HV Polarization Research Articles

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.

Above ground biomass estimation of mangroves located in Negombo - Muthurajawela wetland in Sri Lanka using ALOS PALSAR Images

In Sri Lanka, mangrove forests are scattered along the north-western, north eastern, Jaffna Peninsula and eastern coastal belt. The total estimated extent of mangroves in the country is about 87 km 2 . Estimation of Above Ground Biomass (AGB) of mangroves is a challenging task due to field sampling difficulties. Use of satellite based remote sensing technologies is becoming popular for estimation of AGB for different vegetation types. To overcome the limitations during field sampling and to identify the possibility of using SAR data for AGB estimation, ALOS PALSAR satellite data were used to estimate AGB of mangroves and associated vegetation in Muthurajawela- Negombo wetland in Sri Lanka. Diameter at Breast Height (DBH) measurements over 5cm of eighteen (18) sampling plots (10x10 m) were collected and the relevant allometric equation was used to estimate the AGB. Backscatter coefficient values of HH and HV polarization of ALOS –PALSAR images were used to estimate the AGB of mangroves using a previously derived model. Finally, an AGB map of mangrove associated vegetation was developed for the study area using ALOS PALSAR data as a method of minimizing field work while saving time and cost. According to the results, the average AGB is observed as 65t/ha from the field sampling method (28 -135 t/ha) while it was estimated as 76 t/ha (33-155 t/ha) using PALSAR which shows an overestimation by 17%. A significant overestimation by the remote sensing method is occurred when the tree height is more than 5 m. Though it shows an overestimation, the map developed using this approach is helpful to understand the distribution of AGB within mangrove associated vegetation systems where field sampling is a challenging task.

Examining High-Resolution PiSAR-L2 Textures for Estimating Tropical Forest Carbon Stocks

This study examines the potential of airborne PiSAR-L2 data for estimating forest carbon stocks in central Sumatra. Polarimetric interferometric synthetic aperture radar L-band-2 (PiSAR-L2) is a second-generation airborne sensor developed by JAXA. We acquired full-polarimetric data at a fine spatial resolution of 2.5 m during the PiSAR-L2 flight campaign in August 2012. A total of 59 field measurement plots for aboveground forest carbon stocks (AFCSs) were established in same year where AFCS ranged between 4.8 and $253.5\; \text{Mg}\; \text{C} \;\text{ha}^{-1}$ . The plots comprised natural and plantation forests. These plot-level field data were used for calibrating and validating AFCS estimation models with the SAR data. Various possibilities including direct sigma naught backscatters and their ratios and various types of textures obtained from HH, HV, and VV polarizations were examined by applying regression modeling. The main indicators used for the selection of best potential models in the calibration phase were $\text{R}^{2}$ , variable inflation factor (VIF), p-value, and root-mean-squared errors (RMSEs). The potential models were validated using the leave-one-out (LOO) method. The results indicated that a simple combination of backscatters and their ratios provides an AFCS estimate with an RMSE of $42.37\; \text{Mg}\; \text{C}\; \text{ha}^{-1}$ and an $\text{R}^{2}$ of 0.65. Inclusion of SAR textural parameters improved the AFCS estimates with an RMSE of $30.93\;\text{Mg}\; \text{C}\; \text{ha}^{-1}$ and an $\text{R}^{2}$ of 0.80. This indicates that the airborne PiSAR-L2 full-polarimetric data have the potential to estimate forest carbon stocks with an improved accuracy in the tropical region.

