Backscatter Intensity Research Articles

Exploring the Relationship Between Time Series of Sentinel-1 Interferometric Coherence Data and Wild Edible Mushroom Yields in Mediterranean Forests

Edible wild mushrooms constitute a valuable marketable non-wood forest product with high relevance worldwide. There is growing interest in developing tools for estimation of mushroom yields and to evaluate the effects that global change may have on them. Remote sensing is a powerful technology for characterization of forest structure and condition, both essential factors in triggering mushroom production, together with meteo-climatic factors. In this work, we explore options to apply synthetic aperture radar (SAR) data from C-band Sentinel-1 to characterize, at the plot level, wild mushroom productive forests in the Mediterranean region, which provide saprotroph and ectomycorrhizal mushrooms. Seventeen permanent plots with mushroom yield data collected weekly during the productive season are characterized with dense time series of Sentinel-1 backscatter intensity (VV and VH polarizations) and 6-day interval interferometric VV coherence during the 2018–2021 period. Weekly-regularized series of SAR data are decomposed with a LOESS approach into trend, seasonality, and remainder. Trends are explored with the Theil-Sen test, and periodicity is characterized by the Discrete Fast Fourier transform. Seasonal patterns of SAR time-series are described and related to mycorrhizal and saprotroph guilds separately. Our results indicate that time series of interferometric coherence show cyclic patterns which might be related with annual mushroom yields and may constitute an indicator of triggering factors in mushroom production, whereas backscatter intensity is strongly correlated with precipitation, making noisy signals without a clear interpretable pattern. Exploring the potential of remotely sensed data for prediction and quantification of mushroom yields contributes to improve our understanding of fungal biological cycles and opens new ways to develop tools that improve its sustainable, efficient, and effective management.

Integrating Multisource Data and Machine Learning for Supraglacial Lake Detection: Implications for Environmental Management and Sustainable Development Goals in High Mountainous Regions

The accurate detection and monitoring of supraglacial lakes in high mountainous regions are crucial for understanding their dynamic nature and implications for environmental management and sustainable development goals. In this study, we propose a novel approach that integrates multisource data and machine learning techniques for supra-glacial lake detection in the Passu Batura glacier of the Hunza Basin, Pakistan. We extract pertinent features or parameters by leveraging multisource datasets such as radar backscatter intensity VH and VV parameters from Sentinel-1 Ground Range Detected (GRD) data, near-infrared (NIR), NDWI_green, NDWI_blue parameters from Sentinel-2 Multi-spectral Instrument(MSI) data, and surface slope, aspect, and elevation parameters from topographic data. The entire dataset is partitioned into training and testing sets, with machine learning models including the artificial neural network (ANN), the support vector machine (SVM), logistic regression (LR), random forest (RF), and K-nearest neighbour (KNN) trained on the training data (70%). Accuracy assessment employs testing data and involves the evaluation of metrics such as ROC curves and confusion matrices. The best-performing model, ANN, is validated against manually digitized lake polygons derived from Sentinel-2 and Google Earth Pro imagery. Furthermore, the digitized lake polygons are used to analyze glacial lake dynamics from 2016 to 2022. Key findings of this research presented that the NDWI_green, Sigma0_VH, and elevation are the most significant predictors in detecting supra-glacial lakes. Among the various trained and evaluated models, the Artificial Neural Network (ANN) achieved the highest performance (accuracy: 95%, AUC: 0.99) and accurately mapped supra-glacial lakes regardless of their small size. The findings have significant implications for understanding glacial lake behavior in the context of climate change and informing future research and monitoring efforts.

