Radar Remote Sensing: Applications and Challenges

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RADAR REMOTE
RADAR REMOTE: Applications and Challenges
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Contents
Contributors
Foreword
1 - Basis of radar remote sensing
	1 - Introduction to RADAR remote sensing
		1. Brief history of RADAR remote sensing
		2. Optical versus RADAR remote sensing
		3. Fundamentals of RADAR
			3.1 Radar block diagram and operation
				3.1.1 Transmitter section
				3.1.2 Receiver section
			3.2 The RADAR equation
		4. Types of RADAR
			4.1 Radar altimeter
			4.2 Imaging RADARs
			4.3 Scatterometers
			4.4 Sideways-looking airborne radar
			4.5 Synthetic aperture RADAR
		5. Operational frequencies of RADAR
			5.1 Characteristics of RADAR frequency
				5.1.1 Atmospheric effect
				5.1.2 Wave polarization
		6. Backscatter mechanisms
			6.1 Direct backscatter (γD0)
			6.2 Forward scattering
			6.3 Diffuse scattering
			6.4 Double-bounce scattering (γDB0)
			6.5 Volume scattering (γV0)
		7. Radar image characteristics
			7.1 Sigma-nought
			7.2 Gamma-nought
			7.3 Spectral signature
		8. Application of microwave-based remote sensing
			8.1 Cryosphere
			8.2 Biomass estimation
			8.3 Soil moisture
			8.4 Hydrology and oceanography
			8.5 Air traffic control and navigation
			8.6 Military
		References
	2 - Microwave components and devices for RADAR systems
		1. Introduction
		2. Transmission line
			2.1 Coaxial cable
			2.2 Strip lines
			2.3 Waveguide
		3. Antennas
		4. Microwave filters
		5. Absorbers
		6. Microwave sources
			6.1 Relativistic magnetron
			6.2 Relativistic klystron amplifier
			6.3 Relativistic backward wave oscillator
		7. Mode converter
		8. Network analyzer
		9. Some other important microwave components
		10. Summary
		References
	3 - Theory of monostatic and bistatic radar systems
		1. Introduction
		2. Bistatic and monostatic radar system configuration
		3. Radar equation
			3.1 Point target bistatic radar equation
			3.2 Point target monostatic radar equation
			3.3 Distributed target radar equation for monostatic and bistatic configuration
		4. Radar cross-section per unit area/scattering coefficient system and measurement concepts
			4.1 Fundamentals
			4.2 Calibration and characterization of the radar system
			4.3 Amplitude calibration
			4.4 Subsystem characterization
		5. Measurement procedures
		6. Procedure of bistatic specular scatterometer measurement and its calibration over natural terrain
		7. Summary
		References
	4 - Review of microwave fundamentals and its applications
		1. Introduction
		2. Theory of radiative transfer
			2.1 Microwave brightness temperature
			2.2 Faraday rotation
		3. Electromagnetic interaction with discrete objects
			3.1 Diffraction
			3.2 Scattering
			3.3 Radar cross-section
			3.4 Spectral signature
		4. Interaction with inhomogeneous media
			4.1 Process of transmission
		5. Interaction with a smooth surface
			5.1 Scattering from plane boundaries
			5.2 Emission from plane interface
		6. Interaction with rough surfaces
		7. Microwave interaction with natural surfaces
			7.1 Hydrometeors
			7.2 Seas and oceans
			7.3 Interactions with ice and snow
			7.4 Interaction with glacial ice
			7.5 Interaction with sea ice
			7.6 Interactions with rock and desert
			7.7 Interactions with soil
			7.8 Interactions with vegetation
		8. Summary
		Funding
		References
2 - Conventional methods for radar remote sensing
	5 - Comparative flood area analysis based on change detection and binarization methods using Sentinel-1 synthetic a ...
