Backscattering Coefficient Values Research Articles

Interacting Sentinel-2A, Sentinel 1A, and GF-2 Imagery to Improve the Accuracy of Forest Aboveground Biomass Estimation in a Dry-Hot Valley

Carbon absorption and storage in forests is one of the important ways to mitigate climate change. Therefore, it is essential to use a variety of remote-sensing resources to accurately estimate forest aboveground biomass (AGB) in dry-hot valley regions. In this study, satellite images from the Sentinel-1A, Sentinel-2A, and Gaofen-2 satellites were utilized to estimate the forest AGB in Yuanmou County, Yunnan Province, China. Different combinations of image data, based on selected variables of stepwise regression and their performance in constructing linear stepwise regression (LSR) and random forest (RF) models, were explored. The results showed that: (1) after adding the polarized values of the synthetic aperture radar backscatter coefficients, the combination fitting effect was significantly improved; (2) the fitting effect of the Sentinel-1A + Sentinel-2A + Gaofen-2 data combination was superior to the other combinations, indicating that the effective extraction of forest horizon and vertical information can improve the estimation effect of the forest AGB; and (3) the RF model exhibited superior fitting performance compared to the LSR model across all permutations of remotely sensed image datasets, with R2 values of 0.71 and 0.65, and RMSE values of 30.67 and 33.79 Mg/ha, respectively. These findings lay the groundwork for enhancing the precision of AGB estimation in dry-hot valley areas by integrating Sentinel-2A, Sentinel-1A, and GF-2 imagery, providing valuable insights for future research and applications.

A comprehensive open-access database of electron backscattering coefficients for energies ranging from 0.1KeV to 15MeV.

The characterization of electron backscattering is essential in medical physics for accurately assessing dose deposited around inhomogeneities where backscattering alters the spatial energy distribution pattern and for determining Monte-Carlo code's ability to effectively describe electron scattering and does calculation in a target volume. Recent machine learning advances have provided physicists with powerful tools for effectively extracting information and trends from extensive experiment observations if sufficiently sizeable datasets are available for data mining. We report on the development of a publicly accessible database on electron backscattering coefficients for solid targets. The first database on electron-solid interactions was assembled in 1995. Data for bulk materials, limited to normal incidence and energies up to 100keV, were primarily focusing on electron microscopy. To accommodate broad high-energy applications and include the most recent publications we have created a comprehensive database of electron backscattering coefficients, listed as a function of target atomic number and thickness, electron energy, and incidence angle. These additions resulted in a database of 3566 data points, compared to the previous database of 1430. The data collection includes only published experimental observations (no calculations or results fitting) with no attempt to judge their accuracy or quality. A limited number of data points were compared to recently published Monte-Carlo results. The presented database provides values of electron backscattering coefficients for 50 elements and 19 compounds at electron energies ranging from 0.1keV to 15MeV, presented in ASCII files. Each file contains the electron energy and backscattering coefficient with target thickness or electron incidence angle included where available, and the reference number shown in the last column. Additionally, the presented data were shown in the graphs for better visualization. The online database can be accessed from the website https://doi.org/10.5281/zenodo.7810951. The database provides the most up-to-date source of experimentally obtained electron backscattering coefficients that can be used in theoretical and MC calculations and modeling validations. The data availability is still very limited for many solids and almost non-existent for compounds. Novel machine learning methods should be well adapted to predict these unknown values for various targets, thicknesses, energies, and incident angles utilizing the presented cleaned dataset.

