VV Polarization Research Articles

Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data

Accurate and timely monitoring of flooded crop areas is crucial for disaster rescue and loss assessment. However, most flooded crop monitoring methods based on synthetic aperture radar (SAR) imagery were developed for rice, which is probably inappropriate for crops with complex canopy structures that strongly attenuate SAR signals. Additionally, these methods often rely on empirical thresholds and region-specific reference samples, limiting their reliability and applicability on a larger spatial scale. To address these issues, we developed a novel flooded crop mapping approach at a regional scale using Sentinel-1 time-series data and an unsupervised Gaussian Mixture Model (GMM). Our approach leverages a Flood Separability Index (FSI) derived from the fitted probability density function of flooded and non-flooded crop areas in a GMM. This allows us to overcome the limitations of manual input selection in previous studies. The multi-temporal GMM was constructed using the time-series images with optimal polarization to estimate the flooded crop extents on a regional scale. We also investigated the scattering mechanisms of three typical crop disaster structures within an agricultural landscape area. Our results indicate that the proposed multi-temporal GMM is robust in crop planting areas with complex canopy structures. The performance of both single-temporal and multi-temporal GMMs surpasses that of baseline methods such as Otsu and K-means. Compared with VV polarization, VH polarization exhibits greater potential for accurately mapping flooded crops in complex agricultural regions. Our approach does not require labeled samples or many predefined parameters, making it fast and feasible for mapping flooded crops with complex canopy structures in large spatial areas.

Rice crop growth monitoring with sentinel 1 SAR data using machine learning models in google earth engine cloud

The rainfed rice crop monitoring and yield prediction have been Herculean task with optical remote sensing systems operation under cloud cover. The free-of-cost sentinel 1 based SAR data along with machine learning models in GEE cloud was used for rainfed rice crop monitoring for 214 farm plots on a micro-scale in Hooghly, West Bengal, India. The individual plot rice parcel showed a low median backscattering signature for the SAR data during the land preparation/crop transplanting stages with VH and VV at −17.63 dB and −9.63 dB, respectively; whereas, higher median backscattering was experienced at the peak vegetation stage of VH and VV Polarization with −15.20 dB and −6.34 dB, respectively. The random forest model was found best suited with R2 of 0.87 for total crop biomass estimation. The backscatter values have a sound correlation with Heading NDVI, which validated the suitability of SAR images for crop monitoring under rainfed conditions. Further, crop yield prediction using SAR data and total biomass data through machine learning models showed positive correlation for Random forest, Extreme gradient boosting, and Decision tree models with an Area under receiver operating characteristics curve (AUROC) test accuracy of 0.99. These low-cost, high temporal SAR data based models can be used for near real-time crop monitoring even under overcast conditions in near future.

Sea Clutter Feature Prediction and Parameters Inversion Using Deep Learning Model

The characteristics of sea clutter in real marine environments in different sea areas play a vital role in military industry such as radar detection, remote sensing, SAR imaging, and situational awareness. In this paper, a deep neural network sea clutter model is proposed based on the sea clutter big data under the real marine environment to study the characteristics of sea clutter and parameter inversion. Based on the ERA-Interim reanalysis (2015-2017), a database of marine environmental elements in China's offshore waters was established, and a spatiotemporal prediction model for marine environmental elements was proposed to improve missing values. Considering the scattering mechanism of sea surface at different scales comprehensively, a large database of sea clutter time series of multi-scale real surface is established and comparison with the experimental data. Aiming at the coastal waters of China, the LSTM model and deep neural network (DNN) are used to establish the correlation model between marine environmental elements and sea clutter characteristics, and the prediction and parameter inversion of sea clutter characteristics based on sea clutter big data are studied. The results shown that the coefficients of determination of the predicted fitted curves for the HH and VV polarization amplitudes reach 0.9249 and 0.8872, respectively. The inversion of wave heights in different sea areas is the lowest in the South China Sea (accuracy rate of 78%) and the highest in the East China Sea (accuracy rate is 90%). The results of this paper can help improve the ability of sea surface remote sensing and sea clutter suppression.

