Satellite Microwave Radiometry Research Articles

Radiometry of forest canopies

The work is devoted to satellite microwave radiometric methods, which are of great scientific and practical interest for studies of tropical, temperate and boreal forests. Task: It is to assess the relevance and technological priorities of satellite microwave radiometry in relation to monitoring environmental and climatic processes occurring in forest canopies. The priorities and advantages of using satellite microwave radiometry in studying radiothermal radiation of forest canopies are highlighted. Obstacles in the development of this technology are noted. The results of the study can be used for decision-making in the field of technological renewal and improvement of forest ecosystem analysis processes. The article can also be useful for specialists working in the field of development and implementation of innovative technologies.

Joint 1DVar retrievals of tropospheric temperature and water vapor from Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometer observations

Abstract. Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometry (MWR) are two of the most impactful spaceborne remote sensing techniques for numerical weather prediction (NWP). These two techniques provide complementary information about atmospheric temperature and water vapor structure. GNSS-RO provides high vertical resolution measurements with cloud penetration capability, but the temperature and moisture are coupled in the GNSS-RO retrieval process and their separation requires the use of a priori information or auxiliary observations. On the other hand, the MWR measures brightness temperature (Tb) in numerous frequency bands related to the temperature and water vapor structure but is limited by poor vertical resolution (> 2 km) and precipitation. In this study, we combine these two technologies in an optimal estimation approach, 1D variation method (1DVar), to improve the characterization of the complex thermodynamic structures in the lower troposphere. This study employs both simulated and operational observations. GNSS-RO bending angle and MWR Tb observations are used as inputs to the joint retrieval, where bending can be modeled by an Abel integral and Tb can be modeled by a radiative transfer model (RTM) that takes into account atmospheric absorption, as well as surface reflection and emission. By incorporating the forward operators into the 1DVar method, the strength of both techniques can be combined to bridge individual weaknesses. Applying 1DVar to the data simulated from large eddy simulation (LES) is shown to reduce GNSS-RO temperature and water vapor retrieval biases at the lower troposphere while simultaneously capturing the fine-scale variability that MWR cannot resolve. A sensitivity analysis is also conducted to quantify the impact of the a priori information and error covariance used in different retrieval scenarios. The applicability of 1DVar joint retrieval to the actual GNSS-RO and MWR observations is also demonstrated through combining collocated COSMIC-2 and Suomi-NPP (National Polar-orbiting Partnership) measurements.

Identifying Seismic Anomalies via Wavelet Maxima Analysis of Satellite Microwave Brightness Temperature Observations

This study develops a wavelet maxima-based methodology to extract anomalous signals from microwave brightness temperature (MBT) observations for seismogenic activity. MBT, acquired via satellite microwave radiometry, enables subsurface characterization penetrating clouds. Five surface categories of the epicenter area were defined contingent on position (oceanic/terrestrial) and ambient traits (soil hydration, vegetal covering). Continuous wavelet transform was applied to preprocess annualized MBT readings preceding and succeeding prototypical events of each grouping, utilizing optimized wavelet functions and orders tailored to individualized contexts. Wavelet maxima graphs visually portraying signal intensity variations facilitated the identification of aberrant phenomena, including pre-seismic accrual, co-seismic perturbation, and postseismic remission signatures. The casework found 10 GHz horizontal-polarized MBT optimally detected signals for aquatic and predominantly humid/vegetative settings, whereas 36 GHz horizontal-polarized performed best for arid, vegetated landmasses. Quantitative machine learning methods are warranted to statistically define selection standards and augment empirical forecasting leveraging lithospheric stress state inferences from sensitive MBT parametrization.

