Changes In Backscatter Research Articles

Deep learning of Sentinel-1 SAR for burnt peatland detection in Ireland

Peatlands represent vital carbon reserves; however, once ignited, they release stored carbon, inflicting lasting environmental harm and necessitating prolonged recovery periods. An innovative method merging Sentinel-1 satellite imagery and deep learning (DL) is proposed to monitor burnt peat across diverse regions of Ireland, regardless of weather conditions or time of day. Sentinel-2 images and field measurements were used to train deep neural networks (DNN) and the accuracy in detecting burnt peat areas reached 80 %. This was achieved by combining the VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive) from Sentinel-1. Time-series analysis of Sentinel-1 VV backscatter change for Wicklow Mountains in 2018 highlights the Sentinel-1's capacity to detect various phenomena, including snowfall and burnt peat, evident prior to the peat fire event. Furthermore, an examination of peat fire occurrences in Wicklow Mountains from 2018 to 2023 through time series and mapping shows a significant escalation, with the largest burnt areas detected in 2023 spanning over 40 km².

Optical quality changes of the eye during peak SARS-CoV-2 pandemic in young adults

IntroductionTo determine whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects corneal morphology and optical quality. MethodsIn this cross-sectional study, ophthalmological indicators were examined during the peak of SARS-CoV-2 infection after adjusting for public health control measures. Participants were divided into control (remained uninfected), A (infected during follow-up), and B (infected prior to the first consultation) groups. Effects of varying SARS-CoV-2 infection levels were determined using reverse transcription polymerase chain reaction. Changes in corneal morphology, backscatter, and aberrations were measured. Corneal parameters, such as flat keratometry, steep keratometry, and surface variance, vertical asymmetry, height asymmetry, and height decentration indices were considered. ResultsOverall, 110 participants (208 eyes, 42.7% male; age 17–31 years) were enrolled. Eighteen (16.3%) were infection-free during the outbreak with unchanged corneal morphology, backscatter, and aberration. In group A, 34.73 ± 9.30 days after infection, the backscatter of the anterior corneal layer and central layer (both p=0.000) decreased. Total low-order aberration, defocus, horizontal coma, and spherical aberration of the cornea increased (p<0.05), while corneal morphology after infection did not change (p>0.05). In group B, a decrease in backscattering in the corneal middle layer and an increase in horizontal coma (p<0.05) were noted. ConclusionBackscattering of the anterior and intermediate layers of the cornea decreased and corneal aberrations increased after SARS-CoV-2 infection, which affected corneal optical quality. However, corneal morphology and thickness remained unchanged. Ophthalmological indicators and optical quality should be monitored during SARS-CoV-2 infection.

Corneal densitometry changes post-CXL for keratoconus: Comparative evaluation of epithelium-off, contact lens-assisted, and transepithelial techniques.

To evaluate changes in corneal backscattering after collagen cross-linking (CXL) for progressive keratoconus and compare its course with different techniques - standard epithelium-off CXL (SCXL), contact lens-assisted CXL (CACXL), and transepithelial CXL (TECXL). Advanced Eye Center, Post Graduate Institute of Medical Education and Research, Chandigarh, India. Retrospective comparative study. Ninety-four eyes (SCXL: 47, CACXL: 30, and TECXL: 17) were compared. Corneal haze was quantified using Scheimpflug tomography, pre- and post-CXL at 1, 3, 6, and 12 months. The baseline mean density score of the central anterior stromal layer was 16.14 ± 7.07, 15.85 ± 7.89 and 15.89 ± 7.21 in SCXL, CACXL, and TECXL groups, respectively (P 0.93). After SCXL, the score increased to 28.83 and 31.34 at 1 and 3 months, respectively (both P < 0.001) and dropped at 6 months (28.66, P < 0.001) and 12 months (23.72, P 0.003). Post-CACXL, the mean densitometry peaked at 3 months (20.35, P 0.14) and returned toward baseline at 6 months (18.82, P 0.15). After TECXL, it increased slightly at 1 month (18.47, P 0.17), decreased at 3 months (14.88, P 0.7), and plateaued over 1 year. No correlation with visual acuity was seen. Corneal haze increased significantly after SCXL, peaking at 3 months, declining over 6-12 months, and returning to baseline at 12 months. In contrast, post-TECXL and -CACXL, there was an insignificant increase in anterior corneal haze, which returned to baseline within 3-6 months.

