HV Polarization Research Articles

River ice breakup classification using dual- (HH&HV) or compact-polarization RADARSAT Constellation Mission data

Ice jams and associated flooding during river ice breakup are seasonal hazards for many northern communities. Synthetic Aperture Radar (SAR) data enable timely monitoring of river ice conditions in inclement weather. River ice types and open water are discriminated during breakup using dual-polarized (HH&HV; DPH) and compact polarimetric (CP) C-band SAR imagery from the RADARSAT Constellation Mission (RCM). Study areas comprise nineteen locations on rivers in northern Ontario, the Northwest Territories, and Alberta, Canada. Rubble ice, sheet ice, and open water areas are sampled in RCM imagery using corroborating evidence from shoreline cameras and oblique airborne photography. Samples are obtained in the incidence angle range 19° to 48°, for open water with various wind speeds and flow conditions, and for rubble ice and sheet ice with varying surface roughness, snow cover, and wetness. Discrimination relies on a primary classification of rubble ice, sheet ice, and open water, and a secondary classification of ice roughness. The primary classification model development uses a recursive-partitioning machine-learning technique. Overall accuracies for the best DPH models are 83.8% to 89.6%, depending on image noise floor. DPH models use both HH and HV polarizations for low noise floor image modes, whereas only HH is used for higher noise floor image modes. The best CP model accuracy is 90.2%, using the RL, RR, and RVRH parameters. Secondary classification associates increasing ice roughness with increasing HH backscatter for DPH data, and with increasing RH backscatter for CP data. The angular dependencies of rubble ice or sheet ice are used to normalize backscatter, which is then divided into roughness categories. The DPH and CP classification models are named IceBC-DP and IceBC-CP, respectively. Qualitative analysis illustrates good overall ice type and open water classification. Confounding situations are mixed pixel issues, whitewater and wind roughening of water, moist snow microwave absorption, and superficial water on sheet ice. The development of robust methods for monitoring ice jam formation with RCM is an operational concern for departments within the Government of Canada and within Provincial and Territorial Governments.

Canopy active fuel loads estimation with C-band PolSAR by combining polarization decomposition and vegetation scattering model: a case study in Southwestern China

ABSTRACT The forest canopy active fuel load (CAFL) is easily flammable that is usually consumed in the flaming front, and is a critical factor in crown fire early prediction, suppression and response. Previous studies universally relied on optical remote sensing data, whose effectiveness is extremely limited in regions with cloud cover and haze, such as the southwest of Sichuan, China. Synthetic aperture radar (SAR) can work 24 h and is not affected by weather conditions. This study aims to evaluate the potential of volume backscattering coefficients extracted through decomposition of polarimetric SAR (PolSAR, Radarsat-2 C band) for CAFL estimation. The polarimetric decomposition method used in this study was based on Freeman-Durden decomposition. The separated volume backscattering coefficients were used to estimate CAFL based on the variant of the extended water cloud model (VEWCM). For comparison, the SAR raw observations and extended water cloud model (EWCM) were also used to estimate CAFL. The performance of these two models was validated through quantitative assessment, utilizing CAFL measurements obtained from southwest Sichuan and the leave-one-out cross-validation method. Results show that the VEWCM (R2 = 0.69, RMSE = 0.87 tons/ha) performed better than EWCM (R2 = 0.60, RMSE = 1.48 tons/ha) especially in HV polarization. We concluded that PolSAR has the potential to improve CAFL estimation and further assist with pre-fire risk warnings, fire suppression responses, and post-fire effects assessment.

