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Measuring the size of suspended particles in water is crucial in related fields such as environmental engineering, marine engineering, and hydraulic engineering. Considering the size distribution of suspended particles in real rivers, the amplitude (Amp) of the backscattering of particles with sizes ranging from 0.006 mm to 0.030 mm was analysed in this research using a lightweight ultrasonic meter developed by the authors and four probes with bandwidths ranging from 3.0 MHz to 30.0 MHz. In the analysis of Amp measurements for different particle sizes, using 0.008 mm as the reference particle size and converting to the rest of the particle sizes, if the Rayleigh scattering condition is satisfied between the particle sizes and the probe frequency, the conversion error value will be between −10% and 10%. This verifies the correctness of the theoretically derived particle size-Amp theory (the positive power relationship between the particle sizes and the Amp) and reflects the validity constraints of designing this experimental setup. The measurement method based on particle backscattering Amp analysis utilised in this study will help to achieve real-time measurements of suspended particles at river sites.

Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season’s backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former’s lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring.

With the proliferation of smart devices, the Internet of Things (IoT) is rapidly expanding. This study proposes a miniaturized controllable metamaterial with low control voltage for achieving low-power and compact designs in IoT node devices. Operating at a target frequency of 2.4 GHz, the proposed metamaterial requires only a 3.3 V control voltage and occupies approximately one-third of the wavelength in size. Experimental validation demonstrates its excellent reflective control performance, positioning it as an ideal choice for low-power IoT systems, particularly in the context of miniaturized and low-power IoT node applications.

The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better.

The radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth’s surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the precipitation path-integrated attenuation. Previous work by Meneghini and Jones (2011) showed that while averaging over larger latitude/longitude bins increase the number of samples, it can also increase sample variance due to spatial inhomogeneity in the data. As a result, Meneghini and Kim (2017) proposed a new, adaptive method of database construction, in which the number of measurements averaged depends on the spatial homogeneity. The purpose of this work is to re-visit previous, single-frequency results using dual-frequency data and optimal interpolation (kriging). Results include that (1) temporal inhomogeneity can create similar results as spatial, (2) Ka-band behavior is similar to Ku-band, (3) the Ku-/Ka-band difference has less spatial inhomogeneity than either band by itself, and (4) kriging and the adaptive method can reduce the sample variance. The author concludes that finer spatial and temporal resolution is necessary in constructing the database for single frequencies but less so for the Ku-/Ka-band difference. The adaptive approach reduces sample standard deviation with a relatively modest computational increase.

With the advent of the 5G era, radio frequency identification (RFID) has been widely applied in various fields as one of the key technologies for the Internet of Things (IoT) to realize the Internet of Everything (IoE). In recent years, RFID-based motion sensing has emerged as an important research area with great potential for development. In this paper, an RFID backscatter sport motion sensing scheme is proposed, which effectively solves the multi-classification problem by using the received signal strength (RSS) of the backscattered RFID and the error correcting output coding (ECOC)-based support vector machine (SVM). We conduct extensive experiments to validate the effectiveness of the proposed scheme, in which the signal intensities of different types of action poses are collected and the SVM is used as the classification algorithm to achieve high classification accuracies.

Satellite image analysis is a potentially powerful tool for monitoring coastal shoreline positions. This study explores the use of multi-temporal, dual-polarised Sentinel-1 GRD synthetic aperture radar (SAR) imagery with a spatial resolution of 10 m for delineating shorelines. It was conducted in a data-deficient and complex environment (the Niger delta of Nigeria), in a developing country with a cloud-heavy climate. The study focuses on exploring and testing the capability of using multitemporal waterlines from SAR images to derive shoreline positions at high and low tidal states. From 54 Sentinel-1 images recorded in 2017, the study selected 12 images to represent both high and low tidal states. These were spread across the wet and dry seasons in order to account for seasonal differences. Shoreline positions were obtained by identifying the land–water boundary via segmentation using histogram-minimum thresholding, vectorizing and smoothing that boundary, and averaging its position over multiple waterlines. The land–water segmentation had an overall accuracy of 95–99%. It showed differences between wet and dry season shoreline positions in areas dominated by complex creek networks, but similarities along open coasts. The SAR-derived shorelines deviated from the reference lines by a maximum of 43 m (approximately four pixels), and often less than 10 m (one pixel) in most locations (open coast, estuarine, complex creek networks) at high and low tides, except low tide lines in areas with extensive inter-tidal flats at shorelines 70 m to 370 m from the reference lines. However, for applications such as coastal vulnerability assessment, the high tide shoreline is of greater importance. Thus, depending on the application of interest, problems with low tide shoreline delineation may be irrelevant. Despite limitations, notably the relatively small number of images available that were recorded at high or low tide, the method provides a simple, objective, and cost-effective approach to monitoring shorelines at high and low tide.

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha−1. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha−1. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests’ structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha−1) and overestimated moderate biomass levels (30–45 Mg ha−1). The AGB change map showed values ranging from gains of 43.3 Mg ha−1 to losses of −68.8 Mg ha−1 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics.

The upper part of the Krka River estuary and Prokljan Lake are a specific example of a well-stratified estuarine environment in a submerged river canyon. Here, we reconstructed the geomorphological evolution of the area and classified the data gathered in the study, integrating multibeam echosounder data, backscatter echosounder data, side-scan sonar morpho-bathymetric surveys, and acoustic sub-bottom profiling, with the addition of ground-truthing and sediment analyses. This led to the successful classification of the bottom sediments using the object-based image analysis method. Additional inputs to the multibeam echosounder data improved the segmentation of the seafloor classification, geology, and morphology of the surveyed area. This study uncovered and precisely defined distinct geomorphological features, specifically submerged tufa barriers and carbonate mounds active during the Holocene warm periods, analogous to recent tufa barriers that still exist and grow in the upstream part of the Krka River. Fine-grained sediments, classified as estuarine sediments, hold more organic carbon than coarse-grained sediments sampled on barriers. A good correlation of organic carbon with silt sediments allowed the construction of a prediction map for marine sedimentary carbon in this estuarine/lake environment using multibeam echosounder data. Our findings highlight the importance of additional inputs to multibeam echosounder data to achieve the most accurate results.

