Sentinel-1A SAR Data Research Articles

Land subsidence analysis using InSAR along the RiLan high-speed railway in Heze, China

The Heze section of the Rizhao-Lankao High-Speed Railway (RLHR-HZ) officially began operations in June 2021. Previous studies have shown that the RLHR-HZ crosses two subsidence zones. In this study, we analysed ground displacement along the RLHR-HZ using Sentinel-1A SAR data collected from June 2021 to June 2023, employing both Permanent Scatterer and Distributed Scatterer techniques. The results indicate a reduction in the displacement rate; however, subsidence continues along the RLHR-HZ, with displacement rates ranging from −16 mm/yr to −5 mm/yr. This reduction is likely due to the cessation of mining activities and the replenishment of groundwater.

Digital mapping of dates of transplanting and accumulated thermal requirement of rice (Oryza sativa L.) in the subtropics of North Eastern Hill Region, India

ABSTRACT Accurate and timely mapping of rice areas is crucial for effective agricultural planning and food security management in the North Eastern Hill Region of India, especially amid changing climate conditions. This study developed a method to estimate rice transplanting dates by utilizing VH polarized backscattering-coefficients rom Sentinel-1A SAR data and also assessed thermal requirements for rice productivity in Ri-Bhoi district, Meghalaya during kharif 2021. The methodology involved 3 steps- (i) preprocessing data to construct time-series SAR images (ii) rule-based classification of SAR data integrating phenological and topographical information to identify transplanting windows (iii) assessing mapping accuracy. The results when compared with field data, revealed five distinct transplanting windows from early June to early August across 9031 ha of rice area. Overall accuracy was 85%, with a kappa coefficient of 0.74. User and producer accuracies ranged from 0.74 to 0.93 and 0.75 to 0.92, respectively for various transplanting windows. GDD varied between 1432.8°C day and 3290.4°C day, with higher values corresponding to earlier planting dates. This study formulates a robust methodology for rice mapping in hilly, small-field landscapes with multiple transplanting dates by integrating Sentinel-1A SAR data with ancillary and crop phenology information, contributing to improved crop management and climate change adaptations.

Integration of Sentinel-1A SAR data with crop simulation model for rice yield prediction in Udham Singh Nagar, Uttarakhand

Integration of Sentinel-1A SAR data with crop simulation model for rice yield prediction in Udham Singh Nagar, Uttarakhand

Comparison of three machine learning algorithms for retrieving soil moisture information from Sentinel-1A SAR data in northwest Shandong plain, China

Soil moisture (SM) plays a critical role in the growth and management of grain in semi-humid regions. However, little is known about how to integrate satellite data with machine learning to accurately retrieve SM information in these areas. This study compares the capability of three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), to extract SM information over the Northwest Shandong Plain using multi-phase dual-polarized Sentinel-1A satellite data. The backscattering coefficients were obtained through standard intensity and phase processing to calculate the SAR indices, and several characteristic parameters were extracted as impact factors using the Cloude-Pottier decomposition. The importance of these factors was analyzed, while the performance of each machine learning algorithm was comprehensively evaluated using the K-fold cross-validation method. The best-performing model was utilized to retrieve the spatio-temporal changes in SM in the study area. The findings indicate the following: (1) The first eigenvalue has the greatest impact on retrieval accuracy, followed by entropy, where the intensity component of Shannon's entropy is more important than its polarization component; (2) The addition of more impact factors does not bring a continuous improvement in model performance, but the optimal factor combinations differ for different machine learning retrieval models; (3) The RF model trained using the IM12 combination demonstrates better performance than SVM and ANN in retrieving SM information, with a coefficient of determination (R2) of 0.55 and a root mean square error of 6.12 vol% on the validation set. The level of SM in the Yellow River National Wetland Park is higher than that of the surrounding areas, with substantial seasonal changes. Precipitation, temperature, and vegetation significantly influence the regional variations in SM at the macroscopic level.

Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP)

Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP)

Semi physical and machine learning approach for yield estimation of pearl millet crop using SAR and optical data products

ABSTRACT Pearl millet (Pennisetum glaucum L.R.Br.), is the most widely cultivated food crop after rice, wheat, and maize. The aim of the project is to determine the crop acreage of Pearl millet (Bajra) using Sentinel-1A SAR data and Machine Learning Algorithm to determine the yield estimation of the Pearl millet crop at the tehsil level using the Monteith approach. The classification overall accuracy is found to be 86.48% for Agra district and 80.15% for Firozabad district. The Relative Deviation of yield estimation for the Agra and Firozabad districts is found to be 10.14 and 6, respectively.