Design of Ultrawideband RCS Reduction Metasurface Using Space Mapping and Phase Cancellation

An efficient optimizing method of ultra-wideband radar cross-section (RCS) reduction metasurface involve unit amplitude differences and mutual coupling effects is proposed in the letter. Multiple unit phase cancellation was used to broaden RCS-reduction bandwidth. The genetic algorithm was utilized to further design and select the optimal combination from various tiles. To improve the optimization efficiency and accuracy, the space mapping (SM) technique was exploited to refine the design process, which involves the interactions of scattering fields among tiles and subarrays in wideband. Meanwhile, the constraint of wide-angle oblique incidences is also considered in wideband low RCS design. An ultra-wideband RCS reduction checkboard meatsurface can be accomplished by fast optimization of low-cost coarse model and few parameter mapping courses between fine and coarse model. A 10dB RCS reduction for HH and VV polarizations under normal and other oblique incidence angles can be achieved from 5 to 40 GHz. Good agreements between simulation and experiment results are obtained.

Spatio-temporal analysis of surface water extraction methods reliability using COPERNICUS satellite data

The aim of this research is the comparison and subsequent evaluation of the suitability of using SAR (Synthetic Aperture Radar) and multispectral (MSI) satellite data of the Copernicus program for mapping and accurate identification of surface water bodies. The paper considers sudden changes caused by significant climatological-meteorological influences in the country. The surface guidance extraction methodology includes the standard preprocessing of SAR images and concluding the determination of threshold values in binary mask generation. For MSI images, water masks are generated through automatic algorithmic processing on the Google Earth Engine cloud platform. During SAR image processing, it has been found that the VV polarization configuration type (vertical-vertical) is the most suitable. The Lee and Lee Sigma filters are recommended for eliminating radar noise. The chosen window size for filtering depends on the specific object and its spatial extent. The extraction of water surfaces from the MSI image is conducted using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and Water Ratio Index (WRI). Results are evaluated both graphically and numerically, using quantitative accuracy indicators to refine them. Automatic extraction of water surfaces from MSI images in the GEE platform environment is a fast, efficient, and relatively accurate tool for determining the true extent of groundwater. In conclusion, this research can provide more reliable estimates of hydrological changes and interannual variations in water bodies in the country. When combined with multitemporal monitoring, these results can be an effective tool for permanent monitoring of floods and droughts.The aim of this research is the comparison and subsequent evaluation of the suitability of using SAR (Synthetic Aperture Radar) and multispectral (MSI) satellite data of the Copernicus program for mapping and accurate identification of surface water bodies. The paper considers sudden changes caused by significant climatological-meteorological influences in the country. The surface guidance extraction methodology includes the standard preprocessing of SAR images and concluding the determination of threshold values in binary mask generation. For MSI images, water masks are generated through automatic algorithmic processing on the Google Earth Engine cloud platform. During SAR image processing, it has been found that the VV polarization configuration type (vertical-vertical) is the most suitable. The Lee and Lee Sigma filters are recommended for eliminating radar noise. The chosen window size for filtering depends on the specific object and its spatial extent. The extraction of water surfaces from the MSI image is conducted using the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), a pair of Automated Water Extraction Index (AWEI) indices, and Water Ratio Index (WRI). Results are evaluated both graphically and numerically, using quantitative accuracy indicators to refine them. Automatic extraction of water surfaces from MSI images in the GEE platform environment is a fast, efficient, and relatively accurate tool for determining the true extent of groundwater. In conclusion, this research can provide more reliable estimates of hydrological changes and interannual variations in water bodies in the country. When combined with multitemporal monitoring, these results can be an effective tool for permanent monitoring of floods and droughts.

SAR2NDVI: a pix2pix generative adversarial network for reconstructing field-level normalized difference vegetation index time series using Sentinel-1 synthetic aperture radar data

The normalized difference vegetation index (NDVI) is essential for monitoring urban green space, forest cover, and crop growth from sowing to harvesting. High-resolution images from Sentinel-2 and Landsat-8 satellites are available nowadays to get field-level phenological information at short intervals. However, cloud cover is one of the biggest hindrances in vegetation monitoring using NDVI, resulting in data gaps. To address this issue, we propose an NDVI gap-filling methodology to generate cloud-free NDVI time series from Sentinel-1 SAR data using the pix2pix generative adversarial network (GAN) model. Pix2pix GANs with generators based on U-Net and ResNet were designed, and the performance of the models was compared for both the VV and VH polarizations. The generalization capability of the models was studied using synthetic aperture radar (SAR) and NDVI image pairs with different vegetation, field sizes, and shapes. Experimental results show that the generated synthetic NDVI images can effectively substitute the cloudy images for gap-filling. Compared to ResNet, the U-Net generator has given the best results with PSNR = 29.447 dB, RMSE = 0.055, and Pearson correlation coefficient ρ = + 0.909 for VH polarization.