Classification of full-polarization ALOS-PALSAR imagery using SVM in arid area of Dunhuang

Classification is an important process in interpretation of synthetic aperture radar (SAR) imagery. As an advanced instrument for remote sensing, the polarimetric SAR has been applied widely in many fields. The main aim of this paper is to explore the ability of the full-polarization SAR data in classification. The study area is a part of Dunhuang, Gansu Province, China. An L-band full-polarization image of Dunhuang which includes quad-polarization modes was acquired by the ALOS-PALSAR (Advanced Land Observing Satellite-the Phased Array type L-band Synthetic Aperture Radar). Firstly, new characteristic information was extracted by the difference operation, ratio operation, and principal component transform based on the full-polarization (HH, HV or VH, VV) modes SAR data. Then the single-, dual-, full-polarization SAR data and new SAR characteristic information were used to analyze quantitatively the classification accuracy based on the Support Vector Machines (SVM). The results show that classification overall accuracy of single-polarization SAR data is poor, and the highest is 56.53% of VV polarization. The classification overall accuracy of dual-polarization SAR is much better than single-polarization, the highest is 74.77% of HV & VV polarization data. The classification overall accuracy of full-polarization SAR is 80.21%, adding the difference characteristic information, ratio characteristic information and the first principal component (PC1) respectively, the overall accuracy increased by 3.09%, 3.38%, 4.14% respectively. When the full-polarization SAR data in combination with the all characteristic information, the classification overall accuracy reached to 91.01%. The full-polarization SAR data in combination with the band math characteristic information or the PC1 can greatly improve classification accuracy.

Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests

Synthetic Aperture Radar (SAR) backscatter measurements are sensitive to forest aboveground biomass (AGB), and the observations from space can be used for mapping AGB globally. However, the radar sensitivity saturates at higher AGB values depending on the wavelength and geometry of radar measurements, and is influenced by the structure of the forest and environmental conditions. Here, we examine the sensitivity of SAR at the L-band frequency (~25 cm wavelength) to AGB in order to examine the performance of future joint National Aeronautics and Space Administration, Indian Space Research Organisation NASA-ISRO SAR mission in mapping the AGB of global forests. For SAR data, we use the Phased Array L-Band SAR (PALSAR) backscatter from the Advanced Land Observing Satellite (ALOS) aggregated at a 100-m spatial resolution; and for AGB data, we use more than three million AGB values derived from the Geoscience Laser Altimeter System (GLAS) LiDAR height metrics at about 0.16–0.25 ha footprints across eleven different forest types globally. The results from statistical analysis show that, over all eleven forest types, saturation level of L-band radar at HV polarization on average remains ≥100 Mg·ha−1. Fresh water swamp forests have the lowest saturation with AGB at ~80 Mg·ha−1, while needleleaf forests have the highest saturation at ~250 Mg·ha−1. Swamp forests show a strong backscatter from the vegetation-surface specular reflection due to inundation that requires to be treated separately from those on terra firme. Our results demonstrate that L-Band backscatter relations to AGB can be significantly different depending on forest types and environmental effects, requiring multiple algorithms to map AGB from time series of satellite radar observations globally.

Mapping urban impervious surface with dual-polarimetric SAR data: An improved method

Synthetic aperture radar (SAR) data can provide complementary information to improve the mapping of urban impervious surfaces. However, most studies have focused on using only single polarization SAR data. This paper presents a comparative study on the combined use of multispectral optical data and dual polarization SAR data to identify urban impervious surfaces. The experimental results using SPOT-5, TerraSAR-X and ALOS PALSAR data were consistent compared with our previous results using single polarization SAR data. The two-fold result showed that polarimetric SAR images were generally superior to single polarization SAR data for extracting impervious surface areas, although not every individual polarimetric feature could provide a positive result for impervious surfaces mapping. Compared with using only optical and SAR data, the separate HH and HV polarization data improved the accuracy of the results. The incorporation of both Entropy and Alpha features also improved the accuracy. However, the HH/HV ratio and the separate use of coherence did not provide positive results. Noticeably, a combination of all of the dual-polarimetric SAR features was capable of obtaining the best accuracy, with an improvement of approximately 3.5% compared with that of only using SPOT-5 images. This result indicates the superiority of dual-polarimetric SAR data over single polarimetric SAR data for the mapping of urban impervious surfaces.

