HH Polarization Research Articles

ALOS-2 and Sentinel-1 SAR data sensitivity analysis to surface soil moisture over bare and vegetated agricultural fields

Surface Soil Moisture (SSM) is one of the important variables that help to understand the physical process between the land and the atmosphere. Thus, temporal and spatial distribution of SSM is crucial for a sustainable environment. Radar remote sensing, especially Synthetic Aperture Radar (SAR), is an important technology for the estimation of spatio-temporal SSM. The aim of this study is to examine the potential of L-band ALOS-2 and C-band Sentinel-1 SAR data for SSM estimation over bare and vegetated agricultural fields in a dry season (no irrigation). Field campaigns were carried out simultaneous with ALOS-2 acquisitions on 22 April 2016 (bare surfaces) and 12 June 2016 (vegetation covered surfaces). In addition, in-situ SSM, Plant Coverage (PC) and surface roughness were measured in these campaigns. The sensitivity of ALOS-2 and Sentinel-1 polarizations was investigated for bare and vegetation covered surfaces. For bare soil surfaces, the highest sensitivity was obtained from HH polarization of ALOS-2 (R2 = 0.36) and VV polarization of Sentinel-1 (R2 = 0.74). The results demonstrated that the potential of Sentinel-1 for SSM estimation was higher than ALOS-2 over bare soil surfaces. Three models, namely Dubois semi-empirical model and Multiple Linear Regression (MLR) analysis for bare soil surfaces, and Water Cloud Model (WCM) for vegetated surfaces, were regarded for SSM estimation. Dubois model did not respond well for both data sets. Considering the MLR analyses, the testing results showed that Sentinel-1 (R2 = 0.73 and RMSE = 1.76 vol%) presented better accuracy results than ALOS-2 (R2 = 0.57 and RMSE = 2.29 vol%). WCM was effective in eliminating the vegetation backscattering effect and the inversion of WCM presented satisfactory results in estimating SSM with both ALOS-2 and Sentinel-1 data. Furthermore, L-band ALOS-2 provided slightly better results than C-band Sentinel-1 in WCM analysis.

Simulation of Ku-Band Profile Radar Waveform by Extending Radiosity Applicable to Porous Individual Objects (RAPID2) Model

Similar to light detection and ranging (lidar), profile radar can detect forest vertical structure directly. Recently, the first Ku-band profile radar system designed for forest applications, called Tomoradar, has been developed and evaluated in boreal forest. However, the physical relationships between the waveform and forest structure parameters such as height, leaf area index (LAI), and aboveground biomass are still unclear, which limits later forestry applications. Therefore, it is necessary to develop a theoretical model to simulate the relationship and interpret the mechanism behind. In this study, we extend the Radiosity Applicable to Porous IndiviDual objects (RAPID2) model to simulate the profile radar waveform of forest stands. The basic assumption is that the scattering functions of major components within forest canopy are similar between profile radar and the side-looking radar implemented in RAPID2, except several modifications. These modifications of RAPID2 mainly include: (a) changing the observation angle from side-looking to nadir-looking; (b) enhancing the ground specular scattering in normal direction using Fresnel coefficient; (c) increasing the timing resolution and recording waveform. The simulated waveforms were evaluated using two plots of Tomoradar waveforms at co- and cross- polarizations, which are collected in thin and dense forest stands respectively. There is a good agreement (R2 ≥ 0.80) between the model results and experimental waveforms in HH and HV polarization modes and two forest scenes. After validation, the extended RAPID2 model was used to explore the sensitivity of the stem density, single tree LAI, crown shape, and twig density on the penetration depth in the Ku-band. Results indicate that the backscattering of the profile radar penetrates deeper than previous studies of synthetic aperture radar (SAR), and the penetration depth tends to be several meters in Ku-band. With the increasing of the needle and twig density in the microwave propagation path, the penetration depth decreases gradually. It is worth noting that variation of stem density seems to have the least effect on the penetration depth, when there is no overlapping between the single tree crowns.

