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Accurate measurements of ice hydrometeors are required to improve the representation of clouds and precipitation in weather and climate models. In this study, a newly developed, synergistic retrieval algorithm that combines radar with passive millimeter and sub-millimeter observations is applied to observations of three frontally generated, mid-latitude cloud systems in order to validate the retrieval and assess its capabilities to constrain the properties of ice hydrometeors. To account for uncertainty in the assumed shapes of ice particles, the retrieval is run multiple times while the shape is varied. Good agreement with in situ measurements of ice water content and particle concentrations for particle maximum diameters larger than 200 µm is found for one of the flights for the large plate aggregate and the six-bullet rosette shapes. The variational retrieval fits the observations well, although small systematic deviations are observed for some of the sub-millimeter channels pointing towards issues with the sensor calibration or the modeling of gas absorption. For one of the flights the quality of the fit to the observations exhibits a weak dependency on the assumed ice particle shape, indicating that the employed combination of observations may provide limited information on the shape of ice particles in the observed clouds. Compared to a radar-only retrieval, the results show an improved sensitivity of the synergistic retrieval to the microphysical properties of ice hydrometeors at the base of the cloud. Our findings indicate that the synergy between active and passive microwave observations may improve remote-sensing measurements of ice hydrometeors and thus help to reduce uncertainties that affect currently available data products. Due to the increased sensitivity to their microphysical properties, the retrieval may also be a valuable tool to study ice hydrometeors in field campaigns. The good fits obtained to the observations increase confidence in the modeling of clouds in the Atmospheric Radiative Transfer Simulator and the corresponding single scattering database, which were used to implement the retrieval forward model. Our results demonstrate the suitability of these tools to produce realistic simulations for upcoming sub-millimeter sensors such as the Ice Cloud Image or the Arctic Weather Satellite.

This article presents a study on the power consumption and performance analysis of a small, portable, and ultra-low power total power L-band radiometer. The article explores two processing architectures: the ZYNQ 7010 and the ARM A8 embedded microprocessor. The processing algorithm based in C++ was tested for different clock frequencies, ADC sampling speeds, and sizes of the ADC buffer. To reduce the power consumption and the algorithm execution time, high-level and system-level optimizations, along with fixed-point Q(16,16) data representation, were applied to the main code running on LINUX Debian V8. In the case with the ZYNQ 7010, the optimizations had no notable impact on reducing power or execution time in comparison with the ARM A8, where significant variations were measured, showing a tradeoff between power consumption and algorithm performance that limits the processing capability and the system flight time. The ZYNQ 7010 runs the algorithm faster, but the power consumption is higher than the ARM A8. Using the fixed-point Q(16,16) implementation reduced the power consumption and the execution time in both architectures. Based on these results, we developed a heuristic methodology to minimize power consumption and increase the performance. Energy consumption savings for the radiometer during 20 min of flight was 48%. The size of the radiometer was reduced to 30 cm × 30 cm × 10 cm, with a weight of 1.36 kg, (3 lb) allowing the system be carried by small drones. The results were validated measuring salinity at two locations in Western Puerto Rico.

Across spatial and temporal scales, sea-ice motion has implications on ship navigation, the sea-ice thickness distribution, sea ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today's status-quo (processing from daily averaged maps of brightness temperature). We document that the swath-to-swath (S2S) approach results in many more (two orders of magnitude) sea-ice drift vectors than the daily-maps (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 hour Arctic sea-ice drift is 0.9 km for S2S vectors, and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2. Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geo-location uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a swath-to-swath approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms, and making the user community ready to fully take advantage of such a product.

