Retrieval Of Geophysical Parameters Research Articles

Towards the Mitigation of Discrepancies in Sea Surface Parameters Estimated from Low- and High-Resolution Satellite Altimetry

In this study, we present an extension to existing numerical retrackers of synthetic-aperture radar (SAR) altimetry signals. To our knowledge at the time of writing this manuscript, it offers the most consistent retrieval of geophysical parameters compared to low-resolution mode (LRM) retracking results. We achieve this by additionally estimating the standard deviation of vertical wave-particle velocities σv and a new parameter ux, linked to a residual Doppler in the returned radar echoes, which can be related to wind speed and direction. Including this new parameter into the SAR stack retracker mitigates sea surface height estimation errors by up to two centimeters for Sentinel-6MF SAR mode results. Additionally, we found a closed-form equation to describe ux as a function of eastward and northward wind variables, which allows mitigating the effects of this parameter on a SAR stack within level 1B processing and generating a lookup table to correct sea surface height estimates in SAR mode. This additionally opens up the door to estimating the wind speed and direction from SAR altimetry stacks. Additionally, we discuss how this new retracker performs with respect to different planned future baseline processor changes of Sentinel-6MF, namely F09 and F10, by attempting to imitate their level 2 processing. This is achieved by processing cycles 017 to 051 (nearly a full year) of Sentinel-6MF level 1A data on a global scale. We observe that the new retracking method is, on average, more accurate with respect to LRM. However, there is a slight increase in measurement noise due to the introduction of an additional parameter. To ensure that the results of the new retracker are not biased, we retrack using both the new method and the SINCS-OV ZSK retracker on Sentinel-6MF stack data produced in a Monte Carlo simulation. We analyze the simulation results with respect to accuracy, precision, and correlations between estimated parameters. We show that the accuracy of the new retracker is better than SINCS-OV ZSK but less precise, which could be related to higher correlation coefficients—especially with respect to the new parameter ux—between estimated parameters.

FY-3G Satellite Instruments and Precipitation Products: First Report of China’s Fengyun Rainfall Mission In-Orbit

Precipitation is one of the most important parameters in Earth system but is hard to measure. China began to develop satellites dedicated to precipitation measurements in the second generation of the FengYun polar-orbiting meteorological satellite program (FY-3). The first of total 2 rainfall missions scheduled, FY-3G, was successfully launched on 16 April 2023 and became the world’s third satellite to measure precipitation with space-borne radar after the tropical rainfall measuring mission in 1997 and global precipitation measurement core observatory in 2014. In this manuscript, we illustrate the platform of FY-3G and instruments mounted in great detail, with additional information about ground segments, designed sensor-based products, and retrieval of geophysical parameters. During the 4 months after launch, the specifications of the platform and instruments are under inspection as calibration and validation are carefully conducted. The first images captured by FY-3G are encouraging, and initial results show a strong capability for providing insights into all kinds of precipitation phenomena. The important work of data processing, such as data assimilation, data fusion between space-based and ground-based radar, and that between polar and geostationary satellites, as well as future applications in weather modification, has been prepared in advance. As a pioneer of China’s rainfall missions, FY-3G greatly improves our ability to provide global precipitation measurements, understand Earth’s water and energy cycle, and forecast extreme events for the benefit of society.

A Novel Fully Coupled Physical–Statistical–Deep Learning Method for Retrieving Near-Surface Air Temperature from Multisource Data

Retrieval of near-surface air temperature (NSAT) from remote sensing data is often ill-posed because of insufficient observational information. Many factors influence the NSAT, which can lead to the instability of the accuracy of traditional algorithms. To overcome this problem, in this study, a fully coupled framework was developed to robustly retrieve NSAT from thermal remote sensing data, integrating physical, statistical, and deep learning methods (PS-DL). Based on physical derivation, the optimal combinations of remote sensing bands were chosen for building the inversion equations to retrieve NSAT, and deep learning was used to optimize the calculations. Multisource data (physical model simulations, remote sensing data, and assimilation products) were used to establish the training and test databases. The NSAT retrieval accuracy was enhanced using the land surface temperature (LST) and land surface emissivity (LSE) as prior knowledge. The highest mean absolute error (MAE) and root-mean-square error (RMSE) of the retrieved NSAT data were 0.78 K and 0.89 K, respectively. In a cross-validation against the China Meteorological Forcing Dataset (CMFD), the MAE and RMSE were 1.00 K and 1.29 K, respectively. The actual inversion MAE and RMSE for the optimal band combination were 1.21 K and 1.33 K, respectively. The proposed method effectively overcomes the limitations of traditional methods as the inversion accuracy is enhanced by adding the information of atmospheric water vapor and more bands, and the applicability (portability) of the algorithm is enhanced using LST and LSE as prior knowledge. This model can become a general inversion paradigm for geophysical parameter retrieval, which is of milestone significance because of its accuracy and the ability to allow deep learning for physical interpretation.