Supply chain management aims to achieve both efficiency and low cost. Backscatter technology provides a low-energy consumption approach for critical links in the supply chain, such as warehouse management and cargo identification. Traditional backscatter systems achieve tag data transmission through dedicated hardware or controlled transmission sources. An additional access point (AP) can be used to ensure that the original data are always known in tag data decoding. These requirements increase the deployment costs and are not suitable for large-scale applications. To address these challenges, we introduce CRCScatter, a backscatter system based on a cyclic redundancy check (CRC) reverse algorithm, with an uncontrolled source and a single-AP receiver. The CRCScatter decoder at the receiver uses the constraints within 802.11b WiFi packets to recover the original packet and decode tag data from the backscatter packet. Our Matlab simulation results show that CRCScatter is effective in the low signal-to-noise ratio (SNR) regime, and its average decoding time is independent of the length of tag data. By appending redundant bits in tag data, the decoding accuracy of CRCScatter can be improved. In summary, CRCScatter presents a backscatter communication mode based on ambient WiFi signals with fewer hardware requirements and low deployment costs. Furthermore, the decoding idea of calculating unknown data based on the packet constraints has the potential to expand to different types of excitation packages.

Accurately estimating wheat yield is crucial for informed decision making in precision agriculture (PA) and improving crop management. In recent years, optical satellite-derived vegetation indices (Vis), such as Sentinel-2 (S2), have become widely used, but the availability of images depends on the weather conditions. For its part, Sentinel-1 (S1) backscatter data are less used in agriculture due to its complicated interpretation and processing, but is not impacted by weather. This study investigates the potential benefits of combining S1 and S2 data and evaluates the performance of the categorical boosting (CatBoost) algorithm in crop yield estimation. The study was conducted utilizing dense yield data from a yield monitor, obtained from 39 wheat (Triticum spp. L.) fields. The study analyzed three S2 images corresponding to different crop growth stages (GS) GS30, GS39-49, and GS69-75, and 13 Vis commonly used for wheat yield estimation were calculated for each image. In addition, three S1 images that were temporally close to the S2 images were acquired, and the vertical-vertical (VV) and vertical-horizontal (VH) backscatter were calculated. The performance of the CatBoost algorithm was compared to that of multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) algorithms in crop yield estimation. The results showed that the combination of S1 and S2 data with the CatBoost algorithm produced a yield prediction with a root mean squared error (RMSE) of 0.24 t ha−1, a relative RMSE (rRMSE) 3.46% and an R2 of 0.95. The result indicates a decrease of 30% in RMSE when compared to using S2 alone. However, when this algorithm was used to estimate the yield of a whole plot, leveraging information from the surrounding plots, the mean absolute error (MAE) was 0.31 t ha−1 which means a mean error of 4.38%. Accurate wheat yield estimation with a spatial resolution of 10 m becomes feasible when utilizing satellite data combined with CatBoost.

Ultrasound imaging of the liver is an everyday, worldwide clinical tool. The echoes are produced by inhomogeneities within the interrogated tissue, but what are the mathematical properties of these scatterers? In theory, the spatial correlation function and the backscatter coefficient are linked by a Fourier transform relationship, however direct measures of these are relatively rare. Under the hypothesis that the fractal branching vasculature and fluid channels are the predominant source of scattering in normal tissues, we compare theory and experimental measures of the autocorrelation function, the frequency dependence of scattering, and fractal dimension estimates from high contrast 3D micro-CT data sets of rat livers. The results demonstrate a fractal dimension of approximately 2.2 with corresponding power law estimates of autocorrelation and ultrasound scattering. These results support a general framework for the analysis of ultrasound scattering from soft tissues.

A passive keyless entry and start (PKES) system is an electronic lock for an automobile that provides the great convenience of opening the door when the user is in proximity. However, the system suffers from relay attacks. Recent studies revealed that relayed signals result in valid packets that are sufficient to unlock doors. In particular, the adversary causes proximity errors by injecting a certain time delay before relaying to manipulate the phase rotation in the response signal. To this end, we present a novel relay-resilient proximity detection solution, BackProx, which uses pseudo-random frequency hopping with the assistance of a reference backscattering device. Since the relay adversary transmits the relayed signals from the key fob at long distances, the signals should propagate over longer distances, resulting in inevitable significant phase rotation with different frequencies. Inspired by this finding, BackProx uses an additional backscattering device to ensure the proximity of the key fob using the invariant characteristics of radio frequency signals in the physical layer (i.e., phase rotation). Our evaluation demonstrates the effectiveness of BackProx in resisting three types of relay attacks. The results show that it achieved a 98% true positive rate at close range and a 0.3% false positive rate at long range.

Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km2, considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor’s acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m2/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R2-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios.

Self-sustainable energy harvesting for Internet of Things devices is challenging since ambient energy may be sporadic and unpredictable. This situation leads to frequent power failures that lead to intermittent operations, which prevent the reliability of data communications. This article presents fundamental hardware circuitry that enables reliable intermittent communications over wireless batteryless node networks. We emphasize two main mechanisms that ensure energy awareness and reliability: energy status-sharing and synchronized operation. We introduce novel low-power and self-sustainable plug-and-play circuits to support these mechanisms.

We investigated the possibilities of improving hydrological simulations by assimilating radar backscatter observations from the advanced scatterometer (ASCAT) in the hydrological model SCHEME using a calibrated water cloud model (WCM) as an observation operator. The WCM simulates backscatter based on soil moisture and vegetation data and can therefore be used to generate observation predictions for data assimilation. The study was conducted over two Belgian catchments with different hydrological regimes: the Demer and the Ourthe catchment. The main differences between the two catchments can be summarized in precipitation and streamflow levels, which are higher in the Ourthe. The data assimilation method adopted here was the ensemble Kalman filter (EnKF), whereby the uncertainty of the state estimate was described via the ensemble statistics. The focus was on the optimization of the EnKF, and possible solutions to address biases introduced by ensemble perturbations were investigated. The latter issue contributes to the fact that backscatter data assimilation only marginally improves the overall scores of the discharge simulations over the deterministic reference run, and only for the Ourthe catchment. These performances, however, considerably depend on the period considered within the 5 years of analysis. Future lines of research on bias correction, the data assimilation of soil moisture and backscatter data are also outlined.

