Jakob J. van Zyl

The Soil Moisture Active Passive (SMAP) Mission

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Councils Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earths land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

Vegetation characteristics and underlying topography from interferometric radar

This paper formulates and demonstrates methods for extracting vegetation characteristics and underlying ground surface topography from interferometric synthetic aperture radar (INSAR) data. The electromagnetic scattering and radar processing, which produce the INSAR observations, are modeled, vegetation and topographic parameters are identified for estimation, the parameter errors are assessed in terms of INSAR instrumental performance, and the parameter estimation is demonstrated on INSAR data and compared to ground truth. The fundamental observations from which vegetation and surface topographic parameters are estimated are (1) the cross-correlation amplitude, (2) the cross-correlation phase, and (3) the synthetic aperture radar (SAR) backscattered power. A calculation based on scattering from vegetation treated as a random medium, including the effects of refractivity and absorption in the vegetation, yields expressions for the complex cross correlation and backscattered power in terms of vegetation characteristics. These expressions lead to the identification of a minimal set of four parameters describing the vegetation and surface topography: (1) the vegetation layer depth, (2) the vegetation extinction coefficient (power loss per unit length), (3) a parameter involving the product of the average backscattering amplitude and scatterer number density, and (4) the height of the underlying ground surface. The accuracy of vegetation and ground surface parameters, as a function of INSAR observation accuracy, is evaluated for aircraft INSAR, which is characterized by a 2.5-m baseline, an altitude of about 8 km, and a wavelength of 5.6 cm. It is found that for ≈0.5% accuracy in the INSAR normalized cross-correlation amplitude and ≈5° accuracy in the interferometric phase, few-meter vegetation layer depths and ground surface heights can be determined from INSAR for many types of vegetation layers. With the same observational accuracies, extinction coefficients can be estimated at the 0.1-dB/m level. Because the number of parameters exceeds the number of observations for current INSAR data sets, external extinction coefficient data are used to demonstrate the estimation of the vegetation layer depth and ground surface height from INSAR data taken at the Bonanza Creek Experimental Forest in Alaska. This demonstration shows approximately 5-m average ground truth agreement for vegetation layer depths and ground-surface heights, with a clear dependence of error on stand height. These errors suggest refinements in INSAR data acquisition and analysis techniques which will potentially yield few-meter accuracies. The information in the INSAR parameters is applicable to a variety of ecological modeling issues including the successional modeling of forested ecosystems.

Radar mapping of surface soil moisture

Abstract Intended as an overview aimed at potential users of remotely sensed spatial distributions and temporal variations of soil moisture, this paper begins with an introductory section on the fundamentals of radar imaging and associated attributes. To place the soil moisture sensing task in proper perspective, the prerequisite step of classifying terrain into four basic types—bare surfaces, short vegetation, tall vegetation, and urban—is addressed by demonstrating how a dual-frequency polarimetric radar can correctly classify terrain with an accuracy greater than 90%. Over 5000 image pixels with known terrain identity were involved in the evaluation of the radar image classifier. For bare soil (with vegetation cover shorter than 15 cm), radar can estimate the volumetric moisture content (expressed in per cent) of the top 5 cm soil layer with an r.m.s. error of 3.5%. Based on theoretical model predictions as well as experimental observations, strong evidence exists in support of radars potential for sensing soil moisture under vegetation cover, but no operational algorithm exists at present.

The Shuttle Radar Topography Mission (SRTM): a breakthrough in remote sensing of topography

Abstract The Shuttle Radar Topography Mission (SRTM), flown on the Space Shuttle Endeavour on Flight STS-99 and launched on 11 February 2000, will produce digital elevation data of the Earths land mass between 60 degrees north latitude and 54 degrees south latitude. This data will be at least one order of magnitude more precise in the elevation resolution, and will have postings of 30 m, representing an order of magnitude increase in the density of the postings over currently available data.

Application of Cloude's target decomposition theorem to polarimetric imaging radar data

In this paper we applied Cloudes decomposition to imaging radar polarimetry. We show in detail how the decomposition results can guide the interpretation of scattering from vegetated areas. For multi-frequency polarimetric radar measurements of a clearcut area, the decomposition leads us to conclude that the vegetation is probably thin compared to even the C-band radar wavelength of 6 cm . For a forested area, we notice an increased amount of even number of reflection scattering at P-band and L-band, probably the result of penetration through the coniferous canopy resulting in trunk-ground double reflection scattering. The scattering for the forested area is still dominated by scattering from randomly oriented cylinders, however. It is found that these cylinders are thicker than in the case of clearcut areas, leading us to conclude that scattering from the branches probably dominate in this case.In this paper we applied Cloudes decomposition to imaging radar polarimetry. We show in detail how the decomposition results can guide the interpretation of scattering from vegetated area. For multi-frequency polarimetric radar measurements of a clearcut area, the decomposition leads us to conclude that the vegetation is probably thin compared to even the C-band radar wavelength of 6 cm. For a forested area, we notice an increased amount of even number of reflection scattering at P-band and L-band, probably the result of penetration through the coniferous canopy resulting in trunk-ground double reflection scattering. The scattering for the forested area is still dominated by scattering from randomly oriented cylinders, however. It is found that these cylinders are thicker than in the case of clearcut areas, leading us to conclude that scattering from the branches probably dominate in this case.

