The Nature of Remote Sensing: Introduction. Remote Sensing. Information Extraction from Remote-Sensing Images. Spectral Factors in Remote Sensing. Spectral Signatures. Remote-Sensing Systems. Optical Sensors. Temporal Characteristics. Image Display Systems. Data Systems. Summary. Exercises. References. Optical Radiation Models: Introduction. Visible to Short Wave Infrared Region. Solar Radiation. Radiation Components. Surface-Reflected. Unscattered Component. Surface-Reflected. Atmosphere-Scattered Component. Path-Scattered Component. Total At-Sensor. Solar Radiance. Image Examples in the Solar Region. Terrain Shading. Shadowing. Atmospheric Correction. Midwave to Thermal Infrared Region. Thermal Radiation. Radiation Components. Surface-Emitted Component. Surface-Reflected. Atmosphere-Emitted Component. Path-Emitted Component. Total At-Sensor. Emitted Radiance. Total Solar and Thermal Upwelling Radiance. Image Examples in the Thermal Region. Summary. Exercises. References. Sensor Models: Introduction. Overall Sensor Model. Resolution. The Instrument Response. Spatial Resolution. Spectral Resolution. Spectral Response. Spatial Response. Optical PSFopt. Image Motion PSFIM. Detector PSFdet. Electronics PSFel. Net PSFnet. Comparison of Sensor PSFs. PSF Summary for TM. Imaging System Simulation. Amplification. Sampling and Quantization. Simplified Sensor Model. Geometric Distortion. Orbit Models. Platform Attitude Models. Scanner Models. Earth Model. Line and Whiskbroom ScanGeometry. Pushbroom Scan Geometry. Topographic Distortion. Summary. Exercises. References. Data Models: Introduction. A Word on Notation. Univariate Image Statistics. Histogram. Normal Distribution. Cumulative Histogram. Statistical Parameters. Multivariate Image Statistics. Reduction to Univariate Statistics. Noise Models. Statistical Measures of Image Quality. Contrast. Modulation. Signal-to-Noise Ratio (SNR). Noise Equivalent Signal. Spatial Statistics. Visualization of Spatial Covariance. Covariance with Semivariogram. Separability and Anisotropy. Power Spectral Density. Co-occurrence Matrix. Fractal Geometry. Topographic and Sensor Effects. Topography and Spectral Statistics. Sensor Characteristics and Spectral Stastistics. Sensor Characteristics and Spectral Scattergrams. Summary. Exercises. References. Spectral Transforms: Introduction. Feature Space. Multispectral Ratios. Vegetation Indexes. Image Examples. Principal Components. Standardized Principal Components (SPC) Transform. Maximum Noise Fraction (MNF) Transform. Tasseled Cap Tranformation. Contrast Enhancement. Transformations Based on Global Statistics. Linear Transformations. Nonlinear Transformations. Normalization Stretch. Reference Stretch. Thresholding. Adaptive Transformation. Color Image Contrast Enhancement. Min-max Stretch. Normalization Stretch. Decorrelation Stretch. Color Spacer Transformations. Summary. Exercises. References. Spatial Transforms: Introduction. An Image Model for Spatial Filtering. Convolution Filters. Low Pass and High Pass Filters. High Boost Filters. Directional Filters. The Border Region. Characterization of Filtered Images. The Box Filter Algorithm. Cascaded Linear Filters. Statistical Filters. Gradient Filters. Fourier Synthesis. Discrete Fourier Transforms in 2-D. The Fourier Components. Filtering with the Fourier Transform. Transfer Functions. The Power Spectrum. Scale Space Transforms. Image Resolution Pyramids. Zero-Crossing Filters. Laplacian-of-Gaussian (LoG) Filters. Difference-of-Gaussians (DoG) Filters.Wavelet Transforms. Summary. Exercises. References. Correction and Calibration: Introduction. Noise Correction. Global Noise. Sigma Filter. Nagao-Matsuyama Filter. Local Noise. Periodic Noise. Distriping 359. Global,Linear Detector Matching. Nonlinear Detector Matching. Statistical Modification to Linear and Nonlinear Detector. Matching. Spatial Filtering Approaches. Radiometric Calibration. Sensor Calibration. Atmospheric Correction. Solar and Topographic Correction. Image Examples. Calibration and Normalization of Hyperspectral Imagery. AVIRIS Examples. Distortion Correction. Polynomial Distortion Models. Ground Control Points (GCPs). Coordinate Transformation. Map Projections. Resampling. Summary. Exercises References. Registration and Image Fusion: Introduction. What is Registration? Automated GCP Location. Area Correlation. Other Spatial Features. Orthrectification. Low-Resolution DEM. High-Resolution DEM. Hierarchical Warp Stereo. Multi-Image Fusion. Spatial Domain Fusion. High Frequency Modulation. Spectral Domain Fusion. Fusion Image Examples. Summary. Exercises. References. Thematic Classification: Introduction. The Importance of Image Scale. The Notion of Similarity. Hard Versus Soft Classification. Training the Classifier. Supervised Training. Unsupervised Training. K-Means Clustering Algorithm. Clustering Examples. Hybrid Supervised/Unsupervised Training. Non-Parametric Classification Algorithms. Level-Slice. Nearest-Mean. Artificial Neural Networks (ANNs). Back-Propagation Algorithm. Nonparametric Classification Examples. Parametric Classification Algorithms. Estimation of Model-Parameters. Discriminant Functions. The Normal Distribution Model. Relation to the Nearest-Mean Classifier. Supervised Classification Examples and Comparison to Nonparametric Classifiers. Segmentation. Region Growing. Region Labeling. Sub-Pixel Classification. The Linear Mixing Model. Unmixing Model. Hyperspectral Image Analysis. Visualization of the Image Cube. Feature Extraction. Image Residuals. Pre-Classification Processing and Feature Extraction. Classification Algorithms. Exercises. Error Analysis. Multitemporal Images. Summary. References. Index.