Estimation of Water Spread Area in Selected Tanks of Cuddalore District of Tamil Nadu Using Sentinel-1A SAR Data

The most important aspect of water resource planning and management is mapping and regular monitoring of surface water bodies. Remote sensing has become most widely used method for measuring and monitoring of water body dynamics due its temporal and spatial availability of high-resolution data. A study was conducted to map and estimate the water spread area in the tanks of Kattumannarkoil block of Cuddalore district of Tamil Nadu using Sentinel-1A SAR satellite data. European Space Agency’s Sentinel-1A data provides high quality SAR data which is used widely for monitoring of crop and water resources. The acquisition and preprocessing of satellite data was done using Google Earth Engine. The obtained satellite image from Google Engine was processed using ArcGIS. The water spread area was derived from the processed data using thresholding approach. The back-scattering values generated through pre-processing ranges from -18.00 db to -28.12 db for August 2022 (Pre-rainy season), -15.85 to -26.96 for December 2022 (Post-rainy season) and -15.55 to -24.82 for April 2023(Summer time). The estimated water spread area in selected 12 Tanks were 37.191 ha, 58.34 ha and 59.971 ha on 19th August 2022, 17th December 2022 and 16th April 2023 respectively.

Assessment of Tank Water Spread Area in Cheyyar Sub Basin Using Sentinel 1A Data

A research study was conducted for estimating the water spread area of tanks in Cheyyar sub basin for the year 2022. Tanks are low-cost irrigation water source and play a key role in recharge of ground water. Mapping and regular monitoring of tanks plays an important role in water resource planning. Sentinel-1A SAR data has high potential in estimating water spread area as it is unaffected by cloud cover, light or weather conditions. After processing the obtained satellite data using SNAP software, the water spread area was obtained by threshold approach. The backscattering (dB) value ranged between -30.641 and 22.898 during the course of study. The total water spread area was noticed maximum and minimum during the month of January and July with 5728.20 ha and 1123.44 ha (21.88% and 4.29 % of total tank area) respectively. The total rainfall received in Cheyyar sub basin was about 1290 mm. The water spread area seems to be proportional to the rainfall of previous and current months.

An optimum datasets analysis for monitoring crops using remotely sensed Sentinel-1A SAR data

ABSTRACT To effectively monitor crops, it is necessary to select extremely redundant satellite images and to know the number of acquisitions required for a specific period to analyse cropping patterns, thereby reducing analysis time. In this paper, we have examined an empirical analysis for the optimum dataset (OptD) selection required to monitor the crops. Sentinel-1 dual-polarized SAR datasets were used in this study to illustrate the effectiveness of optimum datasets required for the considered crops (ginger, tobacco, rice, cabbage, and pumpkin). In this work, at first, the entropy and alpha bands were treated as cluster centres for crop decomposition and its scattering mechanism using the cluster-based K-means unsupervised classification technique. The clusters are plotted on the H-α plane to get the H-α plot of dual-polarization SAR data for target decomposition. To understand the dominance of scattering type with crop growth stage, the obtained scattering distribution from the H-alpha plot is scaled to a percentage analysis. Based on qualitative observations of the percent scattering distribution of crop pixels over a h-alpha plot and backscattering coefficient behaviour at different crop growth stages, an empirical approach has been used to select dataset reduction. It has been suggested that the combination of successive repeated data with similar scattering analysis of combined h-alpha plots and backscattering analysis is the best reduced dataset selection for effective crop monitoring. From the analysis, the optimum dataset required for monitoring Ginger (from the flourishing stage), Tobacco, Paddy, Cabbage, and Pumpkin has been identified, and found that the tobacco crop requires fewer datasets, whereas the rice crop requires a greater number of datasets for monitoring. Despite the challenges associated with, p-bias for the crops was achieved at good levels, given that, lowering the datasets to obtain the optimal number without significantly reducing the accuracy of the data.

