Inter-satellite Microwave Radiometer Calibration

The removal of systematic brightness temperature (Tb) biases is necessary when producing decadal passive microwave data sets for weather and climate research. It is crucial to achieve Tb measurement consistency among all satellites in a constellation as well as to maintain sustained calibration accuracy over the lifetime of each satellite sensor. In-orbit inter-satellite radiometric calibration techniques provide a long term, group-wise solution; however, since radiometers operate at different frequencies and viewing angles, Tb normalizations are made before making intermediate comparisons of their near-simultaneous measurements. In this dissertation, a new approach is investigated to perform these normalizations from one satellite\u27s measurements to another. It uses Taylor\u27s series expansion around a source frequency to predict Tb of a desired frequency. The relationship between Tb\u27s and frequencies are derived from simulations using an oceanic Radiative Transfer Model (RTM) over a wide variety of environmental conditions. The original RTM is built on oceanic radiative transfer theory. Refinements are made to the model by modifying and tuning algorithms for calculating sea surface emission, atmospheric emission and attenuations. Validations were performed with collocated WindSat measurements. This radiometric calibration approach is applied to establish an absolute brightness temperature reference using near-simultaneous pair-wise comparisons between a non-sun synchronous radiometer and two sun-synchronous polar-orbiting radiometers: the Tropical Rain Measurement Mission (TRMM) Microwave Imager (TMI), WindSat (on Coriolis) and Advanced Microwave Scanning Radiometer (AMSR) on Advanced Earth Observing System -II (ADEOSII), respectively. Collocated measurements between WindSat and TMI as well as between AMSR and TMI, within selected 10 weeks in 2003 for each pair, are collected, filtered and applied in the cross calibration. AMSR is calibrated to WindSat using TMI as a transfer standard. Accuracy prediction and error source analysis are discussed along with calibration results. This inter-satellite radiometric calibration approach provides technical support for NASA\u27s Global Precipitation Mission which relies on a constellation of cooperative satellites with a variety of microwave radiometers to make global rainfall measurements

A Roughness Correction for Aquarius Ocean Brightness Temperature Using the CONAE MicroWave Radiometer

Aquarius/SAC-D is a joint NASA/CONAE (Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using an L-band radiometer that measures ocean brightness temperature (Tb). The application of L-band radiometry to retrieve SSS is a difficult task, and therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that warms the ocean Tb. The Aquarius (AQ) instrument (L-band radiometer/scatterometer) baseline approach uses the radar scatterometer to provide this ocean roughness correction, through the correlation of radar backscatter with the excess ocean emissivity. In contrast, this dissertation develops an ocean roughness correction for AQ measurements using the MicroWave Radiometer (MWR) instrument Tb measurements at Ka-band to remove the errors that are caused by ocean wind speed and direction. The new ocean emissivity radiative transfer model was tuned using one year (2012) of on-orbit combined data from the MWR and the AQ instruments that are collocated in space and time. The roughness correction in this paper is a theoretical Radiative Transfer Model (RTM) driven by numerical weather forecast model surface winds, combined with ancillary satellite data from WindSat and SSMIS, and environmental parameters from NCEP. This RTM provides an alternative approach for estimating the scatterometer-derived roughness correction, which is independent. The theoretical basis of the algorithm is described and results are compared with the AQ baseline scatterometer method. Also results are presented for a comparison of AQ SSS retrievals using both roughness corrections

The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the <i>R</i><sub>value</sub> data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7%) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7%) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (<i>R</i><sup>2</sup> = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results

SMAP L-Band Microwave Radiometer: Instrument Design and First Year on Orbit

The Soil Moisture Active Passive (SMAP) L-band microwave radiometer is a conical scanning instrument designed to measure soil moisture with 4 percent volumetric accuracy at 40-kilometer spatial resolution. SMAP is NASA's first Earth Systematic Mission developed in response to its first Earth science decadal survey. Here, the design is reviewed and the results of its first year on orbit are presented. Unique features of radiometer include a large 6-meter rotating reflector, fully polarimetric radiometer receiver with internal calibration, and radio-frequency interference detection and filtering hardware. The radiometer electronics are thermally controlled to achieve good radiometric stability. Analyses of on-orbit results indicate the electrical and thermal characteristics of the electronics and internal calibration sources are very stable and promote excellent gain stability. Radiometer NEdT (Noise Equivalent differential Temperature) less than 1 degree Kelvin for 17-millisecond samples. The gain spectrum exhibits low noise at frequencies greater than 1 megahertz and 1 divided by f (pink) noise rising at longer time scales fully captured by the internal calibration scheme. Results from sky observations and global swath imagery of all four Stokes antenna temperatures indicate the instrument is operating as expected

Creating a Consistent Oceanic Multi-decadal Intercalibrated TMI-GMI Constellation Data Record