Soil moisture retrieval over irrigated grassland using X-band SAR data

The aim of this study was to develop an inversion approach to estimate surface soil moisture from X-band SAR data over irrigated grassland areas. This approach simulates a coupling scenario between Synthetic Aperture Radar (SAR) and optical images through the Water Cloud Model (WCM). A time series of SAR (TerraSAR-X and COSMO-SkyMed) and optical (SPOT 4/5 and LANDSAT 7/8) images were acquired over an irrigated grassland region in southeastern France. An inversion technique based on multi-layer perceptron neural networks (NNs) was used to invert the Water Cloud Model (WCM) for soil moisture estimation. Three inversion configurations based on SAR and optical images were defined: (1) HH polarization, (2) HV polarization, and (3) both HH and HV polarizations, all with one vegetation descriptor derived from optical data. The investigated vegetation descriptors were the Normalized Difference Vegetation Index NDVI, Leaf Area Index LAI, Fraction of Absorbed Photosynthetically Active Radiation FAPAR, and the Fractional vegetation COVER FCOVER. These vegetation descriptors were derived from optical images. For the three inversion configurations, the NNs were trained and validated using a noisy synthetic dataset generated by the WCM for a wide range of soil moisture and vegetation descriptor values. The trained NNs were then validated from a real dataset composed of X-band SAR backscattering coefficients and vegetation descriptor derived from optical images. The use of X-band SAR measurements in HH polarization (in addition to one vegetation descriptor derived from optical images) yields more precise results on soil moisture (My) estimates. In the case of NDVI derived from optical images as the vegetation descriptor, the Root Mean Square Error on My estimates was 3.6 Vol.% for NDVI values between 0.45 and 0.75, and 6.1 Vol.% for NDVI between 0.75 and 0.90. Similar results were obtained regardless of the other vegetation descriptor used.

合成開口レーダを用いた遠隔観測型コンクリートひび割れ計測の原理検証実験

This document describes a feasibility study for detecting crack of concrete by using Ka-band (from 26GHz to 40GHz) full polarized synthetic aperture radar (SAR). Recently, it becomes important to detect cracks for checking the damage of infrastructure and used houses. If we need transmissive image through the wallpaper, radio-frequency sensor is an effective solution. We conducted an experiment in radio-frequency anechoic chamber to detect the crack by using full polarized SAR system. The system mainly consisted from a vector network analyzer (VNA), a RF-switch, four horn antennas and an actuator. The four antennas obtained full polarized (VV, VH, HV, HH) SAR imagery. In our experiment, VH or HV polarization is effective for the crack along with the radar line of sight (LOS). On the other hand, VV polarization is effective for the crack across LOS. The experiment shows effectiveness of polarized SAR-based monitoring system.

Estimation of Tropical Forest Structural Characteristics Using ALOS-2 SAR Data

The potential of ALOS-2 SAR data for the estimation of tropical forest structural characteristics was assessed in Vietnam by collecting forest inventory data. The effect of polarization and seasonality of the SAR data on the estimation of forest biomass was analyzed. The combination of HH, HV, and HH/HV polarizations using multiple linear regression did not improve the estimation of biomass compared to using the HV channel independently, as the HH and HH/HV variables were not statistically significant. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies was not very sensitive to the biomass. The strong dependence of the biomass estimates with the season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. The validation results showed that the dry season HV polarization could explain 54% variation of the biomass.

Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data using Random Forests

The European CORINE land cover mapping scheme is a standardized classification system with 44 land cover and land use classes. It is used by the European Environment Agency to report large-scale land cover change with a minimum mapping unit of 5 ha every six years and operationally mapped by its member states. The most commonly applied method to map CORINE land cover change is by visual interpretation of optical/near-infrared satellite imagery. The Sentinel-1A satellite carries a C-band Synthetic Aperture Radar (SAR) and was launched in 2014 by the European Space Agency as the first operational Copernicus mission. This study is the first investigation of Sentinel-1A for CORINE land cover mapping. Two of the first Sentinel-1A images acquired during its ramp-up phase in May and December 2014 over Thuringia in Germany are analysed. 27 hybrid level 2/3 CORINE classes are defined. 17 of these were present at the study site and classified based on a stratified random sample of training pixels from the polygon-eroded CORINE 2006 map. Sentinel-1A logarithmic radar backscatter at HH and HV polarisation (May acquisition), VV and VH polarisation (December acquisition), and the HH image texture are used as input bands to the classification. In addition, a Digital Terrain Model (DTM), a Canopy Height Model (CHM) and slope and aspect maps from the Shuttle Radar Topography Mission (SRTM) are used as input bands to account for geomorphological features of the landscape. In future, elevation data will be delivered for areas with sufficiently high coherence from the Sentinel-1A Interferometric Wide-Swath Mode itself. When augmented by elevation data from radar interferometry, Sentinel-1A is able to discriminate several CORINE land cover classes, making it useful for monitoring of cloud-covered regions. A bistatic Sentinel-1 Convoy mission would enable single-pass interferometric acquisitions without temporal decorrelation.

Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI

Forests and their changes are important to the regional and global carbon cycle, biodiversity and ecosystem services. Some uncertainty about forest cover area in China calls for an accurate and updated forest cover map. In this study, we combined ALOS PALSAR orthorectified 50-m mosaic images (FBD mode with HH and HV polarization) and MODIS time series data in 2010 to map forests in China. We used MODIS-based NDVI dataset (MOD13Q1, 250-m spatial resolution) to generate a map of annual maximum NDVI and used it to mask out built-up lands, barren lands, and sparsely vegetated lands. We developed a decision tree classification algorithm to identify forest and non-forest land cover, based on the signature analysis of PALSAR backscatter coefficient data. The PALSAR-based algorithm was then applied to produce a forest cover map in China in 2010. The resulting forest/non-forest classification map has an overall accuracy of 96.2% and a Kappa Coefficient of 0.91. The resultant 50-m PALSAR-based forest cover map was compared to five forest cover databases. The total forest area (2.02×106km2) in China from the PALSAR-based forest map is close to the forest area estimates from China National Forestry Inventory (1.95×106km2), JAXA (2.00×106km2), and FAO FRA (2.07×106km2). There are good linear relationships between the PALSAR-based forest map and the forest maps from the JAXA, MCD12Q1, and NLCD-China datasets at the province and county scales. All the forest maps have similar spatial distributions of forest/non-forest at pixel scale. Our PALSAR-based forest map recognizes well the agro-forests in China. The results of this study demonstrate the potential of integrating PALSAR and MODIS images to map forests in large areas. The resultant map of forest cover in China in 2010 can be used for many studies such as forest carbon cycle and ecological restoration.

Soil Salinity Characterization Using Polarimetric InSAR Coherence: Case Studies in Tunisia and Morocco

The phenomenon of soil salinization in semi-arid regions is getting amplified and accentuated by both anthropogenic practices and climate change. Land salinization mapping and monitoring using conventional strategies are insufficient and difficult. Our work aims to study the potential of synthetic aperture radar (SAR) for mapping and monitoring of the spatio-temporal dynamics of soil salinity using interferometry. Our contribution in this paper consists of a statistical relationship that we establish between field salinity measurement and InSAR coherence based on an empirical analysis. For experimental validation, two sites were selected: 1) the region of Mahdia (central Tunisia) and 2) the plain of Tadla (central Morocco). Both sites underwent three ground campaigns simultaneously with three Radarsat-2 SAR image acquisitions. The results show that it is possible to estimate the temporal change in soil electrical conductivity (EC) from SAR images through the InSAR technique. It has been shown that the radar signal is more sensitive to soil salinity in HH polarization using a small incidence angle. However, for the HV polarization, a large angle of incidence is more suitable. This is, under considering the minimal influence of roughness and moisture surfaces, for a given InSAR coherence.