A review of recent developments in low-frequency ultra-wideband microwave radiometry for studies of the cryosphere

Over the past decade, a series of airborne experiments in the Arctic and Antarctica explored microwave emission from sea ice and ice sheets at frequencies from 0.5 to 2 GHz. The experiments were motivated by the fact that lower frequencies penetrate deeper into a frozen surface, thus offering the possibility to measure physical temperatures at great depths in ice sheets and, subsequently, other unique geophysical observables including sea ice salinity. These experiments were made feasible by recent engineering advances in electronics, antenna design, and noise removal algorithms when operating outside of protected bands in the electromagnetic spectrum. These technical advances permit a new type of radiometer that not only operates at low frequency, but also obtains continuous spectral information over the band from 0.5 to 2 GHz. Spectral measurements facilitate an understanding of the physical processes controlling emission and also support the interpretation of results from single frequency instruments. This paper reviews the development of low-frequency, wide band radiometry and its application to cryosphere science over the past 10 years. The paper summarizes the engineering design of an airborne instrument and the associated algorithms to mitigate radio frequency interference. Theoretical models of emission built around the morphologic and electrical properties of cryospheric components are also described that identify the dominant physical processes contributing to emission spectra. New inversion techniques for geophysical parameter retrieval are summarized for both Arctic and Antarctic scenarios. Examples that illustrate how the measurements are used to inform on glaciological problems are presented. The paper concludes with a description of new instrument concepts that are foreseen to extend the technology into operation from space.

RFI Source Detection Based on Reweighted ℓ 1-Norm Minimization for Microwave Interferometric Radiometry

Radio frequency interference (RFI) seriously deteriorates the quality of the retrieval of geophysical parameters, e.g., Earth surface moisture and ocean salinity, measured in microwave interferometric radiometry (MIR). The accurate detection of RFI sources is crucial for locating these illegal sources and mitigating their impact. In this article, we propose a new method based on reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -norm minimization to detect RFI sources. First, we exploit the sparsity of RFI sources in the spatial domain and formulate the RFI detection as a problem of reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -norm minimization, by which the RFI signals can be well recovered and the background noises can be suppressed. Then, we present two algorithms, termed RL1 and NRL1, to achieve RFI source detection. The RL1 algorithm employs a fast iterative shrinkage thresholding (FIST) technique, and the NRL1 algorithm combines the FIST with a neighbor-reweighting strategy that helps to further enhance the RFI target regions. Finally, simulations and experiments using Soil Moisture and Ocean Salinity (SMOS) satellite data demonstrate the superiority of the proposed method on the RFI-signal-to-background ratio (RSBR) in recovered images and the detection performance of RFI sources, compared with the existing RFI processing methods in MIR.

Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification

It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step.

Atmospheric Correction to Passive Microwave Brightness Temperature in Snow Cover Mapping Over China

Variable atmospheric conditions are typically ignored in the retrieval of geophysical parameters of the Earth’s surface when using spaceborne passive microwave observations. However, high frequencies, for example, 91.7 GHz, are sensitive to variable atmospheric absorption, even in winter’s dry conditions. In this article, the influence of variable atmospheric absorption on snow cover extent (SCE) mapping was quantitatively investigated. A physical method was derived to enable atmospheric correction for variable atmospheric conditions. The total column precipitable water vapor (TPWV) from Moderate Resolution Imaging Spectroradiometer (MODIS) was parametrized into transmittances in this correction method. The corrected brightness temperature at 19 and 91.7 GHz from the Special Sensor Microwave Imager Sounder (SSMIS) was applied to the threshold algorithm for snow mapping over China. Compared with the Interactive Multisensor Snow and Ice Mapping System (IMS) data in winter from 2012 to 2013, for Qinghai–Tibet plateau (QTP), a significant improvement after correction was obtained from February to March over ephemeral and shallow snow, where the largest daily improvement of accuracy is up to 20%. The accuracy (incl. precision, recall, and F1 index) improved on average is from 0.77 (0.60, 0.68, and 0.63) to 0.79 (0.69, 0.7, and 0.68) over the full winter time from December to March. Over forest-rich Northeast China, where snow in winter is thicker, small improvement was observed at the onset of the snow season and over snow margin area. It was evidenced that high frequency is a promising way of snow cover mapping with the proposed atmospheric correction method.

Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations

This paper presents the processing algorithms for geolocating and calibration of the Cyclone Global Navigation Satellite System (CYGNSS) level 1 land data products, as well as analysis of the spatial resolution of Global Navigation Satellite System Reflectometry (GNSS-R) coherent reflections. Accurate and robust geolocation and calibration of GNSS-R land observations are necessary first steps that enable subsequent geophysical parameter retrievals. The geolocation algorithm starts with an initial specular point location on the Earth’s surface, predicted by modeling the Earth as a smooth ellipsoid (the WGS84 representation) and using the known transmitting and receiving satellite locations. Information on terrain topography is then compiled from the Shuttle Radar Topography Mission (SRTM) generated Digital Elevation Map (DEM) to generate a grid of local surface points surrounding the initial specular point location. The delay and Doppler values for each point in the local grid are computed with respect to the empirically observed location of the Delay Doppler Map (DDM) signal peak. This is combined with local incident and reflection angles across the surface using SRTM estimated terrain heights. The final geolocation confidence is estimated by assessing the agreement of the three geolocation criteria at the estimated surface specular point on the local grid, including: the delay and Doppler values are in agreement with the CYGNSS observed signal peak and the incident and reflection angles are suitable for specular reflection. The resulting geolocation algorithm is first demonstrated using an example GNSS-R reflection track that passes over a variety of terrain conditions. It is then analyzed using a larger set of CYGNSS data to obtain an assessment of geolocation confidence over a wide range of land surface conditions. Following, an algorithm for calibrating land reflected signals is presented that considers the possibility of both coherent and incoherent scattering from land surfaces. Methods for computing both the bistatic radar cross section (BRCS, for incoherent returns) and the surface reflectivity (for coherent returns) are presented. a flag for classifying returns as coherent or incoherent developed in a related paper is recommended for use in selecting whether the BRCS or reflectivity should be used in further analyses for a specific DDM. Finally, a study of the achievable surface feature detection resolution when coherent reflections occur is performed by examining a series of CYGNSS coherent reflections across an example river. Ancillary information on river widths is compared to the observed CYGNSS coherent observations to evaluate the achievable surface feature detection resolution as a function of the DDM non-coherent integration interval.

Untangling the Incoherent and Coherent Scattering Components in GNSS-R and Novel Applications

As opposed to monostatic radars where incoherent backscattering dominates, in bistatic radars, such as Global Navigation Satellite Systems Reflectometry (GNSS-R), the forward scattered signals exhibit both an incoherent and a coherent component. Current models assume that either one or the other are dominant, and the calibration and geophysical parameter retrieval (e.g., wind speed, soil moisture, etc.) are developed accordingly. Even the presence of the coherent component of a GNSS reflected signal itself has been a matter of discussion in the last years. In this work, a method developed to separate the leakage of the direct signal in the reflected one is applied to a data set of GNSS-R signals collected over the ocean by the Microwave Interferometer Reflectometer (MIR) instrument, an airborne dual-band (L1/E1 and L5/E5a), multi-constellation (GPS and Galileo) GNSS-R instrument with two 19-elements antenna arrays with 4 beam-steered each. The presented results demonstrate the feasibility of the proposed technique to untangle the coherent and incoherent components from the total power waveform in GNSS reflected signals. This technique allows the processing of these components separately, which increases the calibration accuracy (as today both are mixed and processed together), allowing higher resolution applications since the spatial resolution of the coherent component is determined by the size of the first Fresnel zone (300–500 meters from a LEO satellite), and not by the size of the glistening zone (25 km from a LEO satellite). The identification of the coherent component enhances also the location of the specular reflection point by determining the peak maximum from this coherent component rather than the point of maximum derivative of the incoherent one, which is normally noisy and it is blurred by all the glistening zone contributions.

The case for increasing the posting rate in delay/Doppler altimeters

In this paper we analyze the effect of increasing the posting rate of delay/Doppler altimeters on the retrieval of open ocean geophysical parameters. The conventional posting rate for synthetic aperture radar altimeters data is 20 Hz, which on-ground corresponds to the typical delay/Doppler along-track resolution of about 320 m. However, the speckle autocorrelation properties of delay/Doppler waveforms over the open ocean show a decorrelation distance much shorter than the typical along-track resolution, suggesting that further information can be extracted if more frequent observations of the surface were obtained. By processing one cycle of Sentinel-3A data at different posting rates we have verified that a relative improvement of more than 20% in the measurement noise of both sea surface height and significant wave height can be achieved, just by increasing the posting rate from 20 to 40 Hz, and an even further improvement is obtained at 80 Hz. We believe this result has significant implications for the current CryoSat-2 and Sentinel-3 missions and the upcoming Sentinel-6/Jason-CS mission.

Evaluating the impact of range walk compensation in delay/Doppler processing over open ocean

Throughout this paper an analysis is presented on the impact on the geophysical retrievals when a second order correction for the range migration is applied in delay/Doppler processing for Synthetic Aperture Radar altimeters. Under the assumption that the range distance is not varying within a burst acquisition, the range migration is usually corrected in delay/Doppler domain allowing for a great simplification of the processing. Range walk is the changing range migration during the burst acquisition and its compensation can be added to delay/Doppler processing aiming at increasing the accuracy of the Level1b power waveforms. In-house processing framework was exploited to process CryoSat Full Bit Rate products up to the ocean geophysical parameters retrieval with and without compensating for the range walk. Experimental results revealed that, compensating for the range walk, lower Significant Wave Height (SWH) is retrieved so that the existing difference in SWH from delay/Doppler with respect to conventional altimetry can be reduced.

Aerosol direct radiative effect over clouds from a synergy of Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) reflectances

Abstract. The retrieval of geophysical parameters is increasingly dependent on synergistic use of satellite instruments. More sophisticated parameters can be retrieved and the accuracy of retrievals can be increased when more information is combined. In this paper, a synergistic application of Ozone Monitoring Instrument (OMI), on the Aura platform, and Moderate Resolution Imaging Spectroradiometer (MODIS), on the Aqua platform, Level 1B reflectances is described, enabling the retrieval of the aerosol direct radiative effect (DRE) over clouds using the differential aerosol absorption (DAA) technique. This technique was first developed for reflectances from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on the Environmental Satellite (Envisat), which had the unique capability of measuring contiguous radiances from the ultraviolet (UV) at 240 to 1750 nm in the shortwave-infrared (SWIR), at a moderate spectral resolution of 0.2 to 1.5 nm. However, the spatial resolution and global coverage of SCIAMACHY was limited, and Envisat stopped delivering data in 2012. In order to continue the DRE data retrieval, reflectances from OMI and MODIS, flying in formation, were combined from the UV to the SWIR. This resulted in reflectances at a limited but sufficient spectral resolution, available at the OMI pixel grid, which have a much higher spatial resolution and coverage than SCIAMACHY. The combined reflectance spectra allow the retrieval of cloud microphysical parameters in the SWIR, and the subsequent retrieval of aerosol DRE over cloud scenes using the DAA technique. For liquid cloud scenes in the south-east Atlantic region with cloud fraction (CF) &gt;0.3, the area-averaged instantaneous aerosol DRE over clouds in June to August 2006 was 25 Wm−2 with a standard deviation of 30 Wm−2. The maximum area-averaged instantaneous DRE from OMI–MODIS in August 2006 was 75.6±13 Wm−2. The new aerosol DRE over-cloud dataset from OMI–MODIS is compared to the SCIAMACHY dataset for the period 2006 to 2009, showing a very high correlation. The OMI–MODIS DRE dataset over the Atlantic Ocean is highly correlated to above-cloud AOT measurements from OMI and MODIS. It is related to AOT measurements over Ascension Island in 2016, showing the transport of smoke all the way from its source region in Africa over the Atlantic to Ascension and beyond.