Impact of ashes from the 2022 Tonga volcanic eruption on satellite ocean color signatures

A powerful eruption within the Hunga Tonga-Hunga Ha’apai (HTHH) volcano (20.64°S, 175.19°W) in the Kingdom of Tonga, occurred on 15 January 2022. The volcanic blast was enormous, leading many scientists to investigate the full impact and magnitude of this event via satellite observations. In this study, we describe a new ocean color signature from a discolored water patch created by the HTHH eruption using NASA and CMEMS products of satellite-derived biological and optical properties. Elevated surface chlorophyll-a concentration (Chl-a) between 0.15 to 2.7 mg.m-3 was not associated with phytoplankton growth, but to basalt-andesitic ash material expelled by the volcano and into the ocean, which resulted in erroneous Chl-a estimates. Distribution of the patch over time was aligned with CMEMS ocean currents for 19 days. The gradual decrease of light attenuation or diffuse attenuation coefficient for downward irradiance at 490 nm, Kd(490), was interpreted as due to the sinking of ash particles with time. It is suggested that due to high porosity of 30-40%, a density close to that of seawater, ash particles stay suspended in the water column for more than 10 days with sustained high values of satellite-derived Chl-a, Kd(490), and particulate backscattering coefficient at 443 nm. The high attenuation of light due to ash, reducing the penetration depth to less than 10 meters during the first period after the eruption may have had implications on ecological processes and biogeochemical cycles in Tongan waters.

Anticyclonic mesoscale eddy induced mesopelagic biomass hotspot in the oligotrophic ocean

Increasing evidence suggests that mesoscale eddies have a strong influence on the distribution and behavior of zooplankton and micronekton. However, the mechanisms driving their responses to mesoscale eddies are not yet fully understood, largely due to the low resolution of previous observations. Underwater gliders equipped with acoustic equipment and environmental sensors provide an ideal means to obtain observations with high temporal and spatial resolutions. In this study, two underwater gliders were used to record environmental parameters and acoustic intensities across an anticyclonic eddy in the oligotrophic western North Pacific subtropical gyre in September 2019. Based on the variations in the sea surface current and sea surface height anomaly (SSHA) along the underwater glider tracks, the study area was divided into three subregions: eddy core (SSHA >27 cm), eddy periphery (27 cm > SSHA >20 cm), and ambient water (SSHA <20 cm). More intense backscattering occurred in the upper 800 m of the eddy core with mean area backscattering coefficient values that were 35.8% higher than those of the eddy periphery and 19.8% higher than those of the ambient water. The convergence associated with the eddy could explain that the largest animal congregations occurred in the upper 100 m of the eddy core at night. Downwelling of warmer and oxygen-rich water in the eddy core induced animals to accumulate toward the deep of the eddy core, while migrating downwards during the day. Both these results and the lack of spatial heterogeneity of chlorophyll -a in and around the anticyclonic eddy indicated that horizontal and vertical distributions of zooplankton and micronekton in the anticyclonic eddy in the oligotrophic ocean were less related to spatial variations in the phytoplankton, but might be associated with the combined effect of the physical process in the anticyclonic eddy and the diel vertical migration behavior of animals. Our results suggest that anticyclonic eddies can dynamically modulate the spatial distribution of biomass, leading to the formation of mesopelagic biomass hotspots in the oligotrophic oceans and strongly affecting the ecology and behaviors of top predators that prey on small animals. • Spatial heterogeneity in biomass distribution was observed in an anticyclonic eddy. • Redistribution of biomass occurs within anticyclonic eddies. • Anticyclonic eddies in oligotrophic oceans support mesopelagic biomass hotspots.

Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery

Airborne SAR is an important data source for crop mapping and has important applications in agricultural monitoring and food safety. However, the incidence-angle effects of airborne SAR imagery decrease the crop mapping accuracy. An active pairwise constraint learning method (APCL) is proposed for constrained time-series clustering to address this problem. APCL constructs two types of instance-level pairwise constraints based on the incidence angles of the samples and a non-iterative batch-mode active selection scheme: the must-link constraint, which links two objects of the same crop type with large differences in backscattering coefficients and the shapes of time-series curves; the cannot-link constraint, which links two objects of different crop types with only small differences in the values of backscattering coefficients. Experiments were conducted using 12 time-series images with incidence angles ranging from 21.2° to 64.3°, and the experimental results prove the effectiveness of APCL in improving crop mapping accuracy. More specifically, when using dynamic time warping (DTW) as the similarity measure, the kappa coefficient obtained by APCL was increased by 9.5%, 8.7%, and 5.2% compared to the results of the three other methods. It provides a new solution for reducing the incidence-angle effects in the crop mapping of airborne SAR time-series images.