For a good interpretation of radar backscatter sensitivity to vegetation water dynamics, we need to know which parts of the vegetation layer control that backscatter. However, backscatter sensitivity to different depths in the canopy is poorly understood. This is partly caused by a lack of observational data to describe the vertical moisture distribution. In this study, we aimed to understand the sensitivity of L-band backscatter to water at different heights in a corn canopy. We studied changes in the contribution of different vertical layers to total backscatter throughout the season and during the day. Using detailed field measurements, we first determined the vertical distribution of moisture in the plants, and its seasonal and sub-daily variation. Then, these measurements were used to define different sublayers in a multi-layer water cloud model (WCM). To calibrate and validate the WCM, we used hyper-temporal tower-based polarimetric L-band scatterometer data. WCM simulations showed a shift in dominant scattering from the lowest 50 cm to 50–100 cm during the season in all polarizations, mainly due to leaf and ear growth and corresponding scattering and attenuation. Dew and rainfall interception raised sensitivity to upper parts of the canopy and lowered sensitivity to lower parts. The methodology and results presented in this study demonstrate the importance of the vertical moisture distribution on scattering from vegetation. These insights are essential to avoid misinterpretation and spurious artefacts during retrieval of soil moisture and vegetation parameters.

We present a new subgrid stress model for the large eddy simulation of turbulent flows based on the solution of transport equations for stress tensor components. The model was a priori term-by-term calibrated against an open DNS database on forced isotropic turbulence (Johns Hopkins University database). After that, it was applied in a large eddy simulation of non-premixed turbulent combustion. To demonstrate the impact of the new subgrid stress model on scalar fields, we excluded the backward effect of heat release on the subgrid stresses, considering an isothermal reaction (i.e., diluted mixture; the density variations associated with chemical heat release can be neglected) and a Burke–Schumann reaction sheet approximation. A periodic box filled with a homogeneous turbulent velocity field and a three-layer top-hat mixture fraction field was studied. Four simulations were performed in which a fixed model for mixture fraction and its variance was combined with either the proposed subgrid stress model or one of the standard models, including Smagorinsky, dynamic Smagorinsky and WALE. Qualitatively correct backscatter was observed in a simulation with the new model. The differences in the statistics of the mixture fraction and reactive component fields caused by the new subgrid stress model were analyzed and discussed. The importance of using an advanced subgrid stress model was highlighted.

The decrease in water resources due to climate change is expected to have a significant impact on agriculture. On the other hand, as the world population increases so does the demand for food. It is necessary to better manage environmental resources and maintain an adequate level of crop production in a world where the population is constantly increasing. Therefore, agricultural activities must be closely monitored, especially in maize fields since maize is of great importance to both humans and animals. Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite images were used to monitor maize growth in this study. Backscatter and interferometric coherence values derived from Sentinel-1 images, as well as Normalized Difference Vegetation Index (NDVI) and values related to biophysical variables (such as Leaf Area Index (LAI), Fraction of Vegetation Cover (fCover or FVC), and Canopy Water Content (CW)) derived from Sentinel-2 images were investigated. Sentinel-1 images were also used to calculate plant heights. The Interferometric SAR (InSAR) technique was applied to calculate interferometric coherence values and plant heights. For the plant height calculation, two image pairs with the largest possible perpendicular baseline were selected. Backscatter, NDVI, LAI, fCover, and CW values were low before planting, while the interferometric coherence values were generally high. Backscatter, NDVI, LAI, fCover, and CW values increased as the maize grew, while the interferometric coherence values decreased. Among all Sentinel-derived values, fCover had the best correlation with maize height until maize height exceeded 260 cm (R2 = 0.97). After harvest, a decrease in backscatter, NDVI, LAI, fCover, and CW values and an increase in interferometric coherence values were observed. NDVI, LAI, fCover, and CW values remained insensitive to tillage practices, whereas backscatter and interferometric coherence values were found to be sensitive to planting operations. In addition, backscatter values were also sensitive to irrigation operations, even when the average maize height was about 235 cm. Cloud cover and/or fog near the study area were found to affect NDVI, LAI, fCover, and CW values, while precipitation events had a significant impact on backscatter and interferometric coherence values. Furthermore, using Sentinel-1 images, the average plant height was calculated with an error of about 50 cm.

Low-power wide-area networks (LPWANs), such as LoRaWAN, play an essential role and are expanding quickly in miscellaneous intelligent applications. However, the collision problem is also expanding significantly with the mass promotion of LPWAN nodes and providing collision-resilient techniques that are urgently needed for these applications. This paper proposes BackLoRa, a lightweight method that enables collision-resilient LoRa transmission with extra propagation information provided by backscatter tags. BackLoRa uses several backscatter tags to create multipath propagation features related to the LoRa nodes’ positions and offers a lightweight algorithm to extract the feature and correctly distinguish each LoRa node. Further, BackLoRa proposes a quick-phase acquisition algorithm with low time complexity that can carry out the iterative recovery of symbols for robust signal reconstructions in low-SNR conditions. Finally, comprehensive experiments were conducted in this study to evaluate the performance of BackLoRa systems. The experimental results show th compared with the existing scheme, our scheme can reduce the symbol error rate from 65.3% to 5.5% on average and improve throughput by 15× when SNR is −20 dB.