Weather Radar Data Processing and Atmospheric Applications: An Overview of Tools for Monitoring Clouds and Detecting Wind Shear

Rain, snow, and volcanic ash clouds contain particles generated by different physical and chemical processes. When electromagnetic radiation interacts with particle distribution, causing absorption and scattering, the backscattered power enables the retrieval of useful geophysical parameters of particle distribution. This is the measurement principle of microwave weather (meteorological) radar, monostatic remote sensing that can exploit Rayleigh and Mie backscattering to remotely probe atmospheric clouds. This article is an overview of weather radar systems and data processing for rain, snow, and volcanic ash clouds. We describe ground-based weather radars along with other platforms, such as airborne and spaceborne configurations. Signal processing of single-polarization weather radar systems in addition to more advanced schemes, such as those that enable Doppler and dual-polarization capability, are discussed. Additionally, we describe Doppler weather radars with narrow beams, which are used to detect low-level wind shear during rain, microbursts, and gusts. Multifrequency radar for snowfall retrieval is also presented, with an emphases on estimation and classification of the microphysical properties of particles. Finally, future directions for signal processing and applications for microwave weather radar systems are presented.

A Simulation Study of GNSS-R Polarimetric Scattering from the Bare Soil Surface Based on the AIEM

In the past two decades, global navigation satellite system-reflectometry (GNSS-R) has emerged as a new remote sensing technique for soil moisture monitoring. Some experiments showed that the antenna of V polarization is more favorable to receive the reflected signals, and the interference pattern technique (IPT) was used for soil moisture and retrieval of other geophysical parameters. Meanwhile, the lower satellite elevation angles are most impacted by the multipath. However, electromagnetic theoretical properties are not clear for GNSS-R soil moisture retrieval. In this paper, the advanced integral equation model (AIEM) is employed using the wave-synthesis technique to simulate different polarimetric scatterings in the specular directions. Results show when the incident angles are larger than 70°, scattering at RR polarization (the transmitted signal is right-hand circular polarization (RHCP), while the received one is also RHCP) is larger than that at LR polarization (the transmitted signal is RHCP, while the received one is left-hand circular polarization (LHCP)), while scattering at LR polarization is larger than that at RR polarization for the other incident angles (1°∼70°). There is an apparent dip for VV and VR scatterings due to the Brewster angle, which will result in the notch in the final receiving power, and this phenomenon can be used for soil moisture retrieval or vegetation corrections. The volumetric soil moisture (vms) effects on their scattering are also presented. The larger soil moisture will result in lower scattering at RR polarization, and this is very different from the scattering of the other polarizations. It is interesting to note that the surface correlation function only affects the amplitudes of the scattering coefficients at much less level, but it has no effects on the angular trends of RR and LR polarizations.

A Statistical Inverse Method for Gridding Passive Microwave Data With Mixed Measurements

When a passive microwave footprint intersects objects on the ground with different spectral characteristics, the corresponding observation is mixed. The retrieval of geophysical parameters is limited by this mixture. We propose to partition the study region into objects following an object-based image analysis procedure and then to refine this partition into small cells. Then, we introduce a statistical method to estimate the brightness temperature (TB) of each cell. The method assumes that TB of the cells corresponding to the same object is identically distributed and that the TB heterogeneity within each cell can be neglected. The implementation is based on an iterative expectation–maximization algorithm. We evaluated the proposed method using synthetic images and applied it to grid the TBs of sample AMSR –2 real data over a coastal region in Argentina.

Satellite monitoring of atmospheric temperature profiles and cloud cover over the Yakutsk EAS array and the TAIGA observatory

We consider the problem of night-time atmosphere monitoring at the locations of the Yakutsk EAS array and the TAIGA observatory for the annual October–April period of observations at these facilities. It is shown that the use of data from instruments aboard Terra, Aqua, Suomi-NPP and NOAA-20 satellites, received by the ground stations of the Altai State University, Russia, and processed to Level 2 (retrieval of geophysical parameters of the atmosphere), gives the possibility of the near-real-time monitoring observations of the cloud cover structure and the temperature profiles at night with a spatial resolution and a frequency sufficient for meteorological correction of the detector readings.

