Dual-polarization Synthetic Aperture Radar Research Articles

A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems

ABSTRACT This paper investigates the capability of the free synthetic aperture radar (SAR) Sentinel-1 (S-1) C-band data for burned area mapping through unsupervised machine learning open-source processing solutions in the Mediterranean forest ecosystems. The study was carried out in two Mediterranean sites located in Portugal (PO) and Italy (IT). The entire processing workflow was developed in Python-based scripts. We analyzed two time-series covering about one month before and after the fire events and using both VH and VV polarizations for each study site. The speckle noise effects were reduced by performing a multitemporal filter and the backscatter time averages of pre- and post-fire datasets. The spectral contrast between changed and unchanged areas was enhanced by calculating two single-polarization radar indices: the radar burn difference (RBD) and the logarithmic radar burn ratio (LogRBR); and two temporal differences of dual-polarimetric indices: the delta modified radar vegetation index (ΔRVI) and the delta dual-polarization SAR vegetation index (ΔDPSVI), all exhibiting greater sensitivity to the backscatter changes. The scene’s contrast was enhanced by extracting the Gray Level Co-occurrence Matrix (GLCM) textures (dissimilarity, entropy, correlation, mean, and variance). A principal component analysis (PCA) was applied for reducing the number of the GLCM image layers. The burned area was delineated through unsupervised classification using the k-means clustering algorithm. A suitable number of clusters (k value) were set using a silhouette score analysis. To assess the accuracy of the resulting detected burned areas, an official burned area map based on multispectral Sentinel-2 (S-2) was used for PO, while for IT, a reference map was produced from S-2 data, based on the normalized burned ratio difference (ΔNBR) index. Recall (r), precision (p) and the F-score accuracy metrics were calculated. Our approach reached the values of 0.805 (p), 0.801 (r) and 0.803 (F-score) for PO, and 0.851 (p), 0.856 (r) and 0.853 (F-score) for IT. These results confirm the suitability of our approach, based on SAR S-1 data, for burned area mapping in heterogeneous Mediterranean ecosystems. Moreover, the implemented workflow, completely based on free and open-source software and data, offers high adaptation flexibility, repeatability, and custom improvement.

Dual-Polarized SAR Ship Grained Classification Based on CNN With Hybrid Channel Feature Loss

This letter proposes a novel convolutional neural network (CNN) method for dual-polarized synthetic aperture radar (SAR) ship grained classification. The network employs hybrid channel feature loss that jointly utilizes the information contained in the polarized channels (VV and VH). It is demonstrated that, by adopting the proposed CNN framework and the novel loss function, the classification performance can be efficiently improved. First, instead of the prevalently used threefold or fourfold division (container ship, oil tanker, bulk carrier, and so on), the proposed method can further divide vessels into eight accurate categories. Second, this method can not only effectively classify targets into eight categories but also its accuracy in terms of fewer category classifications surpasses existing methods. Third, the method can achieve good performance on a small training data set. Experiments conducted on the OpenSARShip data sets indicate that the proposed classification method achieves state-of-the-art results.

A joint change detection method on complex-valued polarimetric synthetic aperture radar images based on feature fusion and similarity learning

ABSTRACT Existing deep learning-based change detection methods in the field of polarimetric synthetic aperture radar (POLSAR) usually directly deal with intensity images. Methods can be easily transferred from optical image processing to synthetic aperture radar (SAR) image processing. However, the polarization information, which is critical in POLSAR data, has been discarded under normal situations. This paper introduces a novel joint change detection network based on similarity learning for coregistered complex-valued SAR data. A pseudo-siamese network takes both amplitude information and polarization information of POLSAR data as the input. The fusion of low-level, middle-level and high-level features enables the network to keep high-resolution and have strong representation ability during training procedures. Our novel sub-networks, which we term C2-Net and Intensity-Net, deal with the covariance matrix of complex SAR data and amplitude SAR data, respectively. The Intensity-Net works as a typical classification network and detects targets directly. The C2-Net attempts to find the relationship between two SAR data patches. An improved cosine similarity function is used to measure the similarity between two generated feature vectors in C2-Net. Output probability vectors of the two sub-networks are combined for final change detection. The two sub-networks are trained jointly and simultaneously. Control experiments show that proposed improvements are working. Experimental results on dual-polarization complex-valued SAR data from Sentinel-1 demonstrate the effectiveness of the proposed method.