Resource abundance of cobalt-rich ferromanganese crusts in the KC-8 seamount, West Pacific

We have estimated the resource potential of cobalt-rich ferromanganese crusts (CFCs) on the summit area of the KC-8 seamount in the West Pacific, based on the acoustic backscatter intensity (ABI), ROV video footage, and CFC thickness measurements. To estimate the spatial distribution of CFC across 13 equally divided blocks within a 360 km2 area, shipboard and deep-tow ABIs were compared with visual data. The presence of CFCs is evident in the areas where ABI is higher than average, validating the use of ABI data to delineate the exposure of CFCs. With high accuracy in distinguishing between CFCs and sediment-dominant areas, the distribution area of CFCs was precisely calculated. The variation in CFC thickness across the KC-8 seamount summit was measured through CFC samples retrieved using a CFC corer. CFC thickness is highly variable even between adjacent sites, but it shows a roughly increasing trend from south to northwest. Based on the CFC distribution area associated with thickness, we calculated a CFC resource abundance. Of the estimation, 82% have remained on the gentle slope (<10°) areas, which can be accessible for future mining. Overall, our study suggests that the combination of an acoustic survey with reliable thickness information is highly effective in estimating CFC abundance.

A Bioengineered Cathepsin B-sensitive Gas Vesicle Nanosystem That Responds With Increased Gray-level Intensity of Ultrasound Biomicroscopic Images

ObjectivesThis work aimed to promote the interaction of a modified Gas Vesicle (GV) with Cathepsin B (CTSB) protease and analyzed their backscattered signal by Ultrasound (US). MethodsWe modified the sequence of the gene coding for GvpC to contain a CTSB cleavage and expressed the protein in an Escherichia coli recombinant system. The protein was purified and added to GVs preparations in which the original GvpC was removed (ΔGV), constituting the modified GV (GV*). Western Blot (WB) test was used to compare GVs with GvpC and engineer GvpC at starting (T0) and after 24 hours (T24) reacting with CTSB. A 21 MHz US B-mode and nonlinear contrast-mode (5% total power) imaged US phantoms having samples of GVwt, ΔGV (stripped GV), GV*, and CTSB+GV*. Also, a 21MHz US B-mode imaged US phantoms having a tumor cell line extracellular fraction (TCEF) and the TCEF+GV* sample. A 100% total US power was applied to collapse the GV structure. ResultsIn WB we detected a decrease in engineered GvpC levels 24 h after the incubation of GV* with CTSB, compared to the concentration at T0, suggesting that CTSB cleaved the engineered GvpC. Regions-of-interest over image of phantom cross-sections were determined and the B-mode image mean gray level intensity resulted in a significant (p<0.05) increase comparing CTSB+GV* with PBS (control), GVwt, ΔGV and GV*. Non-linear mode image gray level intensity from CTSB+GV* increased by 11.79, 7.86 and 14.75 dB from samples containing GVwt, ΔGV and GV*, respectively. GV preparations incubated with TCEF and the TCEF+GV* sample showed an increase of 81% in signal compared to TCEF+GVwt. ConclusionsThe increased ultrasound backscattered signal intensity suggests GVs as a potential biosensor for protease activity, possibly aiding the detection of protease-rich tissue regions.

Incidence angle dependency and seasonal evolution of L and C-band SAR backscatter over landfast sea ice

Abstract We estimate sea-ice type specific incidence angle (IA) dependencies for dual polarized (HH/HV) L and C-band synthetic aperture radar (SAR) for the winter, melt onset and advanced melt seasons for level and deformed ice, using time-series of Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1 imagery off the north-east coast of Greenland. The IA dependencies are used to radiometrically correct the L and C-band backscatter time-series, which enables analysis of their seasonal evolution. From this, we observe that the L-band backscatter intensity increases for both ice types at the transition from winter to melt onset. We use these results to estimate ice type separability and to train an IA aware Bayesian classifier at both frequencies. These results show that while both frequencies are capable of distinguishing level and deformed ice during the winter, only L-band SAR can reliably make this separation during the melt onset season. During the advanced melt season, the overall classification accuracies are similarly low for L and C-band. This study demonstrates the potential of L-band SAR for sea-ice mapping, which is highly relevant in the light of several upcoming L-band SAR missions.

