FLOOD MAPPING AND PERMANENT WATER BODIES CHANGE DETECTION USING SENTINEL SAR DATA

Mapping water bodies with a high accuracy is necessary for water resource assessment, and mapping them rapidly is necessary for flood monitoring. Poyang Lake is the largest freshwater lake in China, and its wetland is one of the most important in the world. Poyang Lake is affected by floods from the Yangtze River basin every year, and the fluctuation of the water area and water level directly or indirectly affects the ecological environment of Poyang Lake. Synthetic Aperture Radar (SAR) is particularly suitable for large-scale water body mapping, as SAR allows data acquisition regardless of illumination and weather conditions. The two-satellite Sentinel-1 constellation, providing C-Band SAR data, passes over the Poyang Lake about five times a month. With its high temporal-spatial resolution, the Sentinel-1 SAR data can be used to accurately monitor the water body. After acquiring all the Sentinel-1 (1A and 1B) SAR data, to ensure the consistency of data processing, we propose the use of a Python and SeNtinel Application Platform (SNAP)-based engine (SARProcMod) to process the data and construct a Poyang Lake Sentinel-1 SAR dataset with a 10 m resolution. To extract water body information from Sentinel-1 SAR data, we propose an automatic classification engine based on a modified U-Net convolutional neural network (WaterUNet), which classifies all data using artificial sample datasets with a high validation accuracy. The results show that the maximum and minimum water areas in our study area were 2714.08 km2 on 20 July 2020, and 634.44 km2 on 4 January 2020. Compared to the water level data from the Poyang gauging station, the water area was highly correlated with the water level, with the correlation coefficient being up to 0.92 and the R2 from quadratic polynomial fitting up to 0.88; thus, the resulting relationship results can be used to estimate the water area or water level of Poyang Lake. According to the results, we can conclude that Sentinel-1 SAR and WaterUNet are very suitable for water body monitoring as well as emergency flood mapping.

In general, changes in the multitemporal synthetic aperture radar (SAR) images are detected by classifying the SAR ratio images into the changed and unchanged classes. However, multitemporal SAR images have either increase or decrease in the backscattering values. Therefore, the changed areas can be further classified into positive and negative changed classes. This paper presents an unsupervised change detection approach for detecting the positive and negative changes based on a multi-objective evolutionary algorithm. In this paper, the widely adopted mean-ratio and log-ratio operators are extended to generate SAR ratio images for distinguishing the positive and negative changes. In order to reduce the corruption of speckle noise present in the multitemporal SAR images, a fuzzy cluster validity index is established to exploit local spatial and gray level information. Then the objective functions are simultaneously optimized by a multi-objective evolutionary algorithm. The experimental results on two simulated data sets and three real SAR data sets confirm the effectiveness of the proposed method.

Palustrine wetland systems are important ecosystems and provide numerous ecosystems services to support society. Unfortunately, they remain under constant threat of devastation due to land use practices and global climate change, which underscores the need to identify, map, and monitor these landscape features. This study explores harmonic coefficients and seasonal median values derived from Sentinel-1 synthetic aperture radar (SAR) data, as well as digital elevation model (DEM)-derived terrain variables, to predict palustrine wetland locations in the Vermont counties of Bennington, Chittenden, and Essex. Support vector machine (SVM) and random forest (RF) machine learning models were used with various combinations of the three datasets: terrain, SAR seasonal medians, and SAR harmonic time series coefficients. For Bennington County, using the harmonic and terrain data with a RF model yielded the most accurate results, with an overall accuracy of 76%. The terrain data alone and RF model produced the highest overall accuracy in Chittenden County with an accuracy of 85%. In Essex County any combination of the three datasets and the RF model yielded the highest overall accuracy of 81%. Generally, this study documented better performance using the RF algorithm in comparison to SVM. Terrain variables were generally important for differentiating wetlands from uplands and waterbodies. However, Sentinel-1 data, represented as harmonic regression coefficients and seasonal medians, provided limited predictive power. Although Sentinel-1 SAR data were of limited value in the explored case studies, findings may not extrapolate to other SAR datasets using different polarizations, wavelengths, and/or spatial resolutions.