The Tropical Rainfall Measuring Mission (TRMM), launched in late November 1997 into a low earth orbit, produced the longest microwave radiometric data time series of 17-plus years from the TRMM Microwave Imager (TMI). The Global Precipitation Measuring (GPM) mission is the follow-on to TRMM, designed to provide data continuity and advance precipitation measurement capabilities. The GPM Microwave Imager (GMI) performs as a brightness temperature (Tb) calibration standard for the intersatellite radiometric calibration (XCAL) for the other constellation members; and before GPM was launched, TMI was the XCAL standard. This dissertation aims at creating a consistent oceanic multi-decadal Tb data record that ensures an undeviating long-term precipitation record covering TRMM-GPM eras. As TMI and GMI share only a 13-month common operational period, the U.S. Naval Research Laboratory\u27s WindSat radiometer, launched in 2003 and continuing today provides the calibration bridge between the two. TMI/WindSat XCAL for their \u3e 9 years\u27 period, and WindSat/GMI XCAL for one year are performed using a robust technique developed by the Central Florida Remote Sensing Lab, named CFRSL XCAL Algorithm, to estimate the Tb bias of one relative to the other. The 3-way XCAL of GMI/TMI/WindSat for their joint overlap period is performed using an extended CFRSL XCAL algorithm. Thus, a multi-decadal oceanic Tb dataset is created. Moreover, an important feature of this dataset is a quantitative estimate of the Tb uncertainty derived from a generic Uncertainty Quantification Model (UQM). In the UQM, various sources contributing to the Tb bias are identified systematically. Next, methods for quantifying uncertainties from these sources are developed and applied individually. Finally, the resulting independent uncertainties are combined into a single overall uncertainty to be associated with the Tb bias on a channel basis. This dissertation work is remarkably important because it provides the science community with a consistent oceanic multi-decadal Tb data record, and also allows the science community to better understand the uncertainty in precipitation products based upon the Tb uncertainties provided

Toward vicarious calibration of microwave remote-sensing satellites in arid environments

The Soil Moisture and Ocean Salinity (SMOS) satellite marks the commencement of dedicated global surface soil moisture missions, and the first mission to make passive microwave observations at L-band. On-orbit calibration is an essential part of the instrument calibration strategy, but on-board beam-filling targets are not practical for such large apertures. Therefore, areas to serve as vicarious calibration targets need to be identified. Such sites can only be identified through field experiments including both in situ and airborne measurements. For this purpose, two field experiments were performed in central Australia. Three areas are studied as follows: 1) Lake Eyre, a typically dry salt lake; 2) Wirrangula Hill, with sparse vegetation and a dense cover of surface rock; and 3) Simpson Desert, characterized by dry sand dunes. Of those sites, only Wirrangula Hill and the Simpson Desert are found to be potentially suitable targets, as they have a spatial variation in brightness temperatures of <4 K under normal conditions. However, some limitations are observed for the Simpson Desert, where a bias of 15 K in vertical and 20 K in horizontal polarization exists between model predictions and observations, suggesting a lack of understanding of the underlying physics in this environment. Subsequent comparison with model predictions indicates a SMOS bias of 5 K in vertical and 11 K in horizontal polarization, and an unbiased root mean square difference of 10 K in both polarizations for Wirrangula Hill. Most importantly, the SMOS observations show that the brightness temperature evolution is dominated by regular seasonal patterns and that precipitation events have only little impact

Spatial patterns in timing of the diurnal temperature cycle

This paper investigates the structural difference in timing of the diurnal temperature cycle (DTC) over land resulting from choice of measuring device or model framework. It is shown that the timing can be reliably estimated from temporally sparse observations acquired from a constellation of low Earth-orbiting satellites given record lengths of at least three months. Based on a year of data, the spatial patterns of mean DTC timing are compared between temperature estimates from microwave Ka-band, geostationary thermal infrared (TIR), and numerical weather prediction model output from the Global Modeling and Assimilation Office (GMAO). It is found that the spatial patterns can be explained by vegetation effects, sensing depth differences and more speculatively the orientation of orographic relief features. In absolute terms, the GMAO model puts the peak of the DTC on average at 12:50 local solar time, 23 min before TIR with a peak temperature at 13:13 (both averaged over Africa and Europe). Since TIR is the shallowest observation of the land surface, this small difference represents a structural error that possibly affects the model's ability to assimilate observations that are closely tied to the DTC. The equivalent average timing for Ka-band is 13:44, which is influenced by the effect of increased sensing depth in desert areas. For non-desert areas, the Ka-band observations lag the TIR observations by only 15 min, which is in agreement with their respective theoretical sensing depth. The results of this comparison provide insights into the structural differences between temperature measurements and models, and can be used as a first step to account for these differences in a coherent way

Wind direction azimuthal signature in the stokes emission vector from the ocean surface at microwave frequencies

Includes bibliographical references (page 431).An ocean polarimetric emission model is presented. It is found that skewness and upwind/cross-wind rms slopes are responsible for the first and second azimuthal harmonic, respectively. Atmospheric effects contribute significantly at low wind speeds, and at horizontal polarization at certain observation angles. Simulation results compare favorably with reported JPL-WINDRAD measurements

An artificial neural network approach for soil moisture retrieval using passive microwave data

Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005