Potential impact of flooding on schistosomiasis in Poyang Lake regions based on multi-source remote sensing images

BackgroundFlooding is considered to be one of the most important factors contributing to the rebound of Oncomelania hupensis, a small tropical freshwater snail and the only intermediate host of Schistosoma japonicum, in endemic foci. The aim of this study was to assess the risk of intestinal schistosomiasis transmission impacted by flooding in the region around Poyang Lake using multi-source remote sensing images.MethodsNormalized Difference Vegetation Index (NDVI) data collected by the Landsat 8 satellite were used as an ecological and geographical suitability indicator of O. hupensis habitats in the Poyang Lake region. The expansion of the water body due to flooding was estimated using dual-polarized threshold calculations based on dual-polarized synthetic aperture radar (SAR). The image data were captured from the Sentinel-1B satellite in May 2020 before the flood and in July 2020 during the flood. A spatial database of the distribution of snail habitats was created using the 2016 snail survey in Jiangxi Province. The potential spread of O. hupensis snails after the flood was predicted by an overlay analysis of the NDVI maps in the flood-affected areas around Poyang Lake. The risk of schistosomiasis transmission was classified based on O. hupensis snail density data and the related NDVI.ResultsThe surface area of Poyang Lake was approximately 2207 km2 in May 2020 before the flood and 4403 km2 in July 2020 during the period of peak flooding; this was estimated to be a 99.5% expansion of the water body due to flooding. After the flood, potential snail habitats were predicted to be concentrated in areas neighboring existing habitats in the marshlands of Poyang Lake. The areas with high risk of schistosomiasis transmission were predicted to be mainly distributed in Yongxiu, Xinjian, Yugan and Poyang (District) along the shores of Poyang Lake. By comparing the predictive results and actual snail distribution, we estimated the predictive accuracy of the model to be 87%, which meant the 87% of actual snail distribution was correctly identified as snail habitats in the model predictions.ConclusionsData on water body expansion due to flooding and environmental factors pertaining to snail breeding may be rapidly extracted from Landsat 8 and Sentinel-1B remote sensing images. Applying multi-source remote sensing data for the timely and effective assessment of potential schistosomiasis transmission risk caused by snail spread during flooding is feasible and will be of great significance for more precision control of schistosomiasis.Graphical

Covered Cracks Detection Using Dual-Polarization Synthetic Aperture Radar Imaging

Detecting covered cracks in metals is critical in many applications. In this article, a single dual-polarized circular aperture antenna and synthetic aperture imaging are devised to detect arbitrarily oriented cracks covered under thick dielectric layer. The devised monostatic quad-polarized system facilitates measurements of the reflection (copolarized) and transmission (cross-polarized) parameters which form the basis for reliable detection. Through analysis and measurements involving dielectric covers with thicknesses ranging from 4.25 to 12.7 mm, it will be demonstrated that the background clutter and the effect of the cover are largely suppressed in the images formed using the cross-polarized parameter. On the other hand, it will be highlighted that the crack could be actually masked by the clutter in the image formed using the reflection measurements commonly used for crack detection.

Crop Mapping Using Sentinel Full-Year Dual-Polarized SAR Data and a CPU-Optimized Convolutional Neural Network With Two Sampling Strategies