Accuracy Assessment of IWCM Soil Moisture Estimation Model in Different Frequency and Polarization Bands

Radar backscattering coefficient has high dependence to dielectric constant of soil and many efforts have been done in the past to estimate soil moisture using Synthetic Aperture Radar (SAR). Soil moisture estimation in vegetated areas has some limitations and difficulties due to the effects of vegetation cover and soil surface roughness on radar backscattering coefficient. One of the widely used soil moisture estimation models in vegetated areas is Water Cloud Model (WCM) which has been improved and known as Improved Water Cloud Model (IWCM) recently. One way of improving soil moisture estimation accuracy in vegetated areas is to use optimum frequency and polarization band so as to minimize the effects of soil surface roughness and vegetation cover on radar back scattering coefficient. In this research, the accuracies of IWCM in different frequencies and polarizations have been assessed. The results showed that the IWCM has its highest accuracy in L-band, HV polarization mode. Also, by using the IWCM, sensitivities of radar waves to moisture of 0–3, 3–6 and 0–6 cm soil depths have been studied. The results demonstrated that radar waves have more sensitivity to the moisture content of 0–3 cm soil depth.

Impacts of Feature Normalization on Optical and SAR Data Fusion for Land Use/Land Cover Classification

Land use/land cover (LULC) classification using optical and synthetic aperture radar (SAR) remote sensing images is becoming increasingly important to produce more accurate LULC products. As an important step, feature normalization techniques have been studied by the areas of pattern recognition. Nevertheless, because of the totally different imaging mechanisms of optical and SAR sensors, most of the existing normalization approaches are not suitable for optical and SAR data fusion. Moreover, whether normalization is a significant step remains unclear regarding optical and SAR fusion. Taking the Satellite Pour l'Observation de la Terre (SPOT-5) and the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band SAR (PALSAR) (HH and HV polarizations) as the optical and SAR data, this letter aims to evaluate the impact of feature normalization. Experimental results indicated that feature normalization is not necessarily significant depending on fusion methods. For instance, distribution-dependent classifiers (e.g., a maximum likelihood classifier) are independent of feature normalization; thus, it has no impact on the results when using these classifiers. Moreover, advanced classifiers (e.g., a support vector machine) with built-in normalization are also not influenced by feature normalization. In contrast, a minimum distance classifier and an artificial neural network (ANN) depend on the input values of optical and SAR features and thus can be influenced by feature normalization. However, our experiments showed a fluctuation in classification accuracy using an ANN with normalized features. Therefore, more experiments are required to investigate the optimal normalization approaches for the optical and SAR images when using an ANN as the fusion method.

Forest and Forest Change Mapping with C- and L-band SAR in Liwale, Tanzania

Abstract. As part of a Tanzanian-Norwegian cooperation project on Monitoring Reporting and Verification (MRV) for REDD+, 2007-2011 Cand L-band synthetic aperture radar (SAR) backscatter data from Envisat ASAR and ALOS Palsar, respectively, have been processed, analysed and used for forest and forest change mapping over a study side in Liwale District in Lindi Region, Tanzania. Land cover observations from forest inventory plots of the National Forestry Resources Monitoring and Assessment (NAFORMA) project have been used for training Gaussian Mixture Models and k-means classifier that have been combined in order to map the study region into forest, woodland and non-forest areas. Maximum forest and woodland extension masks have been extracted by classifying maximum backscatter mosaics in HH and HV polarizations from the 2007-2011 ALOS Palsar coverage and could be used to map efficiently inter-annual forest change by filtering out changes in non-forest areas. Envisat ASAR APS (alternate polarization mode) have also been analysed with the aim to improve the forest/woodland/non-forest classification based on ALOS Palsar. Clearly, the combination of C-band SAR and L-band SAR provides useful information in order to smooth the classification and especially increase the woodland class, but an overall improvement for the wall-to-wall land type classification has yet to be confirmed. The quality assessment and validation of the results is done with very high resolution optical data from WorldView, Ikonos and RapidEye, and NAFORMA field observations.

