The SMAPVEX12 (Soil Moisture Active/Passive Validation Experiment 2012) was carried out over the summer of 2012 in Manitoba, Canada. The goal of the project was to improve the accuracy of satellite-based remote sensing of soil moisture. Data were gathered during a 42-day field campaign with surface measurements on 55 different agricultural fields in south-central Manitoba. The extended duration of the campaign, contrast in soil textures, and variety of crop types over the study region provided an excellent range of soil moisture and vegetation conditions. The study fields ranged from bare to fully vegetated, with volumetric soil moisture levels spanning a range of almost 50%. Remotely sensed data were collected on 17 days by aircraft at 1.4 Ghz with a microwave radiometer at two different resolutions. Observed brightness temperatures from the radiometer showed a typical inverse relationship to the near simultaneous soil moisture measurements from the field. Field-by-field relationships using all sampling dates with both soil and emissivity data were all shown to be significant with the exception of two of the pasture fields and a soybean field. Linear regressions across multiple fields and by flight lines also had statistically significant slopes. The significance of all these relationships improved with the removal of pasture fields from the analysis. On most fields, the sensitivity (slope) of the relationship and correlation coefficient (R2) between emissivity and observed soil moisture increased when vegetation and roughness effects were taken into account. The b parameter that relates vegetation water content to optical depth in the tau-omega model was optimized using the collective slope and R2 values of the individual fields. A b parameter value of 0.06 for horizontal polarization and 0.13 for vertical polarization were found to be optimal across the range of all fields in this analysis. / May 2016

Current North American operational satellite passive microwave snow water equivalent (SWE) retrieval algorithms consistently underestimate SWE levels for tundra environments when compared to four years of regional snow surveys conducted in the Northwest Territories and northern Manitoba, Canada. Almost all contemporary SWE algorithms are based on the brightness temperature difference between the 37GHz and 19GHz frequencies found onboard both past and present spaceborne sensors. This underestimation is likely a result of the distribution and deposition of the tundra snow, coupled with the influence of tundra lakes on brightness temperatures at the 19GHz frequency. To better our understanding concerning the underestimation of passive microwave SWE retrievals on the tundra, Environment Canada collected in situ measurements of SWE, snow depth, and density at 87 sites within a 25km by 25km study domain located near Churchill, Manitoba in March 2006. Coincident multi-scale passive microwave airborne (70m & 500m resolution) and spaceborne (regridded to 12.5km & 25km resolution depending on frequency) data were measured at 6.9GHz, 19GHz, 37GHz and 89 GHz frequencies during the same time period. The snow survey data highlighted small-scale localized patterns of snow distribution and deposition on the tundra that likely influences current SWE underestimation. Snow from the open tundra plains is re-distributed by wind into small-scale vegetated features and micro-topographic depressions such as narrow creekbeds, lake edge willows, small stands of coniferous trees and polygonal wedge depressions. The very large amounts of snow deposited in these spatially-constrained features has little influence on the microwave emission measured by large-scale passive microwave spaceborne sensors and is therefore unaccounted for in current methods of satellite SWE estimation. The analysis of the passive microwave airborne data revealed that brightness temperatures at the 19GHz were much lower over some tundra lakes, effectively lowering SWE at the satellite scale by reducing the 37-19GHz brightness temperature difference used to estimate SWE. The unique emission properties of lakes in the wide open expanse of the tundra plains, coupled with an insensitivity to the large amounts of SWE deposited in small-scale features provides an explanation for current passive microwave underestimation of SWE in the tundra environment.