Although optical remote sensing can capture the Earth's environment with visible and infrared sensors, it is limited by weather conditions. Often, only a few sets of cloud-free optical imagery are available in cloudy regions, where many agricultural towns are located. On the other hand, radar remote sensing can capture imagery under cloudy conditions. In this study, we examined the capability of Sentinel-1 multitemporal dual-polarized synthetic aperture radar (SAR) imagery in a whole year from Google Earth Engine in crop mapping in two study sites in Chongqing, China, and Landivisiau, France. Results show that it is possible to produce better crop classification maps using multitemporal SAR imagery, but the performance is limited by local terrain. Flat agricultural regions, such as Western Europe, are expected to benefit from the multitemporal SAR information. Mountain agricultural regions, such as Southwestern China, will encounter difficulties due to the undulate terrain. We also tested two sampling strategies, i.e., random sampling and regional sampling, and observed high variation in overall accuracy: the former led to a higher accuracy. The gap is caused by the diversity of training sets examined using tSNE visualization. The importance of SAR channels in each month are correlated with their entropy. Data from the growing season are important in distinguishing crop types. The 3-D convolutional neural network (CNN) achieved similar results under a huge computation cost compared with 2-D CNNs. Based on the experiments, we recommend to use a lightweight 2-D CNN that can run on the CPU for real-world crop mapping with SAR data.

ОЦІНКА ПАРАМЕТРІВ ШОРСТКОСТІ ЗЕМНОЇ ПОВЕРХНІ ЗА ДВОХПОЛЯРИЗАЦІЙНИМ РАДІОЛОКАЦІЙНИМ ВІДБИТТЯМ МЕТОДОМ ЗВОРОТНОГО МОДЕЛЮВАННЯ

Радіолокаційне дистанційне зондування є сучасним та ефективним методом аерокосмічних досліджень Землі. Масове впровадження нових радіолокаційних систем високої розрізненності на основі радіолокаторів з синтезованою апертурою (РСА) значно розширило можливості радарного знімання. Доцільність застосування даного методу було підтверджено в сферах, що традиційно використовують дані дистанційного зондування – картографії, сільському і лісовому господарстві, пошуку корисних копалини, охорони навколишнього середовища, моніторингу надзвичайних ситуацій, обороні. Основним фізичним параметром земної поверхні, який реєструється неінтерферометричними РСА, є коефіцієнт зворотного розсіяння (сигма-нуль). Прийнятий радарний сигнал є джерелом для складного моделювання процесів зворотного розсіяння та оцінки вторинних фізичних і біофізичних характеристик земної поверхні -- текстури, зволоженості, діелектричної проникності, структури рослинного покриву тощо. При цьому шорсткість земної поверхні, яка описується середньоквадратичним відхиленням її вертикальних нерегулярностей, виступає одним з найбільш значущих чинників будь-якого моделювання. Отже, задача відновлення шорсткості земної поверхні за даними радіолокаційного дистанційного зондування є досить актуальною. Шорсткість земної поверхні – це незалежна фізична величина, що багато в чому визначає радіолокаційне зворотне розсіяння. Проте радіус кореляції шорсткості залежить від поляризації радіолокаційного сигналу. У статті описується кількісний підхід до відновлення шорсткості земної поверхні за двохполяризаційними зображеннями РСА на основі узгодження вимірів незалежної фізичної величини – діелектричної проникності в різних поляризаціях. Використовується запропонована Багдаді та ін. напівемпіричне калібрування IEM радіолокаційного зворотного розсіяння з урахуванням поляризаційно-залежної величини радіуса кореляції для забезпечення фізичної еквівалентності вимірів діелектричної проникності земної поверхні в різних поляризаціях. Крім того, приведені необхідні розрахункові співвідношення та приклад обробки реального радіолокаційного зображення Sentinel-1. Одержані результати в цілому відповідають відомим фізичним закономірностям і ландшафтним особливостям території дослідження.

Semi-supervised Classification of Paddy Fields from Dual Polarized Synthetic Aperture Radar (SAR) images using Deep Learning

ABSTRACT Rice is the major food grain for many countries in this world, in particular South Asian countries such as Pakistan, India, Bangladesh, Sri Lanka, Myanmar, Thailand, etc. India is one of the leading producers of rice, with agriculture contributing 14% to 15% to the Gross domestic product (GDP). Detection of paddy field requires constant monitoring of a region throughout the year. Hence, we use dual-polarized Sentinel-1 Synthetic Aperture Radar (SAR) data with 20 m resolutions for our study. In this paper, we propose a semi-supervised algorithm that detects paddy fields in various seasons. The technique is divided into two parts, unsupervised and supervised. Feature extraction layers of the Visual Geometry Group (VGG16) model are used to segregate patches into five clusters using an entropy-based loss function. Depending on the availability of the ground truth, data is sampled (stratified sampling), and only a certain amount of data is further used for the supervised section. Clusters showing higher similarity (Jaccard/Tanimoto test) with the paddy or nonpaddy classes are connected to a three-layer neural network used for the supervised method. This has been tested on two study sites using 10 data sets, five from 2018 to 2019, each. The highest accuracy reported for 2018 and 2019 is 85.61% and 84.92%, respectively. The two sites are selected from different districts of West Bengal, India. Our model has also shown higher generalization capability against the conventional-supervised models.