Composite Backscatter Characteristics of Conductive/Dielectric Ships and Sea Surfaces with Breaking Waves under High Sea Conditions.

When a radar detects marine targets, the radar echo is influenced by the shape, size and dielectric properties of the targets, as well as the sea surface under different sea conditions and the coupling scattering between them. This paper presents a composite backscattering model of the sea surface and conductive and dielectric ships under different sea conditions. The ship scattering is calculated using the equivalent edge electromagnetic current (EEC) theory. The scattering of the sea surface with wedge-like breaking waves is calculated using the capillary wave phase perturbation method combined with the multi-path scattering method. The coupling scattering between ship and sea surface is obtained using the modified four-path model. The results reveal that the backscattering RCS of the dielectric target is significantly reduced compared with the conducting target. Furthermore, the composite backscattering of the sea surface and ship increases significantly in both HH and VV polarizations when considering the effect of breaking waves under high sea conditions at low grazing angles in the upwind direction, especially for HH polarization. This research offers valuable insights into optimizing radar detection of marine targets in varying sea conditions.

Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations

In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014–2018) with 1-km Sentinel-1 backscatter (γ°) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019–2021), the γ° simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 γ° remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil γ° holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.

A Near-Real-Time Flood Detection Method Based on Deep Learning and SAR Images

Owning to the nature of flood events, near-real-time flood detection and mapping is essential for disaster prevention, relief, and mitigation. In recent years, the rapid advancement of deep learning has brought endless possibilities to the field of flood detection. However, deep learning relies heavily on training samples and the availability of high-quality flood datasets is rather limited. The present study collected 16 flood events in the Yangtze River Basin and divided them into three categories for different purpose: training, testing, and application. An efficient methodology of dataset-generation for training, testing, and application was proposed. Eight flood events were used to generate strong label datasets with 5296 tiles as flood training samples along with two testing datasets. The performances of several classic convolutional neural network models were evaluated with those obtained datasets, and the results suggested that the efficiencies and accuracies of convolutional neural network models were obviously higher than that of the threshold method. The effects of VH polarization, VV polarization, and the involvement of auxiliary DEM on flood detection were investigated, which indicated that VH polarization was more conducive to flood detection, while the involvement of DEM has a limited effect on flood detection in the Yangtze River Basin. Convolutional neural network trained by strong datasets were used in near-real-time flood detection and mapping for the remaining eight flood events, and weak label datasets were generated to expand the flood training samples to evaluate the possible effects on deep learning models in terms of flood detection and mapping. The experiments obtained conclusions consistent with those previously made on experiments with strong datasets.

SAR and InSAR data linked to soil moisture changes on a temperate raised peatland subjected to a wildfire

Interferometry of Synthetic Aperture Radar (InSAR) can potentially contribute to the cost-effective regional or global monitoring of the degradation and restoration of peatlands. However, there are uncertainties about the links between InSAR results and peatland ecohydrological parameters, especially soil moisture. Here, we analyse the relationships between the temporal evolutions of InSAR coherence, ground displacements, and in-situ soil moisture measurements for a temperate raised bog at Ballynafagh, Co. Kildare, Ireland, in the period 2017-mid-2021. We also investigate the effects of a wildfire in June–July 2019 on those relationships. InSAR-derived ground displacements from Sentinel-1 C-band radar data indicate long-term subsidence of the intact and Active Raised Bog. Superimposed on the long-term displacement trends are annual oscillations that are linked to variations in rainfall and temperature and that are in phase with changes in soil moisture. We show that InSAR coherence is directly related to the change in soil moisture, with large changes causing coherence decrease or loss. The wildfire removed a 10–20 cm thick mossy vegetation layer across 60–70% of the intact bog area. The SAR backscatter intensity in VV polarisation increased after the wildfire, but the InSAR coherence, the InSAR-derived surface displacements and the soil moisture were not noticeably affected. We therefore infer that C-band radar waves attenuate in the active vegetation layer, but penetrate through it into the upper few cm of the underlying peat. The SAR backscatter occurs primarily at this level in the peat, where its coherence is controlled by the soil moisture. These findings underpin application and interpretation of radar for monitoring of peatlands, even if affected by wildfires, which are forecast to increase in both frequency and intensity due to global warming.

