle, indicate better improvements in the precipitation simulation with 3DEnVAR radar assimilation than those with the 3DVAR method.

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

Evaluation of convective boundary layer height estimates using radars operating at different frequency bands

Abstract. Knowledge of the atmospheric boundary layer state and evolution is important for understanding air pollution and low-level cloud development, among other things. There are a number of instruments and methods that are currently used to estimate boundary layer height (BLH). However, no single instrument is capable of providing BLH measurements in all weather conditions. We proposed a method to derive a daytime convective BLH using clear air echoes in radar observations and investigated the consistency of these retrievals between different radar frequencies. We utilized data from three vertically pointing radars that are available at the SMEAR II station in Finland, i.e. the C band (5 GHz), Ka band (35 GHz) and W band (94 GHz). The Ka- or W-band cloud radars are an integral part of cloud profiling stations of pan-European Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS). Our method will be utilized at ACTRIS stations to serve as an additional estimate of the BLH during summer months. During this period, insects and Bragg scatter are often responsible for clear air echoes recorded by weather and cloud radars. To retrieve a BLH, we suggested a mechanism to separate passive and independently flying insects that works for all analysed frequency bands. At the lower frequency (the C band) insect scattering has been separated from Bragg scattering using a combination of the radar reflectivity factor and linear depolarization ratio. Retrieved values of the BLH from all radars are in a good agreement when compared to the BLH obtained with the co-located HALO Doppler lidar and ERA5 reanalysis data set. Our method showed some underestimation of the BLH after nighttime heavy precipitation yet demonstrated a potential to serve as a reliable method to obtain a BLH during clear-sky days. Additionally, the entrainment zone was observed by the C-band radar above the CBL in the form of a Bragg scatter layer. Aircraft observations of vertical profiles of potential temperature and water vapour concentration, collected in the vicinity of the radar, demonstrated some agreement with the Bragg scatter layer.

Educational Low-Cost C-Band FMCW Radar System Comprising Commercial Off-the-Shelf Components for Indoor Through-Wall Object Detection

This paper presents an educational low-cost C-band frequency-modulated continuous wave (FMCW) radar system for use in indoor through-wall metal detection. Indoor remote-sensing applications, such as through-wall detection and positioning, are essential for the comprehensive realization of the internet of things or super-connected societies. The proposed system comprises a two-stage radio-frequency power amplifier, a voltage-controlled oscillator, circuits for frequency modulation and system synchronization, a mixer, a 3-dB power divider, a low-noise amplifier, and two cylindrical horn antennas (Tx/Rx antennas). The antenna yields gain values in the 6.8~7.8 range when operating in the 5.83~5.94 GHz frequency band. The backscattered Tx signal is sampled at 4.5 kHz using the Arduino UNO analog-to-digital converter. Thereafter, the sampled signal is transferred to the MATLAB platform and analyzed using a customized FMCW radar algorithm. The proposed system is built using commercial off-the-shelf components, and it can detect targets within a 56.3 m radius in indoor environments. In this study, the system could successfully detect targets through a 4 cm-thick ply board with a measurement accuracy of less than 10 cm.

Expanding infrastructure and growing anthropogenic impacts along Arctic coasts

The accelerating climatic changes and new infrastructure development across the Arctic require more robust risk and environmental assessment, but thus far there is no consistent record of human impact. We provide a first panarctic satellite-based record of expanding infrastructure and anthropogenic impacts along all permafrost affected coasts (100 km buffer, ≈6.2 Mio km2), named the Sentinel-1/2 derived Arctic Coastal Human Impact (SACHI) dataset. The completeness and thematic content goes beyond traditional satellite based approaches as well as other publicly accessible data sources. Three classes are considered: linear transport infrastructure (roads and railways), buildings, and other impacted area. C-band synthetic aperture radar and multi-spectral information (2016–2020) is exploited within a machine learning framework (gradient boosting machines and deep learning) and combined for retrieval with 10 m nominal resolution. In total, an area of 1243 km2 constitutes human-built infrastructure as of 2016–2020. Depending on region, SACHI contains 8%–48% more information (human presence) than in OpenStreetMap. 221 (78%) more settlements are identified than in a recently published dataset for this region. 47% is not covered in a global night-time light dataset from 2016. At least 15% (180 km2) correspond to new or increased detectable human impact since 2000 according to a Landsat-based normalized difference vegetation index trend comparison within the analysis extent. Most of the expanded presence occurred in Russia, but also some in Canada and US. 31% and 5% of impacted area associated predominantly with oil/gas and mining industry respectively has appeared after 2000. 55% of the identified human impacted area will be shifting to above 0 ∘C ground temperature at two meter depth by 2050 if current permafrost warming trends continue at the pace of the last two decades, highlighting the critical importance to better understand how much and where Arctic infrastructure may become threatened by permafrost thaw.