Microwave technology is very sensitive to Radio Frequency Interferences (RFI). Works previously done within this Master by Marc Jou [1] showed the impossibility to retrieve measurements using L-band radiometers on-board drones. After detecting such issue, Balamis first tried to solve it by hardware: a new antenna design and the extensive use of shielding on the drone were tried without success. Balamis started the development of its first digital radiometer based on the use of Software Defined Radio architecture on 2017, partially funded with the support of CDTI. The resulting minimum viable digital radiometer was ready by June 2019, but it did not include any RFI mitigation capability. Developments done my Master student Ahmad Daoud [2] demonstrated the identification of RFI using Fast Fourier Transform (FFT) over RAW data but could not provide any efficient implementation of its mitigation on-board the L-band radiometer. The proposed solution is the implementation of the FFT and the RFI filters using Field Programmable Gate of Array (FPGA) for the input signals, and its concurrent performance. Filtering an analog signal by introducing in-system FFT of ZYNQ7000 FPGA is implemented in this project. Additionally, the power consumption of FPGA, and the need to dissipate it, forces the development of a temperature control system with cooling capabilities. It is done to improve the previous heating-only thermal control of Balamis radiometer. Such more advanced thermal control will be also used for the Interferometric Ground-based Synthetic Aperture Radar that Balamis is developing. Solving these two goals are therefore the purpose of this Master Thesis.

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in a GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water-leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptable error. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is 103 times faster with CPU or 104 times with GPU processors. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth-observing satellite missions with similar capabilities.

La eficiencia del uso de nitrógeno en la agricultura es muy baja, provocando problemas ambientales y baja productividad de los cultivos, por lo que conocer la distribución espacial y temporal de variables biofísicas que estimen su crecimiento, como la fracción de cobertura vegetal (FCV), y el contenido de nitrógeno del dosel (CND) aportará información valiosa para mejorar esta condición. El uso de imágenes satelitales de libre acceso, como las imágenes del satélite Sentinel-2 (S‑2), puede facilitar esta tarea. Con base en lo anterior, se realizó un estudio en seis parcelas de maíz, cultivadas por productores de la zona de Texcoco de Mora, Estado de México, cuyo primer objetivo fue investigar la relación entre índices espectrales, estimados a partir de la reflectancia, medida con un radiómetro multiespectral a nivel de superficie y de imágenes S-2; con la FCV, estimada mediante fotografías digitales a través de la aplicación para celulares Canopeo. El segundo objetivo fue obtener un modelo lineal para estimar el CND a partir del análisis de diferentes índices espectrales. Se generaron dos modelos para estimar la FCV, a partir de la información radiométrica a nivel de superficie, la validación de estos arrojó un valor alto de R2 = 0.92 y un CME = 7.3% entre datos de FCV medidos y estimados; sin embargo, en el caso de los datos estimados con imágenes S-2 se obtuvieron valores más bajos de R2 = 0.67 y CME = 17.6%, lo cual se atribuyó a un posible efecto de la atmósfera, ya que el periodo de estudio coincidió con la temporada de lluvias. Finalmente, en el caso del CND, los mejores resultados se obtuvieron con el modelo generado a partir del ClGreen, con el cual se obtuvo un R2 de 0.91 y un CME de 0.63 g m-2, al comparar el CND medido con relación al estimado.

In sea surface salinity (SSS) retrieval using L-band passive radiometry, radiometer-independent ocean wind speed is needed as auxiliary data. Wind speed data from scatterometer and weather models are commonly used as auxiliary data in satellite SSS missions. This article's overarching goal is to explore the feasibility of incorporating the cyclone global navigation satellite system (CyGNSS) data into the SSS retrieval algorithm of the soil moisture active and passive (SMAP) mission over tropical and subtropical oceans. As a proof-of-concept study, empirical geophysical model functions in the retrieval algorithm are developed using the statistics of collocated SMAP, CyGNSS, and referenced buoys measurements. The SSS accuracy of CyGNSS-incorporated salinity retrieval is investigated against the SMAP SSS data product. Comparisons show that the proposed CyGNSS-incorporated retrieval algorithm improves the SSS accuracy by 0.1∼0.2 psu at low wind speed (