Remote sensing, models, and methods for image processing

It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available globally or with adequate temporal or spatial sampling. It is recognized that a spaceborne L-band radiometer with a suitable antenna is the most promising way of fulfilling this gap. With these scientific objectives and technical solution at the heart of a proposed mission concept the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) mission as its second Earth Explorer Opportunity Mission. The development of the SMOS mission was led by ESA in collaboration with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L-Band 2-D interferometric radiometer operating in the 1400-1427-MHz protected band . The instrument receives the radiation emitted from Earth's surface, which can then be related to the moisture content in the first few centimeters of soil over land, and to salinity in the surface waters of the oceans. SMOS will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe. SMOS has a revisit time of less than 3 days so as to retrieve soil moisture and ocean salinity data, meeting the mission's science objectives. The caveat in relation to its sampling requirements is that SMOS will have a somewhat reduced sensitivity when compared to conventional radiometers. The SMOS satellite was launched successfully on November 2, 2009.

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The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle

The sections in this article are


1
Radiometers

2
Radar Scattering

3
Radar Scatterometers

4
Radar Altimeters

5
Ground-Penetrating Radars

6
Imaging Radars

7
Real-Aperture Radars

8
Synthetic-Aperture Radars

Microwave Remote Sensing

Despite recent improvements in satellite instrument calibration and the algorithms used to determine reflected solar (SW) and emitted thermal (LW) top-of-atmosphere (TOA) radiative fluxes, a sizeable imbalance persists in the average global net radiation at the TOA from satellite observations. This imbalance is problematic in applications that use earth radiation budget (ERB) data for climate model evaluation, estimate the earth’s annual global mean energy budget, and in studies that infer meridional heat transports. This study provides a detailed error analysis of TOA fluxes based on the latest generation of Clouds and the Earth’s Radiant Energy System (CERES) gridded monthly mean data products [the monthly TOA/surface averages geostationary (SRBAVG-GEO)] and uses an objective constrainment algorithm to adjust SW and LW TOA fluxes within their range of uncertainty to remove the inconsistency between average global net TOA flux and heat storage in the earth–atmosphere system. The 5-yr global mean...

Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget

The ocean surface wind mediates exchanges between the ocean and the atmosphere. These air–sea exchange processes are critical for understanding and predicting atmosphere, ocean, and wave phenomena on many time and space scales. A cross-calibrated multiplatform (CCMP) long-term data record of satellite ocean surface winds is available from 1987 to 2008 with planned extensions through 2012. A variational analysis method (VAM) is used to combine surface wind data derived from conventional and in situ sources and multiple satellites into a consistent nearglobal analysis at 25-km resolution, every 6 h. The input data are cross-calibrated wind speeds derived from the Special Sensor Microwave Imager (SSM/I; F08–F15), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and wind vectors from SeaWinds on the NASA Quick Scatterometer (QuikSCAT) and on the second Japanese Advanced Earth Observing Satellite (ADEOS-...

A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications

The Special Sensor Microwave/Imager (SSM/I) instrument and scan geometry are briefly described. The results of investigations of the stability of the gain, calibration targets and spin rate, the radiometer noise and sensitivity, the coregistration, the beam width and main-beam efficiency of the antenna beams, and the absolute calibration and geolocation of the instrument are presented. The results of this effort demonstrate that the SSM/I is a stable, sensitive, and well-calibrated microwave radiometric system capable of providing accurate brightness temperatures for microwave images of the Earth and for use by environmental product retrieval algorithms. It is predicted that this SSM/I and the 11 future ones currently built or to be built will provide high-performance microwave measurements for determination of global weather and critical atmospheric, oceanographic, and land parameters to operational forecasters and users and the research community for the next two decades. >

SSM/I instrument evaluation

Passive microwave measurements of the sea surface were made from Argus Island tower at 1.41, 8.36, and 19.34 GHz over a range in wind speeds from calm to 15 m/s. These measurements show a definite frequency-dependent correlation between the microwave brightness temperature and wind speed. This dependence results from roughness effects of the compact sea surface associated with wind-driven waves.

Passive Microwave Measurements of Sea Surface Roughness

Analysis of 1.4 GHz Radiometric measurements from Skylab

: This is an interim report on an ongoing program to develop a passive microwave system for the remote all-weather measurement of sea surface temperature from a satellite platform. It presents a general method for obtaining optimal linear algorithms for the reduction of multifrequency radiometer data to determine marine environmental variables. The mean square errors in determining the environmental variables are also determined thereby enabling the evaluation of different passive microwave radiometric systems. This method, called the 'Environmental Transfer Function', includes in its analysis the error contributions from three sources: errors in measuring microwave brightness temperature, the statistical variances of the environmental parameters, and uncertainties in the equations used to relate the brightness temperatures to the environmental parameters. The mathematical formulation is first developed and then an example application is given to evaluate the remote sensing capability of selected radiometric systems. (Author)

Estimation of Marine Environmental Parameters Using Microwave Radiometric Remote Sensing Systems.

: This is an interim report on an ongoing program to develop a passive microwave system for the remote all-weather measurement of sea surface temperature from a satellite platform. It presents the results of a theoretical investigation of the interdependence of the relevant environmental and instrumental parameters and their effect on the measured microwave brightness temperature and the sea surface temperature derived from it. The parameters considered are sea surface temperature, sea salinity, ocean roughness (wind speed), atmospheric water vapor, clouds, and the observational frequency. Detailed descriptions of the calculations of the emission and reflection of the sea surface, the emission and absorption of the atmosphere, the translation and rotation of the antenna properties from the satellite-oriented coordinate system to an earth-oriented system, and the convolution of the antenna reception pattern with the total radiatio over the earth's surface are given. Calculations of the microwave brightness temperature and of the uncertainty of the sea surface temperature derived from it for a range of values and uncertainties of the environmental conditions are presented. These calculations indicate that the optimum observational frequency for the determination of sea surface temperature lies between about 3 and 6 GHz. The dominant source of error is due to uncertainty in the effects of ocean roughness. A multifrequency microwave system will be required to correct for the effects of the various environmental factors and to obtain the sea surface temperature to an uncertainty of less than 1 deg C.

James P. Hollinger

Papers

SSM/I instrument evaluation

Passive Microwave Measurements of Sea Surface Roughness

Analysis of 1.4 GHz Radiometric measurements from Skylab

Estimation of Marine Environmental Parameters Using Microwave Radiometric Remote Sensing Systems.

An Investigation of the Remote Determination of Sea Surface Temperature Using Microwave Radiometry.