Exploring optimal integration schemes for Sentinel-1 SAR and Sentinel-2 multispectral data in land cover mapping across different atmospheric conditions

Synthetic aperture radar (SAR) is an important means to observe the sea surface wind field. Sentinel-1 and GF-3 are located on orbit SAR satellites, but the SAR data quality of these two satellites has not been evaluated and compared at present. This paper mainly studies the data quality of Sentinel-1 and GF-3 SAR satellites used in wind field inversion. In this study, Sentinel-1 SAR data and GF-3 SAR data located in Malacca Strait, Hormuz Strait and the east and west coasts of the United States are selected to invert wind fields using the C-band model 5.N (CMOD5.N). Compared with reanalysis data called ERA5, the root mean squared error (RMSE) of the Sentinel-1 inversion results is 1.66 m/s, 1.37 m/s and 1.49 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively; the RMSE of GF-3 inversion results is 1.63 m/s, 1.45 m/s and 1.87 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively. Based on the data of Sentinel-1 and GF-3 located on the east and west coasts of the United States, CMOD5.N is used to invert the wind field. Compared with the buoy data, the RMSE of the Sentinel-1 inversion results is 1.20 m/s, and the RMSE of the GF-3 inversion results is 1.48 m/s. The results show that both Sentinel-1 SAR data and GF-3 SAR data are suitable for wind field inversion, but the wind field inverted by Sentinel-1 SAR data is slightly better than GF-3 SAR data. When applied to wind field inversion, the data quality of Sentinel-1 SAR is slightly better than the data quality of GF-3 SAR. The SAR data quality of GF-3 has achieved a world-leading level.

In this letter, we develop a variational model for change detection in multitemporal synthetic aperture radar (SAR) images. SAR images are typically polluted by multiplicative noise, therefore ordinary active contour model (ACM), or the snake model, for image segmentation is not suitable for change detection in multitemporal SAR images. Our model is a generalization of ACM under the assumption that the image data fits the Generalized Gaussian Mixture (GGM) model. Our method first computes the log-ratio image of the input multitemporal SAR images. Then the method iteratively executes the following two steps until convergence: (1) estimate the parameters for the generalized Gaussian distributions inside and outside the current evolving curve using maximum-likelihood estimation; (2) evolve the current curve according to the image data and the parameters previously estimated. When convergence is achieved, the location of the evolving curve depicts the changed and the unchanged areas.Experiments were carried out on both semi-simulated data set and real data set. Results showed that the proposed method achieves total error rates of 0.43% and 1.05%, for semi-simulated and real data sets, respectively, which were comparable to other prevalent methods.

Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. Five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles, and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polarisation and the S2 NDWI, respectively, provided better overall accuracies than when thresholding was applied to any of the individual features considered. The accuracies of this dual thresholding technique were comparable to those of machine learning and may thus offer a viable solution for automatic mapping of waterbodies.

<p>A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented.  The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on a dataset from winter 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 91.6%. The error is a bit higher for young ice (76%) and first-year ice (84%). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data.</p><p> </p><p>Our study demonstrates that CNN can be successfully applied for classification of sea ice types in SAR data. The algorithm is applied in small sub-images extracted from a SAR image after preprocessing including thermal noise removal. Validation shows that the errors are mostly attributed to coarse resolution of ice charts or misclassification of training data by human experts.</p><p> </p><p>Several sensitivity experiments were conducted for testing the impact of CNN architecture, hyperparameters, training parameters and data preprocessing on accuracy. It was shown that a CNN with three convolutional layers, two max-pool layers and three hidden dense layers can be applied to a sub-image with size 50 x 50 pixels for achieving the best results. It was also shown that a CNN can be applied to SAR data without thermal noise removal on the preprocessing step. Understandably, the classification accuracy decreases to 89% but remains reasonable.</p><p> </p><p>The main advantages of the new algorithm are the ability to classify several ice types, higher classification accuracy for each ice type and higher speed of processing than in the previous studies. The relative simplicity of the algorithm (both texture analysis and classification are performed by CNN) is also a benefit. In addition to providing ice type labels, the algorithm also derives the probability of belonging to a class. Uncertainty of the method can be derived from these probabilities and used in the assimilation of ice type in numerical models. </p><p><br>Given the high accuracy and processing speed, the CNN-based algorithm is included in the Copernicus Marine Environment Monitoring Service (CMEMS) for operational sea ice type retrieval for generating ice charts in the Arctic Ocean. It is already released as an open source software and available on Github: https://github.com/nansencenter/s1_icetype_cnn.</p>

Time-series Sentinel-1A SAR remote sensing of rice planting methods in Ebonyi State, Nigeria