Passive microwave satellite observations provide critical information for global forecast models, particularly in cloudy and/or precipitating conditions. The limited temporal sampling provided by current operational polar orbiters cannot capture rapidly changing conditions such as the development of convective storms. This is a significant issue for open-ocean weather systems such as tropical cyclones and hurricanes that can only be effectively monitored from satellites. The recent development and demonstration of miniaturized microwave radiometers on-board low-cost CubeSat satellites has the potential to dramatically improve the temporal and spatial sampling of all-sky microwave observations by deploying a substantial constellation of satellites in low Earth orbit. Two constellations of 60 CubeSats in 550 km orbits are compared to the current operational microwave sensors. One approach employs all polar orbiters, while the other approach uses multiple inclination orbits for increased sampling over convective storm regions. Both approaches reduce average revisit times to approximately 20–30 min globally, and the multi-inclination approach also provides irregular 5–10 min sampling over selected latitudes. Improved global temporal sampling would provide all-sky observations to global forecast models over rapidly changing environments, while millimeter-wave observations over convective storm regions would be valuable for both forecasting and studying the development of convective storms. This article demonstrated that a constellation of low-cost CubeSats with microwave radiometers has the potential to provide equivalent temporal resolution to that observed from sensors on geostationary orbit .

We present a relatively simple method to estimate tropical cyclone (TC) surface wind structure (34-, 50-, and 64-kt wind radii) and intensity [maximum wind speed (MWS)] from wind fields acquired from the L-band SMAP radiometer and C-band Sentinel-1A/B and RADARSAT-2 synthetic aperture radar (SAR) between 2015 and 2020. The radiometer and SAR-derived wind radii and MWS are systematically compared with the best-track estimates. The root-mean-square errors (RMSEs) of R34, R50, and R64 are 31.2, 21.8, and 17.0 nmi (1 nmi = 1.852 km) for radiometer, and 21.7, 16.5, and 16.3 nmi for SAR, respectively. These error values are smaller than the averaged best-track uncertainty estimates for the three wind radii. Compared with the best-track reports, the bias and RMSE for the MWS estimates are −0.2 m/s and 5.8 m/s for radiometer, and 4.4 m/s and 9.1 m/s for SAR, respectively. These results are for the wind speeds in the range of 17–80 m/s. For the two typical TCs (Lionrock and Noru) in the Northwest Pacific Ocean, our results show that a combination of the radiometer and SAR wind data acquired within a very short time interval has the potential to simultaneously obtain reasonable measurements of the wind radii and intensity parameters. Moreover, for a TC with a long lifecycle, such as Typhoon Noru, we demonstrate that the high-resolution and multitemporal synergistic observations from SAR and radiometer are valuable for studying fine-scale features of the wind field and characteristics of wind asymmetry associated with intensity change, as well as the evolution of TC surface wind structure and intensity.

The atmosphere-land exchange inverse disaggregation (DisALEXI) algorithm is a multi-scale energy balance model that estimates evapotranspiration (ET) using land-surface temperature (LST) as a driving remote sensing input. Using LST products from ECOSTRESS, a thermal radiometer mounted on the International Space Station, DisALEXI ET products have been produced over the contiguous United States (CONUS) at 70 m resolution. The goal of this study is to demonstrate the accuracy of the CONUS-wide ET produced by the Jet Propulsion Laboratory (JPL) and to compare the results with the original DisALEXI ET produced by researchers at the United States Department of Agriculture (USDA). DisALEXI-USDA has been produced ad-hoc using Landsat LST, and is routinely produced over six target sites using ECOSTRESS LST. DisALEXI-JPL was implemented in order to expand the spatial coverage. DisALEXI-JPL was evaluated at 26 CONUS eddy covariance sites, showing good correlation, with R2 = 0.80 and RMSE = 0.81 mm/day, which is comparable to previous DisALEXI validation studies (RMSE ∼1 mm/day). The two DisALEXI implementations compared well, with R2 = 0.92. This article evaluates DisALEXI-JPL and shows that the algorithm is valid over a larger segment of CONUS. We also show the impact of quality flags, as pixels with high view zenith angles or high aerosol optical depth showed greater deviation from field measurements. As a product demonstration, we show a regional map of fine-scale ET, where the fine-scale variation over wider areas can detect small areas of stress much sooner than products with coarse resolution representing average conditions.

Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the near-infrared band and combined it with four “common bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the near-infrared band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with Rrs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from Rrs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer Rrs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.

Microwave remote sensing can be used to monitor the time evolution of some key parameters over land, such as land surface temperature or surface water extent. Observations are made with instruments, such as the Scanning Microwave Multichannel Radiometer (SMMR) before 1987, the Special Sensor Microwave/Imager (SSM/I) and the subsequent Special Sensor Microwave Imager/Sounder (SSMIS) from 1987 and still operating, and the more recent Global Precipitation Measurement Microwave Imager (GMI). As these instruments differ on some of their characteristics and use different calibration schemes, they need to be inter-calibrated before long-time-series products can be derived from the observations. Here an inter-calibration method is designed to remove major inconsistencies between the SMMR and other microwave radiometers for the 18 and 37 GHz channels over continental surfaces. Because of a small overlap in observations and a ∼6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from 3 years of GMI brightness temperatures are first calculated and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data records of microwave brightness temperatures over continental surfaces.

Fresh water is considered to be one of the most precious natural resources in many countries. Given that soil moisture plays a salient role in vegetation growth, the continuous and timely measurement of soil moisture content is critical to manage water shortages, especially in the agriculture sector [1], [2], which is the largest water use sector in Australia. A 10% water savings in the Australian agriculture sector would be equivalent to a reduction of more than 30% of the combined water consumption from Australian capital cities [3]. Further, soil moisture influences the process of rainfall being partitioned into runoff and infiltration, and saturated soil could turn heavy rain into floods. Therefore, soil moisture information is essential for accurate climate forecasting as well as improving flood and drought prediction [3]-[5].

In a traditional spaceborne microwave radiometer system with a scanning antenna there is often a conflict between spatial and radiometric resolution. Integration over many beams per frequency might be necessary to improve radiometric resolution. Many beams may be generated using many classical feed horns or by a Focal Plane Array (FPA) system. At C-band horns are bulky and replacing several such horns with a FPA is an interesting option. The FPA concept uses many small antenna elements, many receivers, and powerful on-line digital processing. A focal plane array system that can replace 4 horns is evaluated, especially concerning power consumption.

Forward models are a key tool to generate synthetic observations given knowledge of the atmospheric state. In this way, they are an integral part of inversion algorithms that aim to retrieve geophysical variables from observations or in data assimilation. Their application for the exploitation of the full information content of remote sensing observations becomes increasingly important when these are used to evaluate the performance of cloud-resolving models (CRMs). Herein, CRM profiles or fields provide the input to the forward model whose simulation results are subsequently compared to the observations. This paper introduces the freely available comprehensive microwave forward model PAMTRA (Passive and Active Microwave TRAnsfer), demonstrates its capabilities to simulate passive and active measurements across the microwave spectral region for upward- and downward-looking geometries, and illustrates how the forward simulations can be used to evaluate CRMs and to interpret measurements to improve our understanding of cloud processes. PAMTRA is unique as it treats passive and active radiative transfer (RT) in a consistent way with the passive forward model providing upwelling and downwelling polarized brightness temperatures and radiances for arbitrary observation angles. The active part is capable of simulating the full radar Doppler spectrum and its moments. PAMTRA is designed to be flexible with respect to instrument specifications and interfaces to many different formats of input and output, especially CRMs, spanning the range from bin-resolved microphysical output to one- and two-moment schemes, and to in situ measured hydrometeor properties. A specific highlight is the incorporation of the self-similar Rayleigh–Gans approximation (SSRGA) for both active and passive applications, which becomes especially important for the investigation of frozen hydrometeors.