Application of Attenuation Correction to Quantitative Precipitation Estimation on C-Band Weather Radar in Bengkulu

A weather radar is an active system remote sensing tool that observes precipitation indirectly. Weather radar has an advantage in estimating precipitation because it has a high spatial resolution (up to 0.5 km). Reflectivity generated by weather radar still has signal interference caused by attenuation factors. Attenuation causes the Quantitative Precipitation Estimation (QPE) by the C-band weather radar to underestimate. Therefore attenuation correction on C-band weather radar is needed to eliminate precipitation estimation errors. This study aims to apply attenuation correction to determine Quantitative Precipitation Estimation (QPE) on the c-band weather radar in Bengkulu in December 2018. Gate-by-gate method attenuation correction with Kraemer approach has applied to c-band weather radar data from the Indonesian Agency for Meteorology and Geophysics (BMKG) weather radar network Bengkulu. This method uses reflectivity as the only input. Quantitative Precipitation Estimation (QPE) has obtained by comparing weather radar-based rain estimates to 10 observation rain gauges over a month with the Z-R relation equation. Root Mean Square Error (RMSE) is used to calculate the estimation error. Weather radar data are processed using Python-based libraries Wradlib and ArcGIS 10.5. As a result, the calculation between the weather radar estimate precipitation and the observed rainfall obtained equation Z=2,65R1,3. The attenuation correction process with Kreamer's approach on the c-band weather radar has reduced error in the Qualitative Precipitation Estimation (QPE). Corrected precipitation has a smaller error value (r = 0.88; RMSE = 8.38) than the uncorrected precipitation (r = 0.83; RMSE = 11.70).

The Life Cycle of a Stationary Cloud Cluster during the Indian Summer Monsoon: A Microphysical Investigation Using Polarimetric C-Band Radar

Abstract Multiplatform observations of the life cycle of a tropical continental mesoscale cloud cluster (CC) during the Indian summer monsoon, which contributed more than ~70 mm of rainfall over the arid peninsular Indian region, are presented in this study. The CC was characterized by a deep warm cloud layer with isolated convective cells in the initiation phase, merging of several deep cumulus clouds (~6 km) during the mature phase, growing up to ~15 km with mixed-phase and ice-phase cloud microphysical processes. Throughout the life cycle of the CC, polarimetric radar analyses revealed size sorting of falling raindrops, growth of dendritic particles, riming, aggregation, the occurrence of a saggy bright band, etc. The formation of big raindrops is observed during the initial convective rain, associated with the melting of hail. The stratiform counterpart is primarily associated with aggregates, ice crystals, and melting snow, resulting in surface rainfall. Aggregates are found to be the spatially dominant hydrometeor followed by ice crystals. The presence of vertically oriented ice crystals indicates active cloud electrification processes during the spatial aggregation of convective clouds. The dominant microphysical processes and precipitation pathways are illustrated. The study forms a benchmark case for model intercomparisons and evaluations.

Brief communication: Detection of glacier surge activity using cloud computing of Sentinel-1 radar data

Abstract. For studying the flow of glaciers and their response to climate change it is important to detect glacier surges. Here, we compute within Google Earth Engine the normalized differences between winter maxima of Sentinel-1 C-band radar backscatter image stacks over subsequent years. We arrive at a global map of annual backscatter changes, which are for glaciers in most cases related to changed crevassing associated with surge-type activity. For our demonstration period 2018–2019 we detected 69 surging glaciers, with many of them not classified so far as surge type. Comparison with glacier surface velocities shows that we reliably find known surge activities. Our method can support operational monitoring of glacier surges and some other special events such as large rock and snow avalanches.

Gazing inside a giant-hail-bearing Mediterranean supercell by dual-polarization Doppler weather radar

Ongoing climate change is considered to be responsible for the intensification and increased frequency of extreme weather events. The Mediterranean basin is not exempted by such modification processes as testified by the recent increase in the number of severe convective storm and mesocyclones. In recent years this kind of events caused many victims and damages in the such area, due to its geomorphological configuration and to the massive urban development of the last decades. Due to their local-scale nature Mediterranean extreme events can hardly be forecast with the use of numerical weather prediction systems and cannot adequately observed by using satellite platforms. These methods are not as straightforward as those based on radar observations because their spatial-temporal resolution is not necessarily adequate to resolve exceptional atmospheric phenomena as a weather radar can indeed offer. This article uses weather radar observations of an exceptional Mediterranean hail-bearing supercell that hit the central-eastern coast of the Adriatic Sea on 10 July 2019 causing flash flood and giant hail. Two operational dual-polarization Doppler C-band radars, managed by the Civil Protection Department of Italy, were able to observe, in an operational framework, the genesis and the evolution of the supercell, allowing for a detailed analysis of the event. Exploiting the unique time-resolved high-resolution three-dimensional available measurements, the combined use of a wind-field retrieval scheme and the interpretation of the dual-polarization radar observed features reveals some unique microphysical processes, rarely documented through radar observations at the Mediterranean latitudes. Results of this work improve the understanding of mechanisms and processes of giant hail formation and it is crucial to improve the nowcasting and forecasting skill for the early detection of hazardous meteorological events.