Coseismic Source Model of the February 2023 Mw 6.8 Tajikistan Earthquake from Sentinel-1A InSAR Observations and Its Associated Earthquake Hazard

On 23 February 2023, an Mw 6.8 earthquake struck the border of Tajikistan and Xinjiang China, the source mechanism remains controversial according to different seismic inversions. To better comprehend the source characteristics and the surface deformation pattern, we used the ascending and descending orbital Sentinel-1A SAR data to obtain the coseismic deformation of this earthquake based on the traditional two-pass differential interferometric synthetic aperture radar (InSAR). The source model is inverted from the InSAR coseismic deformation results. The possible Coulomb Failure Stress (CFS) transfer is analyzed based on the preferred source model. The results illustrate that the earthquake ruptured a blind left-lateral strike-slip fault of strike 28.1° with a maximum slip of 1.53 m and the total geodetic moment is 1.99 × 1019 N·m (Mw 6.83). The strike direction and the fault characteristics suggest the Seismogenic fault is a secondary fault of the Sarez–Karakul Fault System. The 2015 Mw 7.2 Sarez Earthquake plays a triggering role in the occurrence of the 2023 Tajikistan earthquake. Earthquake hazard on Sarez–Karakul Fault System and Sarez–Murghab Thrust System is enhanced due to the Coulomb stress loaded by the Tajikistan earthquake.

Application of Sentinel-1A SAR Data for Village Level Flood Inundation Mapping in Malda District, West Bengal, India

It’s no exaggeration to say that floods are among the most common and catastrophic natural disasters, with widespread impacts on ecosystems and human lives. The World Bank has concluded that India is one of the most flood-prone countries in the world. This research has been focused on identification of real-time flood hazard area in the Malda district (India) using SAR data, Sentinel-1A (C-band). ArcGIS, Erdas-Imagine, and SNAP were used for this analysis. These inundation layers were intersected with the Malda district village’s database to determine the full extent of the devastation caused by the floods and finally determined the real-time flood-impacted area (380.19 km² on 4th September and 338.87 km² on 10th September, 2019). It was noted that on 4th September, 766 and 10th September 765 villages were seriously devastated owing to water. Moreover, among the 15 blocks of this district, Harischandrapur-II (75.67%), Manikchak (68.60%) and Kaliachak-III (68.00%) have experienced the most flooding. Finally, in order to estimate the overall population, households and affected by the floods, stratified random sampling was carried out in the flood-impacted villages of the Malda district. The safest blocks are Chanchal-I, Gazole, Harischandrapur-I and Kaliachak-I and the highly vulnerable blocks are Harischandrapur-II, Kaliachak-III and Manikchak. According to the flood influences on children, the most vulnerable blocks are Malda Old, English Bazar, Kaliyachak-III, and Harishchandrapur-II.

Monitoring Seasonal Movement Characteristics of the Landslide Based on Time-Series InSAR Technology: The Cheyiping Landslide Case Study, China

Landslides are one of the extremely high-incidence and serious-loss geological disasters in the world, and the early monitoring and warning of landslides are of great importance. The Cheyiping landslide, located in western Yunnan Province, China, added many cracks and dislocations to the surface of the slope due to the severe seasonal rainfall and rise of the water level, which seriously threaten the safety of residents and roads located on the body and foot of the slope. To investigate the movement of the landslide, this paper used Sentinel-1A SAR data processed by time-series interferometric synthetic aperture radar (InSAR) technology to monitor the long-time surface deformation. The landslide boundary was defined, then the spatial distribution of landslide surface deformation from 5 January 2018 to 27 December 2021 was obtained. According to the monthly rainfall data and the temporal deformation results, the movement of the landslide was highly correlated with seasonal rainfall, and the Cheyiping landslide underwent seasonal sectional accelerated deformation. Moreover, the water level change of the Lancang River caused by the water storage of the hydropower station and seasonal rainfall accelerates the deformation of the landslide. This case study contributes to the interpretation of the slow deformation mechanism of the Cheyiping landslide and early hazard warning.

Shallow Sea Topography Detection from Multi-Source SAR Satellites: A Case Study of Dazhou Island in China

Accurate measurement of underwater topography in the coastal zone is essential for human marine activities, and the synthetic aperture radar (SAR) presents a completely new solution. However, underwater topography detection using a single SAR image is vulnerable to the interference of sea state and sensor noise, which reduces the detection accuracy. A new underwater topography detection method based on multi-source SAR (MSSTD) was proposed in this study to improve the detection precision. GF-3, Sentinel-1, ALOS PALSAR, and ENVISAT ASAR data were used to verify the sea area of Dazhou Island. The detection result was in good agreement with the chart data (MAE of 2.9 m and correlation coefficient of 0.93), and the detection accuracy was improved over that of a single SAR image. GF-3 image with 3 m spatial resolution performed best in bathymetry among the four SAR images. Additionally, the resolution of the SAR image had greater influence on bathymetry compared with polarization and radar band. The ability of MSSTD has been proved in our work. Collaborative multi-source satellite observation is a feasible and effective scheme in marine research, but its application potential in underwater topography detection still requires further exploration.

