The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

The status of environmental satellites and availability of their data products

The latest available information about the status of unclassified environmental satellite (flown by the United States) and their data products is presented. The type of environmental satellites discussed include unmanned earth resource and meteorological satellites, and manned satellites which can act as a combination platform for instruments. The capabilities and data products of projected satellites are discussed along with those of currently operating systems

Causal SAR ATR with Limited Data via Dual Invariance

Synthetic aperture radar automatic target recognition (SAR ATR) with limited data has recently been a hot research topic to enhance weak generalization. Despite many excellent methods being proposed, a fundamental theory is lacked to explain what problem the limited SAR data causes, leading to weak generalization of ATR. In this paper, we establish a causal ATR model demonstrating that noise $N$ that could be blocked with ample SAR data, becomes a confounder with limited data for recognition. As a result, it has a detrimental causal effect damaging the efficacy of feature $X$ extracted from SAR images, leading to weak generalization of SAR ATR with limited data. The effect of $N$ on feature can be estimated and eliminated by using backdoor adjustment to pursue the direct causality between $X$ and the predicted class $Y$. However, it is difficult for SAR images to precisely estimate and eliminated the effect of $N$ on $X$. The limited SAR data scarcely powers the majority of existing optimization losses based on empirical risk minimization (ERM), thus making it difficult to effectively eliminate $N$'s effect. To tackle with difficult estimation and elimination of $N$'s effect, we propose a dual invariance comprising the inner-class invariant proxy and the noise-invariance loss. Motivated by tackling change with invariance, the inner-class invariant proxy facilitates precise estimation of $N$'s effect on $X$ by obtaining accurate invariant features for each class with the limited data. The noise-invariance loss transitions the ERM's data quantity necessity into a need for noise environment annotations, effectively eliminating $N$'s effect on $X$ by cleverly applying the previous $N$'s estimation as the noise environment annotations. Experiments on three benchmark datasets indicate that the proposed method achieves superior performance

Time-Offset Fractional-N PLLs for Heterodyne FMCW SAR

This text contains an investigation into the use of time-offset fractional-N phase locked loops (PLLs) for heterodyne frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) and the impact of spurii on such a system. Heterodyne receiver architectures avoid phenomena which limit the sensitivity of their homodyne counterparts, and enable certain inter-antenna feed-through suppression techniques. Despite these advantages, homodyne receivers are more prevalent owing to advantages in size, weight and cost. Designed to address this dilemma, the miloSAR is believed to be the only heterodyne FMCW SAR to employ a pair of time-offset fractional-N PLLs for waveform synthesis to enable low-cost heterodyning and simplify filter-based feed-through suppression. This system architecture is revealed to be susceptible to swept-offset spurii termed spur chirps which hinder the sensor's performance. While integer boundary spurs and phase detector harmonics infamously plague fractional-N PLLs, their resultant spur-chirps have not seen analysis in the context of FMCW SAR. Simulations and measurements reveal that these spurii significantly degrade SAR image quality in terms of peak sidelobe ratio, structural similarity index measure and root mean square error. To combat this, several suppression techniques were assessed, namely: time domain zeroing, PLL loop bandwidth reduction, and a novel method termed range-Doppler spur masking. A subset of these suppression techniques were applied to measured SAR data sets, including car-borne data measured in Iowa, USA and airborne data captured in Oudtshoorn, South Africa. These results show that the impact of spur chirps can be effectively quelled, meaning that time-offset fractional-N PLLs offer an attractive, low-cost approach to the implementation of heterodyne FMCW SAR

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

A critical review on electromagnetic precursors and earthquake prediction

Seismo-electromagnetic precursory-based earthquake prediction studies are criticized in terms of their scientific content, problem complexity, signal excitation and propagation, causal relations with earthquakes, and public awareness and expectations. One aim is to trigger a new debate on this hot topic in the Electromagnetic Society

Water Bodies mapping and monitoring using high-resolution satellite images

Recent developments in satellite optical remote sensors have led to a new age in surface water monitoring. Several methodologies have been developed to identify water bodies using the various spatial, spectral, and temporal properties. Surface water observation is a functional necessity for ecological and hydrological processes. Recently anticipated satellites with enhanced spectral and spatial resolution sensors might lead to broader remote sensing techniques for evaluating and monitoring water bodies. Remote sensing data integration, GPS, and GIS technology are powerful tools to monitor and analyze water bodies. Remotely sensed data could be utilized to construct a geographically positioned permanent database to give a baseline for future comparisons. For many environmental applications, surface water body mapping and monitoring are crucial. This research examines surface water detection, extraction, and monitoring with optical remote sensing, particularly progress within the recent decade. Satellite image delineation of the water body remains challenging due to sensor resolutions, cloud presence, low-albedo surfaces, topography, and atmospheric circumstances in metropolitan locations. This study shows the utility of high spatial resolutions satellite images are suitable for mapping and monitoring surface water bodies, even minor water systems. The suggested technique distinguished water from other land cover features with precision and time. The integrated use of remotely sensed data, GPS, and GIS will allow consultants and natural resource managers to construct management plans for several applications for the management of natural resources. &nbsp