Application of Reflected Global Navigation Satellite System (GNSS-R) Signals in the Estimation of Sea Roughness Effects in Microwave Radiometry

In February-March 2009 NASA JPL conducted an airborne field campaign using the Passive Active L-band System (PALS) and the Ku-band Polarimetric Scatterometer (PolSCAT) collecting measurements of brightness temperature and near surface wind speeds. Flights were conducted over a region of expected high-speed winds in the Atlantic Ocean, for the purposes of algorithm development for salinity retrievals. Wind speeds encountered were in the range of 5 to 25 m/s during the two weeks deployment. The NASA-Langley GPS delay-mapping receiver (DMR) was also flown to collect GPS signals reflected from the ocean surface and generate post-correlation power vs. delay measurements. This data was used to estimate ocean surface roughness and a strong correlation with brightness temperature was found. Initial results suggest that reflected GPS signals, using small low-power instruments, will provide an additional source of data for correcting brightness temperature measurements for the purpose of sea surface salinity retrievals

SGP Cloud and Land Surface Interaction Campaign (CLASIC): Science and Implementation Plan

The Cloud and Land Surface Interaction Campaign is a field experiment designed to collect a comprehensive data set that can be used to quantify the interactions that occur between the atmosphere, biosphere, land surface, and subsurface. A particular focus will be on how these interactions modulate the abundance and characteristics of small and medium size cumuliform clouds that are generated by local convection. These interactions are not well understood and are responsible for large uncertainties in global climate models, which are used to forecast future climate states. The campaign will be conducted from June 8 to June 30, 2007, at the U.S. Department of Energy’s Atmospheric Radiation Measurement Climate Research Facility Southern Great Plains site. Data will be collected using eight aircraft equipped with a variety of specialized sensors, four specially instrumented surface sites, and two prototype surface radar systems. The architecture of Cloud and Land Surface Interaction Campaign includes a high-altitude surveillance aircraft and enhanced vertical thermodynamic and wind profile measurements that will characterize the synoptic scale structure of the clouds and the land surface within the Atmospheric Radiation Measurement Climate Research Facility Southern Great Plains site. Mesoscale and microscale structures will be sampled with a variety of aircraft, surface, and radar observations

Surface heterogeneity impacts on boundary layer dynamics via energy balance partitioning

The role of land-atmosphere interactions under heterogeneous surface conditions is investigated in order to identify mechanisms responsible for altering surface heat and moisture fluxes. Twelve coupled land surface – large eddy simulation scenarios with four different length scales of surface variability under three different horizontal wind speeds are used in the analysis. The base case uses Landsat ETM imagery over the Cloud Land Surface Interaction Campaign (CLASIC) field site for 3 June 2007. Using wavelets, the surface fields are band-pass filtered in order to maintain the spatial mean and variances to length scales of 200 m, 1600 m, and 12.8 km as lower boundary conditions to the model (approximately 0.25, 1.2 and 9.5 times boundary layer height). The simulations exhibit little variation in net radiation. Rather, there is a pronounced change in the partitioning of the surface energy between sensible and latent heat flux. The sensible heat flux is dominant for intermediate surface length scales. For smaller and larger scales of surface heterogeneity, which can be viewed as being more homogeneous, the latent heat flux becomes increasingly important. The simulations showed approximately 50 Wm<sup>−2</sup> difference in the spatially averaged latent heat flux. The results reflect a general decrease of the Bowen ratio as the surface conditions transition from heterogeneous to homogeneous. Air temperature is less sensitive to variations in surface heterogeneity than water vapor, which implies that the role of surface heterogeneity may be to maximize convective heat fluxes through modifying and maintaining local temperature gradients. More homogeneous surface conditions (i.e. smaller length scales), on the other hand, tend to maximize latent heat flux. The intermediate scale (1600 m) this does not hold, and is a more complicated interaction of scales. Scalar vertical profiles respond predictably to the partitioning of surface energy. Fourier spectra of the vertical wind speed, air temperature and specific humidity (<i>w</i><span style="position: relative; top: -.5em; left: -.65em;">~</span><i style=" margin-left:-.7em"></i>, <i>T</i><span style="position: relative; top: -.5em; left: -.65em;">~</span><i style=" margin-left:-.7em"></i> and <i>q</i><span style="position: relative; top: -.5em; left: -.65em;">~</span><i style=" margin-left:-.7em"></i>) and associated cospectra (<i>w</i><span style="position: relative; top: -.5em; left: -.65em;">~</span><i style=" margin-left:-.7em"></i><i>T</i><span style="position: relative; top: -.5em; left: -.65em;">~</span><i style=" margin-left:-.7em"></i>, <i>w</i><span style="position: relative; top: -.5em; left: -.65em;">~</span><i style=" margin-left:-.7em"></i><i>q</i><span style="position: relative; top: -.5em; left: -.65em;">~</span><i style=" margin-left:-.7em"></i> and <i>T</i><span style="position: relative; top: -.5em; left: -.65em;">~</span><i style=" margin-left:-.7em"></i><i>q</i><span style="position: relative; top: -.5em; left: -.65em;">~</span><i style=" margin-left:-.7em"></i>), however, are insensitive to the length scale of surface heterogeneity, but the near surface spectra are sensitive to the mean wind speed