Dynamics of Bubble Plumes Produced by Breaking Waves

Abstract Bubble plumes play a significant role in the air–sea interface by influencing processes such as air–sea gas exchange, aerosol production, modulation of oceanic carbon and nutrient cycles, and the vertical structure of the upper ocean. Using acoustic Doppler current profiler (ADCP) data collected off the west coast of Ireland, we investigate the dynamics of bubble plumes and their relationship with sea state variables. In particular, we describe the patterns of bubble plume vertical extension, duration, and periodicity. We establish a power-law relationship between the average bubble penetration depth and wind speed, consistent with previous findings. Additionally, the study reveals a significant association between whitecapping coverage and observed acoustic volume backscatter intensity, underscoring the role of wave breaking in bubble plume generation. The shape of the probability distribution of bubble plume depths reveals a transition toward stronger and more organized bubble entrainment events during higher wind speeds. Furthermore, we show that deeper bubble plumes are associated with turbulent Langmuir number Lat ∼ 0.3, highlighting the potential role of Langmuir circulation on the transport and deepening of bubble plumes. These results contribute to a better understanding of the complex interactions between ocean waves, wind, and bubble plumes, providing valuable insights for improving predictive models and enhancing our understanding of air–sea interactions. Significance Statement This research contributes to understanding bubble plume dynamics in the upper ocean and their relationship with sea state variables. The establishment of a power-law relationship between the bubble penetration depth and wind speed, along with the association between whitecapping coverage and acoustic backscatter intensity, contributes to improved predictive capabilities for air–sea interactions and carbon dioxide exchange. The identification of the potential influence of Langmuir circulation on bubble plume dynamics expands our understanding of the role of coherent circulations in transporting bubble plumes. Additionally, this study presents a clear methodology using commercial sensors such as an ADCP, which can be easily replicated by researchers worldwide, leading to potential advancements in our comprehension of bubble plume dynamics.

Sensitivity of backscattering to spherical particle physical properties: Size, refractive index, and shape deviations

The purpose of this paper is to study the dependency of light backscattering on particle physical properties: size, refractive index, and shape; and to demonstrate the extreme sensitivity of backscattering on these properties. We demonstrate how the nondescript backscattering pattern evolves seemingly chaotic, in contrast to the orderly forward scattering, with the particle's physical properties. The demonstration was carried out by light scattering from water droplets and with spherical particle Mie theory. Our results show systematic, descriptive evolution of the forward scattering pattern, which is clearly shown in logarithmic Q–space, whereas non-descriptive, chaotic evolution within the backscattering regime is displayed in linear θ–space. Additionally, our study shows that within the last 1°, the backscattering intensity remains constant and featureless for the ∼ 2.5 μm diameter water droplets investigated here.

Analysis of thermal instability for fiber backscatter phase and intensity.

In this Letter, we utilize the speckle model to measure the average random scattering rate of fiber backscatter and analyze its dependence on length, yielding a linear fitting coefficient of 0.23 ppm/m for a PM980-XP fiber. We incorporate the temperature coupling effect into the model and validate the model's accuracy by examining the distribution of the change rate of the backscattering rate relative to the temperature and the amplitude spectral density of the backscattered power. Our findings demonstrate that the typical shoulder-shaped noise in interferometer experiments is limited by stray light, and the dependence of shoulder-shaped noise on the fiber length and temperature noise level is analyzed.

Geological and oceanographic constrains on the deposit of ferromanganese nodules on the archipelagic aprons of seamounts