Sensitivity of Sentinel-1 Backscatter to Management-Related Disturbances in Temperate Forests

The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016–2022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors.

Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

AbstractOver half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.

Total-variation regularized U-Net for wildfire burned area mapping based on Sentinel-1 C-Band SAR backscattering data

Previous studies have shown that Synthetic Aperture Radar (SAR) is able to detect burned areas, serving as a key data source for monitoring active wildfires in situations where optical sensors are hindered by dense smoke or cloud cover. Radar remote sensing provides rich and useful data on historical burned areas, which are critical for large-scale wildfire burned area mapping. This study aims to unveil the potential inherent in Sentinel-1 C-Band SAR data and to investigate the impact of reference masks on wildfire burned area mapping. SAR images frequently exhibit disruptive speckle noise, which manifests as isolated pixels or small regions, thereby hindering the seamless identification of burned areas through SAR data. To mitigate noise and enhance the connectivity of SAR-based burned area delineation, we propose a novel approach: a Total-Variation (TV) regularized U-Net model, tailored to learn from noisy pseudo masks derived from Sentinel-1 C-Band SAR backscattering data. To validate its efficacy, we assembled a dataset encompassing 16 geographically diverse wildfire events, incorporating SAR-based kMap, a SAR-based binary label (designated as SARREF), and an optical-based binary label (referred to as OptREF). The kMap, serving as a relative indicator of SAR backscattering change, is defined as the divergence between post-fire SAR backscattering and the temporal average, normalized by the temporal standard deviation of pre-fire SAR time series. Utilizing SAR-based kMap as input and SARREF or OptREF as labels, we systematically explored the impact of TV regularization on SAR-based burned area mapping. Our experimental findings highlight several key insights: 1) Vanilla U-Net considerably elevates the F1 score from 0.61 to 0.66 when compared to threshold-based approaches; 2) The TV-regularized U-Net exhibits a notable capacity for enhancing both accuracy and the connectivity of SAR-based burned areas. When trained with noisy SARREF, it achieves a substantial F1 score of 0.69 and an IoU score of 0.53. 3) In scenarios involving OptREF, the vanilla U-Net attains peak performance with the highest F1 (0.75) and IoU (0.61) scores, with the expected outcome of TV regularization yielding no additional enhancement.

Fish avoidance of ships during acoustic surveys tested with quiet uncrewed surface vessels

Acoustic surveys to estimate fish biomass and abundance are a major component of many fisheries monitoring programs. An important bias in acoustic surveys is that fish may avoid the survey vessel. Here, we utilized quiet uncrewed surface vessels (USVs) equipped with 120 kHz split beam echosounders to evaluate fish responses to motorized survey vessels. Two of these USVs were deployed in Lakes Michigan and Huron in summer 2021 and compared against three motorized vessels used in conventional fisheries acoustic surveys. Paired comparisons employed vessel-drone passes that provided the opportunity to observe fish response as vessels approached and then overtook their quiet USV counterparts. Sound originating from the vessels was primarily in the 10–1000 Hz range. Overall received sound pressure levels for ships at the closest pass were 100 dB (re 1 µPa @ 1 m) for one and 133–134 dB for the other two vessels. In contrast, sound originating from the USV was not detectable over ambient noise. We examined acoustic data from the USVs for potential changes in total acoustic backscatter, average target depth, and average in situ target strength as vessels approached. We observed weak evidence of an avoidance response by fish to the vessel with the loudest noise profile and highest survey speed but not for the other two vessels. We also compared acoustic data from 33 2-km transects surveyed by both vessels and the USVs, finding few differences between vessel and USV data for water depths between 5 and 80 m. Results from this work suggest that acoustics estimates of fishes in Lakes Michigan and Huron (primarily alewife, rainbow smelt, and bloater) are largely consistent among the vessels used in these two lakes for standard acoustic surveys and that fish avoidance is minimal in water depths > 5 meters.