Current North American operational satellite passive microwave snow water equivalent (SWE) retrieval algorithms consistently underestimate SWE levels for tundra environments when compared to four years of regional snow surveys conducted in the Northwest Territories and northern Manitoba, Canada. Almost all contemporary SWE algorithms are based on the brightness temperature difference between the 37GHz and 19GHz frequencies found onboard both past and present spaceborne sensors. This underestimation is likely a result of the distribution and deposition of the tundra snow, coupled with the influence of tundra lakes on brightness temperatures at the 19GHz frequency. To better our understanding concerning the underestimation of passive microwave SWE retrievals on the tundra, Environment Canada collected in situ measurements of SWE, snow depth, and density at 87 sites within a 25km by 25km study domain located near Churchill, Manitoba in March 2006. Coincident multi-scale passive microwave airborne (70m & 500m resolution) and spaceborne (regridded to 12.5km & 25km resolution depending on frequency) data were measured at 6.9GHz, 19GHz, 37GHz and 89 GHz frequencies during the same time period. The snow survey data highlighted small-scale localized patterns of snow distribution and deposition on the tundra that likely influences current SWE underestimation. Snow from the open tundra plains is re-distributed by wind into small-scale vegetated features and micro-topographic depressions such as narrow creekbeds, lake edge willows, small stands of coniferous trees and polygonal wedge depressions. The very large amounts of snow deposited in these spatially-constrained features has little influence on the microwave emission measured by large-scale passive microwave spaceborne sensors and is therefore unaccounted for in current methods of satellite SWE estimation. The analysis of the passive microwave airborne data revealed that brightness temperatures at the 19GHz were much lower over some tundra lakes, effectively lowering SWE at the satellite scale by reducing the 37-19GHz brightness temperature difference used to estimate SWE. The unique emission properties of lakes in the wide open expanse of the tundra plains, coupled with an insensitivity to the large amounts of SWE deposited in small-scale features provides an explanation for current passive microwave underestimation of SWE in the tundra environment.

This research examines the potential to use passive microwave remote sensing for measuring soil moisture extremes that impact agricultural areas in Canada. A validation was made of three passive microwave remote sensing soil moisture data sets, with weekly averaged values from the Land Parameter Retrieval Model (LPRM) applied to AMSR-E C/X-Band data providing the most accurate results (root mean squared error of 5 to 10%). A further evaluation of this data set against a spatially distributed in situ soil moisture network in Alberta suggests that this data set may be less accurate in regions where dense vegetation or open water is present, particularly on the northern edges of the Canadian agricultural extent. A method to derive soil moisture anomalies was developed that uses homogenous regions to spatially aggregate soil moisture statistics to compensate for a short satellite data record. It was found that these anomalies can be estimated with errors of less than 5% when these regions are 15 pixels or more over a seven year time period. Surface soil moisture anomalies from LPRM showed weak but significant relationships to precipitation based drought indices, suggesting promise for using these anomalies for wider soil moisture extremes monitoring. Soil moisture anomalies from CLASS and in situ networks showed inconsistencies with LPRM anomalies in how they capture soil moisture conditions that are relevant to agricultural yield.. These data sets overall show that this approach to quantifying extremes has potential, but improvement to soil moisture retrieval from LPRM and CLASS, and an integration of the information they provide are needed to optimize these data sets for agricultural monitoring. / National Science and Engineering Research Council, Agriculture and Agri-Food Canada, Canadian Space Agency

No description available.

The understanding of the dynamics of the climatic variables is critical to model and predict climatic and environmental changes. Traditional measures collected by the meteorological stations network are dispersedly located throughout the territory in the northern high latitudes and errors associated to these variations can be considerable. Our goal is to evaluate the contribution of the remote sensing by passive microwaves compared to the ground measurements for better characterizing the variations in the surface temperature during the winter. In the presence of snow, extraction of the surface parameters by microwave measurements is a complex and ill-conditioned problem. We evaluated a semi-empirical relation based on a theoretical analysis to estimate the surface temperatures with the measured brightness temperatures at 19 and 37 GHz (vertical polarization). Simulations of emissivity made with the Helsinki Technology University (HUT) model, the knowledge of the land cover, and the forest biomass enable us to define this relationship.The results of the comparison between the calculated surface temperatures and the air temperatures for the entire Canada (137 stations) and for two winters, 1992-93 and 2002-03 (16359 measurements) show a significant correlation with an estimation error ranging between 4 and 7[degrees Celsius] according to the regions considered. These results are discussed according to region and type of land cover (grassland, forest, tundra).

This thesis provides insight into techniques for the detection and classification of various marginal ice zone roughnesses in the southern Beaufort Sea using in situ and satellite-based microwave remote sensing. A proposed model of surface roughness shows the dependence of circular coherence, a discriminator of roughness, on the roughness and dielectrics. A relationship between ice slopes in azimuth and range direction is derived. Microwave brightness temperature of open water is significantly correlated with wave height but not with the wind speed, having the strongest correlations for the H-polarization at both 37 and 89 GHz. A modified formula for the relationship between non-dimensional form of energy and wave age at wind speeds 0−10 m/s is obtained. The brightness temperature (April−June) of sea ice at H-polarization of 89 GHz is found to decrease with increasing roughness, and is attributed to the dominant contributions from rapidly varying thermodynamic properties of snow-covered sea ice.