Retrieval of the Soil Salinity From Sentinel-1 Dual-Polarized SAR Data Based on Deep Neural Network Regression

Soil salinity is a significant environmental indicator, directly reflecting the land quality and its productivity. In this work, dual-polarized synthetic aperture radar (PolSAR) data were exploited to retrieve the soil salinity in saline-affected fields based on the deep neural network (DNN) theory. Two polarimetric features gotten from Cloude polarization decomposition, together with six other features, were calculated and selected from ten features according to the feature importance. The DNN regression model for soil salinity (DNNR-S) based on one-dimensional convolutional neural network, quantifying the relationship between the eight feature parameters and soil salinity, was established to retrieve the soil salinity in northeastern China. Compared with other five traditional machine learning regression methods [logistic regression (LOR), random forest regression (RFR), support vector regression (SVR), decision tree regression (DTR), and multilayer perceptron regression (MPR)], DNNR-S is the best-performing model with the performance of root mean square error (RMSE) &#x003D; 0.28, mean absolute error (MAE) &#x003D; 0.21, and <inline-formula> <tex-math notation="LaTeX">$r = 0.74$ </tex-math></inline-formula>. Using the proposed model, the soil salinity was retrieved, and 52.37&#x0025; was found to be affected by salinization which was in general agreement with the government report. Due to the training criteria in the proposed model, it was a little more computationally intensive than all the others, but the DNNR-S had the advantage of easy spread to other study areas.

Automatic Extraction of Green Tide Using Dual Polarization Chinese GF-3 SAR Images

Yu, H.F.; Wang, C.Y.; Sui, Y.; Li, J.H., and Chu, J.L., 2020. Automatic extraction of green tide using dual polarization China GF-3 SAR images. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T.W. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 318-325. Coconut Creek (Florida), ISSN 0749-0208.Traditional optical images are not suitable for the all-weather observation of green tide due to the fact that they are greatly affected by clouds. Synthetic aperture radar (SAR) on the other hand, is able to realize all-day and all-weather observation due to a lower sensitivity to clouds, rain and fog. SAR images are therefore an effective supplementary data for the monitoring of green tide. Due to the differences in brightness and noise across the different areas in an image, it is difficult to use the same threshold in order to extract all the green tide information from the image. Based on the iterative threshold method and the histogram bimodal method, this paper presents a new method for the automatic detection of green tide that uses adaptive thresholds for Gaofen-3 (GF-3) satellite dual polarization SAR remote sensing images. In this study, firstly, a sliding window is used to segment the image into multiple sub-images of the same size; secondly, the iterative threshold method is used to obtain the green tide and seawater samples that have the theoretical bimodal structure from each sub-image; then, using the histogram bimodal method, the detection threshold is calculated automatically; and finally, using the threshold segmentation, the green tide areas are extracted from the images. In order to verify the proposed method, the multi-scale segmentation method and the proposed method are both used to detect green tide in China Yellow Sea. The study results show that in the case of GF-3 SAR FSII images, the proposed automatic detection method is superior to the multi-scale segmentation method, as it not only improves the accuracy of green tide detection, but also realizes the automation of green tide extraction. Furthermore, Cross-polarization (HV) images may be more suitable than co-polarization (HH) images for the extraction of green tide due to their lower noise level.