Wall-to-wall mapping of carbon loss within the Chornobyl Exclusion Zone after the 2020 catastrophic wildfire

Key messageWe propose a framework to derive the direct loss of aboveground carbon stocks after the 2020 wildfire in forests of the Chornobyl Exclusion Zone using optical and radar Sentinel satellite data. Carbon stocks were adequately predicted using stand-wise inventory data and local combustion factors where new field observations are impossible. Both the standalone Sentinel-1 backscatter delta (before and after fire) indicator and radar-based change model reliably predicted the associated carbon loss.ContextThe Chornobyl Exclusion Zone (CEZ) is a mosaic forest landscape undergoing dynamic natural disturbances. Local forests are mostly planted and have low ecosystem resilience against the negative impact of global climate and land use change. Carbon stock fluxes after wildfires in the area have not yet been quantified. However, the assessment of this and other ecosystem service flows is crucial in contaminated (both radioactively and by unexploded ordnance) landscapes of the CEZ.AimsThe aim of this study was to estimate carbon stock losses resulting from the catastrophic 2020 fires in the CEZ using satellite data, as field visitations or aerial surveys are impossible due to the ongoing war.MethodsThe aboveground carbon stock was predicted in a wall-to-wall manner using random forest modelling based on Sentinel data (both optical and synthetic aperture radar or SAR). We modelled the carbon stock loss using the change in Sentinel-1 backscatter before and after the fire events and local combustion factors.ResultsRandom forest models performed well (root-mean-square error (RMSE) of 22.6 MgC·ha−1 or 37% of the mean) to predict the pre-fire carbon stock. The modelled carbon loss was estimated to be 156.3 Gg C (9.8% of the carbon stock in burned forests or 1.5% at the CEZ level). The standalone SAR backscatter delta showed a higher RMSE than the modelled estimate but better systematic agreement (0.90 vs. 0.73). Scots pine (Pinus sylvestris L.)-dominated stands contributed the most to carbon stock loss, with 74% of forests burned in 2020.ConclusionThe change in SAR backscatter before and after a fire event can be used as a rough proxy indicator of aboveground carbon stock loss for timely carbon map updating. The model using SAR backscatter change and backscatter values prior to wildfire is able to reliably estimate carbon emissions when on-ground monitoring is impossible.

Quantitative Assessment of Tendon Backscatter Anisotropy in B-Mode Ultrasound

To test the anisotropy of human tendons in conventional B-mode ultrasound, we prospectively performed ultrasound scans of 40 normal patella tendons and 24 patella tendons with chronic tendinopathy in adults. We scanned all tendons in longitudinal orientation (parallel to tendon fibers) using a linear array transducer (8.5 MHz) with beam steering at 0°, 5°, 10°, 15° and 20°. We used ImageJ histogram analysis to process B-mode images offline for assessing backscatter as a function of angle, known as backscatter anisotropy, between normal tendons and the subcutaneous tissues and between normal tendons and tendons with tendinopathy. We compared the results using the slopes of linear regression lines drawn through the angle-dependent data, and we concluded that the tissue anisotropy was significantly different if the 95% confidence intervals of the line slopes for different tissues did not overlap. We observed significant differences between normal tendons and both the adjacent subcutaneous tissues and tendons with tendinopathy. However, the difference in the regression slopes between tendons with tendinopathy and the adjacent subcutaneous soft tissues was not significant. It appears that changes in anisotropic backscatter could be used to detect tendon abnormalities and in assessing the significance of disease and the effectiveness of therapy.

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

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

Quantifying the sensitivity of L-Band SAR to a decade of vegetation structure changes in savannas

Global savannas are the third largest carbon sink with large human populations being highly dependent on their ecosystem services. However, savannas are changing rapidly due to climate change, fire, animal management, and intense fuelwood harvesting. In southern Africa, large trees (>5 m in height) are under threat while shrub cover (<3 m) is increasing. The collection of multi-date airborne LiDAR (ALS) data, initiated over a decade ago in the Lowveld of South Africa, provided a rare opportunity to quantify the ability of L-band SAR to track changes in savanna vegetation structure and this study is the first to do so, to our knowledge. The objective was to test the ability of ALOS PALSAR 1&2, dual-pol (HH, HV) data to quantify woody cover and volume change in savannas over 2-, 8- and 10-year periods through comparison to ALS. For each epoch (2008, 2010, 2018), multiple PALSAR images were processed to Gamma0 (γ0) at 15 m resolution with multi-temporal speckle filtering. ALS data were processed to fractional canopy cover and volume, and then compared to 5 × 5 aggregated (75 m) SAR mean γ0. The ALS cover change (∆CALS) and volume change between pairs of years were highly correlated, with (R2 > 0.8), thus results for cover change applied equally to volume change. Cover change was predicted using (i) direct backscatter change or (ii) the difference between annual cover map product derived using the Bayesian Water Cloud Model (BWCM) and logarithmic models. The linear relationship between ∆γ0 and ∆CALS varied between year pairs but reached a maximum R2 of 0.7 for 2018–2010 and a moderate R2 of 0.4 for 2018–2008. Overall, 1 dB ∆γ0 corresponded to approximately 0.1 cover change. The three cover change models had very similar uncertainties with mean RMSE = 0.15, which is 13% of the observed cover change range (−0.6 to +0.6). The direct backscatter change approach had less underestimation of positive and negative cover change. The L-band backscatter had a higher sensitivity than suggested by previous studies, as it was able to reliably distinguish cover change at 0.25 increments. The SAR-derived cover change maps detected the loss of stands of big trees, and widespread increases in cover of 0.35–0.65 in communal rangelands due to shrub encroachment. In contrast, the maps suggest that cover generally decreased in conservation areas, forming distinct fence-line effects, potentially caused by significant increases in elephant numbers and frequent, intense wildfires in reserves.