Global joint measurements of sea surface winds and currents are planned using satellite-based Doppler scatterometers operating in the Ka-band to achieve improved spatial resolution and retrieval accuracy. Still, the knowledge of sea surface Ka-band backscatter properties is poor, particularly, at high winds (>20 m s−1). Sea surface radar cross-section in the Ka-band, in contrast to that in the lower frequency Ku-/X-/C-/L-bands, is likely more sensitive to sea spray, small-scale particles typically present at high winds. In this paper, tower-based field data collected by a continuous dual-co-polarized Ka-band radar during a strong offshore wind event (with wind speed reaching 33 m s−1) are analyzed. This katabatic wind event (≈12 h long) was also recorded by supplementary wave, wind, and current sensors. At the wave fetch of ≈1 km, the maximum wavelength of observed offshore waves was ≈10 m. For such extremely young wind–sea conditions, an apparent sea spray generation was observed during wind gusts. Radar measurements were performed at 20∘ and 45∘ incidence angles, mostly for cross- and up-wind azimuth look geometry. Based on these high wind measurements, the previously developed Ka-band empirical model is tested and compared with other published geophysical model functions. Dual-co-polarized measurements are used to infer resonant Bragg and non-Bragg scattering components and assess the short wind wave spectrum, which shows a clear tendency for saturation at high winds. The presence of sea spray signatures is apparent in the high-frequency tails of radar Doppler spectra, but their overall contribution to the Doppler centroid frequency is weak. Hence, the standard modulation transfer function approach developed for moderate winds is still applicable at high winds for interpreting the wave-induced Doppler velocity and inferring sea surface currents. These results can also be useful for understanding Doppler scatterometry measurements in tropical cyclones.

Laboratory experiments were conducted to evaluate the feasibility of profiling and characterizing subsurface bubble plumes following a breaking wave event from an above-water Light Detection and Ranging (LiDAR) system. Measurements of LiDAR backscatter profiles of bubble plumes under mechanically generated breaking waves in a wave tank were collected and analyzed. After onset of wave breaking, the LiDAR backscatter increases rapidly by injected bubble plumes of active wave breaking. This intensification reaches a depth of one wave height within one wave period. After active wave breaking, the LiDAR backscatter from dissipated bubble plumes in the upper layer of water column decreases very slowly. The temporal variations of LiDAR backscatter are comparable to the collocated in-water measurements of optical backscatter at 850 nm wavelength and acoustic backscatter at 2000 kHz frequency. The decay rate of LiDAR backscatter of dissipated bubble plumes follows a power-law function consistent with decay rate of void fraction measurements in previous studies. This study demonstrates the viability and potential of using above-water LiDAR remote sensing to characterize subsurface bubble plumes.

This work studies the feasibility of using backscatter-modulated tags to introduce false information into a signal received by a frequency-modulated continuous-wave (FMCW) radar. A proof-of-concept spoofing device was designed in the 24 GHz ISM band. The spoofing device was based on an amplifier connected between two antennas, and modulation was carried out by switching the amplifier bias. The use of an amplifier allowed us to increase the level of spoofing signal compared with other modulated backscattering methods. The simulated and experimental results show that our method has the ability to generate a pair of false targets at different ranges and velocities depending on the modulation frequency of the chosen tag, since sidebands appear due to this modulation. Countermeasures to detect the spoofing attack based on changes in the slope of the frequency sweep between frames are also proposed.

Forest stand height (FSH), or average canopy height, serves as an important indicator for forest monitoring. The information provided about above-ground biomass for greenhouse gas emissions reporting and estimating carbon storage is relevant for reporting for Reducing Emissions from Deforestation and Forest Degradation (REDD+). A novel forest height estimation method utilizing a fusion of backscatter and Interferometric Synthetic Aperture Radar (InSAR) data from JAXA’s Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) is applied to a use case in Savannakhet, Lao. Compared with LiDAR, the estimated height from the fusion method had an RMSE of 4.90 m and an R2 of 0.26. These results are comparable to previous studies using SAR estimation techniques. Despite limitations of data quality and quantity, the Savannakhet, Lao use case demonstrates the applicability of these techniques utilizing L-band SAR data for estimating FSH in tropical forests and can be used as a springboard for use of L-band data from the future NASA-ISRO SAR (NISAR) mission.

The COVID-19 pandemic has highlighted a large amount of challenges to address. To combat the spread of the virus, several safety measures, such as wearing face masks, have been taken. Temperature controls at the entrance of public places to prevent the entry of virus carriers have been shown to be inefficient and inaccurate. This paper presents a smart mask that allows to monitor body temperature and breathing rate. Body temperature is measured by a non-invasive dual-heat-flux system, consisting of four sensors separated from each other with an insulating material. Breathing rate is obtained from the temperature changes within the mask, measured with a thermistor located near the nose. The system communicates by means of long-range (LoRa) backscattering, leading to a reduction in average power consumption. It is designed to establish the relative location of the smart mask from the signal received at two LoRa receivers installed inside and outside an access door. Low-cost LoRa transceivers with WiFi capabilities are used in the prototype to collect information and upload it to a server. Accuracy in body temperature measurements is consistent with measurements made with a thermistor located in the armpit. The system allows checking the correct placement of the mask based on the recorded temperatures and the breathing rate measurements. Besides, episodes of cough can be detected by sudden changes in thermistor temperature.

Ultra-high frequency (UHF) multiple input multiple output (MIMO) passive radio frequency identification (RFID) systems have attracted the attention of many researchers in the last few years. The system modeling and theoretical performance analysis of these systems have been well investigated and revealed in many studies, yet the system prototype and the corresponding experimental results are scarce. In this study, measurements of a 1 × 2 × 1 UHF passive RFID system, including a MIMO UHF passive RFID tag prototype and its corresponding software-defined radio-based reader, taken in a microwave anechoic chamber, are presented. The experimental results are compared with theoretical values and computer simulations. The overall results demonstrate the consistency and the feasibility of UHF MIMO passive RFID systems.

Support arm backscatter and off-axis effects of an electronic portal imaging device (EPID) are challenging for radiotherapy quality assurance. Aiming at the issue, we proposed a simple yet effective method with correction matrices to rectify backscatter and off-axis responses for EPID images. First, we measured the square fields with ionization chamber array (ICA) and EPID simultaneously. Second, we calculated the dose-to-pixel value ratio and used it as the correction matrix of the corresponding field. Third, the correction value of the large field was replaced with that of the same point in the small field to generate a correction matrix suitable for different EPID images. Finally, we rectified the EPID image with the correction matrix, and then the processed EPID images were converted into the absolute dose. The calculated dose was compared with the measured dose via ICA. The gamma pass rates of 3%/3 mm and 2%/2 mm (5% threshold) were 99.6% ± 0.94% and 95.48% ± 1.03%, and the average gamma values were 0.28 ± 0.04 and 0.42 ± 0.05, respectively. Experimental results verified our method accurately corrected EPID images and converted pixel values into absolute dose values such that EPID was an efficient radiotherapy dosimetry tool.