Effects of Wind Wave Spectra, Non-Gaussianity, and Swell on the Prediction of Ocean Microwave Backscatter with Facet Two-Scale Model

The image intensity of high-resolution synthetic aperture radar (SAR) is closely related to the facet scattering distribution. In this paper, the effects of wind wave spectra, non-Gaussianity of the sea surface, and swell on the distribution of the facet normalized radar cross section (NRCS) simulated by the facet two-scale model (TSM) are analyzed by comparing the simulated results with the Sentinel-1 SAR data, the Advanced Scatterometer (ASCAT) data, and the geophysical model function (GMF) at the wind speed range of 3–16 m/s, the wind direction range of 0°–360°, and the incidence angle range of 30°–50° under VV and HH polarizations. The results show that the Apel spectrum achieves a more consistent mean NRCS and NRCS distribution with the reference data at low incidence angles, while the composite spectra perform better at high incidence angles under VV polarization. Under HH polarization, the Apel spectrum always has a better performance. The upwind–downwind asymmetry of backscattering can be predicted well by the modified TSM, which is constructed by incorporating bispectrum correction into the conventional TSM. The distribution of the scattering simulated by the modified TSM deviates from the Gaussian distribution significantly, which is in good agreement with the Sentinel-1 data. Additionally, the introduction of swell widens the spread of the NRCS distribution, and the fluctuation range of the NRCS profile considering swell is larger than that without swell. All these changes caused by the introduction of swell make the distribution of the facet scattering more consistent with the Sentinel-1 data. Moreover, the scattering image patterns and scattering image spectrum of the Sentinel-1 data can be successfully simulated at various sea states with the consideration of swell.

USING MACHINE LEARNING CLASSIFIERS AND REGRESSION MODELS FOR ESTIMATING THE STAND AGES OF FALCATA PLANTATIONS FROM SENTINEL DATA

Abstract. Falcata is a widely planted Industrial Tree Plantation (ITP) species in the Caraga Region, Mindanao, Philippines, significantly contributing to more than 80% of the country's Falcata log production in recent years. Currently, Falcata plantations face several challenges, especially regarding plantation monitoring and management. The information on stand age is essential for the efficient monitoring and sustainable management of ITPs. With advances in technology and freely available satellite data, primarily from the Sentinel satellites, remote sensing has provided an alternative approach to determining stand age information. In this context, this study used multivariate regression models and machine learning classifiers, namely Support Vector Machine (SVM) and Random Forest (RF) to estimate Falcata stand ages from single-date and multitemporal Sentinel-1 and Sentinel-2 images and vegetation indices (VIs). The eleven multivariate regression models have R2 values ranging from 0.23 and 0.81 and with estimation errors of 1.72 to 3.58 years. The best multivariate regression model developed is an exponential model that relates Falcata stand age with the year 2021 Sentinel-2 surface reflectance bands and year 2021 Sentinel-1 VV and VH polarization bands. The model has training and validation data R2 of 0.61 and 0.55, which are the most consistent among the 11 regression models developed. When used for stand age estimation, this model may underestimate, or overestimate stand age by 1.72 years. When a machine learning approach is to be employed, the RF classifier performed better than SVM, particularly when estimating stand ages using multitemporal Sentinel-1 and Sentinel-2 data and VIs. The classifier has an overall accuracy of 84.69%, the highest among the eight classification results generated by the study.

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

Validation and calibration of partitioned integral ocean wave parameters from co-polarized synthetic aperture radar data