1-km soil moisture retrieval using multi-temporal dual-channel SAR data from Sentinel-1 A/B satellites in a semi-arid watershed

Soil moisture is an important variable in agricultural and hydrological applications. Sentinel-1 (A/B) satellite provides high-resolution (∼10 m) synthetic aperture radar data, and its high revisit capability (6 days at the equator) with multi-track data provides great potential for global soil moisture monitoring. In this study, an algorithm was proposed for soil moisture retrieval using the Multi-Temporal Dual-Channel (MTDC) Sentinel-1 data. The MTDC algorithm first conducted an incidence angle normalization method based on time series data to normalize backscattering coefficients from multiple tracks. Subsequently, using the vegetation water content obtained through a vegetation index climatology, soil roughness and soil unfrozen water content could be estimated for the freezing period (unfrozen water content in the soil is assumed to be stable for January and December). The changes in total backscatter between successive observations were expressed as a function of the two-way vegetation transmissivity multiplied by the difference between soil backscatter, which was directly related to the changes in soil moisture. Considering the retrieved soil roughness as a constant for the entire year, and the average unfrozen water content as the initial soil moisture, the changes in and absolute values of soil moisture for each observation can be estimated by minimizing a cost function between the modeled and observed changes in total backscatter. Retrieval results were validated against the soil moisture network in a semi-arid watershed of Shandian River with an unbiased Root Mean Square Error (ubRMSE) of 0.06 cm3·cm−3, and a bias of −0.04 cm3·cm−3. With the initial soil moisture from in-situ measurements, a higher accuracy was achieved for the MTDC algorithm (ubRMSE = 0.06 cm3·cm−3; bias = −0.01 cm3·cm−3). Further, comparisons with Soil Moisture Active Passive (SMAP) enhanced products further verified the efficacy of the proposed algorithm (ubRMSE = 0.03 cm3·cm−3; bias = −0.004 cm3·cm−3), and demonstrated the feasibility of operational soil moisture products using the Sentinel-1 radar dataset.

The method of forest change detection using Sentinel-2 optical satellite imagery and Sentinel-1 radar imagery: A case study in Dak Nong Province, Vietnam

Abstract. Bao TQ, Thi NV, Doanh LS, Duan PV, Khang LN, Cuong NT, Hieu BT, Mai DTT, Tuyen DV. 2022. The method of forest change detection using Sentinel-2 optical satellite imagery and Sentinel-1 radar imagery: A case study in Dak Nong Province, Vietnam. Biodiversitas 23: 4800-4809. This study presents the findings of forest change detection in Dak Nong Province, Vietnam by combining the NDVI of Sentinel-2 satellite imagery and the backscatter (BKS) values of VH and VV polarizations from the first quarter of 2020 to the first quarter of 2021 and using the combination model of BKS and NDVI (CMB) to determine the NDVI and Backscatter Change Index (NBCI) between the two periods, which were processed and analyzed in Google Earth Engine. The verification results on 270 samples for all 3 indices show that the results of forest loss detection using NBCI index, NDVI index, and BKS index reach 93.3%, 83.3% and 73.3%, respectively; the results of identifying the areas with no forest change using NBCI index, NDVI index, and BKS index achieve 97.8%, 92.2% and 83.3%, respectively; the results of forest increase detection using NBCI index, NDVI index, and BKS index reach 90.0%, 85.6% and 68.9%, respectively. The NBCI index for Dak Nong Province indicated that during the study period, the the province lost 1,198.9 hectares forest area, whereas it gained 871.3 hectares of the same.