Analytical Coherent Detection in High-Resolution Dual-Polarimetric Sea Clutter with Independent Inverse Gamma Textures

Polarization diversity has been widely used in maritime radars to improve target detection performance. Full utilization of the polarimetric characteristics of sea clutter is the key to designing effective polarimetric detectors. For high-resolution maritime radars, the HH-HV dual-polarization is an affordable and effective mode to monitor small targets, owing to the simple configuration of single-polarimetric transmit and dual-polarimetric reception and lower clutter powers at the HH and HV polarizations. Enlightened by the analytical coherent detector in compound-Gaussian clutter with inverse Gamma texture, this paper investigates dual-polarimetric coherent detection in dual-polarimetric compound-Gaussian clutter with independent inverse Gamma distributed textures. The analytical dual-polarimetric near-optimum coherent detector is derived, which is a fusion of the generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) at the two polarizations. For short, it is referred to as the P-GLRT-LTD. It is proven that the P-GLRT-LTD is of constant false alarm rate with respect to the Doppler steering vector, scale parameters of textures, and speckle covariance matrices. Moreover, the thresholds of the P-GLRT-LTD are given analytically. Experiments using simulated sea clutter data with the estimated scale and shape parameters from the two measured intelligent pixel processing radar (IPIX) datasets and two measured IPIX datasets with test targets are made to compare P-GLRT-LTD with other existing dual-polarimetric coherent detectors. The results show that the P-GLRT-LTD attains the same detection performance as the existing best-performance detector. The P-GLRT-LTD has a lower computational cost than the existing best-performing one.

Assessment of Pre‐ and Post‐Fire Fuel Availability for Wildfire Management Based on L‐Band Polarimetric SAR

AbstractMany communities coexist with wildfires that lead to loss of lives, property, and ecosystem services. Remote sensing tools can aid disaster response and post‐event assessment, offering fire agencies opportunities for additional surveillance with radar, an all‐weather instrument that can image day or night. The Station (2009) and Bobcat (2020) Fires are the two largest fires in Los Angeles County history, each burning over 100,000 acres. These areas are imaged with NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar L‐band instrument. We test whether polarimetric radar can detect fire scars, burn severity, and different fuel types through its sensitivity to different scattering mechanisms. Polarimetric SAR products are moved into geographic information system‐friendly formats, and in lieu of available field measurements are analyzed alongside agency data showing fire perimeters, burn progression outlines, and soil burn severity. We find that the HV polarization returns and the primary scattering mechanism, quantified through the Cloude‐Pottier decomposition, are the most sensitive parameters. Higher HV values pre‐fire correspond well to areas of moderate and high soil burn severity, and the pattern of fire progression follows higher HV to some extent. Using an HV difference threshold of 1.5 dB, the Bobcat burn scar is identified at 0.70 accuracy when compared with the published fire perimeter. Alpha 1 Angle can also demonstrate sensitivity to soil burn severity pre‐ and post‐fire, showing vegetation types with increased surface scattering post‐fire, which can be used to map burn scars and track recovery by vegetation type.

Sea ice drift vector extraction based on feature matching using CS-1 Images

The study emphasizes the crucial role of sea ice drift in Arctic climate research and the protection of human activities. Employing C-SAR/01 (CS-1) imagery as the primary data source, the study assesses the effectiveness of the ORB feature matching algorithm for extracting Arctic Sea ice drift vectors. Additionally, it investigates the spatial distribution variations in the drift vectors extracted from the HH and HV polarization channels. The accuracy of the extracted drift vectors is validated using manually extracted sea ice drift data. Experimental results reveal a significant disparity in the number of drift vectors extracted from CS-1 HV polarization images compared to HH polarization images. The sea ice drift vectors extracted using the Oriented fast and Roasted Bried (ORB) operator demonstrate an average velocity error of less than 0.27 cm/s (0.31 km/d) and an average direction error of less than 5.66° in HV polarization images.