Anticipated Impacts of Soil Moisture Active Passive (SMAP) Mission Measurements on Hydrologic Applications

No abstract availabl

Cyberinfrastructure for Airborne Sensor Webs

Since 2004 the NASA Airborne Science Program has been prototyping and using infrastructure that enables researchers to interact with each other and with their instruments via network communications. This infrastructure uses satellite links and an evolving suite of applications and services that leverage open-source software. The use of these tools has increased near-real-time situational awareness during field operations, resulting in productivity improvements and the collection of better data. This paper describes the high-level system architecture and major components, with example highlights from the use of the infrastructure. The paper concludes with a discussion of ongoing efforts to transition to operational status

An Initial Assessment of a SMAP Soil Moisture Disaggregation Scheme Using TIR Surface Evaporation Data over the Continental United States

The Soil Moisture Active Passive (SMAP) mission is dedicated toward global soil moisture mapping. Typically, an L-band microwave radiometer has spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of the SMAP active radar within three months of becoming operational, an intermediate (9-km) and finer (3-km) scale soil moisture product solely from the SMAP mission is no longer possible. Therefore, the focus of this study is a disaggregation of the 36-km resolution SMAP passive-only surface soil moisture (SSM) using the Soil Evaporative Efficiency (SEE) approach to spatial scales of 3-km and 9-km. The SEE was computed using thermal-infrared (TIR) estimation of surface evaporation over Continental U.S. (CONUS). The disaggregation results were compared with the 3 months of SMAP-Active (SMAP-A) and Active/Passive (AP) products, while comparisons with SMAP-Enhanced (SMAP-E), SMAP-Passive (SMAP-P), as well as with more than 180 Soil Climate Analysis Network (SCAN) stations across CONUS were performed for a 19 month period. At the 9-km spatial scale, the TIR-Downscaled data correlated strongly with the SMAP-E SSM both spatially (r = 0.90) and temporally (r = 0.87). In comparison with SCAN observations, overall correlations of 0.49 and 0.47; bias of 0.022 and 0.019 and unbiased RMSD of 0.105 and 0.100 were found for SMAP-E and TIR-Downscaled SSM across the Continental U.S., respectively. At 3-km scale, TIR-Downscaled and SMAP-A had a mean temporal correlation of only 0.27. In terms of gain statistics, the highest percentage of SCAN sites with positive gains (>55%) was observed with the TIR-Downscaled SSM at 9-km. Overall, the TIR-based downscaled SSM showed strong correspondence with SMAP-E; compared to SCAN, and overall both SMAP-E and TIR-Downscaled performed similarly, however, gain statistics show that TIR-Downscaled SSM slightly outperformed SMAP-E

