scholarly journals Complementing Optical Remote Sensing with Synthetic Aperture Radar Observations of Hail Damage Swaths to Agricultural Crops in the Central United States

2020 ◽  
Vol 59 (4) ◽  
pp. 665-685 ◽  
Author(s):  
Jordan R. Bell ◽  
Esayas Gebremichael ◽  
Andrew L. Molthan ◽  
Lori A. Schultz ◽  
Franz J. Meyer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Growing Season ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Hail Damage ◽  
Sar Imagery ◽  
Moisture Conditions ◽  
Aperture Radar

AbstractThe normalized difference vegetation index (NDVI) has been frequently used to map hail damage to vegetation, especially in agricultural areas, but observations can be blocked by cloud cover during the growing season. Here, the European Space Agency’s Sentinel-1A/1B C-band synthetic aperture radar (SAR) imagery in co- and cross polarization is used to identify changes in backscatter of corn and soybeans damaged by hail during intense thunderstorm events in the early and late growing season. Following a June event, hail-damaged areas produced a lower mean backscatter when compared with surrounding, unaffected pixels [vertical–vertical (VV): −1.1 dB; vertical–horizontal (VH): −1.5 dB]. Later, another event in August produced an increase in co- and cross-polarized backscatter (VV: 0.7 dB; VH: 1.7 dB) that is hypothesized to result from the combined effects of crop growth, change in structure of damaged crops, and soil moisture conditions. Hail damage regions inferred from changes in backscatter were further assessed through coherence change detections to support changes in the structure of crops damaged within the hail swath. While studies using NDVI have routinely concluded a decrease in NDVI is associated with damage, the cause of change with respect to the damaged areas in SAR backscatter values is more complex. Influences of environmental variables, such as vegetation structure, vegetation maturity, and soil moisture conditions, need to be considered when interpreting SAR backscatter and will vary throughout the growing season.

scholarly journals Tropical Peatland Identification using L-Band Full Polarimetric Synthetic Aperture Radar (SAR) Imagery (Study Case: Siak Regency, Riau Province)

2019 ◽  
Vol 26 (2) ◽  
pp. 63
Author(s):  
Desti Ayunda ◽  
Ketut Wikantika ◽  
Dandy A. Novresiandi ◽  
Agung B. Harto ◽  
Riantini Virtriana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Vegetation Index ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Error Matrix ◽  
Tropical Peatland ◽  
Average Value ◽  
Sar Imagery ◽  
Aperture Radar

From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks).

Estimation of Synthetic Aperture Radar (SAR) soil moisture with the use of fractal roughness

2021 ◽  
Author(s):  
Ju Hyoung Lee ◽  
Notarnicola Claudia ◽  
Jeff Walker
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Accurate Estimation ◽  
Distribution Data ◽  
Fractal Surface ◽  
Fractal Method ◽  
Local Point ◽  
Aperture Radar

<p>To estimate surface soil moisture from Sentinel-1 backscattering, accurate estimation of soil roughness is a key. However, it is usually error source, due to complexity of surface heterogeneity. This study investigates the fractal methods that takes multi-scale roughness into account. Fractal models are widely recognized as one of the best approaches to depict soil roughness of natural system. Unlike the conventional approach of fractal method that uses local roughness measured in the field or Digital Elevation Model information seldom considering a stochastic characteristic of soil surface, fractal surface is generated with the roughness spatially inverted from Synthetic Aperture Radar (SAR) backscatter. Assuming that the land surface in study site is on small to intermediate scales, pseudo-roughness is spatially estimated by modelling SAR roughness with the one-sided power-law spectrum. In addition, it is assumed that both multiple and single scales of roughness affect SAR backscatter in an integrative way. For validation, soil moisture is retrieved with this time-varying roughness. Based upon local validation and cost minimization, as compared with an inversion approach of surface scattering models (Integral Equation Model), a fractal method seems geometrically more sensible than an inversion, based upon a spatial distribution and a priori knowledge in the field. Although inverted roughness is used as an input, fractal model does not reproduce the same roughness. Results will show local point validation, fractal surface, and estimation of coefficients, and various spatial distribution data. This study will be useful for future satellite missions such as NASA-ISRO SAR mission.</p>

scholarly journals RASF and DEDR-POCS Image Fusion through Multi-layer Perceptron in SAR Imagery Systems

2019 ◽  
pp. 20-26
Author(s):  
Israel Yañez-Vargas ◽  
Joel Quintanilla-Domínguez ◽  
Gabriel Aguilera-Gonzalez
Keyword(s):  
Synthetic Aperture Radar ◽  
Image Fusion ◽  
Low Cost ◽  
Synthetic Aperture ◽  
Discrete Wavelet ◽  
Multi Layer Perceptron ◽  
Robust Adaptive ◽  
Sar Imagery ◽  
Design Regularization ◽  
Aperture Radar

This paper presents a novel multi-layer perceptron (MLP) based image fusion technique, which fuses two synthetic aperture radar (SAR) images, obtained from the same spatial reflectivity map, acquired with a conventional low-cost fractional synthetic aperture radar (Fr-SAR) system, enhanced via two different methodologies. The first image is enhanced using the traditional descriptive experiment design regularization (DEDR) framework through the projection onto convex solution sets (POCS) method; the second image is enhanced with the DEDR framework by incorporating the robust adaptive spatial filtering (RASF) solution operator. This work describes a MLP based technique applied to the pixel level multi-focus fusion problem characterized by the use of image windows with the idea of reducing noise and determining which pixel is clearer between the two images. Experimental results show that the proposed novel method outperforms the discrete wavelet transform based most competing approach.

scholarly journals Refocusing of Multiple Moving Targets Based on the Joint Sparse Processing of One Channel Synthetic Aperture Radar Imagery Patches

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1215 ◽  
Author(s):  
Xin Wang ◽  
Ling Qiao
Keyword(s):  
Synthetic Aperture Radar ◽  
Moving Objects ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Moving Targets ◽  
Lp Norm ◽  
Radar Imagery ◽  
Sar Imagery ◽  
Norm Constraint ◽  
Aperture Radar

A sparse-based refocusing methodology for multiple slow-moving targets (MTs) located inside strong clutter regions is proposed in this paper. The defocused regions of MTs in synthetic aperture radar (SAR) imagery were utilized here instead of the whole original radar data. A joint radar projection operator for the static and moving objects was formulated and employed to construct an optimization problem. The Lp norm constraint was utilized to promote the separation of MT data and the suppression of clutter. After the joint sparse imaging processing, the energy of strong static targets could be suppressed significantly in the reconstructed MT imagery. The static scene imagery could be derived simultaneously without the defocused MT. Finally, numerical simulations were used verify the validity and robustness of the proposed methodology.

Better Utilization of SAR Dynamic Range

1977 ◽  
Vol 21 (3) ◽  
pp. 235-240
Author(s):  
Edward J. Dragavon
Keyword(s):  
Computer Simulation ◽  
Synthetic Aperture Radar ◽  
Real Time ◽  
Image Enhancement ◽  
Dynamic Range ◽  
Synthetic Aperture ◽  
Analytic Methods ◽  
Sar Imagery ◽  
Gray Shade ◽  
Aperture Radar

Three general classes of image enhancement techniques for synthetic aperture radar (SAR) video were investigated through non-real-time computer simulation. The general categories were 1) monochromatic adaptive gray shade transformations, 2) pseudocolor encoding, and 3) feature analytic methods. The class of feature analytic techniques was found to have the greatest potential for improving the operational utility of SAR imagery.

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