Spatial-social evaluations of ecosystem services of adaptive aquaculture models using SAR and multivariate analyses: a case in the Vietnamese Mekong Delta.

The presented study is conducted to investigate the efficiency of two important aquaculture models of the Vietnamese Mekong Delta (VMD)'s Soc Trang province via quantifying and mapping the supporting ecosystem services (ES). The study targets the two most prevalent rearing practices, intensive and semi-intensive, covering four rural districts: My Xuyen, Tran De, Cu Lao Dung, and Vinh Chau. A mixed-method approach was applied, combining remote sensing, grass-root social survey, and multivariate statistical analyses. First, image analysis using Sentinel-1A time-series data was conducted to detect the aquaculture areas across the study area based on temporal changes of VV backscatter of different land use/land cover (LULC) types, in which aquaculture receives relatively low backscatter values compared to other LULC categories except river and deeper water surfaces. Our analysis yields an overall accuracy of 91% with a kappa coefficient of 0.82. Second, using semi-structured questionnaires, a total of 140 shrimp farming households across the four focused districts were interviewed for their rearing experience. Thereupon, the collected responses were analyzed using two multivariate analyses, including principal component analysis (PCA) and hierarchical cluster analysis (HCA). In general, the intensive model could have generated more economic values of ecosystem services than the artisanal/semi-intensive model. Our analyses also took note of the potential barriers hindering the semi-intensive farmers from achieving higher economic income. These include (i) geographical factors, i.e., locations; (ii) social factors, i.e., experience, farming calendar, education; (iii) financial factors, i.e., investments; and (iv) technical factors, i.e., farm areas, productivity, rearing concentration. Since semi-intensive food is more appropriate for small-scale farming households, it is recommendable that addressing these factors can enhance the efficiency of this model as a profitable livelihood option.

Seasonal change of multifrequency backscatter in three Baltic Sea habitats

This study investigated the seasonality of acoustic backscatter intensities, exploring three habitats in the southwestern Baltic Sea: 1) a mussel-covered reef, 2) coarse sand and gravel, and 3) seagrass meadows. Backscatter information of different, partly calibrated frequencies (200, 400, 550, and 700 kHz) was collected in three seasons (May, August, and October). The acoustic data were supported by point samples and video profiles for grain size and benthic community analysis. Angular response curves helped to quantify the seasonal backscatter response of the different frequencies. The multifrequency and multiseasonal backscatter maps distinguish the three habitats and reveal variable seasonal differences in acoustic backscatter, but not all changes in the benthic community can be recognized in the acoustic data. 1) The high-backscatter response of the mussel-covered reef shows little seasonal differences and was frequency independent. 2) The ecologically valuable coarse sand and gravel areas show small-scale seasonal alterations in the sediment composition and morphology, mainly caused by changes in local hydrodynamics. Higher frequencies were found best suited to identify coarse sand and gravel. 3) Seagrass meadows seasonality is dominated by growth of seagrass blades, increasing the backscatter response compared to bare sand. The use of multiple frequencies is beneficial as the low frequency is sensitive to changes in the shallow subsurface and benthic features such as seagrass rhizomes, while the higher frequency highlights changes related to coarser sediment.

Age-related changes in dynamic corneal backscatter observed in Scheimpflug imaging.

Corneal visualization Scheimpflug technology (Corvis ST) has the potential to indirectly provide information on corneal structure via the analysis of statistical properties of the backscatter. The aim of this work was to ascertain whether there are age-related changes in the dynamics of corneal backscatter during an air-puffed induced corneal deformation. Retrospective data from Corvis ST measurements of 151 young subjects (19-30 years) and 82 older subjects (50-87 years) were considered. Each measurement consisted of 140 frames (sampling frequency: 4330 fps). For every frame the cornea was first segmented, then regions of interest, encompassing temporal, central and nasal parts of cornea were selected, to which the parameters of Weibull distribution (scale and shape) were fitted, leading to time series of the estimated parameters. Apparent differences were found between the parameters of Weibull distribution between the two considered groups that manifest themselves mostly in the nasal region of the cornea. However, those differences cannot be attributed to the age alone. For this, a normalization method is proposed that leads to a much better separation between the groups in all considered regions. Clinical Relevance- The parameters of the corneal backscat-ter are widely used to asses corneal clarity (so-called corneal densitometry). Recently the parameters of Weibull distribution fitted to the corneal backscatter data have been used to support diagnosis of keratoconus. This work contributes to the assessment of corneal clarity by identifying the apparent age-related differences in those parameters when dynamic raw data is considered highlighting the need for such parameters to be appropriately normalized. Further it shown that the shape parameter of Weibull distribution unlike the scale parameter carries that information already for the raw non-normalized data.