Classification of GaoFen-3 full-polarization data based on combining polarization characteristics

To explore potential applications of GaoFen-3 (GF-3) fully polarimetric synthetic aperture radar (SAR) data in ground-feature classification, we selected Jiangxiang Town, Nanchang County as the research area and used GF-3 quad-polarization strip I (QPSI) data for classification. Pauli decomposition, Krogager decomposition, and the H / A / α decomposition model were used to decompose the GF-3 full-polarization data; combined with the backscattering coefficient values of GF-3’s HH, HV, VH, and VV polarization data, feature optimization was performed, and the optimal ground-object features for feature classification were obtained. The random forest (RF) method was used to classify the optimal features, and the results were then compared and verified. The results demonstrate that the component after polarization decomposition of H / A / α is more important in classification than the original backscattering coefficient values of HH, HV, VH, and VV. GF-3 single-phase SAR data were used for ground-feature classification in a crop-intensive area of South China, and the optimal feature optimization results included H / A / α decomposition and synthesis components of the alpha, anisotropy, red, entropy, blue, green, and polarization backscattering coefficients of VV. The RF method’s classification accuracy was higher than that of the maximum-likelihood, support-vector-machine, and neural-network methods. Using this approach, GF-3 data classification’s overall accuracy was up to 78.17%, and the Kappa coefficient was 0.738. This study has clear practical significance and reference value for the selection of classification methods for ground features based on GF- 3 data in cloudy and rainy areas.

Multisensor approach to land use and land cover mapping in Brazilian Amazon

Remote sensing has an important role in the Land Use and Land Cover (LULC) mapping process worldwide. Combining spaceborne optical and microwave data is essential for accurate classification in areas with frequent cloud cover, such as tropical regions. In this study, we investigate the possible improvements, when SAR data is incorporated into the classification process along with optical data. We used MSI/Sentinel-2 and SAR/Sentinel-1 to provide LULC mapping in the Roraima State, Brazil, in 2019. This State is located in a tropical area, where the cloud cover is frequent over the year. Cloud cover becomes substantial, especially during the May-August period when crops are grown. Twenty-nine scenarios involving a combination of optical- and SAR-based features, as well as times of data acquisition, were considered in this study. Our results showed that optical or SAR data used individually are not enough to provide accurate LULC mapping. The best results in terms of overall accuracy (OA) were achieved using metrics of multi-temporal surface reflectance and vegetation index (VI) for optical imagery, and values of backscatter coefficient in different polarizations and their ratios yielding an OA of 86.41 ± 1.74%. Analysis of three periods of data (January to April, May to August, and September to December) used for classification allowed us to identify the optimal period for distinguishing specific classes. When comparing our LULC map with a LULC product derived within the MapBiomas project we observed that our method performed better to map annual and perennial crops and water classes. Our methodology provides a more accurate LULC for the Roraima State, and the proposed technique can be applied to benefit other regions that are affected by persistent cloud cover.

Аэрозольный зонд обратного рассеяния для ракетных и баллонных исследований

The article briefly describes an optical probe for direct measurements and studies of the vertical distribution of the aerosol component of the atmosphere. The operation principle is based on the measurement of backscattering from a sequence of powerful probing pulses. The analyzed air volume is located at a close (0.5 m) distance from the radiation source. LEDs at 470 nm and 940 nm are used as radiation sources. The probe can be easily integrated with all types of standard aerological radiosondes, meteorological rockets, and having its own navigation module and telemetry system it can also be used in autonomous launches. The results of measurements carried out at the Dolgoprudny aerological station in November 2020, which recorded low values of the aerosol backscattering coefficients in the stratosphere, are presented.

