scholarly journals Inundation Extent Mapping by Synthetic Aperture Radar: A Review

2019 ◽  
Vol 11 (7) ◽  
pp. 879 ◽  
Author(s):  
Xinyi Shen ◽  
Dacheng Wang ◽  
Kebiao Mao ◽  
Emmanouil Anagnostou ◽  
Yang Hong
Keyword(s):  
Synthetic Aperture Radar ◽  
Optical Sensors ◽  
Risk Mitigation ◽  
Numerical Models ◽  
Synthetic Aperture ◽  
Spatiotemporal Resolution ◽  
Urban Flood ◽  
Inundation Mapping ◽  
Sar Data ◽  
Aperture Radar

Recent flood events have demonstrated a demand for satellite-based inundation mapping in near real-time (NRT). Simulating and forecasting flood extent is essential for risk mitigation. While numerical models are designed to provide such information, they usually lack reference at fine spatiotemporal resolution. Remote sensing techniques are expected to fill this void. Unlike optical sensors, synthetic aperture radar (SAR) provides valid measurements through cloud cover with high resolution and increasing sampling frequency from multiple missions. This study reviews theories and algorithms of flood inundation mapping using SAR data, together with a discussion of their strengths and limitations, focusing on the level of automation, robustness, and accuracy. We find that the automation and robustness of non-obstructed inundation mapping have been achieved in this era of big earth observation (EO) data with acceptable accuracy. They are not yet satisfactory, however, for the detection of beneath-vegetation flood mapping using L-band or multi-polarized (dual or fully) SAR data or for urban flood detection using fine-resolution SAR and ancillary building and topographic data.

scholarly journals High-Resolution Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Terrain Data

2020 ◽  
Vol 12 (6) ◽  
pp. 900 ◽  
Author(s):  
Fernando Aristizabal ◽  
Jasmeet Judge ◽  
Alejandro Monsivais-Huertero
Keyword(s):  
Machine Learning ◽  
Synthetic Aperture Radar ◽  
Predictive Value ◽  
Synthetic Aperture ◽  
Flood Inundation ◽  
Dense Vegetation ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Land Covers ◽  
Aperture Radar

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset Height Above Nearest Drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features dual-polarized SAR only and dual-polarized SAR along with HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+36.0%), critical success index (CSI) (+39.95%), true positive rate (TPR) (+42.02%), and negative predictive value (NPV) (+17.26%). A marginal change of +0.15% was seen for positive predictive value (PPV), but true negative rate (TNR) fell −14.4%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

Multi-Temporal Synthetic Aperture Radar Data for Paddy Crop Area Estimation in Eastern part of Godavari Delta, Andhra Pradesh, India.

2020 ◽  
Vol 8 (3) ◽  
pp. 208-218
Author(s):  
S.K. Tiwari ◽  
Prasada Rao G
Keyword(s):  
Synthetic Aperture Radar ◽  
Cloud Cover ◽  
Andhra Pradesh ◽  
Synthetic Aperture ◽  
Crop Acreage ◽  
The Third ◽  
Godavari Delta ◽  
Sar Data ◽  
Paddy Crop ◽  
Aperture Radar

In the present study, an attempt is made to estimate the area under paddy crop during Rabi, 2013-14 using Microwave satellite data in the eastern part of Godavari delta. Clouds veil nearly the entire sky in both (Kharif & Rabi) seasons of Andhra Pradesh and hinder the estimation of crop acreage through optical satellite sensors. Microwaves can penetrate clouds and be used to detect crops during the day and night, regardless of cloud cover. Radar Imaging SATellite-1 (RISAT-1), microwave sensor, dual-polarization Horizontal-Horizontal (HH), Horizontal-Vertical (HV), Medium Resolution scanSAR Mode (MRS) data (18 m pixel spacing and 37° incidence angle) of three different dates (in December, January, and February) with 25 days interval was used. The backscatter (dB) values of the early, mid, and late-season transplanted stages of paddy crop were used to estimate the paddy crop acreage coupled with ground truth information during different stages of the crop. It was observed that the dB values at the transplanting stage rapidly increased with plant growth in the early season sown areas and mid-season sowed paddy illustrate a dip in dB values in the second date due to change in transplantation and increased backscatter coefficient values in the third date because of crop growth after transplantation. The backscatter signature value of late sowing paddy crop showed first and second dates with high backscatter due to previous crop/vegetation and then a sudden dip in the third date as submerged field ready for transplantation. The dB values of the above stages were used in decision-based classifier to estimate paddy crop acreage. The paddy area was compared at Mandal (sub-district level) estimates observed the significant coefficient of determination (R² = 0.89) between traditional estimates and Synthetic Aperture Radar (SAR) data assessment. This study robustly suggests the utilization of SAR data in agricultural crop monitoring during cloud cover.

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

2015 ◽  
Vol 158 ◽  
pp. 180-192 ◽  
Author(s):  
Akhmad Solikhin ◽  
Virginie Pinel ◽  
Jean Vandemeulebrouck ◽  
Jean-Claude Thouret ◽  
Muhamad Hendrasto
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Dual Polarization ◽  
Pyroclastic Deposits ◽  
Sar Data ◽  
Aperture Radar

scholarly journals Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

2021 ◽  
Author(s):  
Adam Collingwood ◽  
Paul Treitz ◽  
Francois Charbonneau ◽  
David M. Atkinson
Keyword(s):  
Synthetic Aperture Radar ◽  
High Arctic ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Optical Data ◽  
Neural Network Modeling ◽  
Artificial Neural ◽  
Sar Data ◽  
The Relationship ◽  
Aperture Radar

Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

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