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Understanding Moisture Dynamics in the Vadose Zone: Transcending the Darcy Scale

Soil moisture forms the interface at which the partitioning of the energy, carbon and water budget for the land-surface occurs. Its variability impacts different fields of application at varying extent scales like agriculture at the field scale, meteorology at the regional scale and climate change assessment at the global scale. However, past literature has focused on understanding soil moisture dynamics at this diverse range of extent scales using soil moisture data at the Darcy support scale which cannot effectively cater to soil moisture dynamics for the current eco-hydrologic models that describe complex heterogeneous domains at remote sensing footprint scales. This dissertation serves to push the envelope of our understanding of soil moisture dynamics and its dependence on land-surface heterogeneity at the coarse remote sensing scales. The research questions answered in this dissertation include 1) determining the dominant land-surface controls of near-surface soil moisture dynamics at scales varying between the Darcy (of the order of a few centimeters) support and satellite footprint scale (25.6 km); 2) generating a framework for quantifying the relationships between antecedent wetness, land-surface heterogeneity and near-surface soil moisture at remote sensing scales and 3) evaluating variability in the root zone moisture dynamics as evaluated through evapo-transpiration estimates at different remote sensing footprint scales. The dominant land-surface factors controlling soil moisture distribution at different scales were determined by developing a new Shannon entropy based technique and non-decimated wavelet transforms. It was found that the land-surface controls on soil moisture vary with hydro-climate and antecedent wetness conditions. In general, the effect of soil was found to reduce with coarsening support scale while the effect of topography and vegetation increased. A novel Scale-Wetness-Heterogeneity (SWHET) cuboid was developed to coalesce the relationship between soil moisture redistribution and dominant physical controls at different land-surface heterogeneity and antecedent wetness conditions across remote sensing scales. The SWHET cuboid can potentially enable spatial transferability of the scaling relationships for near-surface soil moisture. It was found that results from the SWHET cuboid enabled spatial transferability of the scaling relationships between two similar hydro-climates (Iowa, U.S.A and Manitoba, Canada) under some wetness and land-surface heterogeneity conditions. Evapotranspiration estimates were computed at varying scales using airborne and satellite borne remotely sensed data. The results indicated that in a semi-arid row cropped orchard environment, a remote sensing support scale comparable to the row spacing and smaller or comparable to the canopy size of trees overestimates the land surface temperature and consequently, underestimates evapotranspiration

Passive Microwave Remote Sensing of Snow Layers Using Novel Wideband Radiometer Systems and RFI Mitigation