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

Glacier melt detection at different sites of Greenland ice sheet using dual-polarized Sentinel-1 images

ABSTRACT Synectic Aperture Radar (SAR) backscatter coefficient is sensitive to glacier surface physical characteristic changes, including the states of melting and refreezing, but it is also sensitive to incidence angle variation. This study explores the capability of monitoring Greenland Ice Sheet (GrIS) melting status with Sentinel-1 dual-polarized images by referring to Automatic Weather Station (AWS) records. Sentinel-1 SAR images at five coastal regions of the GrIS are obtained from 2017 to 2021. The backscatter coefficients are normalized to an incidence angle of 30° with an empirical model. Time series of five backscatter coefficients profiles covering AWS illustrates different patterns of the ice surface dielectric constant dynamics in different elevations. The wet snow radar zone shows clear backscatter coefficients decreasing during the melting seasons, but the bare ice radar zone behaves more complexly during the melting seasons. The numbers of melting days at different elevations are also derived for each profile based on −3 dB backscatter coefficient decrease of HH and/or HV polarization, showing the heterogeneous ablation processes over the GrIS. The daily maximum 2 m air temperature on two consecutive days (before and on the SAR acquisition day) exceeds 0°C, and the daily average 2 m air temperature exceeds −0.5°C on the SAR acquisition day that was recorded by the AWS finds good agreements with the −3 dB decrease of the backscatter coefficients, suggesting the GrIS surface melting can be well captured by dual-polarized Sentinel-1 C-band SAR images. The overall agreement and Kappa coefficients are mostly better than 0.85 and 0.70, respectively, for HH images and 0.80 and 0.60, respectively, for HV images, suggesting a better performance of the co-polarized image. High temporal resolution and wide-swath SAR sequence imagery provide suitable data sources for monitoring glacier surface melting-refreezing stats; further analysis is requested to quantitatively link the volume of melting with backscatter coefficient and other SAR data sources.

Multi-band SAR intercomparison study in the Antarctic Peninsula for sea ice and iceberg detection

Synthetic aperture radar (SAR) systems are one of the best resources to gather information in polar environments, but the detection and monitoring of sea ice types and icebergs using them is still a challenge. Limitations using single-frequency images in sea ice characterization are well known, and using different SAR bands has been revealed to be useful. In this paper, we present the quantitative results of an intercomparison experiment conducted by the Argentine Naval Hydrographic Service (SHN) using X-, C-, and L-bands from COSMO-SkyMed, Sentinel-1, and SAOCOM satellites, respectively. The aim of the experiment was to evaluate SAOCOM for its use on SHN products. There were 25 images with different SAR parameters that were analyzed, incorporating the diversity in the information that everyday Ice Services attend to. Particularly, iceberg detections, fast first-year ice, and belts and strips were studied in the Antarctic Sound, the surroundings of Marambio Island, and Erebus and Terror Gulf. The results show that the HV polarization channel of the L-band provides useful information for iceberg detection and fast first-year ice surface feature recognition and is a promising frequency for the study of strip identification under windy sea conditions and snow accumulation on first-year ice.

PolSAR Image Classification by Introducing POA and HA Variances

A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target classification. However, current methods experience difficulties in separating forests and buildings, especially oriented buildings. To address this issue, inspired by the three-component decomposition method, multiple new scattering models were proposed to describe the difference between forest scattering and building scattering. However, this problem cannot effectively be solved with scattering power alone since HV polarization records significant scattering powers from building areas that are similar to vegetation. Therefore, in this study, two new parameters, the polarization orientation angle (POA) variance and helix angle (HA) variance, were defined to describe the distributions of buildings and forests. By combining scattering power with POA and HA variances, the random forest algorithm was used to conduct the land cover classification, focusing on distinguishing between forests and oriented buildings. Finally, the C- and L-band polarimetric SAR data acquired by the GF-3, ALOS1 PALSAR, and SAOCOM systems were selected to test the proposed method. The results indicate that it is feasible to improve PolSAR classification accuracy by introducing polarimetric parameters. Quantitatively, the classification accuracies increased by 23.78%, 10.80%, and 12.97% for the ALOS1 PALSAR, GF-3, and SAOCOM data, respectively.