The archipelagic aprons of the large deep-sea seamounts of the northwestern Pacific Ocean (NWPO) show potential areas for significant reserves of ferromanganese nodules (FMNs). This study used datasets such as depth, backscatter intensity (BI), and optical coverage in conjunction with mineralogical, element geochemical, Sr-Nd-Pb isotopic, and chronological analyses of FMNs of the Suda Guyot (SG), which was located on the central area of the Marcus-Wake seamounts, in the NWPO. The results indicated a Y-shaped distribution of the deposit on the northern apron of the SG. Landslides predated the mineralization processes of the FMN deposit, and the ubiquitous channels in the apron had largely minimal influence on the distribution of nodules. Current mineralization of the deposit has been ongoing for ∼10 Myrs. Continuous weakening of the Antarctic Bottom Water (AABW) resulted in a gradual decrease in bottom water oxygen contents around the SG. This in turn resulted in a decrease in cryptocrystalline Fe-vernadite (δ-MnO2) and elemental contents associated with δ-MnO2 of FMNs, such as Mo, Te, and Tl. Meanwhile, the contribution of Asian dust to the study area increased, leading to increased Fe, which in turn increased amorphous ferrihydrite (FeOOH), and FeOOH-associated elements such as Ti, Pb, and Th. Productivity gradually increased to its peak value around 4–5 Myrs ago, leading to similar trends in REY, Ba, and U. REY contents exhibited a certain correlation with water depth around the SG. The results of this study suggest that the Carbonate Compensation Depth (CCD) variation resulted in higher content of REY of the FMNs in the shallower apron.

Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels

Accurately measuring river width has been one of greatest challenges due to the presence of mixed land–water pixels intersecting river boundaries. Therefore, this study proposed a novel mechanical method (RW-vebasud), instead of traditionally empirical models, to estimate river width within a pixel in vegetation areas based on time series analysis of Sentinel-1 and Sentinel-2 spaceborne multispectral images. We initially explored the mechanism of variation in backscatter intensity (σ) with enhanced vegetation index (EVI) whereby we successfully removed noises in σ–EVI relationship resulted from vegetation growth. Then, for the first time a smooth functional relationship between water area proportion and backscatter intensity within a ROI (or region of interest) was constructed. Consequently, subpixel water–land separation based on the mechanism process was realized. The novel method could not only work at large-scaled rivers (satellite-visible) but perform well at small-scaled rivers within a water-land mixed pixel (satellite-invisible). A total of 197 measurements for river widths in China during 2016 ∼ 2021 were used for model verification, demonstrating a positive correlation between EVI and σ, with R2 ranging from 0.16 to 0.69 (P<0.05). The RW-vebasud exhibited superior accuracy in calculating river width compared to the widely used MNDWI (modified normalized difference water index). The Root Mean Square Error (RMSE) decreased by 4.32 ∼ 6.65 m when the river width was less than 90 m and by 66.12 % when it exceeded 90 m, compared to MNDWI. Remarkably, RW-vebasud maintains satisfactorily high accuracy (the Nash-Sutcliffe efficiency coefficient: NSE=0.70 and RMSE=3.19) even at the spatial scale less than 3 times the image resolution, breaking the internationally accepted limit that river width extraction can only be accurate when the river width is greater than 3 times the satellite resolution. Moreover, the accuracy of this method is better than that with the currently well-known global river width datasets GRWL and MERIT Hydro. For the RW-Vebasud/GRWL/MERIT Hydro datasets, the NSE=0.99 /0.93/0.87, the RMSE=5.99/42.33/54.27, and the R2 = 0.99/0.91/0.74, respectively. The application of RW-vebasud in China shows that river widths in wet and dry seasons exhibited an increasing trend over the previous six years (2016–2021), as global warming accelerated glacier melting and increased rainfall quantity, with an average growth rate of 2.26 m/year (wet, P<0.05) and 2.17 m/year (dry, P<0.05), respectively. In response to the summer/winter Asian monsoons, most rivers widen in summer. The largest river width occurs in the Yellow River Basin (YLRB, 155.28 m on average), while the smallest occurs in the Hai River Basin (HARB, 22.99 m on average). The method proposed in this study can provide efficient techniques for surface river-width reconstruction which can greatly facilitate global resource and environmental modelling.