On the Problems of Monitoring Atmospheric Humidity Fields Using Satellite Microwave Radiometry Methods When Studying Cyclogenesis in the Tropical Atlantic

On the Problems of Monitoring Atmospheric Humidity Fields Using Satellite Microwave Radiometry Methods When Studying Cyclogenesis in the Tropical Atlantic

On the Problems of Monitoring Atmospheric Humidity Fields Using Satellite Microwave Radiometry Methods When Studying Cyclogenesis in the Tropical Atlantic

Some problems of microwave radiometric methods for monitoring fields of total atmospheric moisture content from satellites in areas of hurricane formation in the tropical regions of the Atlantic are considered. The possibilities of using polar-orbiting satellites (DMSP, EOS Aqua, GCOM-W1) for these tasks are analyzed a feature of which is the presence of gaps in satellite data (blind spots) resulting from divergence of scanning bands at lower latitudes. It is shown through a number of examples that the joint use of various satellites makes it possible to reduce the influence of this effect and obtain a clear picture of the evolution of atmospheric humidity fields in the tropical Atlantic and their connection with the evolution of tropical waves that initiate the formation of hurricanes.

Landsat-8 Observations of Foam Coverage under Fetch-Limited Wave Development

In this paper, we aimed to clarify the problem of foam coverage dependence on wave fetch, which is of interest in satellite microwave radiometry, but for which controversial results were reported previously. The classical approach to investigating developing waves was applied. That is, the waves are considered as coming from the coast under approximately constant wind velocity. The study includes two scenes of intensive katabatic winds in the Gulf of Lion and the Gulf of Tehuantepec. We used two Bands of Landsat OLI images to extract the wave spectral peak frequency and the sea fraction covered by foam simultaneously along the wave fetch. The distributions of the spectral peak frequency along the fetch obeying the classical wave growth law clearly showed that we observed the developing waves. Along the fetch, the sea surface covered with foam grows about three times with the power law. This development of foam coverage occurred at the range of dimensionless fetches from 50 up to 7000 if the fetch is scaled using wind velocity and gravity acceleration. A simple model of the foam coverage growth with wave fetch is suggested. We modeled wave energy dissipation rate using the JONSWAP wave spectrum for developing seas. The model explains the observations at the quantitative level. Reported results can be applied to investigations of tropical cyclones using satellite microwave radiometry.

The sensitivity of satellite microwave observations to liquid water in the Antarctic snowpack

Abstract. Surface melting on the Antarctic Ice Sheet has been monitored by satellite microwave radiometry for over 40 years. Despite this long perspective, our understanding of the microwave emission from wet snow is still limited, preventing the full exploitation of these observations to study supraglacial hydrology. Using the Snow Microwave Radiative Transfer (SMRT) model, this study investigates the sensitivity of microwave brightness temperature to snow liquid water content at frequencies from 1.4 to 37 GHz. We first determine the snowpack properties for eight selected coastal sites by retrieving profiles of density, grain size and ice layers from microwave observations when the snowpack is dry during wintertime. Second, a series of brightness temperature simulations is run with added water. The results show that (i) a small quantity of liquid water (≈0.5 kg m−2) can be detected, but the actual quantity cannot be retrieved out of the full range of possible water quantities; (ii) the detection of a buried wet layer is possible up to a maximum depth of 1 to 6 m depending on the frequency (6–37 GHz) and on the snow properties (grain size, density) at each site; (iii) surface ponds and water-saturated areas may prevent melt detection, but the current coverage of these waterbodies in the large satellite field of view is presently too small in Antarctica to have noticeable effects; and (iv) at 1.4 GHz, while the simulations are less reliable, we found a weaker sensitivity to liquid water and the maximal depth of detection is relatively shallow (<10 m) compared to the typical radiation penetration depth in dry firn (≈1000 m) at this low frequency. These numerical results pave the way for the development of improved multi-frequency algorithms to detect melt intensity and the depth of liquid water below the surface in the Antarctic snowpack.