Mapping tropical forest aboveground biomass using airborne SAR tomography

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha−1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

Identification of Fault Zone in Bali Using GGMPlus Gravity and Alos-2 Palsar-2 Data

The local active fault in Bali has a small magnitude (M<5) but has destructive potential because it is very close to residential areas. Mapping the fault area on Bali is needed to identify the parameters of faults. This study used gravity data from GGMplus, topographic data from DEMNAS, and lineaments using ALOS-2 PALSAR-2 data. Validation and interpretation using the geological map of Bali and seismicity data. We interpret the subsurface using the gravity derivative method to identify the type of fault movement. Identify fault locations using lineament extraction from SAR data processed by directional filters. The composite image red-green-blue (RGB) for HH, HV, and VV polarization was used for automatic lineament extraction and then corrected manually. The results of the gravity method succeeded in identifying 29 of the 30 faults from the geological map of the Bali sheet and a new spot from PALSAR-2. Bali land has 12 thrust faults, 11 strike-slip faults and six normal faults. The image of PALSAR-2 (L band) has succeeded in making a fault lineament map for the Bali region. The lineament extraction results from PALSAR-2 obtained four new faults (Pesanggaran, Sepang, Tegal Badeng, and Banyuwedang), while four faults were not identified (Tampaksiring Fault, Plaga, Mambal, and Munduk-Rajasa). NE-SW dominates the strike directions, and the dip angles are 45-80 degrees. We propose 30 faults in Bali, including 26 defects from geological maps with changes in length and location shift and four new marks extracted from automatic lineament.Keywords: Remote Sensing, Earthquake, Derivative Gravity, Lineament, SAR

On the Effects of the Incidence Angle on the L-Band Multi-Polarisation Scattering of a Small Ship

The monitoring of ships is of paramount importance for ocean and coastal area surveillance. The synthetic aperture radar is shown to be a key sensor to provide effective and continuous observation of ships due to its unique imaging capabilities. When advanced synthetic aperture radar imaging systems are considered, the full scattering information is available that was demonstrated to be beneficial in developing improved ship detection and classification algorithms. Nonetheless, the capability of polarimetric synthetic aperture radar to observe marine vessels is significantly affected by several imaging and environmental parameters, including the incidence angle. Nonetheless, how changes in the incidence angle affect the scattering of ships still needs to be further investigated since only a sparse analysis, i.e., on different kinds of ships of different sizes observed at multiple incidence angles, has been performed. Hence, in this study, for the first time, the polarimetric scattering of the same ship, i.e., a small fishing trawler, which is imaged multiple times under the same sea state conditions but in a wide range of incidence angles, is analysed. This unique opportunity is provided by a premium L-band UAVSAR airborne dataset that consists of five full-polarimetric synthetic aperture radar scenes collected in the Gulf of Mexico. Experimental results highlight the key role played by the incidence angle on both coherent, i.e., co-polarisation signature and pedestal height, and incoherent, i.e., multi-polarisation and total backscattering power, polarimetric scattering descriptors. Experimental results show that: (1) the polarised scattering component is more sensitive to the incidence angle with respect to the unpolarised one; (2) the co-polarised channel under horizontal polarisation dominated the polarimetric backscattering from the fishing trawler at lower angles of incidence, while both co-polarised channels contribute to the polarimetric backscattering at higher incidence angles; (3) the HV polarisation provides the largest target-to-clutter ratio at lower incidence angles, while the HH polarisation should be preferred at higher angles of incidence.

Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting forest carbon content, analyzing forest degradation and restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, the spaceborne Light Detection and Ranging (LiDAR) sensor specifically designed to acquire forest structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used to extract forest canopy height information over large areas. Yet, GEDI has no spatial coverage for most forested areas in Canada and other high latitude regions. On the other hand, the spaceborne LiDAR called Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a global coverage but was not specially developed to study forested ecosystems. Nonetheless, both spaceborne LiDAR sensors obtain point-based information, making spatially continuous forest canopy height estimation very challenging. This study compared the performance of both spaceborne LiDAR, GEDI and ICESat-2, combined with ALOS-2/PALSAR-2 and Sentinel-1 and -2 data to produce continuous canopy height maps in Canada for the year 2020. A set-aside dataset and airborne LiDAR (ALS) from a national LiDAR campaign were used for accuracy assessment. Both maps overestimated canopy height in relation to ALS data, but GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI. PALSAR-2 HV polarization was the most important covariate to predict canopy height, showing the great potential of L-band in comparison to C-band from Sentinel-1 or optical data from Sentinel-2. The approach proposed here can be used operationally to produce annual canopy height maps for areas that lack GEDI and ICESat-2 coverage.