Machine learning-based meta-classifier for Kharif Bajra (pearl millet) discrimination in the mixed cropping environment using multi-temporal SAR data

Study on pearl millet discrimination in mixed cropping environment was carried out as it forms the major coarse cereal crop in India. The study aims at determining the capabilities of Sentinel-1A SAR data and Machine Learning (ML) algorithms for pearl millet discrimination. Six ML algorithms, that is, k-nearest neighbour (k-NN), Decision Tree (DT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP) and Naïve Bayes (NB) were evaluated and compared. Meta-classifier using majority voting technique was proposed by ensembling classifiers (RF, SVM and NB) which were diverse and have comparable accuracies. The diversity assessment was conducted using Cochran Q statistics for ensembling purpose. RF classifier (83.76%) using VH polarization produced better result indicating importance of cross-polarization in crop classification. Further, meta-classifier proposed in the study improved the accuracy to 88.15% (approximately by 5%) and class accuracies of pearl millet resulting in improved discrimination of pearl millet in mixed cropping environment.

Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm

After a coal mine is closed, the coal rock mass could undergo weathering deterioration and strength reduction due to factors such as stress and groundwater, which in turn changes the stress and bearing capacity of the fractured rock mass in the abandoned goaf, leading to secondary or multiple surface deformations in the goaf. Currently, the spatiotemporal evolution pattern of the surface deformation of closed mines remains unclear, and there is no integrated monitoring and prediction model for closed mines. Therefore, this study proposed to construct an integrated monitoring and prediction model for closed mines using small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) and a deep learning-based long short-term memory (LSTM) neural network algorithm to achieve evolution pattern and dynamic prediction of spatiotemporal surface deformation of closed mines. Taking a closed mine in the western part of Xuzhou, China, as an example, based on Sentinel-1A SAR data between 21 December 2015, and 11 January 2021, SBAS InSAR technology was used to obtain the spatiotemporal evolution pattern of the surface during the 5 years after mine closure. The results showed that the ground surface subsided in the early stage of mine closure and then uplifted. In 5 years, the maximum subsidence rate in the study area is −43 mm/a, and the cumulative maximum subsidence is 310 mm; the maximum uplift rate is 29 mm/a, and the cumulative maximum uplift is 135 mm. Moreover, the maximum tilt and curvature are 3.5 mm/m and 0.19 mm/m2, respectively, which are beyond the safety thresholds of buildings; thus, continuous monitoring is necessary. Based on the evolution pattern of surface deformation, the surface deformation prediction model was proposed by integrating SBAS InSAR and an LSTM neural network. The experiment results showed that the LSTM neural network can accurately predict the deformation trend, with a maximum root mean square error (RMSE) of 5.1 mm. Finally, the relationship between the residual surface deformation and time after mine closure was analyzed, and the mechanisms of surface subsidence and uplift were discussed, which provide a theoretical reference for better understanding the surface deformation process of closed mines and the prevention of surface deformation.

Crustal Deformation of the Kendeng Fault Branches Area from GNSS and InSAR Data in Surabaya City, Indonesia

Slip rates on active faults derived from the interseismic velocity field are critical to understanding seismic hazards in metropolitan cities. This study integrated the data from the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) to evaluate the interseismic velocities in the second-largest city in Indonesia, Surabaya, where branches of the Kendeng fault (the Surabaya and the Waru faults) pass through. Data from 16 campaign-mode GNSS stations collected between 2017 and 2020 from previous research are reprocessed to estimate the velocity field. Horizontal velocities under the ITRF frame range between -23.8 mm/yr and 47.9 mm/yr toward the southeast. Vertical velocities generally range between -1.3 mm/yr and -112.2 mm/yr. Sentinel-1A SAR data of both ascending and descending tracks acquired between November 2014 and July 2020 were used to generate the interferograms with the InSAR Scientific Computing Environment (ISCE) software. Furthermore, cumulative displacement time series were constructed using the Small BAseline Subset (SBAS) technique within the Generic InSAR Analysis Toolbox (GIAnT). This study also carried out the detection of outlier SAR epochs to improve the precision of Line-of-sight (LOS) velocity estimates. The LOS velocities range from -14.8 to 10.8 mm/yr in the ascending track and from -12.7 to 9.5 mm/yr in the descending track. These results will facilitate the detection of coupling behaviors on the Kendeng fault branches, which can improve our understanding of seismic risks in the Surabaya area.