Properties of the 67P/Churyumov-Gerasimenko interior revealed by CONSERT radar

The Philae lander provides a unique opportunity to investigate the internal structure of a comet nucleus, providing information about its formation and evolution in the early solar system. We present Comet Nucleus Sounding Experiment by Radiowave Transmission (CONSERT) measurements of the interior of Comet 67P/Churyumov-Gerasimenko. From the propagation time and form of the signals, the upper part of the “head” of 67P is fairly homogeneous on a spatial scale of tens of meters. CONSERT also reduced the size of the uncertainty of Philae’s final landing site down to approximately 21 by 34 square meters. The average permittivity is about 1.27, suggesting that this region has a volumetric dust/ice ratio of 0.4 to 2.6 and a porosity of 75 to 85%. The dust component may be comparable to that of carbonaceous chondrites.

A General Characterization for Polarimetric Scattering From Vegetation Canopies

Current polarimetric model-based decomposition techniques are limited to specific types of vegetation because of their assumptions about the volume scattering component. In this paper, we propose a generalized probability density function based on the nth power of a cosine-squared function. This distribution is completely characterized by two parameters; a mean orientation angle and the power of the cosine-squared function. We show that the underlying randomness of the distribution is only a function of the power of the cosine-squared function. We then derive the average covariance matrix for various different elementary scatterers showing that the result has a very simple analytical form suitable for use in model-based decomposition schemes.

Introduction to the Physics and Techniques of Remote Sensing: Elachi/Introduction to the Physics and Techniques of Remote Sensing, Second Edition