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

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

Global seasonal Sentinel-1 interferometric coherence and backscatter data set

This data set is the first-of-its-kind spatial representation of multi-seasonal, global C-band Synthetic Aperture Radar (SAR) interferometric repeat-pass coherence and backscatter signatures. Coverage comprises land masses and ice sheets from 82° Northern to 79° Southern latitudes. The data set is derived from multi-temporal repeat-pass interferometric processing of about 205,000 Sentinel-1 C-band SAR images acquired in Interferometric Wide-Swath Mode from 1-Dec-2019 to 30-Nov-2020. The data set encompasses three sets of seasonal (December-February, March-May, June-August, September-November) metrics produced with a pixel spacing of three arcseconds: 1) Median 6-, 12-, 18-, 24-, 36-, and 48-days repeat-pass coherence at VV or HH polarizations, 2) Mean radiometrically terrain corrected backscatter (γ0) at VV and VH, or HH and HV polarizations, and 3) Estimated parameters of an exponential coherence decay model. The data set has been produced to obtain global, spatially detailed information on how decorrelation affects interferometric measurements of surface displacement and is rich in spatial and temporal information for a variety of mapping applications.

Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland

Abstract. Snow cover (SC) and timing of snowmelt are key regulators of a wide range of Arctic ecosystem functions. Both are strongly influenced by the amplified Arctic warming and essential variables to understand environmental changes and their dynamics. This study evaluates the potential of Sentinel-1 (S-1) synthetic aperture radar (SAR) time series for monitoring SC depletion and snowmelt with high spatiotemporal resolution to capture their understudied small-scale heterogeneity. We use 97 dual-polarized S-1 SAR images acquired over northeastern Greenland and 94 over southwestern Greenland in the interferometric wide swath mode from the years 2017 and 2018. Comparison of S-1 intensity against SC fraction maps derived from orthorectified terrestrial time-lapse imagery indicates that SAR backscatter can increase before a decrease in SC fraction is observed. Hence, the increase in backscatter is related to changing snowpack properties during the runoff phase as well as decreasing SC fraction. We here present a novel empirical approach based on the temporal evolution of the SAR signal to identify start of runoff (SOR), end of snow cover (EOS) and SC extent for each S-1 observation date during melt using backscatter thresholds as well as the derivative. Comparison of SC with orthorectified time-lapse imagery indicates that HV polarization outperforms HH when using a global threshold. The derivative avoids manual selection of thresholds and adapts to different environmental settings and seasonal conditions. With a global configuration (threshold: 4 dB; polarization: HV) as well as with the derivative, the overall accuracy of SC maps was in all cases above 75 % and in more than half of cases above 90 %. Based on the physical principle of SAR backscatter during snowmelt, our approach is expected to work well in other low-vegetation areas and, hence, could support large-scale SC monitoring at high spatiotemporal resolution (20 m, 6 d) with high accuracy.

Addressing critical influences on L-band radar backscatter for improved estimates of basal area and change