The multitemporal acquisition of images from the Sentinel-1 satellites allows continuous monitoring of a forest. This study focuses on the use of multitemporal C-band synthetic aperture radar (SAR) data to assess the results for forest type (FTY), between coniferous and deciduous forest, and tree species (SPP) classification. We also investigated the temporal stability through the use of backscatter from multiple seasons and years of acquisition. SAR acquisitions were pre-processed, histogram-matched, smoothed, and temperature-corrected. The normalized average backscatter was extracted for interpreted plots and used to train Random Forest models. The classification results were then validated with field plots. A principal component analysis was tested to reduce the dimensionality of the explanatory variables, which generally improved the results. Overall, the FTY classifications were promising, with higher accuracies (OA of 0.94 and K = 0.86) than the SPP classification (OA of 0.66 and K = 0.54). The use of merely winter images (OA = 0.89) reached, on average, results that were almost as good as those using of images from the entire year. The use of images from a single winter season reached a similar result (OA = 0.87). We conclude that multiple Sentinel-1 images acquired in winter conditions are feasible to classify forest types in a hemi-boreal Swedish forest.

The effective normalized radar cross section (NRCS) of breaking waves, σwb, is empirically derived based on joint synchronized Ka-band radar and video records of the sea surface from a research tower. The σwb is a key parameter that, along with the breaker footprint fraction, Q, defines the contribution of non-polarized backscattering, NP =σwbQ, to the total sea surface NRCS. Combined with the right representation of the regular Bragg and specular backscattering components, the NP component is fundamental to model and interpret sea surface radar measurements. As the first step, the difference between NRCS values for breaking and non-breaking conditions is scaled with the optically-observed Q and compared with the geometric optics model of breaker backscattering. Optically-derived Q might not be optimal to represent the effect of breaking waves on the radar measurements. Alternatively, we rely on the breaking crest length that is firmly detected by the video technique and the empirically estimated breaker decay (inverse wavelength) scale in the direction of breaking wave propagation. A simplified model of breaker NRCS is then proposed using the geometric optics approach. This semi-analytical model parameterizes the along-wave breaker decay from reported breaker roughness spectra, obtained in laboratory experiments with mechanically-generated breakers. These proposed empirical breaker NRCS estimates agree satisfactorily with observations.

This work proposes the use of a modulated tag for direct communication between two vehicles using as a carrier the wave emitted by an FMCW radar installed in the vehicle for advanced driver assistance. The system allows for real-time signals detection and classification, such as stop signal, turn signals and emergency lights, adding redundancy to computer video sensors and without incorporating additional communication systems. A proof-of-concept tag has been designed at the microwave frequency of 24 GHz, consisting of an amplifier connected between receiving and transmitting antennas. The modulation is performed by switching the power supply of the amplifier. The tag is installed on the rear of the car and it answers when it is illuminated by the radar by modulating the backscattered field. The information is encoded in the modulation switching rate used. Simulated and experimental results are given showing the feasibility of the proposed solution.

The TU Wien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (ASCAT) on the Metop series of satellites as part of the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The incidence angle dependence of backscatter is described by a second-order Taylor polynomial, the coefficients of which are used to normalize ASCAT observations to the reference incidence angle of 40∘ and for correcting vegetation effects. Recently, a kernel smoother was developed to estimate the coefficients dynamically, in order to account for interannual variability. In this study, we used the kernel smoother for estimating these coefficients, where we distinguished for the first time between their two uses, meaning that we used a short and fixed window width for the backscatter normalisation while we tested different window widths for optimizing the vegetation correction. In particular, we investigated the impact of using the dynamic vegetation parameters on soil moisture retrieval. We compared soil moisture retrievals based on the dynamic vegetation parameters to those estimated using the current operational approach by examining their agreement, in terms of the Pearson correlation coefficient, unbiased RMSE and bias with respect to in situ soil moisture. Data from the United States Climate Research Network were used to study the influence of climate class and land cover type on performance. The sensitivity to the kernel smoother half-width was also investigated. Results show that estimating the vegetation parameters with the kernel smoother can yield an improvement when there is interannual variability in vegetation due to a trend or a change in the amplitude or timing of the seasonal cycle. However, using the kernel smoother introduces high-frequency variability in the dynamic vegetation parameters, particularly for shorter kernel half-widths.

A novel methodology is proposed to reliably predict the resonant characteristics of a multipatch backscatter-based radio frequency identification (RFID) chipless tag. An ultra-wideband impulsion radio (UWB-IR)-based reader interrogates the chipless tag with a UWB pulse, and analyzes the obtained backscatter in the time domain. The RFID system consists of a radar cross-section (RCS)-based chipless tag containing a square microstrip patch antenna array in which the chipless tag is interrogated with a UWB pulse by an UWB-IR-based reader. The main components of the backscattered signal, the structural mode, and the antenna mode were identified and their spectral quality was evaluated. The study revealed that the antenna-mode backscatter includes signal carrying information, while the structural mode backscatter does not include any tag information. The simulation findings were confirmed by experimental measurements obtained in an anechoic chamber environment using a 6-bit multipatch chipless RFID tag. Finally, the novel technique does not use calibration tags and can freely orient tags with respect to the reader.

The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R2 = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations.

This paper presents a method for low data rate transmission for devices implanted in the body using backscattered Long Range (LoRa) signals. The method uses an antenna loaded with a switch that changes between two load impedances at the rate of a modulating oscillator. Consequently, the LoRa signal transmitted by a LoRa node is reflected in the adjacent channels and can be detected with a LoRa gateway tuned to the shifted channels. A prototype developed to operate at Medical Implant Communication Service (MICS) and the Industrial Scientific and Medical (ISM) 433 MHz band is presented. The prototype uses a commercial ceramic antenna with a matched network tuned to the frequency band with high radiation efficiency. The effect of the coating material covering the antenna was studied. Simulated and experimental results using a phantom show that it is feasible to read data from deep implanted devices placed a few meters from the body because of the high sensitivity of commercial LoRa receivers.