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

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

Determination of electron backscattering coefficient of beryllium by a high-precision Monte Carlo simulation

We present an up-to-date Monte Carlo simulation of electron backscattering coefficient of beryllium, which is an important material in fusion reactor, at an impact energy range of electrons between 100 eV and 100 keV. The Mott’s cross section calculated with more accurate scattering potential and a relativistic dielectric functional formalism with full Penn algorithm and experimental optical data are used in the modelling of electron elastic and inelastic scatterings, respectively. This Monte Carlo simulation modelling enabled us to derive comprehensive theoretical values of backscattering coefficient, which are found significantly smaller than the previous published experimental data particularly at energies below 10 keV. Simulation of electron backscattering spectra was then carried out with and without Auger electron emission, which confirmed that Auger electrons have negligible contribution to backscattering coefficient. To validate our simulation results, we have also performed calculation for amorphous boron and carbon, while the comparison with the available experimental data shows reasonable agreement. Further simulation for carbon and water covered Be sample has revealed that the surface contamination in low vacuum conditions with several atomic/molecular layers can drastically alter the measurement values for beryllium at low energies. The low backscattering coefficient values of beryllium are partly attributed to the extremely strong forward elastic scattering for low atomic number element. Therefore, we recommend to use the here reported backscattering coefficient data of beryllium for applications.

Retrieving soil moisture profiles based on multifrequency polarimetric radar backscattering observations. Theoretical case study

ABSTRACT In this theoretical work, a dual-frequency polarimetric method is proposed for measuring moisture profiles in the topsoil up to 0.30 m thick. A case of measuring soil moisture profiles, which monotonically changes with depth, during 37 days after irrigation is considered. Original values of backscattering coefficients are calculated by the Oh model and by the small perturbation method at frequencies of 5.4 GHz and 435 MHz, respectively. In these calculations, we used measured moisture profiles and spectroscopic refractive mixing dielectric model of non-saline mineral soil with a clay fraction of 9.1%. Soil moisture profiles are retrieved by solving the inverse problem, the cost function of which is constructed based on the co- and cross-polarized ratios, calculated at two frequencies for the measured and modelled soil moisture profiles. An exponential function is used as a modelled soil moisture profile. It is shown that the standard deviation between the retrieved and measured soil moisture values in the surface layer 0.30 m thick appears to be ≤0.02 m3 m−3 (theoretical limit), and the determination coefficient is 0.881. The study shows a promising path towards developing multi-frequency radar systems for remote sensing of soil moisture profiles using satellites-based and unmanned aerial vehicles air-based platforms.

Monte Carlo simulation study of electron yields from compound semiconductor materials

A systematic study has been performed based on a Monte Carlo simulation for the investigation of secondary electron yields, backscattering coefficients, and total electron yields for eight compound semiconductor materials, i.e., AlN, TiN, VN, VC, GaAs, InAs, InSb, and PbS, at different incident electron energies in the range 0.1–10 keV. Our Monte Carlo simulation model is based on the Mott cross section for electron elastic scattering as calculated by a partial wave method and a dielectric functional approach to electron inelastic scattering with the full Penn algorithm. We used Palik's optical data for lower photon energies below 100 eVs and Henke's data for higher photon energies. The cascade production of secondary electrons in electron inelastic scattering and low energy is included in the simulation. The simulated results of electron backscattering coefficients are compared with the experimental data available in the literature. Considering the fact that the experimental data for these compound materials are not available, we have compared them with experimental data for elements having the nearest mean atomic numbers. The simulation predicted much larger backscattering coefficient values than the empirical Staub formula.