L-band synthetic aperture radar (SAR) backscatter intensity is sensitive to land cover and can be used to estimate vegetation measures such as basal area (BA) and biomass. However, the estimation of BA, and especially change in BA, can be hampered by the influences upon backscatter of external factors such as imaging geometry, terrain topology, prevailing moisture conditions and even SAR sensor characteristics. This paper describes a method of reducing the adverse effects of such extraneous influences on vegetation and change estimates derived from single-channel SAR data. Empirical corrections for terrain slope and cross-track tendencies were applied and linear least squares difference minimization used to normalize the backscatter differences between scenes. The method was applied to state-wide coverage of L-band, fine-mode, HV polarization Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data over New South Wales (NSW), Australia. The data were acquired with different sensors over two “observational epochs”: ALOS PALSAR in 2009 and ALOS-2 PALSAR-2 in 2016/17. The SAR datasets presented significant variations in backscatter intensity beyond those attributable to changes in vegetation cover. The corrective procedures resulted in improved uniformity of observed backscatter dependence on vegetation. Variations in backscattering coefficient between swaths were reduced by as much as 1.75 dB and 25% of the standard deviation in mean backscattering coefficients in common areas and at near- and far-range. This corresponded to a correction in BA estimate of 4.4 m2 ha−1. The method was observed to reduce ambiguities in regrowth estimates at swath boundaries and correct estimates of BA change by as much as 30% over large areas. The resulting estimates of 7-year change in BA provide spatially explicit forest structural information that is assisting in monitoring changes in woody vegetation across NSW.

Thermal Denoising of Cross-Polarized Sentinel-1 Data in Interferometric and Extra Wide Swath Modes

An updated algorithm for removal of thermal noise in Sentinel-1 synthetic aperture radar (SAR) Level-1 Ground Range Detected (GRD) data in cross-polarization is presented. The algorithm is comprised of two steps: correction of the annotated thermal noise magnitude (previously proposed in park2018) and a novel correction of the annotated thermal noise range dependence. The magnitude of the annotated thermal noise is corrected by applying scale and offset coefficients tuned on a few hundred of Sentinel-1 data acquired over surfaces with low backscatter in Interferometric Wide and Extra Wide swath modes in HV and VH polarizations. The values of coefficients for all modes and polarizations for data processed with Instrument Processing Facility (IPF) version 3.1 -3.3 are provided. The range dependence is corrected by minimizing a cost function between the annotated range profiles of thermal noise and antenna pattern gain (APG). An objective validation metric based on comparison of averaged backscatter at inter-swath boundaries is proposed. Validation is performed on hundreds of Sentinel-1 scenes acquired over open ocean, doldrums, deserts and sea ice. It shows that the new algorithm outperforms the standard thermal noise removal algorithm proposed by European Space Agency in almost all cases. Analysis shows that the new algorithm worsens noise correction in cases when the range dependence of the annotated APG does not match with the observed signal, indicating either problems with signal processing on IPF or imprecise annotation of APG.

Carbon stock estimation and mapping of mangrove forest using ALOS-2 PALSAR-2 in Benoa Bay Bali, Indonesia

Mangrove forests in tropics coastlines area play an essential role in carbon fixation and carbon storage. Mangrove forests in coastal areas are very effective and efficient in reducing the concentration of carbon dioxide (CO2) in the atmosphere because mangroves can absorb CO2 through photosynthesis by diffusion through stomata and then store carbon in the form of biomass. With the lack of efforts to manage mangrove forests, it needs to be developed so that forest functions can be utilized sustainably. This paper describes examining the use of remote sensing data, particularly dual-polarization ALOS-2 PALSAR-2 data, with the primary objective to estimate the carbon stock of mangrove forests in Benoa Bay, Bali. The carbon stock was estimated by analyzing HV Polarization, Above Ground Biomass (AGB), and ground biomass (BGB). The total carbon stock was obtained by multiplying the total biomass with the organic carbon value of 0.47. The potential carbon stock in the mangrove Benoa Bay area is 209,027.28 ton C to absorb carbon dioxide (CO2) of 767,130.11 ton CO2 Sequestration same with 3.87 X 1011 bottles in 2015 and 204.422,59 ton C to absorb carbon dioxide (CO2) of 750.230,93 ton CO2 Sequestration same with 3.79 x 1011 bottles in 2020.

Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2

Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (RHH/HV) and HV/HH (RHH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m3/m3. This was comparable to the RVI and RHH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m3/m3, respectively. The findings of this study suggest that SAR-based indicators, RHH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop.