Assimilation of SMOS Retrievals in the Land Information System

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm(sub 3 cm(sub -3). These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve

Satellite Observed Salinity Distributions at High Latitudes in the Northern Hemisphere: A Comparison of Four Products

Global surface ocean salinity measurements have been available since the launch of SMOS in 2009 and coverage was further enhanced with the launch of Aquarius in 2011. In the polar regions where spatial and temporal changes in sea surface salinity (SSS) are deemed important, the data has not been as robustly validated because of the paucity of in situ measurements. This study presents a comparison of four SSS products in the ice-free Arctic region, three using Aquarius data and one using SMOS data. The accuracy of each product is assessed through comparative analysis with ship and other in situ measurements. Results indicate RMS errors ranging between 0.33 and 0.89 psu. Overall, the four products show generally good consistency in spatial distribution with the Atlantic side being more saline than the Pacific side. A good agreement between the ship and satellite measurements were also observed in the low salinity regions in the Arctic Ocean, where SSS in situ measurements are usually sparse, at the end of summer melt seasons. Some discrepancies including biases of about 1 psu between the products in spatial and temporal distribution are observed. These are due in part to differences in retrieval techniques, geophysical filtering, and sea ice and land masks. The monthly SSS retrievals in the Arctic from 2011 to 2015 showed variations (within approximately 1 psu) consistent with effects of sea ice seasonal cycles. This study indicates that spaceborne observations capture the seasonality and interannual variability of SSS in the Arctic with reasonably good accuracy

Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over a Vineyard Field

The objective of this study was to compare several approaches to soil moisture (SM) retrieval using L-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30-60). Based on a three year data set (2010-2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as 'Mattar' and 'Saleh'). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and Mattar) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the reference SM data set derived from the multi-angular observations (R2 around 0.90, RMSE varying between 0.035 and 0.056 m3m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the Saleh methods based on either bi-angular or bipolarization observations (R2 around 0.93, RMSE around 0.035 m3m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the 'reference' SM time series (R2 around 0.75, RMSE around 0.055 m3m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals

Radio frequency interference in microwave radiometry: statistical analysis and study of techniques for detection and mitigation

Microwave radiometry field has been increasing its performance with higher accuracy measurements, leading to a more presence in the remote sensing field. Several space-borne, air-borne and ground-based radiometers have been developed to perform measurement campaigns; however, the actual sensitivity of a radiometer is often limited by man-made radio emissions such as radars, broadcasting emissions, wireless communications and many other communication systems based on electromagnetic waves, limiting the improvement in the radiometers¿ performance. Consequently, in order to maintain the accuracy in the radiometric measurements, it has been researched in the Radio Frequency Interference (RFI) detection and mitigation systems and algorithms for the microwave radiometry field. The scope of this doctoral thesis is the development and testing of RFI detection and mitigation algorithms in order to enhance radiometric measurements performed by the Multifequency Experimental Radiometer with Interference Tracking for Experiments over Land and Littoral (MERITXELL). The MERITXELL has been developed during this thesis with the idea studying the RFI present in several radiometric bands and the way to mitigate it, as well as to obtain data from diverse frequency bands and devices in only one measurement campaign

Winona Daily News

https://openriver.winona.edu/winonadailynews/1700/thumbnail.jp

Millimeter-wave sensing of the environment: A bibliographic survey

This literature survey was conducted to examine the field of millimeter wave remote sensing of the environment and collect all relevant observations made in the atmospheric windows near 90, 140, and 230 GHz of ocean, terrain, man-made features, and the atmosphere. Over 170 articles and reports were examined; bibliographic references are provided for all and abstracts are quoted when available. Selected highlights were extracted from the pertinent articles

Spartan Daily, December 11, 1984

Volume 83, Issue 67https://scholarworks.sjsu.edu/spartandaily/7253/thumbnail.jp