Coupling remote sensing data with in-situ optical measurements to estimate suspended particulate matter under the Evros river influence (North-East Aegean sea, Greece)

ABSTRACTMonitoring the riverine output of Suspended Particulate Matter (SPM) distribution in marine embayment is a crucial factor for the water quality of neighbouring coastal regions. This study presents satellite-derived SPM calculations against in-situ measurements in the continental shelf of North-East Aegean surrounding the transboundary Evros river mouth. Surface SPM, Inherent Optical Properties (IOPs) and remote sensing reflectance (Rrs) data were collected in a field campaign during low river discharge period (June 2016). The relationship between the optical backscattering coefficient (bbp) and the in-situ SPM concentrations was investigated. Subsequently, an empirical single band model was applied for estimating SPM concentrations by using the Landsat-8 Operational Land Imager (L8/OLI) red band and the model was then locally tuned within the study area. Furthermore, a multi-band SPM-retrieval algorithm was developed using the in-situ surface reflectance Rrs for calibration and it was validated using the Leave-One-Out Cross Validation technique (LOOCV). The relationship between in-situ SPM and backscattering coefficient values showed good proportionality, thus, nominating the predominance of terrestrial mineral particles. Validation against field measurements indicated that the SPM concentrations derived from the newly-developed multi-band algorithm had an improved significance correlation (96%), compared to both the single band model (not-tuned) (coefficient of determination, R2 = 0.82) and its locally tuned version (R2 = 0.83). Most importantly, the generated multi-band model apart from exhibiting the best performance (R2 = 0.93), it revealed high SPM spots which were not detected by the locally tuned single band model, indicating additional processes originating from river outflows, coastal erosion and subaqueous thermal springs in the area. In contrast, the locally tuned single band model overestimated SPM values in offshore waters, where low concentrations are encountered under the influence of the clear Black Sea Water (BSW).

Ultrasound assessment of the conversion of sound energy into heat in tissue phantoms enriched with magnetic micro- and nanoparticles.

Nowadays, the improvement of ultrasonic hyperthermia therapy is often achieved by adding hard particles to the sonicated medium in order to increase the heating efficiency. The explanation of the phenomenon of ultrasonic heating still requires testing on tissue mimicking materials (TMMs), enriched with particles of different sizes and physical properties. Our goal was to determine, by comparing their quantitative acoustic properties, which TMMs, with magnetic micro- or nanoparticles, convert more ultrasonic energy into heat or which of the particles embedded in the agar gel act as more effective thermal sonosensitizers. We manufactured a pure agar gel and an agar gel with the addition of magnetic micro- or nanoparticles in two proportions of 8 and 16mg/ml. Ultrasound quantitative techniques, the broadband reflection substitution technique and backscattered spectrum analysis were used to characterize the samples by speed of sound (SOS), frequency-dependent attenuation, and backscattering coefficients. The integrated backscattering coefficients were also calculated. The quantitative parameters, scattering, and attenuation coefficients of ultrasound in phantoms with micro- and nanoparticles were estimated. Based on the attenuation and scattering of ultrasound in the samples, the ultrasonic energy absorption, which determines the heating efficiency, was evaluated. Additionally, the temperature increase during sonication of the phantoms by an ultrasonic beam was directly measured using thermocouples. The density of the materials with nanoparticles was higher than for the materials with microparticles with the same fractions of particles. The SOS for all materials ranged from 1489 to 1499m/s. The attenuation in the whole frequency range (3-8MHz) was higher for the materials with nanoparticles than for the materials with microparticles. For the materials with the lower content (8mg/ml) of particles, the attenuation coefficient was 0.2dB/(MHzcm). For the 16mg/ml concentration of nanoparticles and microparticles, the attenuation coefficients were 0.66 and 0.45dB/(MHz cm), respectively. The value of backscattering coefficient in the whole frequency range was greater for the materials with microparticles than for the materials with nanoparticles. The values of the integrated backscattering coefficient were 0.05 and 0.081/m for the materials with nanoparticles and 0.46 and 0.821/m for the materials with microparticles and concentrations of 8 and 16mg/ml, respectively. The rates of temperature increase in the first 3s due to ultrasonic heating were higher for the materials with nanoparticles than for the materials with microparticles. Based on acoustical measurements, we confirmed that all materials can be used as tissue phantoms in the study of ultrasonic hyperthermia, as their properties were in the range of soft tissue properties. We found that the nanoparticle-doped materials had greater attenuation and smaller scattering of ultrasound than the materials with microparticles, so absorption in these materials is greater. Thus, the TMMs with nanoparticles convert more acoustic energy into heat and we conclude that magnetic nanoparticles are more effective thermal sonosensitizers than microparticles. This conclusion is confirmed by direct measurement of the temperature increase in the samples subjected to sonification.