The warming climate is threatening to alter inland water resources on a global scale. Within all waterbody types, lake and river systems are vital not only for natural ecosystems but, also, for human society. Snowmelt phenology is also altered by global warming, and snowmelt is the primary water supply source for many river and lake systems around the globe. Hence, (1) monitoring snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced river and lake systems, and (3) quantifying the causal effect of snowmelt conditions on these waterbodies are critical to understand the cryo-hydrosphere interactions under climate change. Previous studies utilized in-situ or multispectral sensors to track either the surface areas or water levels of waterbodies, which are constrained to small-scale regions and limited by cloud cover, respectively. On the contrary, in the present study, we employed the latest Sentinel-1 synthetic aperture radar (SAR) and Sentinel-3 altimetry data to grant a high-resolution, cloud-free, and illumination-independent comprehensive inland water dynamics monitoring strategy. Moreover, in contrast to previous studies utilizing in-house algorithms, we employed freely available cloud-based services to ensure a broad applicability with high efficiency. Based on altimetry and SAR data, the water level and the water-covered extent (WCE) (surface area of lakes and the flooded area of rivers) can be successfully measured. Furthermore, by fusing the water level and surface area information, for Lake Urmia, we can estimate the hypsometry and derive the water volume change. Additionally, for the Brahmaputra River, the variations of both the water level and the flooded area can be tracked. Last, but not least, together with the wet snow cover extent (WSCE) mapped with SAR imagery, we can analyze the influence of snowmelt conditions on water resource variations. The distributed lag model (DLM) initially developed in the econometrics discipline was employed, and the lagged causal effect of snowmelt conditions on inland water resources was eventually assessed.

Abstract. We present a method to combine CryoSat-2 (CS2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara seas. From the viewpoint of tactical navigation, along-track altimeter SIT estimates are sparse, and the goal of our study is to develop a method to interpolate altimeter SIT measurements between CS2 ground tracks. The SIT estimation method developed here is based on the interpolation of CS2 SIT utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from the AARI ice charts; to ORAS5, PIOMAS and TOPAZ4 ocean–sea ice data assimilation system reanalyses; to combined CS2 and Soil Moisture and Ocean Salinity (SMOS) radiometer weekly SIT (CS2SMOS SIT) charts; and to the daily MODIS (Moderate Resolution Imaging Spectroradiometer) SIT chart. We studied two approaches: CS2 directly interpolated to SAR segments and CS2 SIT interpolated to SAR segments with mapping of the CS2 SIT distributions to correspond to SIT distribution of the PIOMAS ice model. Our approaches yield larger spatial coverage and better accuracy compared to SIT estimates based on either CS2 or SAR data alone. The agreement with modelled SIT is better than with the CS2SMOS SIT. The average differences when compared to ice models and the AARI ice chart SIT were typically tens of centimetres, and there was a significant positive bias when compared to the AARI SIT (on average 27 cm) and a similar bias (24 cm) when compared to the CS2SMOS SIT. Our results are directly applicable to the future CRISTAL mission and Copernicus programme SAR missions.

Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas. Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel—1 SAR data. The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year’s flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 94.3% and 94.1% respectively. The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year’s same season (August 2015–2017) classified outputs. Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses.

Improved estimation of non-photosynthetic vegetation cover using a novel multispectral slope difference index with soil information, Sentinel-1 data, and machine learning

Spatial information about the sand saltation threshold is essential to quantify sand transport and dust emission. Quantitatively estimating the threshold over a wide area is challenging, however, because its main influencing factors (surface roughness elements) are unknown. Here, we explored the use of Sentinel-1 Synthetic Aperture Radar (SAR) data to quantify surface roughness conditions and their temporal changes and map the threshold. Thresholds were obtained by in situ observations in dry spring periods during 2018–2020 at three year-round and eight temporary sites during natural sand and dust storms. We found a linear relationship (r = 0.91, p < 0.0001) between the SAR gamma naught VV intensity and the observed threshold where the site surface was stony or vegetated, indicating that SAR data can be used to quantify the spatial-temporal distribution of the threshold during dry periods. We then estimated thresholds from SAR data for spring 2017–2021 and found high thresholds on slopes and mountains, mainly due to stone and vegetation effects, whereas thresholds in the topographic depression varied because of changes in surface roughness conditions. Thresholds estimated from SAR data should greatly improve wide-area simulations of sand horizontal transport and dust emission fluxes by numerical dust models.

This study introduces a methodology for land cover mapping across extensive areas, utilizing multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) data. The objective is to effectively process SAR data to extract spatio-temporal features that encapsulate temporal patterns within various land cover classes. The paper outlines the approach for processing multitemporal SAR data and presents an innovative technique for the selection of training points from an existing Medium Resolution Land Cover (MRLC) map. The methodology was tested across four distinct regions of interest, each spanning 100 × 100 km2, located in Siberia, Italy, Brazil, and Africa. These regions were chosen to evaluate the methodology’s applicability in diverse climate environments. The study reports both qualitative and quantitative results, showcasing the validity of the proposed procedure and the potential of SAR data for land cover mapping. The experimental outcomes demonstrate an average increase of 16% in overall accuracy compared to existing global products. The results suggest that the presented approach holds promise for enhancing land cover mapping accuracy, particularly when applied to extensive areas with varying land cover classes and environmental conditions. The ability to leverage multitemporal SAR data for this purpose opens new possibilities for improving global land cover maps and their applications.