Multibeam water column image super-resolution by combining morphological and intensity features

ABSTRACT Multibeam water column images (WCI) play a crucial role in the detection and recognition of underwater targets. However, WCI is not a complete image of underwater space due to the gaps between beams inherent in multibeam systems. In this study, we design the WCI super-resolution network (WCISRN) based on Swin-transformer to adapt to the uneven and sparse distribution of water column data. To enable WCISRN to learn morphological features effectively, we produced simulated datasets for pre-training. Subsequently, we conduct self-supervised training on the measured WCI dataset to fine-tune the network parameters, aiming to better adapt WCISRN to the backscattering intensity features of WCI. Finally, by performing network cross-fusion, the pre-trained network and self-supervised training network are combined to form a new network that achieves a balance between image morphology and intensity features. The proposed method is evaluated through visual analysis and quantitative comparison using simulated and real-world datasets. The experimental results demonstrate that our method exhibits stronger detail restoration ability and clearer image morphology. The average peak signal-to-noise ratio (PSNR) on the test set improved from 23.75–26.14, indicating a significant enhancement in the imaging quality of WCI.

Evaluation of REFIX Technology on the Remineralization of Artificial Early Enamel Caries Lesion by Laser Speckle Tracking Analysis.

To assess the effectiveness of the REFIX technology in the remineralization process of initial caries simulated on bovine enamel. The assessment involved the analysis of backscatter intensity, which was determined from laser speckle images. Twenty-one bovine teeth were divided into three groups: G1 and G7 were submitted to treatment with the REFIX technology for 1 and 7 days, respectively. The control group was treated with deionized water. A significant difference in backscatter was found between the carious and sound areas in all groups (p = 0.0038, p < 0.0001, and p = 0.0002 for the control group, G1, and G7, respectively). The intergroup comparison revealed no significant difference among the groups studied. REFIX technology did not alter the optical properties of the samples of bovine teeth with simulated initial caries lesions after 1 and 7 days of treatment.

Multitemporal sentinel-1 SAR data sensitivity analysis at phenological growth stages of green gram

ABSTRACT This research investigated the green gram crop by employing a time series of Sentinel-1A Single Look Complex (SLC) and Sentinel-2 datasets, which corresponded to different phenological growth stages of the crop. Field campaigns were conducted on the acquisition dates of Sentinel-1A datasets for the entire crop cycle to estimate crop height and soil moisture. In line with these observations, temporal variations in 10 salient Synthetic Aperture Radar (SAR) parameters, obtained from Sentinel-1A datasets, are systematically analysed, which elucidated the importance of dual-polarized SAR parameters in the diverse phenological growth stages of the crop. Moreover, the Normalized Difference Vegetation Index (NDVI) values from Sentinel-2 datasets, estimated at various green gram growth stages, are categorized into initial (NDVI ≤ 0.5), intermediate (0.5 < NDVI < 0.8), and maximum growth of the crop (NDVI ≥ 0.8), which are then aligned with corresponding SAR and ground-measured parameters. The inversion model development and sensitivity analysis were carried out to correlate ground-measured parameters with SAR parameters across various growth stages based on the categorization of NDVI values as well as for the entire growing season of the crop. Backscattering intensities are found to have good sensitivity to crop height at its intermediate growth (r ≥ 0.75). Among vegetation indices, the Polarimetric Radar Vegetation Index (PRVI) showed significant sensitivity to crop height in its intermediate growth stage (r ≈ 0.77). H-A-Alpha decomposition parameters displayed notable sensitivity to soil moisture in the initial and maximum growth stages of green gram. This study demonstrates the effectiveness of Sentinel-1 dual-polarized SAR parameters in monitoring green gram phenological growth stages. The parameters show substantial sensitivity to both green gram height and soil moisture.

Light Scattering Measurements of KCl Particles as an Exoplanet Cloud Analog

Salt clouds are predicted to be common on warm exoplanets, but their optical properties are uncertain. The Exoplanet Cloud Ensemble Scattering System (ExCESS), a new apparatus to measure the scattering intensity and degree of linear polarization for an ensemble of particles, is introduced here and used to study the light scattering properties of KCl cloud analogs. ExCESS illuminates particles with a polarized laser beam (532 nm) and uses a photomultiplier tube detector to sweep the plane of illumination. Scattering measurements for KCl particles were collected for three size distributions representative of modeled clouds for the warm exoplanet GJ 1214b. Our measurements show that Lorenz–Mie calculations, commonly used to estimate the light scattering properties of assumedly spherical cloud particles, offer an inaccurate depiction of cubic and cuboid KCl particles. All of our measurements indicate that Lorenz–Mie scattering overestimates the backscattering intensity of our cloud analogs and incorrectly predicts the scattering at mid-phase angles (∼90°) and the preferential polarization state of KCl scattered light. Our results align with the general scattering properties of nonspherical particles and underscore the importance of further understanding the effects that such particles will have on radiative transfer models of exoplanet atmospheres and reflected light observations of exoplanets by the upcoming Nancy Grace Roman Space Telescope and Habitable Worlds Observatory.