Spaceborne remote sensing can track ecosystems changes thanks to continuous and systematic coverage at short revisit intervals. Active remote sensing from synthetic aperture radar (SAR) sensors allows day and night imaging as they are not affected by cloud cover and solar illumination and can capture unique information about its targets. However, SAR observations are affected by the coupled effect of viewing geometry and terrain topography. The study aims to assess the impact of global digital elevation models (DEMs) on the normalization of Sentinel-1 backscattered intensity and interferometric coherence. For each DEM, we analyzed the difference between orbit tracks, the difference with results obtained with a high-resolution local DEM, and the impact on land cover classification. Tests were carried out at two sites located in mountainous regions in Romania and Spain using the SRTM (Shuttle Radar Topography Mission, 30 m), AW3D (ALOS (Advanced Land Observation Satellite) World 3D, 30 m), TanDEM-X (12.5, 30, 90 m), and Spain national ALS (aerial laser scanning) based DEM (5 m resolution). The TanDEM-X DEM was the global DEM most suitable for topographic normalization, since it provided the smallest differences between orbital tracks, up to 3.5 dB smaller than with other DEMs for peak landform, and 1.4–1.9 dB for pit and valley landforms.

Icebergs are ocean hazards which require extensive monitoring. Synthetic Aperture Radar (SAR) satellites can help with this, however, SAR backscattering is strongly influenced by the properties of icebergs, together with meteorological and environmental conditions. In this work, we used five images of quad-pol ALOS-2/PALSAR-2 SAR data to analyse 1332 icebergs in five locations in west and east Greenland. We investigate the backscatter and polarimetric behaviour, by using several observables and decompositions such as the Cloude–Pottier eigenvalue/eigenvector and Yamaguchi model-based decompositions. Our results show that those icebergs can contain a variety of scattering mechanisms at L-band. However, the most common scattering mechanism for icebergs is surface scattering, with the second most dominant volume scattering (or more generally, clouds of dipoles). In some cases, we observed a double bounce dominance, but this is not as common. Interestingly, we identified that different locations (e.g., glaciers) produce icebergs with different polarimetric characteristics. We also performed a multi-scale analysis using boxcar 5 × 5 and 11 × 11 window sizes and this revealed that depending on locations (and therefore, characteristics) icebergs can be a collection of strong scatterers that are packed in a denser or less dense way. This gives hope for using quad-pol polarimetry to provide some iceberg classifications in the future.

Owing to its temporal resolution of 10-day and its polar orbit allowing several crossings over large lakes, the US National Aeronautics and Space Administration (NASA) and the French Centre National d’Etudes Spatiales (CNES) missions including Topex/Poseidon, Jason-1/2/3 demonstrated strong capabilities for the continuous and long-term monitoring (starting in 1992) of large and medium-sized water bodies. However, the presence of heterogeneous targets in the altimeter footprint, such as ice cover in boreal areas, remains a major issue to obtain estimates of water level over subarctic lakes of similar accuracy as over other inland water bodies using satellite altimetry (i.e., R ≥ 0.9 and RMSE ≤ 10 to 20 cm when compared to in-situ water stages). In this study, we aim to automatically identify the Jason-2 altimetry measurements corresponding to open water, ice and transition (water-ice) to improve the estimations of water level during freeze and thaw periods using only the point measurements of open water. Four Canadian lakes were selected to analyze active (waveform parameters) and passive (brightness temperature) microwave data acquired by the Jason-2 radar altimetry mission: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. To determine lake surface states, backscattering coefficient and peakiness at Ku-band derived from the radar altimeter waveform and brightness temperature at 18.7 and 37 GHz measured by the microwave radiometer contained in the geophysical data records (GDR) of Jason-2 were used in two different unsupervised classification techniques to define the thresholds of discrimination between open water and ice measurements. K-means technique provided better results than hierarchical clustering based upon silhouette criteria and the Calinski-Harabz index. Thresholds of discrimination between ice and water were validated with the Normalized Difference Snow Index (NDSI) snow cover products of the MODIS satellite. The use of open water threshold resulted in improved water level estimation compared to in situ water stages, especially in the presence of ice. For the four lakes, the Pearson coefficient (r) increased on average from about 0.8 without the use of the thresholds to more than 0.90. The unbiased RMSE were generally lower than 20 cm when the threshold of open water was used and more than 22 cm over smaller lakes, without using the thresholds.

The development of multibeam echosounders (MBES) as a seabed mapping tool has resulted in the widespread uptake of backscatter intensity as an indicator of seabed substrate properties. Though increasingly common, the lack of standard calibration and the characteristics of individual sonars generally produce backscatter measurements that are relative to a given survey, presenting major challenges for seabed mapping in areas that comprise multiple MBES surveys. Here, we explore methods for backscatter dataset harmonization that leverage areas of mutual overlap between surveys for relative statistical calibration—referred to as “bulk shift” approaches. We use three multispectral MBES datasets to simulate the harmonization of backscatter collected over multiple years, and using multiple operating frequencies. Results suggest that relatively simple statistical models are adequate for bulk shift harmonization procedures, and that more flexible approaches may produce inconsistent results that risk statistical overfitting. While harmonizing datasets collected using the same operating frequency from separate surveys is generally feasible given reasonable temporal limitations, results suggest that the success at harmonizing datasets of different operating frequencies partly depends on the extent to which the frequencies differ. We recommend approaches and diagnostics for ensuring the quality of harmonized backscatter mosaics, and provide an R function for implementing the methods presented here.

Between 2006 and 2011, the Advanced Land Observing Satellite (ALOS) Phased Array L-type Synthetic Aperture Radar (PALSAR) instrument acquired multi-temporal datasets under several environmental conditions and multiple configurations of look angle and polarization. The extensive archive of SAR backscatter observations over the forest test sites of Krycklan (boreal) and Remningstorp (hemi-boreal), Sweden, was used to assess the retrieval of stem volume at stand level. The retrieval was based on the inversion of a simple Water Cloud Model with gaps; single estimates of stem volume are then combined to obtain the final multi-temporal estimate. The model matched the relationship between the SAR backscatter and the stem volume under all configurations. The retrieval relative Root Mean Square Error (RMSE) differed depending upon environmental conditions, polarization and look angle. Stem volume was best retrieved in Krycklan using only HV-polarized data acquired under unfrozen conditions with a look angle of 34.3° (relative RMSE: 44.0%). In Remningstorp, the smallest error was obtained using only HH-polarized data acquired under predominantly frozen conditions with a look angle of 34.3° (relative RMSE: 35.1%). The relative RMSE was below 30% for stands >20 ha, suggesting high accuracy of ALOS PALSAR estimates of stem volumes aggregated at moderate resolution.

Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR data is effective for mapping rice areas and flooding/transplanting dates with an overall accuracy of 90% and a kappa of 0.72 and that hypertemporal moderate-resolution optical imagery is effective for the basic characterization of rice areas with an overall accuracy that ranged from 62% to 87% and a kappa of 0.52 to 0.72. This study has also provided the first assessment of the temporal variation in the backscatter of rice from CSK and TSX using large incidence angles covering all rice crop stages from pre-season until harvest. This complementarity in optical and SAR data can be further exploited in the near future with the increased availability of space-borne optical and SAR sensors. This new information can help improve the identification of rice areas.

Mongolian steppe is one of the largest and important ecosystems. The degradation of grassland and the expansion of desert are occurring due to drought and desertification processes. We attempted monitoring of the broad-scale vegetation in Mongolia by a space-borne precipitation radar, which may complement typical approaches of vegetation monitoring (such as NDVI). We utilized the Global Precipitation Mission’s (GPM) dual-frequency precipitation radar (DPR). We characterized backscatter (σ0) of GPM/DPR’s two microwave bands (Ku and Ka) with respect to the dominant vegetation zones (forest, grassland, desert). Both Ku and Ka radars’ σ0 values were investigated for incidence angle dependency and the seasonal variation. As a result, the use of multi-angle, multi-band observations of GPM/DPR could help to characterize the vegetation zones. Especially, the σ0 at incidence angles between 1° and 8° represented useful characteristics of vegetation. Based on it, by using unsupervised clustering, we produced annual maps describing vegetation zones from 2014 to 2018. The result indicated that Mongolia experienced extensive changes in grassland and desert areas during the study years.

Quantitative acoustic marine habitat mapping needs to consider the impact of macrobenthic organisms on backscatter data. However, the sensitivity of hydroacoustic systems to epibenthic life is poorly constrained. This study explores the impact of a benthic community with sparse abundance on seafloor microroughness and acoustic backscatter at a sandy seafloor in the German North Sea. A multibeam echo sounder survey was ground-truthed by lander measurements combining a laser line scanner with sub-mm resolution and broad-band acoustic transducers. Biotic and abiotic features and spatial roughness parameters were determined by the laser line scanner. At the same locations, acoustic backscatter was measured and compared with an acoustic scatter model utilizing the small-roughness perturbation approximation. Results of the lander experiments show that a coverage with epibenthic features of 1.6% increases seafloor roughness at spatial wavelengths between 0.005−0.03 m, increasing both spectral slope and intercept. Despite the fact that a strong impact on backscatter was predicted by the acoustic model based on measured roughness parameters, only a minor (1.1 dB) change of backscatter was actually observed during both the lander experiments and the ship-based acoustic survey. The results of this study indicate that benthic coverage of less than 1.6% is insufficient to be detected by current acoustic remote sensing.

In the present study, we explore the value of employing both vegetation indexes as well as land surface temperature derived from Project for On-Board Autonomy—Vegetation (PROBA-V) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, respectively, to support the detection of total (wet + dry) snow cover extent (SCE) based on a simple tuning machine learning approach and provide reliability maps for further analysis. We utilize Sentinel-1-based synthetic aperture radar (SAR) observations, including backscatter coefficient, interferometric coherence, and polarimetric parameters, and four topographical factors as well as vegetation and temperature information to detect the total SCE with a land cover-dependent random forest-based approach. Our results show that the overall accuracy and F-measure are over 90% with an ’Area Under the receiver operating characteristic Curve (ROC)’ (AUC) score of approximately 80% over five study areas located in different mountain ranges, continents, and hemispheres. These accuracies are also confirmed by a comprehensive validation approach with different data sources, attesting the robustness and global transferability. Additionally, based on the reliability maps, we find an inversely proportional relationship between classification reliability and vegetation density. In conclusion, comparing to a previous study only utilizing SAR-based observations, the method proposed in the present study provides a complementary approach to achieve a higher total SCE mapping accuracy while maintaining global applicability with reliable accuracy and corresponding uncertainty information.

The knowledge about heterogeneity on agricultural fields is essential for a sustainable and effective field management. This study investigates the performance of Synthetic Aperture Radar (SAR) data of the Sentinel-1 satellites to detect variability between and within agricultural fields in two test sites in Germany. For this purpose, the temporal profiles of the SAR backscatter in VH and VV polarization as well as their ratio VH/VV of multiple wheat and barley fields are illustrated and interpreted considering differences between acquisition settings, years, crop types and fields. Within-field variability is examined by comparing the SAR backscatter with several crop parameters measured at multiple points in 2017 and 2018. Structural changes, particularly before and after heading, as well as moisture and crop cover differences are expressed in the backscatter development. Furthermore, the crop parameters wet and dry biomass, absolute and relative vegetation water content, leaf area index (LAI) and plant height are related to SAR backscatter parameters using linear and exponential as well as multiple regression. The regression performance is evaluated using the coefficient of determination (R 2 ) and the root mean square error (RMSE) and is strongly dependent on the phenological growth stage. Wheat shows R 2 values around 0.7 for VV backscatter and multiple regression and most crop parameters before heading. Single fields even reach R 2 values above 0.9 for VV backscatter and for multiple regression related to plant height with RMSE values around 10 cm. The formulation of clear rules remains challenging, as there are multiple influencing factors and uncertainties and a lack of conformity.

