Roger D. De Roo

Temperature-Dependable Microwave Dielectric Model for an Arctic Soil

Dielectric measurements of an organic-rich permafrost soil over the range from 1.0 to 16 GHz and from -30°C to +25°C are presented. The measured shrub soil contains up to 90% organic matter and is the first soil of this composition for which the soil dielectric has been characterized. The measurements were fitted to the generalized refractive mixing dielectric model (GRMDM) recently proposed by Mironov et al., which combines the complex refractive indexes for the major components of the soil. These components were found to be the solid content, bound water, transient bound water, liquid capillary water, and moistened ice water. The dielectric properties of the frequency-dispersive components are each described by their own Debye relaxation spectrum. The GRMDM has been modified to incorporate the temperature dependence of the Debye parameters. The phase transformation of the soil water components at the freezing temperature is taken into account. As a result, a temperature-dependable GRMDM (TD GRMDM) has been developed, including model parameters which have a physical interpretation. This TD GRMDM predicts the dielectric for this soil in the whole range of moistures, frequencies, and temperatures measured. The model prediction errors are on the same order as that of dielectric measurements. The model proposed is the first of its kind to provide a physical basis for radar and radiothermal remote sensing algorithms that retrieve the freeze/thaw state and the volumetric moisture in the upper layer of an Arctic soil.

Uncertainty in Soil Moisture Retrievals Using the SMAP Combined Active–Passive Algorithm for Growing Sweet Corn

The baseline active and passive (AP) algorithm of the NASA Soil Moisture Active Passive (SMAP) mission disaggregates the brightness temperature (TB) from a spatial resolution of 36 km to 9 km for the soil moisture (SM) using the radar backscattering coefficient (σ0) at 3 km. This algorithm was derived based upon an assumption of a linear relationship between TB and σ0. In this study, we investigated the robustness of this assumption with plot-scale AP measurements obtained under different conditions of surface roughness and stages of growing sweet corn. The uncertainties in the estimated TB at 9 km and, hence, the retrieved SM, due to uncertainties in the algorithm parameters, β and Γ, were assessed under different landcover heterogeneities. Overall, the linear regression was robust, with r2 > 0.75 under bare soil conditions when surface scattering is dominant and >0.52 during the growing season. The uncertainties in β and Γ due to AP observations result in uncertainties in retrieved SM <; 0.04 m3 /m3 for most conditions of heterogeneity. The differences in TB at 9 km, obtained when using β derived from vegetation water content (VWC) and using those from radar vegetation index, were also assessed. The errors in retrieved SM could reach as high as 0.5 m3 /m3 for the worst-case scenario, when an intermediate scale contains high VWC, but the coarse scale region has low averaged VWC. These results suggest that determination of growth stages using a biophysical parameter is essential for β estimations, particularly for highly heterogeneous landcovers.

Evaluation of the kurtosis algorithm in detecting radio frequency interference from multiple sources

A few of the issues faced by the kurtosis detection algorithm on recent field campaigns is discussed here. The performance of the kurtosis algorithm in detecting multiple-source Radio Frequency Interference (RFI) is characterized. A new RFI statistical model is presented in the paper to take into account the behavior of RFI sources under a large foot-print. Results indicate the behavior of the kurtosis ratio under central-limit conditions due to large number of RFI sources.

Remote sensing using coherent multipath interference of wideband planck radiation

A novel microwave radiometric technique, known as wideband autocorrelation radiometry (WiBAR), is introduced as a direct method to remotely measure the layer thickness of low-loss terrain covers such as snow and ice. This is done by measuring the propagation time τdelay from the autocorrelation function (ACF) of multipath microwave emission. We report measurements of the snowpack thickness using WiBAR at the University of Michigan Biological Station (UMBS) in winter 2015. The observations are done at frequencies from 1 to 3 GHz. At these frequencies, the volume and surface scattering are small in the snowpacks. This technique is inherently low-power since there is no transmitter as opposed to active remote sensing techniques.