Enhanced Delaunay Triangulation Sea Ice Tracking Algorithm with Combining Feature Tracking and Pattern Matching

Sea ice drift detection has the key role of global climate analysis and waterway planning. The ability to detect sea ice drift in real-time also contributes to the safe navigation of ships and the prevention of offshore oil platform accidents. In this paper, an Enhanced Delaunay Triangulation (EDT) algorithm for sea ice tracking was proposed for dual-polarization sequential Synthetic Aperture Radar (SAR) images, which was implemented by combining feature tracking with pattern matching based on integrating HH and HV polarization feature information. A sea ice retrieval algorithm for feature detection, matching, fusion, and outlier detection was specifically developed to increase the system’s accuracy and robustness. In comparison with several state-of-the-art sea ice drift retrieval algorithms, including Speeded Up Robust Features (SURF) and the Oriented FAST and Rotated BRIEF (ORB) method, the results of the experiment provided compelling evidence that our algorithm had a higher accuracy than the SURF and ORB method. Furthermore, the results of our method were compared with the drift vector and direction of buoys data. The drift direction is consistent with buoys, and the velocity deviation was about 10 m. It was proved that this method can be applied effectively to the retrieval of sea ice drift.

First-Year and Multiyear Sea Ice Incidence Angle Normalization of Dual-Polarized Sentinel-1 SAR Images in the Beaufort Sea

Automatic and visual sea ice classification of SAR imagery is impeded by the incidence angle dependence of backscatter intensities. Knowledge of the angular dependence of different ice types is therefore necessary to account for this effect. While consistent estimates exist for HH polarization for different ice types, they are lacking HV polarization data, especially for multiyear sea ice. Here we investigate the incidence angle dependence of smooth and rough/deformed first-year and multiyear ice of different ages for wintertime dual-polarization Sentinel-1 C-band SAR imagery in the Beaufort Sea. Assuming a linear relationship, this dependence is determined using the difference in incidence angle and backscatter intensities from ascending and descending images of the same area. At cross-polarization rough/deformed first-year sea ice shows the strongest angular dependence with $-\text{0.11}$ dB/ $1^{\circ }$ followed by multiyear sea ice with $-\text{0.07}$ dB/ $\text{1}^{\circ }$ , and old multiyear ice (older than three years) with $-\text{0.04}$ dB/ $\text{1}^{\circ }$ . The noise floor is found to have a strong impact on smooth first-year ice and estimated slopes are therefore not fully reliable. At co-polarization, we obtained slope values of $-0.24$ , $-0.20$ , $-\text{0.15},$ and $-\text{0.10}$ dB/ $\text{1}^{\circ }$ for smooth first-year, rough/deformed first-year, multiyear, and old multiyear sea ice, respectively. Furthermore, we show that imperfect noise correction of the first subswath influences the obtained slopes for multiyear sea ice. We demonstrate that incidence angle normalization should not only be applied to co-polarization but should also be considered for cross-polarization images to minimize intra ice type variation in backscatter intensity throughout the entire image swath.

A Superpixel-Based Coastline Extraction Algorithm for Single-Polarized ENVISAT and ERS Imagery

In single-polarization synthetic aperture radar imagery, coastline extraction is a challenging task due to complex coastal region condition, low incidence angles (<30°), VV or HH polarization, speckle and sea wind, which have to be handled by visual inspection and manual operation for coastline extraction. To solve this problem, this article proposes a coastline extraction algorithm by utilizing superpixel as the basic unit. An improved line finder is presented and involved in the simple linear iterative clustering to obtain better results around the dam. Then, Gabor feature is extracted to characterize the superpixels. Finally, the hidden Markov random field model is utilized to classify superpixels. A coarse to fine strategy is utilized to extract coastline for the experimental imagery. Four VV polarization ENVISAT and ERS images with incidence angle (30.9°−36.6°, 17°−42°, 17°−42°, 19°−26°) are used as the experimental imagery. Four sites in each imagery are chosen to evaluate the performance of proposed superpixel-based algorithm under the condition of complex coastal region such as a thin dam, sharp corner, weak boundary, together with low-to-moderate sea wind. By using the manually traced coastlines as the standards in the qualitative evaluations with the extracted results, with mean offset (MO) in pixels, root-mean-square error (RMSE) in pixels, proportion of overlapped pixels (OLP) in percentage, distance within one pixel (WOP) in percentage, and distance within two pixels (WTP) in percentage as performance comparison, the accuracy improvements in detecting coastline along the dam are at least about 0.08, 0.19, 2.07% in MO, RMSE, and OLP. The accuracy improvements of the sharp corner are at least about 0.07, 0.09, 2.44%, 2.69%, and 2.77%. The accuracy improvements of the weak boundary are about 2.63, 2.99, 5.26%, 11.46%, and 13.96%. While the proposed algorithm shows weaknesses with the comparison algorithms in WOP, WTP for the dam, OLP, WOP, and WTP for sea wind. In addition, the processing time of the proposed algorithm is a burden to obtain better accuracy performance. Experimental results show that the proposed coastline extraction algorithm is more effective and suitable for coastline extraction in comparison with other existing algorithms in the dam, weak boundary, and sharp corner.