		1. Introduction
		2. Study area
		3. Materials and methods
			3.1 Sentinel-1A (synthetic aperture radar) satellite data
			3.2 PERSIANN-Cloud Classification System satellite-based rainfall
			3.3 Floodwater pixel extraction using Sentinel-1A synthetic aperture radar data
				3.3.1 Floodwater pixel extraction using binarization technique
				3.3.2 Floodwater pixel extraction using change detection method
		4. Results
		5. Discussion
		6. Conclusions
		Acknowledgments
		References
	6 - Subsurface feature identification using L Band Synthetic Aperture Radar (SAR) data over Jaisalmer, India
		1. Introduction
		2. Study area
		3. Data used
			3.1 Synthetic aperture radar data
				3.1.1 ALOS-1 Phased Array L-Band Synthetic Aperture Radar
				3.1.2 Sentinel-1A
			3.2 Optical data
				3.2.1 Landsat data
				3.2.2 Sentinel-2A
		4. Methodology
		5. Result
			5.1 ALOS-1 Phased Array L-Band Synthetic Aperture Radar data interpretation
			5.2 Validation for a subsurface feature in visible data
			5.3 Validation for subsurface feature in C-band synthetic aperture radar data
			5.4 Ground evidence for subsurface feature identification
		6. Conclusion
		Acknowledgments
		Author contributions
		References
	7 - Terrestrial water budget through radar remote sensing
		1. Introduction
		2. Precipitation from radar remote sensing
			2.1 Extreme precipitation events using radar precipitation features
			2.2 Reliability of satellite-derived precipitation: case study on India
				2.2.1 Case study
				2.2.2 Result and discussion
		3. Soil moisture from radar remote sensing
			3.1 Spatial downscaling of satellite soil moisture measurements
				3.1.1 Satellite-based data fusion method
				3.1.2 Downscaling using ancillary geoinformation data
				3.1.3 Model-based method
				3.1.4 Statistical models
				3.1.5 Downscaling using hydrologic models and data assimilation
			3.2 Influence of soil heterogeneity in terrestrial water budget
			3.3 Addressing scale issues in soil heterogeneity
			3.4 Impact of soil moisture dynamics on triggering deep convection
		4. Water levels from radar altimetry
		5. Summary and conclusions
		Acknowledgments
		References
		Further reading
	8 - Application of synthetic aperture radar remote sensing in forestry
		1. Introduction
			1.1 Case study
		2. Polarimetric matrix generation
		3. Polarimetric speckle filtering
		4. Orientation angle correction
		5. Polarimetric decomposition
			5.1 Pauli decomposition
			5.2 Freeman-Durden decomposition
			5.3 Yamaguchi decomposition
		6. Terrain correction
		7. Polarimetric classification
			7.1 Supervised training
		8. Summary and final remarks
			8.1 Summary
		References
		Further reading
	9 - Classification of Radar data using Bayesian optimized two-dimensional Convolutional Neural Network
		1. Introduction
		2. Background
			2.1 Sentinel-1A data
			2.2 Classification
			2.3 Bayesian optimization
		3. Dataset and ground data collection
		4. Dataset preparation for classification
		5. Methodology
			5.1 Data preprocessing
			5.2 Classification using Bayesian-optimized two-dimensional convolutional neural network
		6. Results and discussion
		7. Conclusion
		Acknowledgment
		References
	10 - Modeling and simulation of synthetic aperture radar dataset for retrieval of soil surface parameters
		1. Introduction
		2. Study area and collection of field data
		3. Collection and processing of satellite data
		4. Soil moisture modeling
			4.1 Evaluation of Oh, calibrated integral equation, and modified dubois models
			4.5 Vegetation correction
		5. Results and discussion
		6. Conclusion
		References
	11 - Flood inundation mapping from synthetic aperture radar and optical data using support vector machine: a case s ...
		1. Introduction
		2. Study area
		3. Material and methods
			3.1 Dataset used
			3.2 Synthetic aperture radar data processing
			3.3 Flood inundation mapping
			3.4 Sentinel 2 Multispectral Instrument classification and Normalized Difference Water and Modified and Normalized Difference W ...
		4. Result and discussion
		5. Conclusion
		References
	12 - Performance assessment of phased array type L-band Synthetic Aperture Radar and Landsat-8 used in image classi ...
		1. Introduction
		2. Datasets
			2.1 Study area
			2.2 Datasets: Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar
			2.3 Datasets: Landsat-8
		3. Methodology
			3.1 Image classification techniques
			3.2 Artificial neural networks
			3.3 Support vector machines
			3.4 Accuracy assessment
				3.4.1 Kappa accuracy and coefficients
				3.4.2 Quantity disagreement and allocation disagreement
		4. Results and discussion
			4.1 Land use/land cover using Landsat-8 satellite imagery
			4.2 Land use/land cover using Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar image
			4.3 Diagnostic evaluation of image classification performance and applicability
			4.4 Comparative analysis
		5. Conclusions and future work
		Acknowledgments
		References
	13 - Evaluation of speckle filtering methods using polarimetric Sentinel-1A data
		1. Introduction
		2. Study site and data used
		3. Methodology
			3.1 Speckle filtering methods
			3.2 Evaluation of speckle filters
				3.2.1 Relative standard deviation
				3.2.2 Speckle suppression index
				3.2.3 Equivalent number of looks
		4. Results and discussion
		5. Conclusion
		Acknowledgment
		References
3 - Advanced methods for radar remote sensing
	14 - Emerging techniques of polarimetric interferometric synthetic aperture radar for scattering-based characterization
		1. Introduction
		2. Synthetic aperture radar polarimetry
		3. Polarimetric decomposition
			3.1 Freeman-Durden decomposition
			3.2 Yamaguchi four-component decomposition
			3.3 Multiple-component scattering model decomposition
			3.4 Other works in polarimetric decomposition modeling
		4. Polarization orientation angle
		5. Probability distributions
		6. Polarimetric synthetic aperture radar interferometry
			6.1 Polarimetric synthetic aperture radar interferometry coherence and optimization
		7. Polarimetric synthetic aperture radar interferometry coherence-based decomposition
		8. Polarimetric synthetic aperture radar interferometry decorrelation-based decomposition model
			8.1 Polarimetric synthetic aperture radar interferometry decorrelation
		Acknowledgment
		References
	15 - Advanced method for radar remote sensing: circularly polarized synthetic aperture radar
		1. Introduction
		2. Circularly polarized scattering for remote sensing
		3. Specification of circular polarized synthetic aperture radar for microsatellite
		4. Radio-frequency system of circular polarized synthetic aperture radar
		5. Flight test and images
			5.1 Flight test of circular polarized synthetic aperture radar system
			5.2 Images of circularly polarized signal-to-noise ratio
		6. Summary and future research
		References
	16 - A processing chain for estimating crop biophysical parameters using temporal Sentinel-1 synthetic aperture rad ...
		1. Introduction
		2. Methodology
			2.1 Study area and dataset
			2.2 GEE4Bio: Sentinel-1 data processing chain in Google Earth Engine for biophysical parameter estimation
				2.2.1 Sentinel-1 data fetching
				2.2.2 Cloud filtering
				2.2.3 Image preprocessing
				2.2.4 Vegetation modeling and calibration
				2.2.5 Model inversion and crop biophysical parameter map generation
		3. Results and discussion
			3.1 Water cloud model calibration
			3.2 Water cloud model inversion and validation
				3.2.1 Validation of plant area index
				3.2.2 Validation of wet biomass
			3.3 Generation of plant area index and wet biomass maps using GEE4Bio
		4. Conclusion
		Acknowledgments
		Code availability
		References
	17 - Fuzzy logic for the retrieval of kidney bean crop growth variables using ground-based scatterometer measurements
		1. Introduction
		2. Method and observations
			2.1 Bistatic scatterometer setup and measurements
			2.2 Kidney bean crop variables measurements
		3. Fuzzy inference system
		4. Results and discussion
			4.1 Time series analysis of bistatic scattering coefficients and crop variables
			4.2 Retrieval of kidney bean crop variables
		5. Conclusion
		References
	18 - Monitoring tropical peatlands subsidence by time-series interferometric synthetic aperture radar (InSAR) technique
		1. Introduction
		2. Interferometry synthetic aperture radar for tropical peatlands
			2.1 Interferometry synthetic aperture radar and time-series interferometry synthetic aperture radar
			2.2 Peatland interferometry synthetic aperture radar
		3. Case study: Sintang, Indonesia
			3.1 Methods
			3.2 Results and discussion
		4. Summary
		Acknowledgments
		References
	19 - Toward a North American continental wetland map from space: wetland classification using satellite imagery and ...