Use of calculations to validate beam quality and relative dose measurements for a kilovoltage X-ray therapy unit.

Relative dosimetry measurements are required to fully commission kilovoltage X-ray units for superficial and orthovoltage X-ray therapy. Validation of these relative dosimetry measurements with Monte Carlo methods is advantageous being independent of the measurement process. In this study use is made of the X-ray spectrum generating program SpekPy along with the EGSnrc Monte Carlo code to calculate depth doses and explore the dosimetry effect of changes in backscatter. These calculations are compared with previously reported measurements for the Pantak SXT 150 X-ray therapy unit. SpekPy can also be used to generate half value layer (HVL) values and these are also compared to previously reported HVL measurements for the same X-ray therapy unit. It was found that agreements of the order of 5% in HVL, 3% in depth dose and 1% in backscatter doses were found between Monte Carlo calculations and the previously published measured data. Exit doses in conditions of lack of full backscatter were explored with Monte Carlo calculations demonstrating reduced exit dose up to 20% in these conditions. It is concluded that SpekPy with Monte Carlo codes such as EGSnrc provides a straightforward approach to validating various relative dosimetry measurements in kilovoltage X-ray dosimetry.

Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps

Abstract. Seasonal snow is an essential water resource in many mountain regions. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) at regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We investigate the ability of the Sentinel-1 mission to monitor snow depth at sub-kilometer (100 m, 500 m, and 1 km) resolutions over the European Alps for 2017–2019. The Sentinel-1 backscatter observations, especially in cross-polarization, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in an empirical change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. Compared to in situ measurements at 743 sites in the European Alps, dry snow depth retrievals at 500 m and 1 km resolution have a spatio-temporal correlation of 0.89. The mean absolute error equals 20 %–30 % of the measured values for snow depths between 1.5 and 3 m. The performance slightly degrades for retrievals at the finer 100 m spatial resolution as well as for retrievals of shallower and deeper snow. The results demonstrate the ability of Sentinel-1 to provide snow estimates in mountainous regions where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resource management, flood forecasting, and numerical weather prediction. However, future research is recommended to further investigate the physical basis of the sensitivity of Sentinel-1 backscatter observations to snow accumulation.

Sensitivity of Multifrequency Polarimetric SAR Data to Postfire Permafrost Changes and Recovery Processes in Arctic Tundra

We used full-polarimetric L-band and P-band synthetic aperture radar (SAR) data collected from the recent NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign and Sentinel-1 C-band dual-polarization data to understand the sensitivity of radar backscatter intensity and phase to fire-induced changes in the surface and subsurface soil processes in Arctic tundra underlain by permafrost. The 2007 Anaktuvuk River fire on the Alaska North Slope was used as a case study. At ~10-year postfire, we observed a strong increase (>~3–4 dB) in the low-frequency radar backscatter in severely burned areas during the thaw season, in contrast to limited (< ~0.5 dB) C-band backscatter differences (VV and VH) between burned and unburned areas. However, C-band winter backscatter is generally higher (>1 dB) in burned areas than the adjacent unburned areas. Polarimetric decomposition analysis indicated a general trend toward more random surface scattering, and strong increases in double-bounce scattering and volume scattering power at both P- and L-band in the burned areas. The ice-rich yedoma region shows the largest backscatter increases in burned areas and the highest correlation with burn severity and microtopography changes. The above backscatter changes are attributed to increasing surface roughness and microtopography due to ice-wedge degradation and thermokarst development and increasing subsurface scattering due to an overall drier and deeper active layer in burned areas. Among all frequencies, P-band shows consistently larger contrast in backscatter power and phase between burned and unburned areas, which makes it potentially more useful to study fire–permafrost interactions in the Arctic over decadal time scales.