Study of Coniferous Forests Using Multifrequency Polarimetric Synthetic-Aperture Radar

Specific features of radio reflection of coniferous forest in decimeter- and meter-wavelength ranges are considered. The study is stimulated by the upcoming (in five-to-seven years) spaceborne experiments using synthetic-aperture radars (SARs) working in the P-band (430 MHz), which is important for sounding of forests. Experimental results on the reflection characteristics of pine forests obtained with the aid of multifrequency polarimetric SAR are presented. Reflection from forests is simulated for the meter-wavelength range for horizontal (HH) and vertical (VV) polarizations. It is shown that different effects may determine reflection of meter-wavelength waves with HH and VV polarizations. New results on specific features of airborne and spaceborne radar images (RIs) of forests in the L, P, and VHF bands are obtained. Typical features of the RIs of coniferous forests are revealed. Such features are difficult to interpret and need to be further studied using RI analysis and results of simultaneous measurements of soil and forest parameters. Several examples show that RI interpretations may cause doubts concerning the correctness.

Influence of Wave Polarization on the Texture Characteristics of Objects in Radar Images

The influence of wave polarization on texture parameters of objects in radar images (RIs) is examined by calculations of the second-order statistics (the fractal dimension and the Haralick textural features) on the basis of polarimetric data acquired with the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) system for two wavelengths of the C- and L-ranges. The variance analysis of the samples of fractal dimension values for natural forested and water objects have revealed no influence of polarization on the values before the speckle filtering; however, after speckle filtering, statistically valid differences have been found in the mean values of some pairs of samples. For sample pairs of the copolarizations VV–HH and cross polarizations HV–VH, a random nature of the differences observed in the mean fractal dimension values has been shown. This conclusion is also true for the Haralick textural features of a water object—the contrast, inverse moment, and entropy. Statistically valid differences in the mean values of the Haralick textural features for urban and water objects have been revealed both before and after speckle filtering. For a forested object, variance analysis shows that the polarization-dependent differences in the mean values of the textural feature samples are statistically unreliable.

On Quad-Polarized SAR Measurements of the Ocean Surface

This paper provides improved quantitative estimates of the wind-ruffled roughness contributions to dual co- and cross-polarized radar signals. Expanding previous approaches, 1696 RADARSAT-2 quad-polarized synthetic aperture radar (SAR) measurements, co-located with 65 in situ National Data Buoy Center (NDBC) buoy observations, are analyzed. Considering all wind conditions, the impact of breaking and near-breaking waves on dual co- and cross-polarized radar signals is robustly documented. For VV polarized measurements, the contribution of breaking waves decreased from 60% to 20% with increasing incidence angle, whereas for HH polarization and cross-polarization measurements, it can amount to about 60%–70% for all incidence angles. Building on the large analyzed data set, robust empirical dependencies between breaking waves and their impact on co- and cross-pol signals are then derived, as functions of wind speeds, incidence angles, and azimuth directions.