Climate change can reduce the availability of water resources in many regions, and it will affect agriculture, industry, and energy supply. Snowpack monitoring is important in water resource management as well as flood and avalanche protection. The rapid melting process due to global warming changes the snowpacks' annual statistics, including the extent, and the snow water equivalent (SWE) of seasonal snowpacks, which results in non-stationary annual statistics that should be monitored in nearly daily intervals. The development of advanced radiometric sensors capable of accurately measuring the snowpack thickness and SWE is needed for the long-term study of the snowpack parameters' statistical changes. Passive microwave radiometry provides a means for measuring the microwave emission from a scene of snow and ice. A Wideband Autocorrelation Radiometer (ac{WiBAR}) operating from 1-2~GHz measures spontaneous emission from snowpack at long wavelengths where the scattering is minimized, but the snow layer coherent effects are preserved. By using a wide bandwidth to measure the spacing between frequencies of constructive and destructive interference of the emission from the soil under the snow, it can reveal the microwave travel time through the snow, and thus the snow depth. However, narrowband radio frequency interference (RFI) in the WiBAR's frequency of operations reduces the ability of the WiBAR to measure the thickness accurately. In addition, the current WiBAR system is a frequency domain, FD-WiBAR, system that uses a field-portable spectrum analyzer to collect the data and suffers from high data acquisition time which limits its applications for spaceborne and airborne technologies. In this work, a novel frequency tunable microwave comb filter is proposed for RFI mitigation. The frequency response of the proposed filter has a pattern with many frequencies band-pass and band rejection that preserves the frequency span while reducing the RFI. Moreover, we demonstrate time-domain WiBAR, TD-WiBAR, which presented as an alternative method for FD-WiBAR, and is capable of providing faster data acquisition. A new time-domain calibration is also developed for TD-WiBAR and evaluated with the frequency domain calibration. To validate the TD-WiBAR method, simulated laboratory measurements are performed using a microwave scene simulator circuit. Then the WiBAR instrument is enhanced with the proposed comb filter and showed the RFI mitigation in time-domain mode on an instrument bench test. Furthermore, we analyze the effects of an above snow vegetation layer on brightness temperature spectra, particularly the possible decay of wave coherence arising from volume scattering in the vegetation canopy. In our analysis, the snow layer is assumed to be flat, and its upward emission and surface reflectivities are modeled by a fully coherent model, while an incoherent radiative transfer model describes the volume scattering from the vegetation layer. We proposed a unified framework of vegetation scattering using radiative transfer (RT) theory for passive and active remote sensing of vegetated land surfaces, especially those associated with moderate-to-large vegetation water contents (VWCs), e.g., forest field. The framework allows for modeling passive and active microwave signatures of the vegetated field with the same physical parameters describing the vegetation structure. The proposed model is validated with the passive and active L-band sensor (PALS) acquired in SMAPVEX12 measurements in 2012, demonstrating the applicability of this model.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169653/1/maryamsa_1.pd

Understanding and Improving the Soil Moisture Retrieval Algorithm under Space, Time and Heterogeneity

The spatial and temporal monitoring of soil moisture from remote sensing platforms plays a pivotal role in predicting the future food and water security. That is, improving soil moisture estimation at remote sensing platforms has remarkable impacts in the fields of meteorology, hydrology, agriculture, and global climate change. However, remote sensing of soil moisture for long is hindered by spatial heterogeneity in land surface variables (soil, biomass, topography, and temperature) which cause systematic and random errors in soil moisture retrievals. Most soil moisture improvement methods to date focused on the downscaling of either coarse resolution soil moisture or brightness temperature based on fine scale ancillary information of land surface variables. Comparatively little work has been done on improving the parameterization of most sensitive variables to radiative transfer model that impact soil moisture retrieval accuracy. In addition, the classic radiative transfer model assumes the vegetation and surface roughness parameters, as constant with space and time which undermines the retrieval accuracy. Also, it is largely elusive so far the discussion on the non-linearity of microwave radiative transfer model and its relationship with energy and water fluxes. In order to address the above mentioned limitations, this dissertation aims to develop and validate a soil moisture modeling framework with associated improved parameterizations for surface roughness and vegetation optical depth (VOD) in the homogeneous and heterogeneous environments. To this end, the following research work is specifically conducted: (a) conduct comprehensive sensitivity analysis on radiative transfer model with space, time and hydroclimates; (b) develop multi-scale surface roughness model which incorporates small (soil) and large (topography) surface undulations to improve soil moisture retrievals; (c) improve the parameterization of vegetation topical depth (VOD) using within-pixel biomass heterogeneity to improved soil moisture accuracy; (d) investigate the non-linearity in microwave radiative transfer model, and its association with thermal energy fluxes. The results of this study showed that: (a) the total (linear + non-linear) sensitivity of soil, temperature and biomass variables varied with spatial scale (support), time, and hydro climates, with higher non-linearity observed for dense biomass regions. This non-linearity is also governed by soil moisture availability and temperature. Among these variables, surface roughness and vegetation optical depth are most sensitive variables to radiative transfer model (RTM); (b) considering the spatial and temporal variability in parameterization of surface roughness and VOD has improved soil moisture retrieval accuracy, importantly in cropland and forest environments; and (c) the soil moisture estimated through evaporative fraction (EF) correlates higher with VOD corrected soil moisture