Signal Level Comparison Between TerraSAR-X and COSMO-SkyMed SAR Sensors

Soil and vegetation biophysical parameter retrieval using synthetic-aperture-radar images requires radiometrically well-calibrated sensors. In this letter, a comparison of signal levels between TerraSAR-X (TSX) and the COSMO-SkyMed (CSK) constellation (CSK1, CSK2, CSK3, and CSK4) was carried out in order to analyze the ability to use jointly all current X-band sensors. The analysis of the X-band signal over forest stands showed a stable signal (variation lower than 1 dB) over time for each of the studied sensors, but a significant difference was observed between the different X-band sensors. Differences between radar signals were higher in HH than in HV polarization. TSX and CSK4 showed similar backscatter signals, with signal level differences of 0.6 dB in HH and 1.4 dB in HV. The CSK3 signal was observed to be lower than those from TSX and CSK4 by about 2.1 dB and 1.5 dB in HH against 3.2 dB and 1.8 dB in HV, respectively. Moreover, CSK2 and CSK1 which showed slightly different backscatter signals (within 1.1 dB in HH and 1.9 dB in HV) had signal levels lower than those obtained from TSX (2.2-3.3 dB in HH and 3.2-5.1 dB in HV for about 29° incidence angle). These results show that it is currently difficult to use jointly the available X-band satellites (CSK and TSX) for estimating the biophysical parameters of soil or vegetation. This is due to the significant difference in the radar signal level between some of the analyzed satellites, which will cause a high overor underestimation of biophysical parameters.

Geomorphology Analysis of Lava Flow of Mt. Guntur in West Java Using Synthetic Aperture Radar (SAR) with Fully Polarimetry

Mt. Guntur is one of active volcanoes in Indonesia, which has a very unique product of lava flows than other volcanoes. In order to minimize the adverse effects of volcanic eruptions of Mt. Guntur, one mitigation measure that can be done is to map the volcano landform through geomorphological analysis and identification of lava flow characteristics. It is very obvious that Mt. Guntur lava flow is visible and clear so it is possible to do the landform lava flow mapping. However, this kind of study is very rare especially in Indonesia. Thus, this research aims to analyse the geomorphologic and identify lava flows using high-resolution optical imagery through characterization (signature) landforms by using L band SAR polarimetry backscattering in combination with decision tree classification techniques using the QUEST algorithm (Quick, Unbiased, Efficient Statistical Trees). Moreover, the accuracy of the classification results is calculated using matrix analysis accuracy and Kappa coefficient calculation. In addition, the characterization of the object is also performed using analysis of spectral separation. The analysis showed that the geomorphological analysis can be used for volcanic (V) landforms mapping based on morfocronologi aspect. The identification of the image depends on the spatial resolution of the image. The objects were identified using IKONOS imagery, Google Earth, and PALSAR imagery. The results of showed that the HV and VV polarization serves as the best combination to identify the lava flows. Classification results showed a good accuracy value which is 51.80% with a Kappa coefficient of 0.43. This suggests that the identification of a lava flow using linear polarization combined with decision tree classification has a good level of confidence.

Interrelationship Analysis of L-Band Backscattering Intensity and Soil Dielectric Constant for Soil Moisture Retrieval Using PALSAR Data