A Review on Land Cover Classification Techniques for Major Fruit Crops in India - Present Scenario and Future Aspects

India is second largest fruit producer in the word. Mango (Mangifera indica L.) and banana (Musa sp.) are two major fruit crops grown in India. Fruit crops are very important for improving land productivity, economic condition of farmers by increasing income, generating employment and providing nutritional security. For better management of crops and bringing more area under fruit crops, the information on current status of crop production must be known. In brief literature review, it is observed that many researchers have used the satellite images for crop production by utilizing their surface reflectance or backscattering coefficient values. Classification of satellite images is the main source of information retrieval about the crop production and it also forms the basis and is an important step for crop cover classification, crop identification, acreage estimation, assessment of crop health and/or crop stress, change detection, yield prediction etc. There are several methods for satellite image classification such as ISO-DATA, K-Means, Maximum Likelihood, Minimum Distance, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree Classification (DTC), etc. Many researchers have used these methods for various purposes. The suitability/performance of these methods is also different and depends on the context of use, quality of imagery and ground truth data. In this paper, comparative suitability of unsupervised classifiers (ISODATA and K-Means), supervised classifiers (Parellepiped, Mahalanobis Distance, Maximum Likelihood and ANN) and Decision Tree Classification (DTC) for crop classification are reviewed.

Simulation of SAR images of urban areas by using the ray tracing method with measured values of backscatter coefficients

ABSTRACTThis study uses the ray tracing method to simulate synthetic aperture radar (SAR) images of urban areas. The images are constructed for polarisations: horizontal-horizontal (HH) and vertical-vertical (VV), and different types of buildings, vegetation, and streets. Simulated images of a given area are compared with real SAR images of the same area acquired by the TerraSAR-X satellite. The simulations use the measured backscatter coefficient for HH and VV polarisations and for five different classes of terrain: houses, trees, shrubs, grass, and ground. For multiple reflections, we apply the generalized bistatic Lambertian model. The results show that, despite the limits of the ray tracing method and the approximations involved in modelling three-dimensional objects in the simulated scene, the simulated SAR images correspond well with the actual scene. All features present in the real image are reproduced in the simulated image; in particular, the double reflections of buildings and the surrounding ground appear clearly. However, discrepancies exist, and these are also discussed.

Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data

The study discusses the soil moisture estimation using dual polarimetric RISAT-1. The semi-empirical approach of Modified Dubois Model (MDM) derived by (SrinivasaRao 2013) is worked out using (σ ̊HH) and (σ ̊HV) for soil moisture estimation using dual polarimetric backscattering image. IRS LISS IV data have been used to analyze the site suitability of different land use/cover types. The retrieval of backscattering coefficient values (σ ̊) from SAR is the common principle factor for soil moisture estimation. The surface roughness was measured in the selected sample location, for which the same backscattering values derived from the SAR is linearly correlated showing r2 = 0.93. The estimated surface roughness is used for retrieval of dielectric constant using MDM. The dielectric constant derived from MDM in combination with the Topps model proposed by (Topp 1980), is used to derive satellite-based soil moisture estimation. Linear regression analysis was performed, and the soil moisture derived from SAR are well correlated with the volumetric soil moisture showing the value of r2 = 0.63.