Exploring the optimal combination of image fusion and classification techniques

This study explores an optimal combination of certain selected image fusion and image classification techniques for improvement in classification accuracy. For this purpose, Sentinel-1A SAR and Sentinel-2A multispectral (MS) data have been obtained. Four fusion techniques Brovey Transform, Hue-Saturation-Value (HSV), PCA, and Gram-Schmidt (GS) Transform, have been compared along with two classification methods, Support Vector Machine (SVM) and Maximum Likelihood (ML) have been explored. Improvement in five classes Urban, Water, Baresoil, Forest and Vegetation Agricultural has been analysed. The result indicates that the GS-ML combination (87.56%) gives superior classification accuracy than the GS-SVM combination (84.35%). In contrast, PCA in combination with SVM (82.62%) provides superior accuracy compared to the combination of PCA-ML (82.56%).

Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis.

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as a routine tool for certain critical application areas, such as the assessment of millimetre-scale differential displacements in airport runways, is still debated. This research aims to demonstrate the viability of using medium-resolution Copernicus ESA Sentinel-1A (C-Band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, “Runway n.3” of the “Leonardo Da Vinci International Airport” in Fiumicino, Rome, Italy was investigated as an explanatory case study, in view of historical geotechnical settlements affecting the runway area. In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. The geostatistical analysis provided ample information on the spatial continuity of the Sentinel 1 data in comparison with the high-resolution COSMO-SkyMed data and the ground-based topographic levelling data. Furthermore, a comparison between the PSI outcomes from the Sentinel-1A SAR data—interpolated through Ordinary Kriging—and the ground-truth topographic levelling data demonstrated the high accuracy of the Sentinel 1 data. This is proven by the high values of the correlation coefficient (r = 0.94), the multiple R-squared coefficient (R2 = 0.88) and the Slope value (0.96). The results of this study clearly support the effectiveness of using Sentinel-1A SAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways, paving the way for the development of more efficient and sustainable maintenance strategies for inclusion in next generation Airport Pavement Management Systems (APMSs).

A Large Old Landslide in Sichuan Province, China: Surface Displacement Monitoring and Potential Instability Assessment

Using advanced Differential Interferometric Synthetic Aperture Radar (InSAR) with small baseline subsets (SBAS) and Permanent Scatter Interferometry (PSI) techniques and C-band Sentinel-1A data, this research monitored the surface displacement of a large old landslide at Xuecheng town, Lixian County, Sichuan Province, China. Based on the MassMov2D model, the effect of the dynamic process and deposit thickness of the potentially unstable rock mass (deformation rate < −70 mm/year) on this landslide body were numerically simulated. Combined with terrain data and images generated by an Unmanned Aerial Vehicle (UAV), the driving factors of this old landslide were analyzed. The InSAR results show that the motion rate in the middle part of the landslide body is the largest, with a range of −55 to −80 mm/year on average, whereas those of the upper part and toe area were small, with a range of −5 to −20 mm/year. Our research suggests that there is a correlation between the LOS (line of sight) deformation rate and rainfall. In rainy seasons, particularly from May to July, the deformation rate is relatively high. In addition, the analysis suggests that SBAS can provide smoother displacement time series, even in areas with vegetation and the steepest sectors of the landslide. The simulation results show that the unstable rock mass may collapse and form a barrier dam with a maximum thickness of about 16 m at the Zagunao river in the future. This study demonstrates that combining temporal UAV measurements and InSAR techniques from Sentinel-1A SAR data allows early recognition and deformation monitoring of old landslide reactivation in complex mountainous areas. In addition, the information provided by InSAR can increase understanding of the deformation process of old landslides in this area, which would enhance urban safety and assist in disaster mitigation.

Paddy area estimation in Nagapattinam district using sentinel-1A SAR data

A Research study was conducted to estimate the paddy area in the Nagapattinam district of Tamil Nadu using Sentinel 1A SAR data through multi-temporal feature extraction. Multi-temporal Sentinel 1A data of GRD data at VH polarizations were obtained for the study area at 12 days intervals. Collected data were processed using MAP scape-RICE software. Sentinel 1A is an active SAR microwave data, which captures the crop characteristics irrespective of the weather condition and illumination. Ground truth observations collected during cropping period were used to derive the paddy signature from the processed satellite images. The dB values extracted as signature then subjected to the multi-temporal feature extraction method for delineating the paddy growing areas. Around 99, 00, 700 ha was mapped as the paddy growing area in Nagapattinam district. Accuracy assessment was done with 40 percent of the ground truth data. The overall classification accuracy was 94 percent with a kappa score of 0.88.