The purpose of this paper is to study about the interrelationship between the backscattering intensity of PALSAR data and the laboratory measurement of dielectric constant and soil moisture. The characterization of the dielectric constant of arid soils in the 0.3 - 3 GHz frequency range, particularly focused in L-band was analyzed in varied soil moisture content and soil textures. The interrelationship between the relative dielectric constant with soil textures and backscattering of PALSAR data was also analyzed and statistical model was computed. In this study, after collecting the soil samples in the field from top soil (0 - 10 cm) in a homogeneous area then, the dielectric constant was measured using a dielectric probe tool kit. For investigated of the characteristics and behaviors of the dielectric constant and relationship with backscattering a variety of moisture content from 0% to 40% and soil fraction conditions was tested in laboratory condition. All data were analyzed by integrating it with other geophysical data in GIS, such as land cover and soil texture. Thus, the regression model computed between measured soil moisture and backscattering coefficient of PALSR data which were extracted as same point of each soil sample pixel. Finally, after completing the preprocessing, such as removing the speckle noise by averaging, the model was applied to the PALSAR data for retrieving the soil moisture map in arid region of Iran. The analysis of dielectric constant properties result has shown the soil texture after the moisture content has the largest effected on dielectric constant. In addition, the PALSAR data in dual polarization are also able to derive the soil moisture using statistical method. The dielectric constant and backscattering shown have the exponential relationship and the HV polarization mode is more sensitive than the HH mode to soil moisture and overestimated the soil moisture as well. The validation of result has shown the 4.2 Vol-% RMSE of soil moisture. It means that the backscattering analysis should consider about other factors such a surface roughness and mix pixel of vegetation effective.

Comparison of Simulated Backscattering Signal and ALOS PALSAR Backscattering over Arid Environment Using Experimental Measurement

The purpose of this paper is to simulate the backscattered signal by experimental data and field working then, comparing with the backscattered signal from actual L-band SAR data over arid to semi-arid environments. The experimental data included the laboratory-measured dielectric constant of soil samples and the roughness parameter. A backscattering model used to simulate the backscattering coefficient in sparse vegetation land cover. The backscattering coefficient (σ0) simulated using the AIEM (advanced integral equation model) based on the experimental data. The roughness data were considered by the field observation, chain method measuring and photogrammetry simulation technique by stereo image of ground real photography. The simulated backscattering coefficients were compared with the real extracted backscattering coefficient (σ0) from the ALOS PALSAR single and dual polarization mode data. The most problem in backscattering simulation was the vegetation water content. Therefore, the water-cloud model using the water index result of optical data applied on the simulated backscatter model for enhancement the backscattering heterogeneity from vegetation water contents due to the mix pixel of vegetation in spars vegetation. At the results the AIEM model overestimated the backscattering simulation, it might be cause of high sensitivity of this model to roughness. The ALOS PALSAR HV polarization mode is more sensitive than the HH mode to vegetation water content. The water-cloud model could improve the result and the correlation function of the samples was increased but, the difficulties were the input the A and B parameters to model.

Mapping the 2010 Merapi pyroclastic deposits using dual-polarization Synthetic Aperture Radar (SAR) data

L-band ALOS-PALSAR images acquired before, during and after the 2010 Merapi eruption have been used to classify and map the pyroclastic deposits emplaced during this VEI-4 event. We characterize the deposits using direct-polarized and cross-polarized L-band SAR data and by combining the information of amplitude evolution with temporal decorrelation. Changes in amplitude of the radar signal enable us to map the pyroclastic density currents (PDCs) and tephra-fall deposits. Radar amplitudes in direct (HH) and cross (HV) polarizations decrease where the valley-confined and overbank block-and-ash flow (BAF) deposits (D1) are emplaced. Rainfall- and runoff-reworked PDC deposits (D2) are characterized by an increase in ground backscattering for HH polarization and a decrease for HV polarization. Ground backscattering transiently increases in both polarizations after pyroclastic surge (D3) and tephra fall (D4) deposition. We use a supervised classification method based on maximum likelihood to map the deposits D1–D4. The temporal decorrelation of the radar signal and the amplitude evolution improve the quality of classification results. Classification derived from ALOS-PALSAR images using the maximum likelihood classification provides a result with 70% classification accuracy for deposits overall. The estimated areas of valley-confined and overbank PDC deposits (either primary or reworked by rainfall and runoff) are consistent with the areas measured by other studies, while the large discrepancy in area estimated for pyroclastic-surge deposits can be partly explained by the strong erosion due to intense rainfall that removed a large part of these thin deposits.