Machine learning classifiers have been rarely used for the identification of seafloor sediment types in the rapidly changing dredge pits for coastal restoration. Our study uses multiple machine learning classifiers to identify the sediment types of the Caminada dredge pit in the eastern part of the submarine sandy Ship Shoal of the Louisiana inner shelf of the United States (USA), and compares the performance of multiple supervised classification methods. High-resolution bathymetry and backscatter data, as well as 58 sediment grab samples were collected in the Caminada pit in August 2018, about two years after dredging. Two primary features (bathymetry and backscatter) and four secondary features were selected in the machine learning models. Three supervised classifications were tested in the study area: Decision Trees, Random Forest, and Regularized Logistic Regression. The models were trained using three different combinations of features: (1) all six features, (2) only bathymetry and backscatter features, and (3) a subset of selected features. The best performing model was the Random Forest method, but its performance was relatively poor when dealing with a few mixed (sand and mud) surficial sediment samples. The model provides a new and efficient method to predict the change of sediment distribution inside the Caminada pit over time, and is more reliable when predicting mixed bed with rough pit bottoms. Our results can be used to better understand the impacts on biological communities by (1) direct defaunation after initial sand excavation, (2) later mud accumulation in topographic lows, and (3) other geological and physical processes. In the future, the deposition and redistribution of mud inside the Caminada pit will continue, likely impacting benthos and water quality. Backscatter, roughness derived from bathymetry, rugosity derived from backscatter, and bathymetry (in the importance order from high to low) were identified as the most effective predictors of sediment texture for mineral resources management.

The U.S. Geological Survey (USGS) and the National Oceanic Atmospheric Administration (NOAA) have collected approximately 5400 km2 of geophysical and hydrographic data on the Atlantic continental shelf between Delaware and Virginia over the past decade and a half. Although originally acquired for different objectives, the comprehensive coverage and variety of data (bathymetry, backscatter, imagery and physical samples) presents an opportunity to merge collections and create high-resolution, broad-scale geologic maps of the seafloor. This compilation of data repurposes hydrographic data, expands the area of geologic investigation, highlights the versatility of mapping data, and creates new geologic products that would not have been independently possible. The data are classified using a variety of machine learning algorithms, including unsupervised and supervised methods. Four unique classes were targeted for classification, and source data include bathymetry, backscatter, slope, curvature, and shaded-relief. A random forest classifier used on all five source data layers was found to be the most accurate method for these data. Geomorphologic and sediment texture maps are derived from the classified acoustic data using over 200 ground truth samples. The geologic data products can be used to identify sediment sources, inform resource management, link seafloor environments to sediment texture, improve our understanding of the seafloor structure and sediment pathways, and demonstrate how ocean mapping resources can be useful beyond their original intent to maximize the footprint and scientific impact of a study.

Predefined classification schemes and fixed geographic scales are often used to simplify and cost-effectively map the spatial complexity of nature. These simplifications can however limit the usefulness of the mapping effort for users who need information across a different range of thematic and spatial resolutions. We demonstrate how substrate and biological information from point samples and photos, combined with continuous multibeam data, can be modeled to predictively map percentage cover conforming with multiple existing classification schemes (i.e., HELCOM HUB; Natura 2000), while also providing high-resolution (5 m) maps of individual substrate and biological components across a 1344 km2 offshore bank in the Baltic Sea. Data for substrate and epibenthic organisms were obtained from high-resolution photo mosaics, sediment grab samples, legacy data and expert annotations. Environmental variables included pixel and object based metrics at multiple scales (0.5 m−2 km), which improved the accuracy of models. We found that using Boosted Regression Trees (BRTs) to predict continuous models of substrate and biological components provided additional detail for each component without losing accuracy in the classified maps, compared with a thematic model. Results demonstrate the sensitivity of habitat maps to the effects of spatial and thematic resolution and the importance of high-resolution maps to management applications.

Multi-year field measurements of sea surface Ka-band dual-co-polarized (vertical transmit–receive polarization (VV) and horizontal transmit–receive polarization (HH)) radar Doppler characteristics from an oceanographic platform in the Black Sea are presented. The Doppler centroid (DC) estimated using the first moment of 5 min averaged spectrum, corrected for measured sea surface current, ranges between 0 and ≈1 m/s for incidence angles increasing from 0 to 70 ∘ . Besides the known wind-to-radar azimuth dependence, the DC can also depend on wind-to-dominant wave direction. For co-aligned wind and waves, a negative crosswind DC residual is found, ≈−0.1 m/s, at ≈20 ∘ incidence angle, becoming negligible at ≈ 60 ∘ , and raising to, ≈+0.5 m/s, at 70 ∘ . For our observations, with a rather constant dominant wave length, the DC is almost wind independent. Yet, results confirm that, besides surface currents, the DC encodes an expected wave-induced contribution. To help the interpretation, a two-scale model (KaDOP) is proposed to fit the observed DC, based on the radar modulation transfer function (MTF) previously developed for the same data set. Assuming universal spectral shape of energy containing sea surface waves, the wave-induced DC contribution is then expressed as a function of MTF, significant wave height, and wave peak frequency. The resulting KaDOP agrees well with independent DC data, except for swell-dominated cases. The swell impact is estimated using the KaDOP with a modified empirical MTF.

The establishment of multibeam echosounders (MBES), as a mainstream tool in ocean mapping, has facilitated integrative approaches towards nautical charting, benthic habitat mapping, and seafloor geotechnical surveys. The combined acoustic response of the seabed and the subsurface can vary with MBES operating frequency. At worst, this can make for difficulties in merging the results from different mapping systems or mapping campaigns. However, at best, having observations of the same seafloor at different acoustic wavelengths allows for increased discriminatory power in seabed classification and characterization efforts. Here, we present the results from trials of a multispectral multibeam system (R2Sonic 2026 MBES, manufactured by R2Sonic, LLC, Austin, TX, USA) in the Bedford Basin, Nova Scotia. In this system, the frequency can be modified on a ping-by-ping basis, which can provide multi-spectral acoustic measurements with a single pass of the survey platform. The surveys were conducted at three operating frequencies (100, 200, and 400 kHz), and the resulting backscatter mosaics revealed differences in parts of the survey area between the frequencies. Ground validation surveys using a combination of underwater video transects and benthic grab and core sampling confirmed that these differences were due to coarse, dredge spoil material underlying a surface cover of mud. These innovations offer tremendous potential for application in the area of seafloor geological and benthic habitat mapping.