The inversion of wave spectrum is the embodiment of the fine observation of ocean swells by synthetic aperture radar. Due to the nonlinear distortion from velocity bunching, the operational Sentinel-1 Wave Mode (WV) level-2 (L2) Ocean (OCN) wave products retrieved by quasi-linear inversion are still quite different from the real wave spectrum, requiring further calibration. Limited by the azimuth cut-off, the quasi-linear inversed wave spectrum mostly concentrates on swells with a wavelength longer than 150 m. 2D spectral partitioning separates different swell systems, denoted as partitions in the Sentinel-1 WV L2 OCN product. We compared and calibrated the Sentinel-1 WV L2 OCN products' swell partitions (More specifically partitioned integral ocean wave parameters: partitioned wave height, partitioned wavelength, and partitioned wave direction) with WAVEWATCH III (WW3) in different polarization modes (HH and VV polarization) and different incidence angles (∼23°-WV1 and ∼ 36°-WV2). The accuracy of swell partitions in the OCN products varies with peak wave direction relative to the Sentinel-1 flight direction, and it is difficult to calibrate the partitions derived from quasi-linear inversion unless deleting partitions severely distorted by nonlinearity and large-scale features. A machine learning method is proposed to calibrate the wave partitions. Spectral distances of collocated Sentinel-1 and WW3 partition pairs larger than two are considered “bad” partitions to be filtered. Inputting high-dimensional features (twenty-one features) derived from Sentinel-1 WV L2 OCN products into the extreme gradient boosting (XGBoost) model can effectively reduce the bias (root-mean-square error, RMSE) of significant wave height (SWH) of all the partitions from ∼0.10 m (∼0.70 m) to ∼0.00 m (∼0.46 m) and the bias (RMSE) of wavelengths of all the partitions from ∼20 m (∼35 m) to ∼0.20 m (∼26 m) compared with WW3 swell partitions. This method improves the precision of the wave partition and retains more partitions in the Sentinel-1 WV L2 OCN products than previous studies, especially for wave partitions with moderate wave height (< 4 m), potentially improving the understanding of swell dynamics globally.

A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l’Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data.

Sentinel-1 SAR Polarization Combinations for Flood Inundation Spatial Distribution Mapping (Case Study: South Kalimantan)

41.98% of disaster events occurred in 2021 in Indonesia were flood disasters. Rain of 2.08 billion m3 during the second week of January 2021 occurred in South Kalimantan, Indonesia, where the volume of rainwater was not proportional to the capacity of the Barito Watershed. It caused flooding in 11 cities/regencies. In the discourse of flood disaster mitigation, it is important to be able to map the flood inundation spatial distribution. Research with remote sensing methods, especially active remote sensing, which can penetrate clouds and free from weather disturbances can be an option in analysing flood inundation distribution. Sentinel-1 is one of the active remote sensing with Synthetic Aperture Radar (SAR) C-band imaging mission which is being widely used for flood disaster analysis. Generally, research on flooding is carried out using only VV or VH polarization. Only some researches use combination of both polarizations. Therefore, in this paper, combinations of those two polarizations are used in order to explore the use polarization combination in mapping the distribution of flood inundation, especially for case studies of flood disasters in South Kalimantan. However, in this study case, VV and VH polarization combination are not suitable in mapping flood inundation. VH polarization only showed best result in mapping flood inundation with overall accuracy of 89%, while VV and VH polarization combination resulted overall accuracy of 73%.

Estimation of above-ground carbon of mangrove forest using Sentinel-1 (SAR) and field inventory data: a case study in Quang Yen, Quang Ninh province

Tide-dominated mangroves are found along shallow shorelines with modest slope where they receive freshwater runoff and nutrients from rainfall. They have been globally recognized as they play a vital role in preventing coastal erosion, mitigating effects of wave actions, and protecting coastal habitats and adjacent shoreline land-uses from extreme coastal events. By using Sentinel-1A data, the study has constructed the spatial distribution of mangrove cover compared to PlanetScope image in Quang Yen. In fact, this study used the Sentinel-1A-derived VV/VH polarizations for mangrove cover mapping with thresholds of -12.8&lt;VH&lt;-3.8 and -16.65&lt;VV&lt;-3.3. Upon using VV and VH polarizations for mangrove cover mapping compared to PlanetScope data, it has been confirmed that these polarizations are suitable for mangrove cover monitoring along the coast of Quang Yen with overall accuracy over 92.0% and Kappa coefficient greater than 0.80 in 2022. This study also developed mangrove AGB models based on the field survey data and SAR data for estimating AGB and AGC of mangrove forests in Quang Yen. Overall, selected AGB models (AGB = 288.17 + 21.57*VV, R2=0.553, p-value&lt;0.001) have provided promising options for carbon estimation and monitoring to the coastal areas where the cloudy cover is mostly present and optical remote sensing data is usable. To have more accurate AGB models based on SAR data, this study also suggests that further study should be carried out using more advanced machine learning models based on Sentinel-1A and Sentinel-1B for carbon estimation of mangrove forests in Quang Yen.