Exploring Distributed Scatterers Interferometric Synthetic Aperture Radar Attributes for Synthetic Aperture Radar Image Classification

Land cover classification of Synthetic Aperture Radar (SAR) imagery is a significant research direction in SAR image interpretation. However, due to the unique imaging methodology of SAR, interpreting SAR images presents numerous challenges, and land cover classification using SAR imagery often lacks innovative features. Distributed scatterers interferometric synthetic aperture radar (DS-InSAR), a common technique for deformation extraction, generates several intermediate parameters during its processing, which have a close relationship with land features. Therefore, this paper utilizes the coherence matrix, the number of statistically homogeneous pixels (SHPs), and ensemble coherence, which are involved in DS-InSAR as classification features, combined with the backscatter intensity of multi-temporal SAR imagery, to explore the impact of these features on the discernibility of land objects in SAR images. The results indicate that the adopted features improve the accuracy of land cover classification. SHPs and ensemble coherence demonstrate significant importance in distinguishing land features, proving that these proposed features can serve as new attributes for land cover classification in SAR imagery.

How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and InSAR Coherence across Multiple Years and Orbits

Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant’s phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape.

Field comparison of Antarctic krill (Euphausia superba) backscatter and aggregation types using NORTEK and SIMRAD echosounders

Abstract Temporal distributions of Antarctic krill (Euphausia superba) density and aggregation types were characterized and compared using Nortek Signature100 and SIMRAD Wideband Autonomous Transceiver (WBAT) upward-looking echosounders. Noise varied between the two echosounders. With the Signature100, it was necessary to correct data for background, transient, and impulse noises, while the WBAT data needed to be corrected for background noise only. For selected regions with no visible backscatter, the signal-to-noise ratio of Sv values (i.e. the ratio between the signal and the background noise level) did not vary between the two echosounders. Surface echo backscatter was similar during similar time periods. Descriptive metrics were used to quantify spatial and temporal krill vertical distributions: volume backscatter, mean depth, center of mass, inertia, equivalent area, aggregation index, and proportion occupied. Krill backscatter density differed between the two instruments but was detected at similar mean depths. Krill aggregations were identified at each mooring location and classified in three types based on morphological characteristics. Each type of aggregation shape differed at the two spatially separated moorings, while the acoustic density of each aggregation type was similar. The Signature100 detected a lower number of krill aggregations (n = 133) compared to the WBAT (n = 707). Although both instruments can be used for autonomous deployment and sampling of krill over extended periods, there is a strong caveat for the use of the Signature100 due to significant differences in noise characteristics and krill detection.

Using SAR Data for Monitoring of Agricultural Crops in the South of the Russian Far East