The Contribution of Sea-Ice Contamination to Inaccuracies in Sea-Ice Concentration Retrieval from Satellite Microwave Radiometry Data during the Ice-Melt Period

The Contribution of Sea-Ice Contamination to Inaccuracies in Sea-Ice Concentration Retrieval from Satellite Microwave Radiometry Data during the Ice-Melt Period

Analysis of Sea-Ice Areas Undetectable by the ASI Algorithm Based on Satellite Microwave Radiometry in the Arctic Ocean

Analysis of Sea-Ice Areas Undetectable by the ASI Algorithm Based on Satellite Microwave Radiometry in the Arctic Ocean

Comparative Analysis of Arctic Sea Ice Area Derived from Satellite Microwave Radiometry Data (VASIA2 Algorithm) with Ice Charts of the Arctic and Antarctic Research Institute

Comparative Analysis of Arctic Sea Ice Area Derived from Satellite Microwave Radiometry Data (VASIA2 Algorithm) with Ice Charts of the Arctic and Antarctic Research Institute

Comparison of Vertically Integrated Fluxes of Atmospheric Water Vapor According to Satellite Radiothermovision, Radiosondes, and Reanalysis

The atmospheric advection of water vapor is one of the most important components of the planetary hydrological cycle. Radiosondes are a means for regular observations of water vapor fluxes. However, their data are sparse in space and time. A more complete picture is provided by reanalysis assimilating these data. However, a statistically representative check of the reanalysis estimates of the water vapor fluxes far from regularly operating weather stations is difficult. The previously proposed and developed method of satellite radiothermovision makes it possible to reconstruct the vertically integrated advective water vapor fluxes from satellite microwave radiometry. In this work, for the first time, the results of direct comparisons of long (5 year) time series of zonal vertically integrated daily water vapor fluxes based on the data of radiosondes, reanalysis, and satellite radiothermovision are performed and presented. It is shown that all the data series are statistically reliably correlated (at a confidence level of 0.995). The regression factor between the fluxes from reanalysis and satellite radiothermovision was close to 1, but with a noticeable bias (the latter were about 60 kg/(m·s) less on average). Grounds are given for the hypothesis that calculations based on satellite radiothermovision mainly characterize water vapor fluxes in the lower troposphere (up to heights of about 4 km). Its verification, as well as the analysis of the noted cases of violation of the correlation between the fluxes from satellite radiothermovision and reanalysis, requires further research.

Спутниковая СВЧ-радиометрия для решения задач дистанционного зондирования Земли

The issues of development of the direction of satellite microwave radiometry in Russia in the interests of operational meteorology and oceanography are considered. The analysis of the current state of Russian and foreign radiometric ERS equipment in the microwave range is carried out. The technical characteristics of onboard multichannel microwave radiometers, combining the functions of a scanner and a sounder, are analyzed. The issues of metrological support of microwave measurements of equipment installed on Russian satellites of the Meteor-M series are considered. The original method of internal calibration of the MTVZA-GYA microwave scanner/sounding device is analyzed in detail in order to form the antenna temperature scale. The MTVZA-GYA calibration unit measures the radiation intensity of two matched loads with known brightness temperatures (“hot” and “cold”). An on-board calibrator is used as a “hot” load, it serves as an imitator of an absolutely black body, its brightness temperature of which is in the range of 240–300 K. Absolute (external) calibration is a transition from antenna to brightness temperatures and is performed using high-precision radiation calculations for specially selected natural testing sites. The issues of preliminary processing of MTVZA-GYA data are considered and examples of microwave images of the Earth in the scale of brightness temperatures are given.

Features of the Intrinsic L-Band Radiation of the Gulf of Ob during the Freeze-Up Period

This article analyzes seasonal and interannual dependences of brightness temperature in different zones of the Gulf of Ob according to data from the MIRAS radiometer (SMOS satellite). It is shown that, while the brightness temperature dynamics of the freshwater zone of the Gulf of Ob (the estuary and the central zone) is similar to that of freshwater lakes, this dynamics in the seawater zone is different. The authors explain this difference by an increase in water salinity and, consequently, an increase in radiation absorption in the lower ice layer, which is in contact with the water surface. The regularities of seasonal variations of brightness temperature in different zones of the Gulf of Ob and related ice-cover phases can be used to assess salinity and water mixing in large estuaries of the Arctic in winter based on satellite microwave radiometry.