Monitoring of rice lodging using Sentinel-1 data

Synthetic Aperture Radar (SAR) can monitor rice regardless of time and weather condition, and the ability of crop lodge monitoring has been demonstrated by previous studies. However, there have no study about monitoring of rice lodging using satellite-based SAR. In this study, we extracted backscatter coefficient (BC) and H/A/Alpha polarimetric parameters from dual-polarized Sentinel-1 SAR data, and a lodging sensitivity factor γ was constructed for selecting optimal radar feature parameter (ORFP) which is highly sensitivity to lodging rice. Then, the decision tree classification method was used with multiple ORFPs to classify the healthy and lodging rice plots. We firstly acquired shape of rice region using Sentinel-2 image and Maximum likelihood classification method for eliminating the influence of other features before classification of lodging rice. The result showed that the overall accuracy of 87.18% is achieved with the combination of ORFPs and decision tree.

Retrieval of ice/water observations from synthetic aperture radar imagery for use in lake ice data assimilation

High-resolution lake ice/water observations retrieved from satellite imagery through efficient, automated methods can provide critical information to lake ice forecasting systems. Synthetic aperture radar (SAR) data is well-suited to this purpose due to its high spatial resolution (approximately 50 m). With recent increases in the volume of SAR data available, the development of automated retrieval methods for these data is a priority for operational centres. However, automated retrieval of ice/water data from SAR imagery is difficult, due to ambiguity in ice and open water signatures, both in terms of image tone and in terms of parameterized texture features extracted from these images. Convolutional neural networks (CNNs) can learn features from imagery in an automated manner, and have been found effective in previous studies on sea ice concentration estimation from SAR. In this study the use of CNNs to retrieve ice/water observations from dual-polarized SAR imagery of two of the Laurentian Great Lakes, Lake Erie and Lake Ontario, is investigated. For data assimilation, it is crucial that the retrieved observations are of high quality. To this end, quality control measures based on the uncertainty of the CNN output to eliminate incorrect retrievals are discussed and demonstrated. The quality control measures are found to be effective in both dual-polarized and single-polarized retrievals. The ability of the CNN to downscale the coarse resolution training labels is demonstrated qualitatively.

Active/Passive Multiple Polarization Sea Ice Detection During Initial Freeze-Up

An algorithm for ice/water detection of ice less than 30 cm in thickness (thin ice) using dual-polarized synthetic aperture radar (SAR) and passive microwave data has been developed based on insights from previous work. The frequency chosen from those available was in the C-band. For the radiometer layer ice concentrations, land contaminated pixels were removed. For the SAR layers, a novel approach to their use in ice detection was developed using the coefficient of variation (COV), defined as the ratio of the standard deviation of the normalized radar cross section, σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , to the mean σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . The COV reduced the complicated signatures of breaking waves and thin ice to bright ice pixels on a dark water background. The co-pol and cross-pol COV layers and the radiometer layer were merged according to physics-based rules, except in regions adjacent to land or the ice edge where only the SAR layers were valid. The detection algorithm was applied to 233 Sentinel 1a and 1b dual-polarization images of the freeze-up from 2015, 2016, 2018, and 2019 in the Beaufort and Chukchi Seas. The synergistic ice retrievals showed improved agreement with manual operational analyses in the marginal ice zone over other automated operational products.

APPLICATION OF U-NET CONVOLUTIONAL NEURAL NETWORK TO BUSHFIRE MONITORING IN AUSTRALIA WITH SENTINEL-1/-2 DATA

Abstract. This paper aims to define a pipeline architecture for near real-time identification of bushfire impact areas using Geoscience Australia Data Cube (AGDC). A series of catastrophic bushfires from late 2019 to early 2020 have captured international attention with their scale of devastation across four of the most populous states across Australia; New South Wales, Queensland, Victoria and South Australia. The extraction of burned areas using multispectral Sentinel-2 observations are straightforward when no cloud or haze obstruction are present. Without clear-sky observations, precisely locating the bushfire affected regions are difficult to achieve. Sentinel-1 C-band dual-polarized (VH/VV) Synthetic Aperture Radar (SAR) data is introduced to effectively elicit and analyse useful information based on backscattering coefficients, unaffected by adverse weather conditions and lack of sunlight. Burned vegetation results in significant volume scattering; co-/cross-polarised response decreases due to leafless trees, as well as coherence change over fire-disturbed areas; two sensors acquired images in a shortened revisit time over the same effected areas; all of which provided discriminative features for identifying burnt areas. Moreover, applying U-Net deep learning framework to train the recent and historical satellite data leads to an effective pre-trained segmentation model of burnt and non-burnt areas, enabling more timely emergency response, more efficient hazard reduction activities and evacuation planning during severe bushfire events. The advantages of this approach could have profound significance for a more robust, timely and accurate method of bushfire detection, utilising a scalable big data processing framework, to predict the bushfire footprint and fire spread model development.