The use of SAR data to monitoring agricultural crops is a promising area of research designed to complement existing methods and technologies based on the analysis of multispectral images. The main advantages of vegetation indices calculated from SAR data include their sensitivity to the polarimetric properties of the backscatter intensity, its scattering characteristics, and independence from cloud cover. This is especially important for the territory of the south of the Russian Far East, whose monsoon climate provides humid and cloudy weather during the period when crops gain maximum biomass. For arable lands in the Khabarovsk Territory and the Amur Region, a total of 64 Sentinel-1 SAR images were obtained from May to October 2021. For each date, the values of the DpRVI, RVI, VH/VV indices were calculated and time series were constructed for the entire observation period for individual fields (342 fields in total). NDVI time series were constructed from Sentinel-2 multispectral images using a cloud mask. The characteristics of time series extremes were calculated for different types of arable land: soybeans, oats, and fallows. It was shown that for each crop the seasonal curves DpRVI, RVI, VH/VV had a characteristic appearance. It was found that the DpRVI demonstrated the highest stability – the coefficients of variation of the seasonal variation of DpRVI were significantly lower than those for RVI and VH/VV. It was also revealed that the similarity between the curves of these indices remained for regions quite distant from each other - the Khabarovsk Territory and the Amur Region. The main characteristics of the seasonal variation of time series of radar indices were calculated in comparison with NDVI - the magnitude of the maximum, the date of the maximum and the values of the coefficient of variation for these indicators. It was found, firstly, that the values of these indicators in different regions are similar to each other; secondly, the variability of the maximum and the day of the maximum for DpRVI is lower than for RVI and VH/VV; thirdly, the variability of the maximum and the day of the maximum for DpRVI is comparable to NDVI. Thus, time series of radar indices DpRVI, RVI, VH/VV for the main types of agricultural lands in the south of the Far East have distinctive features and can be used in classification problems, yield modeling and crop rotation control.

A temporal difference matrix for historical cumulative change detection in time series PolSAR data

To detect historical cumulative changes in land cover, this study introduces a novel time series PolSAR change detection method. The objective is to address the inefficiency of traditional detection methods that use multiple pairwise comparisons, as well as to alleviate false positives and false negatives caused by the insufficient utilization of polarization and spatial context information in previous methods. The proposed method initially constructs a temporal difference matrix for time series PolSAR images and computes the difference image (DI) using the maximum eigenvalue of this matrix to depict the cumulative changes over time. Subsequently, the DI is segmented using an MRF-based image segmentation method with a limited amount of supervised information, yielding the cumulative change binary map. The efficacy of the proposed method is validated using three time series datasets covering different scenes acquired by different sensors, namely GaoFen3, Radarsat2 and Sentinel-1. The experimental results demonstrate that the method effectively characterizes cumulative changes in the time series while simultaneously considering disparities in radar backscatter intensity and polarization correlation. As a result, it significantly reduces erroneous and missed change detections and surpasses the existing methods by achieving the highest Recall, F1-score, IOU, Kappa, and overall accuracy. Furthermore, the DI calculation method serves as a versatile change descriptor applicable not only for capturing cumulative changes in time series but also for describing changes between dual temporal images.

Full-Scale Aggregated MobileUNet: An Improved U-Net Architecture for SAR Oil Spill Detection.

Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneous detections of oil spills. In addition, SAR images of the ocean include multiple targets, such as sea surface, land, ships, and oil spills and their look-alikes. The training of a multi-category classifier will encounter significant challenges due to the inherent class imbalance. Addressing this issue requires extracting target features more effectively. In this study, a lightweight U-Net-based model, Full-Scale Aggregated MobileUNet (FA-MobileUNet), was proposed to improve the detection performance for oil spills using SAR images. First, a lightweight MobileNetv3 model was used as the backbone of the U-Net encoder for feature extraction. Next, atrous spatial pyramid pooling (ASPP) and a convolutional block attention module (CBAM) were used to improve the capacity of the network to extract multi-scale features and to increase the speed of module calculation. Finally, full-scale features from the encoder were aggregated to enhance the network's competence in extracting features. The proposed modified network enhanced the extraction and integration of features at different scales to improve the accuracy of detecting diverse marine targets. The experimental results showed that the mean intersection over union (mIoU) of the proposed model reached more than 80% for the detection of five types of marine targets including sea surface, land, ships, and oil spills and their look-alikes. In addition, the IoU of the proposed model reached 75.85 and 72.67% for oil spill and look-alike detection, which was 18.94% and 25.55% higher than that of the original U-Net model, respectively. Compared with other segmentation models, the proposed network can more accurately classify the black regions in SAR images into oil spills and their look-alikes. Furthermore, the detection performance and computational efficiency of the proposed model were also validated against other semantic segmentation models.