VULNERABILITY ASSESSMENT AT MT. BROMO INDONESIA BY USING TIME-SERIES LAND SURFACE DEFORMATION AND GIS

Among the 127 active volcanoes located in eastern Java, Indonesia. Mt. Bromo is the most famous active volcano, type of Mt. Bromo is a strombolian. Many aspects that make volcano an interesting, we conduct a critical and comprehensive study and analysis concerning of volcano eruption based on remote sensing and GIS approaches. Nowadays, remote sensing play an important role to observe volcanic activity and facilitate real-time information. The method used in this study is the determination level of risk in the Mt. Bromo by Pairwise Comparison method. Vulnerability parameters to be obtained from the potential of land deformation, population density, and distance from the volcano dome. In addition, we used SAR data to observe time-series land surface deformation which derived from PALSAR sensor and the images which L-band frequency characteristic on board from Advanced Land Observing Satellite (ALOS) with active microwave sensor to achieve cloud-free and day-and-night land observation. The dataset is composed of 24 SAR images, collected from 24 May 2007 to 5 July 2016 (Descending passes, HH polarization). Consequently, the information result has been created and processed at a municipal or city level including thematic maps, the database has been built, classified and analyzed by using GIS environment. The main idea is providing hazard mitigation map at Mt. Bromo to provide adequate guidance for disaster-prone areas to determine the level of disaster risk.

Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data.

The biomass of three agricultural crops, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with R2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the R 2 > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show R 2 < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other.

Using Long-Term SAR Backscatter Data to Monitor Post-Fire Vegetation Recovery in Tundra Environment

Wildfires could have a strong impact on tundra environment by combusting surface vegetation and soil organic matter. For surface vegetation, many years are required to recover to pre-fire level. In this paper, by using C-band (VV/HV polarization) and L-band (HH polarization) synthetic aperture radar (SAR) images acquired before and after fire from 2002 to 2016, we investigated vegetation change affected by the Anaktuvuk River Fire in Arctic tundra environment. Compared to the unburned areas, C- and L-band SAR backscatter coefficients increased by up to 5.5 and 4.4 dB in the severely burned areas after the fire. Then past 5 years following the fire, the C-band SAR backscatter differences decreased to pre-fire level between the burned and unburned areas, suggesting that vegetation coverage in burned sites had recovered to the unburned level. This duration is longer than the 3-year recovery suggested by optical-based Normalized Difference Vegetation Index (NDVI) observations. While for the L-band SAR backscatter after 10-year recovery, about 2 dB higher was still found in the severely burned area, compared to the unburned area. The increased roughness of the surface is probably the reason for such sustained differences. Our analysis implies that long records of space-borne SAR backscatter can monitor post-fire vegetation recovery in Arctic tundra environment and complement optical observations.

Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces

The monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification.Object-oriented supervised classifications, based on a Random Forest algorithm, and majority zoning post-processing are used. This study emerges from the experiment on multi-sensor crop monitoring (MCM'10, Baup et al., 2012) conducted in 2010 on a mixed farming area in the southwest of France, near Toulouse. This experiment enabled the regular and quasi-synchronous collection of multi-sensor satellite data and in situ observations, which are used in this study. 211 plots with contrasting characteristics (different slopes, soil types, aspects, farming practices, shapes and surface areas) were monitored to represent the variability of the study area. They can be grouped into four classes of land cover: 39 grassland areas, 100 plots of wheat, 13 plots of barley, 20 plots of rapeseed, and 2 classes of bare soil: 23 plots of small roughness and 16 plots of medium roughness. Satellite radar images in the X-, C- and L-bands (HH polarization) were acquired between 14 and 18 April 2010. Optical images delivered by Formosat-2 and corresponding field data were acquired on 14 April 2010.The results show that combining images acquired in the L-band (Alos) and the optical range (Formosat-2) improves the classification performance (overall accuracy = 0.85, kappa = 0.81) compared to the use of radar or optical data alone. The results obtained for the various types of land cover show performance levels and confusions related to the phenological stage of the species studied, with the geometry of the cover, the roughness states of the surfaces, etc. Performance is also related to the wavelength and penetration depth of the signal providing the images. Thus, the results show that the quality of the classification often increases with increasing wavelength of the images used.