This paper is an overview of the progress in sky radiometer technology and the development of the network called SKYNET. It is found that the technology has produced useful on-site calibration methods, retrieval algorithms, and data analyses from sky radiometer observations of aerosol, cloud, water vapor, and ozone. A formula was proposed for estimating the accuracy of the sky radiometer calibration constant F0 using the improved Langley (IL) method, which was found to be a good approximation to observed monthly mean uncertainty in F0, around 0.5 % to 2.4 % at the Tokyo and Rome sites and smaller values of around 0.3 % to 0.5 % at the mountain sites at Mt. Saraswati and Davos. A new cross IL (XIL) method was also developed to correct an underestimation by the IL method in cases with large aerosol retrieval errors. The root-mean-square difference (RMSD) in aerosol optical thickness (AOT) comparisons with other networks took values of less than 0.02 for λ≥500 nm and a larger value of about 0.03 for shorter wavelengths in city areas and smaller values of less than 0.01 in mountain comparisons. Accuracies of single-scattering albedo (SSA) and size distribution retrievals are affected by the propagation of errors in measurement, calibrations for direct solar and diffuse sky radiation, ground albedo, cloud screening, and the version of the analysis software called the Skyrad pack. SSA values from SKYNET were up to 0.07 larger than those from AERONET, and the major error sources were identified as an underestimation of solid viewing angle (SVA) and cloud contamination. Correction of these known error factors reduced the SSA difference to less than 0.03. Retrievals of other atmospheric constituents by the sky radiometer were also reviewed. Retrieval accuracies were found to be about 0.2 cm for precipitable water vapor amount and 13 DU (Dobson Unit) for column ozone amount. Retrieved cloud optical properties still showed large deviations from validation data, suggesting a need to study the causes of the differences. It is important that these recent studies on improvements presented in the present paper are introduced into the existing operational systems and future systems of the International SKYNET Data Center.

Reliable reference measurements over the ocean are essential for the evaluation and improvement of satellite- and model-based aerosol datasets. Within the framework of the Maritime Aerosol Network, shipborne reference datasets have been collected over the Atlantic Ocean since 2004 with Microtops Sun photometers. These were recently complemented by measurements with the multi-spectral GUVis-3511 shadowband radiometer during five cruises with the research vessel Polarstern. The aerosol optical depth (AOD) uncertainty estimate of both shipborne instruments of ±0.02 can be confirmed if the GUVis instrument is cross calibrated to the Microtops instrument to account for differences in calibration, and if an empirical correction to account for the broad shadowband as well as the effects of forward scattering is introduced. Based on these two datasets, a comprehensive evaluation of aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) flown on NASA's Earth Observing System satellites, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat satellite, and the Copernicus Atmosphere Monitoring Service reanalysis (CAMS RA) is presented. For this purpose, focus is given to the accuracy of the AOD at 630 nm in combination with the Ångström exponent (AE), discussed in the context of the ambient aerosol type. In general, the evaluation of MODIS AOD from the official level-2 aerosol products of C6.1 against the Microtops AOD product confirms that 76 % of data points fall into the expected error limits given by previous validation studies. The SEVIRI-based AOD product exhibits a 25 % larger scatter than the MODIS AOD products at the instrument's native spectral channels. Further, the comparison of CAMS RA and MODIS AOD versus the shipborne reference shows similar performance for both datasets, with some differences arising from the assimilation and model assumptions. When considering aerosol conditions, an overestimation of AE is found for scenes dominated by desert dust for MODIS and SEVIRI products versus the shipborne reference dataset. As the composition of the mixture of aerosol in satellite products is constrained by model assumptions, this highlights the importance of considering the aerosol type in evaluation studies for identifying problematic aspects.

The vegetation temperature condition index (VTCI) has been shown to perform well for drought monitoring using multiyear advanced very high-resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data. Compared to single-year VTCI, the VTCI calculated by multiyear data can quantitatively reflect the degree of drought and precipitation in a region. Sentinel-3 is a recently launched remote sensing satellite with high temporal resolution and is similar to the satellites carrying AVHRR and MODIS sensors. One year of Sentinel-3 data is available for calculating the VTCI, and given the need for developing quantitatively drought monitoring capabilities, the aim of this study is to investigate the methods used to calculate the potential multiyear Sentinel-3 VTCI for quantitative drought monitoring. This is based on a comparison with multiyear Terra MODIS VTCI and a correlation analysis with cumulative precipitation data. The analysis results indicate that the potential multiyear Sentinel-3 VTCI can be accurately calculated from the single-year Sentinel-3 VTCI based on the linear correlation between the single-year VTCI and multiyear VTCI derived from Terra MODIS, which do not exhibit obvious systematic deviations from the multiyear Terra MODIS VTCI (the absolute value of the average bias 2 = 0.731, P