This research concerns the CONAE Microwave Radiometer (MWR), on board the Aquarius/SAC-D platform. MWR's main purpose is to provide measurements that are simultaneous and spatially collocated with those of NASA's Aquarius radiometer/scatterometer. For this reason, knowledge of the MWR antenna beam footprint geolocation is crucial to mission success. In particular, this thesis addresses an on-orbit validation of the MWR antenna beam pointing, using calculated MWR instantaneous field of view (IFOV) centers, provided in the CONAE L-1B science data product. This procedure compares L-1B MWR IFOV centers at land/water crossings against high-resolution coastline maps. MWR IFOV locations versus time are computed from knowledge of the satellite's instantaneous location relative to an earth-centric coordinate system (provided by on-board GPS receivers), and a priori measurements of antenna gain patterns and mounting geometry. Previous conical scanning microwave radiometer missions (e.g., SSM/I) have utilized observation of rapid change in brightness temperatures (T_B) to estimate the location of land/water boundaries, and subsequently to determine the antenna beam-pointing accuracy. In this thesis, results of an algorithm to quantify the geolocation error of MWR beam center are presented, based upon two-dimensional convolution between each beam's gain pattern and land-water transition. The analysis procedures have been applied to on-orbit datasets that represent land-water boundaries bearing specific desirable criteria, which are also detailed herein. The goal of this research is to gain a better understanding of satellite radiometer beam-pointing error and thereby to improve the geolocation accuracy for MWR science data products.

No description available.

The accurate estimation of snow water equivalent (SWE) in the Canadian sub-arctic is integral to climate variability studies and water availability forecasts for economic considerations (drinking water, hydroelectric power generation). Common passive microwave (PM) snow water equivalent (SWE) algorithms that utilize the differences in brightness temperature (Tb) at 37 GHz – 19 GHz falter in lake-rich tundra environments because of the inclusion of lakes within PM pixels. The overarching goal of this research was to investigate the use of multiple platforms and methodologies to observe and quantify the effects of lake ice and sub-ice water on passive microwave emission for the purpose of improving snow water equivalent (SWE) retrieval algorithms. Using in situ snow and ice measurements as input, the Helsinki University of Technology (HUT) multi-layer snow emission model was modified to include an ice layer below the snow layer. Emission for 6.9, 19, 37 and 89 GHz were simulated at horizontal and vertical polarizations, and were validated by high resolution airborne passive microwave measurements coincident with in situ sampling sites over two lakes near Inuvik, Northwest Territories (NWT). Overall, the general magnitude of brightness temperatures were estimated by the HUT model for 6.9 and 19 GHz H/V, however the variability was not. Simulations produced at 37 GHz exhibited the best agreement relative to observed temperatures. However, emission at 37 GHz does not interact with the radiometrically cold water, indicating that ice properties controlling microwave emission are not fully captured by the HUT model. Alternatively, active microwave synthetic aperture radar (SAR) measurements can be used to identify ice properties that affect passive microwave emission. Dual polarized X-band SAR backscatter was utilized to identify ice types by the segmentation program MAGIC (MAp Guided Ice Classification). Airborne passive microwave transects were grouped by ice type classes and compared to backscatter measurements. In freshwater, where there were few areas of high bubble concentration at the ice/water interface Tbs exhibited positive correlations with cross-polarized backscatter, corresponding to ice types (from low to high emission/backscatter: clear ice, transition zone between clear and grey ice, grey ice and rafted ice). SWE algorithms were applied to emission within each ice type producing negative or near zero values in areas of low 19 GHz Tbs (clear ice, transition zone), but also produced positive values that were closer to the range of in situ measurements in areas of high 19 GHz Tbs (grey and rafted ice). Therefore, cross-polarized X-band SAR measurements can be used as a priori ice type information for spaceborne PM algorithms, providing information on ice types and ice characteristics (floating, frozen to bed), integral to future tundra-specific SWE retrieval algorithms.