Metamaterial‐based linear phased array antenna with improved wide‐angle scanning bandwidth by parasitic metal strips

Two metamaterial-based linear phased array antennas with improved wide-angle scanning bandwidth are designed in this study. Metamaterial antenna element consists of a metamaterial patch, periodic metal strips, distributed metal strips and slot coupling feeding structure. The bandwidth of the metamaterial antenna is improved by loading the periodic metal strips. Wide-angle scanning bandwidth of arrays is attributed to the distributed metal strips. For an E-plane array, the central element has a relative bandwidth of 30.0% (4.33-5.86 GHz) for Voltage standing wave ratio (VSWR) <2 and the main beam can scan up to ±70° with a gain fluctuation less than 3.3 dB. For an H-plane array, the central element has a relative bandwidth of 26.2% (4.41–5.74 GHz) for VSWR <2 and the main beam can scan up to ±70° with a gain fluctuation less than 2.5 dB. Two 7-element linear phased array antennas are fabricated to verify the scanning performance. The measured and simulation results are in good agreement. The proposed antennas can be promising candidates for C-band radar and satellite communication.

A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion

Monitoring agricultural soil organic carbon (SOC) has played an essential role in sustainable agricultural management. Precise and robust prediction of SOC greatly contributes to carbon neutrality in the agricultural industry. To create more knowledge regarding the ability of remote sensing to monitor carbon soil, this research devises a state-of-the-art low cost machine learning model for quantifying agricultural soil carbon using active and ensemble-based decision tree learning combined with multi-sensor data fusion at a national and world scale. This work explores the use of Sentinel-1 (S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and an innovative machine learning (ML) approach using an integration of active learning for land-use mapping and advanced Extreme Gradient Boosting (XGBoost) for robustness of the SOC estimates. The collected soil samples from a field survey in Western Australia were used for the model validation. The indicators including the coefficient of determination (R2) and root - mean – square - error (RMSE) were applied to evaluate the model's performance. A numerous features computed from optical and SAR data fusion were employed to build and test the proposed model performance. The effectiveness of the proposed machine learning model was assessed by comparing with the two well-known algorithms such as Random Forests (RF) and Support Vector Machine (SVM) to predict agricultural SOC. Results suggest that a combination of S1 and S2 sensors could effectively estimate SOC in farming areas by using ML techniques. Satisfactory accuracy of the proposed XGBoost with optimal features was achieved the highest performance (R2 = 0.870; RMSE = 1.818 tonC/ha) which outperformed RF and SVM. Thus, multi-sensor data fusion combined with the XGBoost lead to the best prediction results for agricultural SOC at 10 m spatial resolution. In short, this new approach could significantly contribute to various agricultural SOC retrieval studies globally.

Potential of C-band Synthetic Aperture Radar Sentinel-1 time-series for the monitoring of phenological cycles in a deciduous forest

Annual time-series of the two satellites C-band SAR (Synthetic Aperture Radar) Sentinel-1A and 1B data over five years were used to characterize the phenological cycle of a temperate deciduous forest. Six phenological metrics of the start (SOS), middle (MOS) and end (EOS) of budburst and leaf expansion stage in spring, and the start (SOF), middle (MOF) and end (EOF) of leaf senescence in autumn were extracted using an asymmetric double sigmoid function (ADS) fitted to the time-series of the ratio (VV/VH) of backscattering at co-polarization VV (vertical–vertical) and at cross polarization VH (vertical-horizontal). Phenological metrics were also derived from other four vegetation proxies (Normalized Difference Vegetation Index NDVI time-series from Sentinel-2A and 2B images, and in situ measurements of NDVI measurements, Leaf Area Index LAI and litterfall temporal dynamics). These estimated phenological metrics were compared to phenological observations obtained by visual observations from the ground, achieved using binoculars by three inter-calibrated observers, on a bi-weekly basis during the budburst and weekly during the senescence. We observe a decrease in the backscattering coefficient (σ0) at VH cross polarization during the leaf development and the expansion phase in spring and an increase during the senescence phase, contrary to what is usually observed on various types of crops. In vertical polarization, σ0VV shows very little variation throughout the year. S-1 time-series of VV/VH ratio provide a good description of the seasonal vegetation cycle allowing the estimation of spring and autumn phenological metrics. Estimates provided by VV/VH of budburst dates using MOS criterion differ by approximately 8 days on average (mean average deviation) from phenological observations. During senescence phase, estimates using MOF criterion are later and deviate by about 20 days from phenological observations of leaf senescence while the differences are of the order of 2 to 4 days between the phenological observations and estimates based on in situ NDVI and LAI time-series, respectively. A deviation of about 7 days, comparable to that observed during budburst, is obtained between the estimates of senescence (MOF) from S-1 and those determined from the in situ monitoring of litterfall. While in spring, leaf emergence and expansion described by LAI or NDVI explain the increase of VV/VH (or the decrease of σ0VH), during senescence, S-1 VV/VH is decorrelated from LAI or NDVI and is better explained by litterfall temporal dynamics. This behavior resulted in a hysteresis phenomenon observed on the relationships between VV/VH and NDVI or LAI. For the same LAI or NDVI, the response of VV/VH is different depending on the phenological phase considered. This study shows the high potential offered by Sentinel-1 SAR C-band time-series for the detection of forest phenology, thus overcoming the limitations caused by cloud cover in optical remote sensing of vegetation phenology.