Sensitivity of PolSAR decomposition to forest disturbance and regrowth dynamics in a managed forest

Managed forests, dynamic with activities of harvest and afforestation, have become an area of imminent research opportunity to understand the effects of these disturbances and regrowth dynamics. As low frequency polarized radar signals have the ability to penetrate forest canopies, the fully polarized Synthetic Aperture Radar (SAR) data gives an ideal representation of conditions in the forest. However, this valuable information has not been optimally utilized in understanding the forest disturbances and regrowth dynamics. Several forest change monitoring studies have been performed using single or dual polarization SAR data, whereas limited studies utilized full polarimetric information. In this study, L-band ALOS/PALSAR and ALOS-2/PALSAR-2 fully polarimetric data is used to study and understand the behavioural dynamics of homogeneous and heterogeneous forest compartments having single and multiple species composition, respectively. Radar scattering pattern of different species, and its variation with respect to phenology, disturbances, harvesting and plantation regrowth are analysed to understand the trend in scattering components with respect to above said forest changes. Based on the scattering properties and their analyses, a two stage rule based classifier has been developed to categorize the forest based on its disturbance and regrowth status into disturbed, re-growth, stable/no change and non forest classes. The classifier performed with, considerably, high accuracy having an overall accuracy of 85.39% and a kappa coefficient of 0.8. The classifier developed in this work presents the potential of fully polarimetric data in understanding the level and extent of disturbances and regrowth dynamics of a forest.

Profile of dielectric constant of peat in Ogan Komering Ilir Regency, Indonesia using SAR Sentinel-1 and ground penetrating radar

Profile of dielectric constant of a substance or a surface indicates its electromagnetic parameters. The profile is associated with electric polarization, permeability with magnetic polarization, and conductivity with an electric current field. In the practice, the Synthetic Aperture Radar (SAR) and the Ground Penetrating Radar (GPR) are commonly utilized to determine how much velocity of a substance and its scattering profiles. However, information about the profile radar scattering mechanisms in tropical peatland is still limited. Therefore, we evaluated dielectric constant from the field using GPR and estimated its spatial variation from a dual-polarization Sentinel-1A type C-band SAR in Ogan Komering Ilir, South Sumatera Province, Indonesia. We measured dielectric constant at the two major types of land use, namely oil palm and acacia. Results indicated that the dielectric constant derived from SAR Sentinel-1A and GPR are affected by peat moisture. It increases as the peat moisture increases. Results show that during peat dry conditions, the profiles of dielectric constant derived from dual-pol Sentinel-1A images are relatively similar to the dielectric constant of peat in Siak Regency. It ranges between 31.78 and 59.3. The dielectric constant derived from GPR ranges between 69 and 70.

On Suitability of ALOS-2/PALSAR-2 Dual-Polarized SAR Data for Arctic Sea Ice Parameter Estimation

In this article, ALOS-2/PALSAR-2 dual-polarized [horizontal-transmit–horizontal-receive and horizontal-transmit–vertical-receive (HH/HV)] ScanSAR mode L-band synthetic aperture radar (SAR) imagery over an Arctic study area was evaluated for their suitability for operational sea ice (SI) monitoring. The L-band SAR data are studied for the estimation of different SI parameters: SI concentration, SI thickness, SI type, and SI drift. Also, some comparisons with nearly coincident C-band data over the same study area have been made. The results indicate that the L-band SAR data from ALOS-2/PALSAR-2 are very useful for estimating the studied SI parameters and equally good or better than using the conventional operational dual-polarized C-band SAR satellite data.