GEOPHYSICAL PARAMETER RETRIEVAL FOR MICROWAVE C BAND SYNTHETIC APERTURE RADAR (SAR) DATASET USING INTEGRAL EQUATION MODEL

Abstract. The microwave remote sensing is highly useful, as it provides synoptic observation of the Earth’s surface or planetary bodies, regardless of day or night and the atmospheric conditions, propagation through ionosphere with minimum loss. One of the best microwave technology for imaging system is the Synthetic Aperture Radar (SAR) remote sensing. The microwave SAR currently represents the best approach for obtaining spatially distributed geophysical parameter present on the Earth’s surface or planetary bodies. In the present work, geophysical parameters viz., Soil Moisture, Surface Roughness, Dielectric Constant (ε) and Backscattering Coefficients (σ0) will be retrieved. The modelling makes the process of estimating information beyond the real observation range for data interpretation. In the present paper most widely used modelling techniques for the microwave SAR dataset is an Integral Equation Model (IEM) which is implemented for above said geophysical parameters retrieval. The aim of the present work is to estimate accurate, reliable and skillful measurements of geophysical parameters from the microwave SAR dataset. In the present study microwave C band SAR dataset is used. The overall processing was done by using PolSARPro Ver. 5.0 software. In the present work, geophysical parameters are measured with the help IEM modelling, the statistical parameter and occurrence plane, estimated from the microwave SAR image, which was very helpful for retrieving geophysical parameters. From the overall paper work, it was concluded that the IEM modelling is a one of the realistic modelling methods for retrieving geophysical parameters for microwave C band SAR dataset.

IMPACT OF OZONE CONCENTRATION ON OCEAN COLOUR RETRIEVALS FOR OCM-2

Abstract. Ozone being an absorbing gas have a significant impact on OCM-2 bands 510 nm, 555 nm and 620 nm which are using for retrieval of major geophysical parameters like Chlorophyll and suspended sediment concentrations. A study has been carried out to analyse the impact of Ozone concentration by ingesting near real time from OMI (Ozone Monitoring Instrument) on Aura instead of Climatology V2013 (2004–13) with a resolution of 1 × 1 deg for ocean colour retrievals from OCM-2 sensor using SeaWiFS Data Analysis System (SeaDAS). The spectral behaviour of Ozone transmittance has been studied for various bands of OCM-2 by using both Climatology and near real time inputs. We could observe maximum relative error percentage about 12 % from Climatology to NRT in Ozone concentration and 0.28 %, 0.66 % and 0.72 % of maximum mean relative error in ozone transmittance at 512 nm, 557 nm and 620 nm. We calculated error budgets induced by ozone in remote sensing reflectance(/sr) where in we could observe mean relative error percentage of 0.52 % in 491 nm, 1.12 % in 512 nm and 4.28 % 557 nm a bands respectively.

Object-Based Multipass InSAR via Robust Low-Rank Tensor Decomposition

The most unique advantage of multipass synthetic aperture radar interferometry (InSAR) is the retrieval of long-term geophysical parameters, e.g., linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed by Kang, as an alternative to the typical single-pixel methods, e.g., persistent scatterer interferometry (PSI), or pixel-cluster-based methods, e.g., SqueeSAR. This enables the exploitation of inherent properties of InSAR phase stacks on an object level. As a follow-on, this paper investigates the inherent low rank property of such phase tensors and proposes a Robust Multipass InSAR technique via Object-based low rank tensor decomposition. We demonstrate that the filtered InSAR phase stacks can improve the accuracy of geophysical parameters estimated via conventional multipass InSAR techniques, e.g., PSI, by a factor of 10-30 in typical settings. The proposed method is particularly effective against outliers, such as pixels with unmodeled phases. These merits, in turn, can effectively reduce the number of images required for a reliable estimation. The promising performance of the proposed method is demonstrated using high-resolution TerraSAR-X image stacks.

Cross validations of radio-frequency interference signature in AMSR-E data using two detection methods

Radio-frequency interference (RFI) detection for low-frequency microwave measurements is an important step before these data are applied to geophysical parameter retrieval or data assimilation. The...