Dry snowpack and freshwater icepack remote sensing using wideband Autocorrelation radiometry

A recently developed microwave radiometric technique, known as wideband autocorrelation radiometry (WiBAR), offers a deterministic method to remotely sense the propagation time τdelay of multi-path microwave emission of low-loss terrain covers and other layered surfaces. Terrestrial examples are the snow and lake ice packs. The microwave propagation time τdelay through the pack yields a measure of its vertical extent. We report measurements of the icepack on Lake Superior, and the snowpack at University of Michigan Biological Station (UMBS) in winter 2014 and 2015, respectively. The observations are done at frequencies from 7 to 10 GHz for icepack and 1 to 3 GHz for snowpack. At these frequencies, the volume and surface scattering are small in the packs. This technique is inherently low-power since there is no transmitter as opposed to active remote sensing techniques. In this paper the system design parameters of the WiBAR is discussed and it is shown that the microwave travel time within a dry snow pack and lake ice pack can be readily measured for a wide range of layer thicknesses observed during the experiment.

The process of unfrozen water freezing with decreasing temperature studied by dielectric measurement in the case of an Arctic soil

Dielectric measurements of an organic-rich permafrost soil over the range from 1.0 to 16 GHz and from −30 °C to +25 °C are presented. The measured shrub soil contains up to 90% organic matter and is the first soil of this composition for which the soil dielectric has been characterized. Using the dielectric data thus obtained, the process of freezing has been analyzed of unfrozen water contained in the shrub tundra sample1.

Lake Icepack and Dry Snowpack Thickness Measurement Using Wideband Autocorrelation Radiometry

A novel microwave radiometric technique, wideband autocorrelation radiometry (WiBAR), is introduced. The radiometer offers a direct method to remotely measure the microwave propagation time difference of multipath microwave emission from low-loss layered surfaces, such as a dry snowpack and a freshwater lake icepack. The microwave propagation time difference through the pack yields a measure of its vertical extent; thus, this technique provides a direct measurement of depth. It is also a low-power sensing method, since there is no transmitter. We present a simple geophysical forward model for the multipath interference phenomenon and derive the system requirements needed to design a WiBAR instrument. An X-band instrument fabricated from commercial-off-the-shelf (COTS) components measured the thickness of the freshwater lake ice at the University of Michigan Biological Station. Ice thickness retrieval is demonstrated from nadir to 59°. The WiBAR was able to directly measure the lake icepack thickness of about 36 cm with an accuracy of 2 cm over this range of incidence angles.

Sampling requirements for wideband autocorrelation radiometric (WIBAR) remote sensing of dry snowpack and lake icepack

Wideband autocorrelation radiometry (WiBAR) is a new method to remotely sense the microwave propagation time τdelay of multi-path microwave emission of low loss layered surfaces such as dry snowpack and freshwater lake icepack. The microwave propagation time τdelay through the pack yields a measure of its vertical extent; thus, this technique is a direct measurement of depth. This technique is inherently low-power since there is no transmitter in contrast to active remote sensing techniques. In this paper, the system design parameters and physics of operation of the WiBAR are discussed, and it is shown that the microwave propagation time can be readily measured for lake icepack at incidence angles away from nadir to at least 59.1°.

A first overview of SnowEx ground-based remote sensing activities during the winter 2016–2017

NASA SnowExs goal is estimating how much water is stored in Earths terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016–2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex.

A spatio-temporal data fusion algorithm for estimating high-resolution soil moisture in agricultural regions

In this study, a data-fusion algorithm is developed for estimation of high-resolution brightness temperatures (TB) at 1km from Soil Moisture Active Passive (SMAP) fine-grid TB product at 9km. It uses image segmentation to spatio-temporally cluster the study region based on meteorological and land cover similarity, followed by a support vector machine based regression that computes the value of the high-resolution TB at all pixels. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, enhanced vegetation index, precipitation, soil texture, and land-cover were used for disaggregation. The algorithm was implemented in Iowa, United States, from May to September 2016, and compared with the field observations of TB from Microwave Water and Energy Balance Experiment conducted as a part of the Soil Moisture Active Passive Validation Experiment (SMAPVEX16-MicroWEX). Additionally, they were also compared with the Sentinel downscaled SMAP TB at 1km. High resolution soil moisture is subsequently derived from high resolution TB using inverse models.