Mapping the spatial-temporal variability of tropical forests by ALOS-2 L-band SAR big data analysis

Insufficient knowledge about spatial-temporal forest heterogeneities in the tropics is a major impediment to better estimation of carbon storage and prevention of forest loss by remote sensing. Seasonality induced fluctuations can severely impede the reliability of synthetic aperture radar (SAR) applications like forest classification, deforestation detection and biomass estimation. In the past, the seasonality effects on SAR data acquired over tropical forests have been largely ignored due to the lack of consistent time-series data for the entire region. To overcome this knowledge gap, this paper presents the first comprehensive statistical assessment of L-band SAR backscatter variability in tropical forests by means of a homogeneous big data analysis on multitemporal ALOS-2/PALSAR-2 dual-polarization ScanSAR data. Virtually the entire tropical forest region was monitored from 2016 to 2018 with 42 days intervals. Approximately 250 TB of ScanSAR data were computed on a 0.05 decimal degrees grid to produce forest maps which provide more detailed and reliable information about the spatial and temporal behavior of tropical forests. The first, easy to understand global forest variability maps provide unseen insights into forest structures for the entire tropical belt from local to continental scale. While the differences between the evergreen rainforest and the (semi-) deciduous dry forest is obviously large in the spatial domain, in the temporal domain seasonal changes can easily cause variations in the same order of magnitude within the same forest class. Depending on the area, natural environmental factors like flooding or leaf shedding can result in seasonal gamma naught ranges exceeding 3 dB in both HH and HV polarization. The findings of this study should have important implications for all future remote sensing research efforts on tropical forests. With drastically increasing availability of more frequent high-resolution L-band observations in the coming decade, these characteristic forest variabilities must be considered in the development of active and passive microwave remote sensing algorithms. The better understanding of the underlying dynamics and processes is expected to improve forest remote sensing applications for the upcoming L-band SAR satellite generation including ALOS-4, NISAR, and Tandem-L.

Modeling L- and X-band backscattering of wheat and tests over fields of Pampas

ABSTRACTA discrete scattering model and a detailed set of ground measurements are used to simulate the backscattering coefficients of wheat fields during the whole growth cycle. Simulations are carried out at L- and X-band, and at HH, VV, and HV polarizations. Wheat fields are located in Pampas (Argentina), and are characterized by low values of plant density. Simulations show that the backscattering coefficient is driven by variations of soil moisture at L-band, particularly for HH polarization, with low vegetation effects. Conversely, the attenuation of vegetation is dominant in producing variations of backscattering coefficients at X-band, particularly for VV polarization. Simulations are compared against experimental data collected over the same Pampas region, using airborne SARAT SAR at L-band and COSMO-SKYMED at X-band. Assuming a surface height standard deviation in a 0.4–0.7 cm range, the simulations generally agree with experimental data, with an RMSE lower than about 2 dB at L-band and X-band, except a limited number of cases. Discrepancies observed in specific conditions are discussed. Overall, the results indicate that a joint use of L- and X-band has a good potential to monitor both soil moisture and vegetation growth.

Experimental Study of Polarized Radar Scattering from the Tree Canopy at the Ka-Band

In this study, experimental measurements were carried out to estimate the polarized backscattering coefficient at the Ka-band through deciduous and coniferous trees. By utilizing a ground-based scatterometer, the returned signal was obtained in three polarized states at an incident angle ranging from 30 to 80 degrees. The scattering coefficients were estimated from the radar equation for the distributed target under narrow beam approximation. Radiometric calibration was conducted using a metal sphere, and a set of corner reflectors was used as the calibration target to ensure measurement accuracy. Both frequency and angular response of backscattering coefficients with three polarizations were analyzed for the cases of deciduous (banyan) and coniferous (pine) trees. The results indicated that the dynamic range for HV polarization is higher than that of HH and VV polarizations for both banyan and pine trees. Comparatively, the dynamic range for the pine tree is larger than that of the banyan tree.

Added Value of Coherent Copolar Polarimetry at X-Band for Crop-Type Mapping

A set of six spotlight TerraSAR-X images acquired at HH and VV polarizations in 2009 over an agricultural site in Germany are employed to evaluate the potential contribution of polarimetric features derived from this copolar mode to crop-type mapping. Results show that the inclusion of the correlation between copolar channels in the set of input features of the classifier consistently improves the classification performance with respect to the use of only backscattering coefficients. An increase around 8%–10% in overall accuracy, depending on the experiment setup, is achieved. Both user and producer accuracies are improved for all crop types, being the most noticeable contribution for barley, oat, and sugar beet. Different sets of input features, as well as classification and evaluation strategies, are tested in order to assess the robustness of this contribution.