The advanced microwave radiometer-climate quality (AMR-C) is a part of the European Sentinel-6A/B series, a collaboration between ESA and NASA, of two Earth-observing satellites, which will be launched in 2020 and 2025. Compared to its predecessor, Jason-3, the two AMR-C radiometer instruments have an external calibration system which enables higher radiometric stability accomplished by moving the secondary mirror between well-defined targets. Sentinel-6 allows continuing the study of the ocean circulation, climate change, and sea-level rise for at least another decade. Besides the external calibration for the AMR heritage radiometer (18.7, 23.8, and 34 GHz channels), the AMR-C contains a high-resolution microwave radiometer (HRMR) with radiometer channels at 90, 130, and 168 GHz. This subsystem allows for a factor of 5× higher spatial resolution at coastal transitions. This article presents a brief description of the instrument and the measured performance of the completed AMR-C-A and AMR-C-B instruments.

Recently, polarimetry has been used to enhance classical photometry to infer aerosol optical properties, as polarized radiation contains additional information about the particles. Therefore, we have equipped the Sun–sky automatic radiometer (SSARA) with polarizer filters to measure linearly polarized light at 501.5 nm. We describe an improved radiometric and polarimetric calibration method, which allows us to simultaneously determine the linear polarizers' diattenuation and relative orientation with high accuracy (0.002 and 0.1∘, respectively). Furthermore, we employed a new calibration method for the alt-azimuthal mount capable of correcting the instrument's pointing to within 32 arcmin. So far, this is limited by the accuracy of the Sun tracker. Both these methods are applicable to other Sun and sky radiometers, such as the Cimel CE318-DP instruments used in the AErosol RObotic NETwork (AERONET). During the A-LIFE (Absorbing aerosol layers in a changing climate: aging, LIFEtime and dynamics) field campaign in April 2017, SSARA collected 22 d of data. Here, we present two case studies. The first demonstrates the performance of an aerosol retrieval from SSARA observations under partially cloudy conditions. In the other case, a high aerosol load due to a Saharan dust layer was present during otherwise clear-sky conditions.

Since the late 1970s, space-borne microwave radiometers have been providing measurements of radiation emitted by the Earth’s surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions, and coverage need to be overcome. Here, we present a new series of long-term VOD products, the VOD Climate Archive (VODCA). VODCA combines VOD retrievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, WindSat, and AMSR2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely the Ku-band (period 1987–2017), X-band (1997–2018), and C-band (2002–2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our merging approach builds on an existing approach that is used to merge satellite products of surface soil moisture: first, the data sets are co-calibrated via cumulative distribution function matching using AMSR-E as the scaling reference. To do so, we apply a new matching technique that scales outliers more robustly than ordinary piecewise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data. The characteristics of VODCA are assessed for self-consistency and against other products. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. Spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and with leaf area index observations from the MODIS instrument as well as with Vegetation Continuous Fields from the AVHRR instruments. Long-term trends in Ku-band VODCA show that since 1987 there has been a decline in VOD in the tropics and in large parts of east-central and north Asia, while a substantial increase is observed in India, large parts of Australia, southern Africa, southeastern China, and central North America. In summary, VODCA shows vast potential for monitoring spatial–temporal ecosystem changes as it is sensitive to vegetation water content and unaffected by cloud cover or high sun zenith angles. As such, it complements existing long-term optical indices of greenness and leaf area. The VODCA products (Moesinger et al., 2019) are open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.2575599.