Preface. 1. Introduction. 1-1 Types and Classes of Remote Sensing Data. 1-2 Brief History of Remote Sensing. 1-3 Remote Sensing Space Platforms. 1-4 Transmission Through the Earth and Planetary Atmospheres. References and Further Reading. 2. Nature and Properties of Electromagnetic Waves. 2-1 Fundamental Properties of Electromagnetic Waves. 2-2 Nomenclature and Definition of Radiation Quantities. 2-3 Generation of Electromagnetic Radiation. 2-4 Detection of Electromagnetic Radiation. 2-5 Interaction of Electromagnetic Waves with Matter: Quick Overview. 2-6 Interaction Mechanisms Throughout the Electromagnetic Spectrum. Exercises. References and Further Reading. 3. Solid Surfaces Sensing in the Visible and Near Infrared. 3-1 Source Spectral Characteristics. 3-2 Wave-Surface Interaction Mechanisms. 3-3 Signature of Solid Surface Materials. 3-4 Passive Imaging Sensors. 3-5 Types of Imaging Systems. 3-6 Description of Some Visible/Infrared Imaging Sensors. 3-7 Active Sensors. 3-8 Surface Sensing at Very Short Wavelengths. 3-9 Image Data Analysis. Exercises. References and Further Reading. 4. Solid-Surface Sensing: Thermal Infrared. 4-1 Thermal Radiation Laws. 4-2 Heat Conduction Theory. 4-3 Effect of Periodic Heating. 4-4 Use of Thermal Emission in Surface Remote Sensing. 4-5 Use of Thermal Infrared Spectral Signatures in Sensing. 4-6 Thermal Infrared Sensors. Exercises. References and Further Reading. 5. Solid-Surface Sensing: Microwave Emission. 5-1 Power-Temperature Correspondence. 5-2 Simple Microwave Radiometry Models. 5-3 Applications and Use in Surface Sensing. 5-4 Description of Microwave Radiometers. 5-5 Examples of Developed Radiometers. Exercises. References and Further Reading. 6. Solid-Surface Sensing: Microwave and Radio Frequencies. 6-1 Surface Interaction Mechanism. 6-2 Basic Principles of Radar Sensors. 6-3 Imaging Sensors: Real-Aperture Radars. 6-4 Imaging Sensors: Synthetic-Aperture Radars. 6-5 Nonimaging Radar Sensors: Scatterometers. 6-6 Nonimaging Radar Sensors: Altimeters. 6-7 Nonconventional Radar Sensors. 6-8 Subsurface Sounding. Exercises. References and Further Readings. 7 Ocean Surface Sensing. 7-1 Physical Properties of the Ocean Surface. 7-2 Mapping of the Ocean Topography. 7-3 Surface Wind Mapping. 7-4 Ocean Surface Imaging . Exercises. References and Further Reading. 8. Basic Principles of Atmospheric Sensing and Radiative Transfer. 8-1 Physical Properties of the Atmosphere. 8-2 Atmospheric Composition. 8-3 Particulates and Clouds. 8-4 Wave Interaction Mechanisms in Planetary Atmospheres. 8-5 Optical Thickness. 8-6 Radiative Transfer Equation. 8-7 Case of a Nonscattering Plane Parallel Atmosphere. 8-8 Basic Concepts of Atmospheric Remote Sounding. Exercises. References and Further Reading. 9. Atmospheric Remote Sensing in the Microwave Region. 9-1 Microwave Interactions with Atmospheric Gases. 9-2 Basic Concept of Downlooking Sensors. 9-3 Basic Concept for Uplooking Sensors. 9-4 Basic Concept for Limblooking Sensors. 9-5 Inversion Concepts. 9-6 Basic Elements of Passive Microwave Sensors. 9-7 Surface Pressure Sensing. 9-8 Atmospheric Sounding by Occultation. 9-9 Microwave Scattering by Atmospheric Particles. 9-10 Radar Sounding of Rain. 9-11 Radar Equation for Precipitation Measurement. 9-12 The Tropical Rainfall Measuring Mission (TRMM). Exercises. References and Further Reading. 10. Millimeter and Submillimeter Sensing of Atmospheres. 10-1 Interaction with Atmospheric Constituents. 10-2 Downlooking Sounding. 10-3 Limb Sounding. 10-4 Elements of a Millimeter Sounder. Exercises. References and Further Reading. 11. Atmospheric Remote Sensing in the Visible and Infrared. 11-1 Interaction of Visible and Infrared Radiation with the Atmosphere. 11-2 Downlooking Sounding. 11-3 Limb Sounding. 11-4 Sounding of Atmospheric Motion. 11-5 Atmospheric Sensing at Very Short Wavelengths. Exercises. References and Further Reading. 12. Ionospheric Sensing. 12-1 Properties of Planetary Ionospheres. 12-2 Wave Propagation in Ionized Media. 12-3 Ionospheric Profile Sensing by Topside Sounding. 12-4 Ionospheric Profile by Radio Occultation. Exercises. References and Further Reading. Appendix A. Use of Multiple Sensors For Surface Observations. Appendix B. Summary of Orbital Mechanics Relevant to Remote Sensing. B-1 Circular Orbits. B-1-1 General Characteristics. B-1-2 Geosynchronous Orbits. B-1-3 Sun-Synchronous Orbits. B-1-4 Coverage. B-2 Elliptical Orbits. B-3 Orbit Selection. Exercises. Appendix C. Simplified Weighting Functions. C-1 Case of Downlooking Sensors (Exponential Atmosphere). C-2 Case of Downlooking Sensors (Linear Atmosphere). C-3 Case of Upward Looking Sensors. Appendix D. Compression of a Linear FM Chirp Signal. Index.

Inference of surface power spectra from inversion of multifrequency polarimetric radar data

During the summer of 1988 an intensive field experiment was conducted in the vicinity of Pisgah lava field in the Mojave Desert of southern California. As part of the experiment, physical properties such as microtopography, composition, soil moisture and dielectric constant at five different sites representing surfaces with r.m.s. heights varying from less than one centimeter to tens of centimeters, were measured. In addition, polarimetric radar images at P-band (68 cm wavelength), L-band (24 cm) and C-band (5.7 cm) were acquired at three different incidence angles with the NASA/JPL airborne imaging radar polarimeter. Using trihedral corner reflectors deployed in the area prior to imaging, the radar images were calibrated to provide σ0 values for each resolution element in each scene. This paper reports on the derivation of the power spectrum of surface microtopography by solution of the small perturbation model for multiple incidence angle and multiple frequency radar data. Power-law fits to the power spectra have exponents (slope in log-log plots) that are nearly the same for all surfaces. These values are close to those from measured microtopography profiles. The offset in log-log plots shows very good correlation with measured power spectrum offsets, however the image-derived offsets are consistently lower than measured values. This may be the result of calibration errors, using the wrong dielectric constants in the inversion, or the fact that not all observed energy was scattered by the surface interface alone.

Radar interferometry studies of the Earth's topography

Topographic information is required for many geological and geophysical investigations. For example, detailed topographic data alone can be used to map geological structure and thus reveal the effects of tectonic deformation. Additionally, they can be combined with gravity field measurements to constrain models of lithospheric structure and rheology [e.g., Topographic Science Working Group, 1988].