Mapping spatio-temporal water level variations over the central Congo River using PALSAR ScanSAR and Envisat altimetry data

ABSTRACTPrevious studies using synthetic aperture radar (SAR) backscattering coefficients have been used to distinguish vegetation types, to monitor flood conditions, and to assess soil moisture variations over the wetlands. Here, we attempted to estimate spatio-temporal water level variations over the central Congo mainstem covered with aquatic plants using the backscattering coefficients from the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) Scanning SAR (ScanSAR) images and water levels from Envisat altimetry data. First, permanent open water, forest, macrophytes, and herbaceous plants have been classified over the central Congo Basin based on statistics of the backscattering coefficient values. Second, we generated multi-temporal water level maps over part of the Congo mainstem based on the relationship between Envisat altimetry-derived river-level changes and PALSAR ScanSAR backscattering coefficient variations. Finally, the water level maps were validated with Ice, Cloud and land Elevation Satellite (ICESat) altimetry-derived water levels. We obtained overall root mean square difference (RMSD) of 67.27 cm at 100-m scale resolution of PALSAR ScanSAR. Our study shows that we can obtain reasonable estimates of water levels of the rivers covered with seasonally floating or emergent macrophytes from backscattering coefficients. Furthermore, it is expected that the generated water level maps can be used as a ‘true’ data set to perform pre-launch study of the Surface Water Ocean Topography (SWOT) mission to be launched in 2021.

In-Situ Measurement of Backscattering in Hollow-Core Fiber Based Resonant Cavities

We measure the light backscattered by resonant optical cavities based on two types of hollow-core photonic bandgap fibers, namely, 7-cell and 19-cell fibers. The measurement of the intensity backscattered by the cavity at resonance permits to deduce the value of the fiber backscattering coefficient. We find backscattering coefficients of the order of $2.0\times 10^{-6}$ and $1.0\times 10^{-6}\;\mathrm{m}^{-1}$ for the 7-cell and 19-cell fibers, respectively, two orders of magnitude larger than the one for standard solid-core single-mode fiber.

Local-scale flood mapping on vegetated floodplains from radiometrically calibrated airborne LiDAR data

Knowledge about the magnitude of localised flooding of riverine areas is crucial for appropriate land management and administration at regional and local levels. However, detection and delineation of localised flooding with remote sensing techniques are often hampered on floodplains by the presence of herbaceous vegetation. To address this problem, this study presents the application of full-waveform airborne laser scanning (ALS) data for detection of floodwater extent. In general, water surfaces are characterised by low values of backscattered energy due to water absorption of the infrared laser shots, but the exact strength of the recorded laser pulse depends on the area covered by the targets located within a laser pulse footprint area. To account for this we analysed the physical quantity of radiometrically calibrated ALS data, the backscattering coefficient, in relation to water and vegetation coverage within a single laser footprint. The results showed that the backscatter was negatively correlated to water coverage, and that of the three distinguished classes of water coverage (low, medium, and high) only the class with the largest extent of water cover (>70%) had relatively distinct characteristics that can be used for classification of water surfaces. Following the laser footprint analysis, three classifiers, namely AdaBoost with Decision Tree, Naïve Bayes and Random Forest, were utilised to classify laser points into flooded and non-flooded classes and to derive the map of flooding extent. The performance of the classifiers is highly dependent on the set of laser points features used. Best performance was achieved by combining radiometric and geometric laser point features. The accuracy of flooding maps based solely on radiometric features resulted in overall accuracies of up to 70% and was limited due to the overlap of the backscattering coefficient values between water and other land cover classes. Our point-based classification methods assure a high mapping accuracy (∼89%) and demonstrate the potential of using full-waveform ALS data to detect water surfaces on floodplain areas with limited water surface exposition through the vegetation canopy.