		1. Introduction
		2. Wetland classification systems
		3. Wetland field data
		4. Remote sensing data
		5. Cloud computing platforms and machine learning algorithms
		6. Wetland classification results for Canada
		7. Conclusion1
		References
		Further reading
4 - Future challenges in radar remote sensing
	20 - Challenges in Radar remote sensing
		1. Introduction
			1.1 Challenges in radar remote sensing for biophysical parameters
			1.2 Challenges in radar remote sensing for flood monitoring
			1.3 Challenges in radar remote sensing of soil moisture
			1.4 Challenges in radar remote sensing of drought
			1.5 Challenges in radar remote sensing of snow
			1.6 Challenges in radar remote sensing for sensor development and implementation
		2. Conclusion
		References
	21 - The study of Indian Space Research Organization's Ku-band based scatterometer satellite (SCATSAT-1) in agricul ...
		1. Introduction
		2. Background of SCATSAT-1
		3. Applications in agriculture
			3.1 Soil moisture
				3.1.1 Framework steps
			3.2 Paddy crop
				3.2.1 Methodology
				3.2.2 Methodology
				3.2.3 Methodology
			3.3 Leaf area index
		4. Summary and conclusions
		Acknowledgments
		References
	22 - Radar remote sensing of soil moisture: fundamentals, challenges & way-out
		1. Introduction
		2. Effect of target parameters on SAR sensitivity toward soil moisture
			2.1 Target parameters that affect SAR backscatter from an agricultural land
				2.1.1 Soil moisture
				2.1.2 Soil texture
				2.1.3 Soil surface roughness
				2.1.4 Crop/vegetation cover
		3. Addressing the effect of target parameters on SAR sensitivity toward soil moisture
			3.1 Incorporating the effect of soil texture
			3.2 Scientific rationale of the soil moisture measure (SM_WAP)
			3.3 Incorporating the effect of surface roughness
			3.4 Incorporating the effect of crop cover
		4. Effect of the sensor parameters on SAR sensitivity toward soil moisture
			4.1 Addressing the effect of wavelength and transmit/receive polarization on SAR sensitivity toward soil moisture
			4.2 Potential of multifrequency, multipolarized SAR in the field of soil moisture retrieval at different soil depths
			4.3 Impact of crop structure and soil moisture on multifrequency, multipolarized SAR backscatter
			4.4 Addressing the effect of incidence angle on SAR sensitivity toward soil moisture
		5. To identify sensitive polarimetric parameters derived from fully and hybrid polarimetric SAR for soil moisture
			5.1 Correlation coefficients
			5.2 Eigen vector decomposition: entropy, alpha, and anisotropy
			5.3 Freeman-Durden surface, volume, and dihedral scattering decomposition
			5.4 Yamaguchi surface, volume, dihedral, and helix scattering decomposition
			5.5 Stokes parameters and parameters derived from Stokes parameters
			5.6 Relating soil moisture and soil surface roughness to polarimetric parameters
		6. Addressing the various challenges involved in ground truth planning and ground truth data collection for radar remote sensi ...
			6.1 Determination of size of sampling field
			6.2 Identification of sampling fields on SAR image
		7. Addressing the challenges involved in development of a soil moisture retrieval model using radar remote sensing
			7.1 How many farm fields (number of sampling fields)?
			7.2 Number of validation samples
		8. Addressing challenges involved in SAR data processing due to a huge data volume
		9. Addressing the issue of interval and scale of a soil moisture map
		10. Conclusion
		References
Index
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	V
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	Y
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