Evaluation of modified Dubois model for estimating surface soil moisture using dual polarization RISAT-1 C-band SAR data

The present study focuses on the soil moisture estimation using dual polarimetric RISAT-1, C-band SAR data in HH and HV polarization. The semi empirical approach derived by Dubois and the same modified and proposed as modified Dubois model (MDM) is worked for winter wheat crop from initial vegetative to maturity stage. Surface roughness model derived roughness is replaced with ‘s’ parameter for retrieval of dielectric constant which is further used in Topp’s model to derive soil moisture. Model derived soil moisture and ground measured soil moisture demonstrates pragmatic correlation after performing linear regression. The behaviour of inversion model varies at different crop stages depending on the soil exposure and crop cover. Normalized difference vegetation index from moderate resolution imaging spectroradiometer has also been evaluated to monitor crop status. Performance of MDM is promising for early and maturity stages of crop for soil moisture estimation.

The Performance of Dual-Frequency Polarimetric Scatterometer in Sea Surface Wind Retrieval

The wind retrieval performance of HY-2A scanning scatterometer operating at Ku-band in HH and VV polarizations has been well evaluated in the wind speed range of 0–25 ms−1. In order to obtain more accurate ocean wind field, a potential extension of dual-frequency (C-band and Ku-band) polarimetric measurements is investigated for both low and very high wind speeds, from 5 to 45ms−1. Based on the geophysical model functions of C-band and Ku-band, the simulation results show that the polarimetric measurements of Ku-band can improve the wind vector retrieval over the entire scatterometer swath, especially in nadir area, with the wind direction root-mean-square error (RMSE) less than 12° in the wind speed range of 5–25 m s−1. Furthermore, the results also show that C-band cross-polarization plays a very important role in improving the wind speed retrieval, with the wind speed retrieval accuracy better than 2 ms−1 for all wind conditions (0–45 ms−1). For extreme winds, the C-band HH backscatter coefficients modeled by CMOD5.N(H) and the ocean co-polarization ratio model at large incidence are used to retrieve sea surface wind vector. This result reveals that there is a big decrease of wind direction retrieval RMSE for extreme wind fields, and the retrieved result of C-band HH polarization is nearly the same as that of C-band VV polarization for low-to-high wind speed (5–25 ms−1). Thus, to improve the wind retrieval for all wind conditions, the dual-frequency polarimetric scatterometer with C-band and Ku-band horizontal polarization in inner beam, and C-band horizontal and Ku-band vertical polarization in outer beam, can be used to measure ocean winds. This study will contribute to the wind retrieval with merged satellites data and the future spaceborne scatterometer.

Mapeamento do Gelo Marinho na Península Antártica com Imagens Sentinel 1A

The sea ice acts as a thermal insulation and in its absence the ocean is free to emit heat to the atmosphere, causing changes in the oceanic circulation. Remote sensing of the sea ice cover comprises several techniques and operates over a wide range of frequencies in the electromagnetic spectrum. This work proposes the classification and identification of free areas of sea ice by means of SAR images in the C band, through Sentinel 1A satellite, under HH polarization and IW imaging mode. The supervised classification was performed using the Random Forest method, with sample training through texture analysis with Sentinel 2 optical images, K-Means unsupervised classification and analysis of HH/HV co-polarizations in Sentinel 1B images under EW imaging. Backscatter coefficients were identified from -10.2 to -13.5 dB for young ice and/or first-year ice, -17.5 to -19.6 dB for new ice, and -23.7 to -26.8 dB to open water in the western region of the Antarctic Peninsula during the austral winter.

Estimasi Cadangan Karbon Biomassa di Atas Permukaan pada Tegakan Mangrove Menggunakan Pengindraan Jauh di Tongke-Tongke, Sulawesi Selatan

Mangrove is one of the most intensive carbon sinks and plays a major role in the carbon cycle. However, the existence of mangrove is decreasing due to land use change that are not in accordance with its allocation, and disrupt the carbon cycle in the ecosystem. This study aims to estimate mangrove carbon stock using remote sensing technique in Tongke-tongke, South Sulawesi. Estimation using remote sensing usually has a low accuracy, therefore this research use multispectral (Landsat) and radar (PALSAR) sensor to increase the accuracy. Total carbon stocks in the study area based on model built for HH and HV polarization were 5662.85 ton and 6431.46 ton, respectively.