Land surface temperature (LST) is a key parameter and plays an important role in hydrology, ecology, environment, and biogeochemistry. It is difficult for the existing satellites to acquire global LST with spatial and temporal consistency. The Moon-based Earth observation platform with a long life, large coverage can observe continuously the Earth, and obtain the global-scale LST. At present, various approaches for retrieving LST from passive microwave remote sensing data have been developed for the satellite remote sensing data with small and constant viewing zenith angle, however, the Moon-based Earth observation platform located outside the Earth's ionosphere has the viewing zenith angle of 0-90°. In this study, a modified physical-based method of LST retrieval from passive microwave data was developed for wide viewing zenith angles, and the LST and emissivity can be simultaneously estimated through the analysis of various atmospheric and ionosphere parameters. Three types of data, including the FengYun-3B satellite microwave radiation imager data, the multichannel Advanced Microwave Scanning Radiometer data, and Moon-based microwave radiation simulation data with the viewing zenith angles of 52-53°, 55°, and 0-90°, respectively, were used to retrieve LST. Results show that the estimation accuracy of LST decreases with the increase of viewing zenith angle. The 23.8 and 36.5 GHz brightness temperature is optimum for the LST estimation under a large-scale viewing zenith angle, and the root mean square errors of the LST are 5.18, 5.44, and 4.79 K, respectively.

The Compact Ocean Wind Vector Radiometer (COWVR) was developed at the Jet Propulsion Laboratory as a proof of concept technology demonstration mission for the Air Force. COWVR is a fully polarimetric imaging radiometer system operating at 18.7, 23.8, and 33.9 GHz . Its receiver subsystem is based on the Jason-3 advanced microwave radiometer, which was launched in early 2016 and is currently in operation. Its enabling components are presented in this article, including the phase-matched waveguide components, noise injection, receivers, and polarimetric backend units.

A two-dimensional variational retrieval (2D-Var) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are explicitly simulated to attempt retrieval of near-surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters judged by analysis of 2D-Var averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels' sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2D-Var for cloud-free retrievals and opens the possibility of stand-alone 3D-Var retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution.

Subject and purpose. The main factor which affects the development of modern marine radars and increase of their efficiency is the correct justification of the concept of construction and selection of technical requirements for the development of new devices. The purpose of this work is to analyze the existing methods of marine radar construction, generalize the results of long-term radio physical researches in different regions of the World Ocean and in the Black Sea water area as well, and on their basis to develop the concept for applied complex radar for surface situation control and navigation. Methods and methodology. During the development of construction concept and analysis of the offered radar features the method of mathematical modeling was used. Results. The concept for the complex radar system construction for surface situation control and navigation consisting of 3-cm radar and 8-mm radar with radiometer device is proposed. When choosing 3-cm wave range, the results of long-term radio physical researches conducted by Usikov IRE NASU in different regions of the World Ocean and the Black Sea water area were considered. The usage of 8-mm wave range allows one to carry out radar observation in the near-field zone where signals of 3-cm radar interfere and create strong reflections from the sea surface. The necessity to use 8-mm wave range radiometer is caused by the fact that reduction of the effective scattering cross section due to applied radio absorbing materials leads to increasing of object’s own radio brightness radiation. Therefore to detect barely visible (produced under Stealth technology) and low-sized targets the radiometer which allows to increase target contrast can be used. Conclusion. Conclusions on practical importance of the offered concept for construction of complex radar for surface situation control and navigation are made.

The moon images have been in recent years employed as a referential light source for in-orbit radiometric characterization of on-board radiometer in spacecraft. The irradiance obtained by the integration of the radiance of moon pixels is compared to the referential irradiance that derives from the astrophysical model, and the comparison result includes the gain characteristics of radiometer. In addition to this radiometric purpose, the limb between moon disk and deep space is also an ideal edge in optical aspect. The edge response of radiometer obtained in the limb includes the characteristics of MTF (modulation transfer function) and straylight. In this paper, in-orbit straylight characterization and correction of SGLI (Second Generation Global Imager) radiometer that is flying on JAXA's remote sensing satellite "SHIKISAI" is reported. It is shown that LSF (line spread function) measured in the moon limb is effective trend monitor of radiometer's geometric property in orbit and is also a key to verify the validity of straylight correction model implemented in the ground data processing system. This paper proposes the methodology that constructs PSF (point spread function) by LSFs in two orthogonal direction and corrects straylight of not only the Moon but the earth image by using the constructed PSF.