Retrieval of Snow Depth and Snow Water Equivalent Using Dual Polarization SAR Data

This paper deals with the retrieval of snow depth (SD) and snow water equivalent (SWE) using dual-polarization (HH-VV) synthetic aperture radar (SAR) data. The effect of different snowpack conditions on the SD and SWE inversion accuracy was demonstrated by using three TerraSAR-X acquisitions. The algorithm is based on the relationship between the SD, the co-polar phase difference (CPD), and particle anisotropy. The Dhundi observatory in the Indian Himalaya was selected as a validation test site where a field campaign was conducted for ground truth measurements in January 2016. Using the field measured values of the snow parameters, the particle anisotropy has been optimized and provided as an input to the SD retrieval algorithm. A spatially variable snow density ( ρ s ) was used for the estimation of the SWE, and a temporal resolution of 90 m was achieved in the inversion process. When the retrieval accuracy was tested for different snowpack conditions, it was found that the proposed algorithm shows good accuracy for recrystallized dry snowpack without distinct layering and low wetness (w). The statistical indices, namely, the root mean square error (RMSE), the mean absolute difference (MAD), and percentage error (PE), were used for the accuracy assessment. The algorithm was able to retrieve SD with an average MAE and RMSE of 6.83 cm and 7.88 cm, respectively. The average MAE and RMSE values for SWE were 17.32 mm and 21.41 mm, respectively. The best case PE in the SD and the SWE retrieval were 8.22 cm and 18.85 mm, respectively.

Oil Spill Discrimination by Using General Compact Polarimetric SAR Features

Ocean surveillance is one of the important applications of synthetic aperture radar (SAR). Polarimetric SAR provides multi-channel information and shows great potential for monitoring ocean dynamic environments. Oil spills are a form of pollution that can seriously affect the marine ecosystem. Dual-polarimetric SAR systems are usually used for routine ocean surface monitoring. The hybrid dual-pol SAR imaging mode, known as compact polarimetry, can provide more information than the conventional dual-pol imaging modes. However, backscatter measurements of the hybrid dual-pol mode depend on the transmit wave polarization, which results in lacking consistent interpretation for various compact polarimetric (CP) images. In this study, we will explore the capability of different CP modes for oil spill detection and discrimination. Firstly, we introduce the general CP formalism method to formulate an arbitrary CP backscattered wave, such that the target scattering vector is characterized in the same framework for all CP modes. Then, a recently proposed CP decomposition method is investigated to reveal the backscattering properties of oil spills and their look-alikes. Both intensity and polarimetric features are studied to analyze the optimal CP mode for oil spill observation. Spaceborne polarimetric SAR data sets collected over natural oil slicks and experimental biogenic slicks are used to demonstrate the capability of the general CP mode for ocean surface surveillance.

Synergistic potential of dual-polarized synthetic aperture radar and multispectral optical imagery for invasive alien species detection and mapping

Invasive alien species are a major threat to global biodiversity and result in adverse environmental and socioeconomic implications, such as reduced ecosystem services, landscape productivity, and costly eradication initiatives. The ability to monitor the extent and spread of alien species invasions provides valuable insight for the mitigation of these adverse implications. The generation of Earth Observation (EO) Sentinel sensor provides unprecedented freely available imagery, which is suitable for both local and regional invasive species monitoring. Specifically, its radar (S1) and optical (S2) sensors offer unique tandem datasets valuable for landscape analysis. Hence, we sought to determine the synergistic potential of fused S1 dual-polarized synthetic aperture radar (SAR) imagery with S2 optical imagery for invasive alien species detection and mapping. S1 and S2 imageries were fused at the feature level, and the support vector machine algorithm used for the multiclass image classification. Results indicated that the fusion of the S1 dual-polarized imagery with S2 optical imagery produced the highest classification accuracy (85%), whereas stand-alone S2 optical bands produced the lowest classification accuracy (79%). Findings from this study underline the significant synergistic potential and complementarity of new-age S2 optical imagery and dual-polarized S1 SAR imagery for invasive alien species detection and mapping. Due to large swath, higher pixel resolution, free availability, and possible tandem complementarity between optical and SAR sensors, we recommend Sentinel EO imagery as an economically viable option for the invasive alien species detection and mapping.