Microwave Satellite Systems for Hydrological Monitoring

This paper considers existing and promising satellite microwave radiometry systems suitable for the evaluation of geophysical (hydrological) parameters of atmosphere, ocean, and land. A comparative analysis is provided for data sets available for end-users. Algorithms and tools for processing and visualization of satellite data are discussed. The capabilities of modern satellite systems to perform specific tasks of remote sensing are described using the example of a river flood in the Altai region in 2014. Monitoring soil moisture of upper layers of soil on floodplains combined with meteorological forecasts allows assessment of the probability of river flooding at certain areas using values of maximum soil moisture capacity. The effect of changes in the physical properties of ice during its destruction is discussed. This effect has been discovered by analyzing the dynamics of daily satellite measurements of brightness temperatures. It can be considered as a harbinger of ice condition changes of large freshwater bodies. The analysis of brightness temperature seasonal variations is presented using the example of Lake Big Bear (Canada).

Comparison of Satellite Microwave and Visual Shipborne Data on Sea Ice Concentration

In this paper we compare the sea ice concentration data obtained by satellite microwave radiometry using the NASA Team, ASI, and VASIA2 algorithms and by special visual ship observations. A database of visual observations of the sea ice concentration obtained during 15 expeditions in the Arctic was used. The comparison for various concentration gradations (very open, open, close, very close, and compact ice) was carried out separately for summer and winter periods. On average, during the summer period, the NASA Team algorithm underestimates the sea ice concentration by 1 point, while the ASI and VASIA2 algorithms underestimate by 0.5 points when compared to ship observations. All three algorithms overestimate the total concentration in the zones of very open ice and underestimate it in the zones of close, very close, and compact ice. The maximal average errors are characteristic of open ice that is most often observed in ice edge zones. In winter, the average error for all algorithms is no more than 1 point when compared with ship observations. However, in very open and close, the average error in winter is significantly higher than that in summer. To assess the effect of melting processes on the concentration values obtained by satellite microwave radiometry, we used ship data on ice melt stage. The average error at the maximal area of ​​melt ponds on the ice surface reaches –2.9 points for the NASA Team algorithm, –2.8 points for the ASI algorithm, and –5.0 points for the VASIA2 algorithm. The results will be useful for determining the extent of ice-cover degradation in the Arctic Ocean observed in recent years.

On Evaluating the Depth of Soil Freezing Based on SMOS Satellite Data

The results of a comparative analysis of the brightness temperatures determined from the SMOS satellite and the corresponding depths of soil freezing, measured at weather stations located at the test sites of the Kulunda Plain, are presented. Based on the daily satellite measurement of brightness temperature, the effect of soil freezing on the microwave radiation of the underlying surface was studied. A theoretical calculation of the dependence of soil brightness temperature on the depth of freezing is performed with the model of microwave radiation of a plane-layered inhomogeneous non-isothermal medium. The real parameters of the Kulunda plain soil as well as the climatic characteristics of the sites under study, obtained from the weather stations for the same period, were used as the input parameters of the model. The analysis of satellite, field and model data showed that the evaluation of the depth of soil freezing with satellite microwave radiometry is limited by the need to conduct the contact measurements of physical properties of soil in the areas, for which the SMOS product on the brightness temperature is given.

Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data

The paper presents a comparison of sea ice concentration (SIC) derived from satellite microwave radiometry data and dedicated ship observations. For the purpose, the NASA Team (NT), Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI), and Variation Arctic/Antarctic Sea Ice Algorithm 2 (VASIA2) algorithms were used as well as the database of visual ice observations accumulated in the course of 15 Arctic expeditions. The comparison was performed in line with the SIC gradation (in tenths) into very open (1–3), open (4–6), close (7–8), very close and compact (9–10,10) ice, separately for summer and winter seasons. On average, in summer NT underestimates SIC by 0.4 tenth as compared to ship observations, while ASI and VASIA2 by 0.3 tenth. All three algorithms overestimate total SIC in regions of very open ice and underestimate it in regions of close, very close, and compact ice. The maximum average errors are typical of open ice regions that are most common in marginal ice zones. In winter, NT and ASI also underestimate SIC on average by 0.4 and 0.8 tenths, respectively, while VASIA2, on the contrary, overestimates by 0.2 tenth against the ship data, however, for open and close ice the average errors are significantly higher than in summer. In the paper, we also estimate the impact of ice melt stage and presence of new ice and nilas on SIC derived from NT, ASI, and VASIA2.

An Attempt to Improve Snow Depth Retrieval Using Satellite Microwave Radiometry for Rough Antarctic Sea Ice

Snow depth on sea ice is a major constituent of the marine cryosphere. It is a key parameter for the derivation of sea-ice thickness from satellite altimetry. One way to retrieve the basin-scale snow depth on sea ice is by satellite microwave radiometry. There is evidence from measurements and inter-comparison studies that current retrievals likely under-estimate the snow depth over deformed, rough sea ice. We follow up on an earlier study, where satellite passive microwave data were combined with information on the sea-ice topography from the satellite laser altimeter on board the Ice, Cloud and land Elevation Satellite (ICESat) in a hybrid approach. Such topography information is spatiotemporally limited because of ICESat’s operation mode. In this paper, we aim to derive a proxy for this topography information from satellite microwave radiometry. For this purpose, we co-locate parameters describing the sea-ice deformation taken from visual ship-based observations and the surface elevation standard deviation derived from ICESat laser altimetry with the microwave brightness temperatures (TB) measured via the Advanced Microwave Scanning Radiometer aboard Earth Observation Satellite (AMSR-E) and aboard Global Change Observation Mission-Water 1 (GCOM-W1) (AMSR2). We find that the TB polarization ratio at 6.9 GHz and the TB gradient ratio between 10.7 GHz (horizontal polarization) and 6.9 GHz (vertical polarization), might be suited as such a proxy. Using this proxy, we modify the above-mentioned hybrid approach and compute the snow depths on sea ice from the AMSR-E and AMSR2 data. We compare our snow depths with those of the commonly used approach, the hybrid approach, with the ship-based observations for the years 2002 through 2015 and with the measurements made by drifting buoys for the period of 2014 through 2018. We find a convincing overall agreement with the hybrid approach and some improvement over the common approach. However, our approach is sensitive to the presence of thin ice—here, the retrieved snow depths are too large; and our approach performs sub-optimally over old ice—here, the retrieved snow depths are too small. More investigations and, in particular, more evaluations are required to optimize our approach so that the snow depths retrieved for the combined AMSR-E/AMSR2 period could serve as a data set for sea-ice thickness retrieval based on satellite altimetry.

Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Abstract. Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath >1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn–dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger “hole at the pole”. Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

On evaluation of depth of soil freezing based on Smos satellite data

The results of a comparative analysis of the brightness temperatures determined from the SMOS satellite and the corresponding depths of soil freezing, measured at weather stations located at the test sites of the Kulunda Plain, are presented. Based on the daily satellite measurement of brightness temperature, the effect of soil freezing on the microwave radiation of the underlying surface was studied. A theoretical calculation of the dependence of soil brightness temperature on the depth of freezing is performed with the model of microwave radiation of a plane-layered inhomogeneous non-isothermal medium. The real parameters of the Kulunda plain soil as well as the climatic characteristics of the sites under study, obtained from the weather stations for the same period, were used as the input parameters of the model. The analysis of satellite, field and model data showed that the evaluation of the depth of soil freezing with satellite microwave radiometry is limited by the need to conduct the contact measurements of physical properties of soil in the areas, for which the SMOS product on the brightness temperature is given.