A Block-Scale FFT Filter Based on Spatial Autocorrelation Features of Speckle Noise in SAR Image

In order to reduce the impact of noise on the accuracy of inversion products based on SAR images, many filtering algorithms have been developed for noise reduction of SAR images. This paper proposes a filtering method based on the spatial autocorrelation feature of the block fast Fourier transform (BFFT). The method statistically analyses the autocorrelation length of speckle noise on Sentinel-1B images for different features and then constructs a relationship between autocorrelation length and noise period. After that, the size of the optimal FFT filtering window radius was determined based on the relationship between the noise period and the components in the image frequency domain. Finally, we filtered the SAR image within the parcels. We compared BFFT with six commonly used filtering methods. The results show that: (1) The noise periods of the soybean, corn, paddy, and water objects on the SAR image have little difference, with noise periods of 3.36, 3.17, 3.13, and 3.14 pixels on the VV polarization and 3.49, 3.17, 2.94, and 2.42 pixels on the VH polarization; (2) after the BFFT filtering in the land parcel area, the mean value of the backscattering coefficient (BC) kept constant, whilst at the same time, the standard deviation (STD) was reduced to half of that before the filtering and (3) the BFFT and NLM filtering methods have a better effect on noise reduction inside the block. The BFFT filtering method retains the variation trend between different regions within the block and preserves the block boundary’s clarity. This study provides a new idea for refined image processing.

Analyzing of land subsidence by Sentinel-1 time-series images using PSInSAR method: A case study of Thai Nguyen, Vietnam

Natural disasters and human activities are now causing an increase in land subsidence, or surface displacement. The effects of land subsidence cause landslides and construction cracking. PSInSAR (Persistent Scatter Interferometry) was identified to estimate surface displacement from a time series of Synthetic Aperture Radar (SAR) images. This technique is a subset of the DInSAR (Differential Interferometric Synthetic Aperture Radar) method. In this article, the authors determined and analyzed land subsidence in Thai Nguyen Province, Vietnam, using time series Sentinel-1A data with VV polarization from July 2019 to December 2020 and the PSInSAR method. There are numerous mineral exploitation mines in Thai Nguyen Province. It is one of the causes of an unusual amount of land subsidence in the region. According to the results determined by the InSAR technique, the velocity of displacement along the line of sight (LOS) of the study area ranges from -23.2 mm per year to +21.0 mm per year. The analysis of time-series SAR images reveals anomalous land subsidence at persistent scatter (PS) points. By analyzing the time-series displacement at PS points using the StaMPS Visualizer tool, the land subsidence during the image acquisition period and surface displacement trends over time were determined. According to this, coal mining regions have the highest land subsidence values ranging from -40 mm to -60 mm. The city and mine regions of Thai Nguyen, where operations have stopped, are largely stable. In addition, the time-series analysis at PS points will allow us to identify unusual displacement points, enabling the implementation of early warning plans.

Small- to Medium-Sized Stream Waterbody Extraction Using Sentinel-1 Images Based on Shortest Plane Distance

In this study, a method for extracting the waterbody of small- to medium-sized streams is verified using SAR (Synthetic Aperture Radar) images based on the shortest plane distance (DST). The areas investigated are the Hantangang River and Jungnangcheon stream. To verify the waterbody extraction method, SAR satellite image data (Sentinel-1) and PlanetScope, which is a high-resolution optical satellite, are used simultaneously. After preprocessing the Sentinel-1 images, their brightness distribution is equalized via histogram matching. To realize an efficient stream extraction, a weight, which is the DST from the stream centerline, is added. After combining this weight value and Sentinel-1's VH and VV polarizations, the optimal parameter value is obtained using the k-means method. This value allows the waterbody to be extracted from Sentinel-1 images with maximum accuracy, depending on the resolution limit. Based on the correlation among the VV, VH, and DST, the waterbody extraction can be calculated using an elliptic equation. Results show that the average accuracy is 0.45-0.75, and the average Kappa coefficient is 0.60-0.85. This study shows that the waterbody area can be estimated using the DST. Additionally, the proposed method extracts the waterbody more simply and rapidly than the existing waterbody extraction method.