Radiometric Measurements Research Articles

Towards consistent assessments of in situ radiometric measurements for the validation of fluorescence satellite missions

The upcoming Fluorescence Explorer (FLEX) satellite mission aims to provide high quality radiometric measurements for subsequent retrieval of sun-induced chlorophyll fluorescence (SIF). The combination of SIF with other observations stemming from the FLEX/Sentinel-3 tandem mission holds the potential to assess complex ecosystem processes. The calibration and validation (cal/val) of these radiometric measurements and derived products are central but challenging components of the mission. This contribution outlines strategies for the assessment of in situ radiometric measurements and retrieved SIF. We demonstrate how in situ spectrometer measurements can be analysed in terms of radiometric, spectral and spatial uncertainties. The analysis of more than 200 k spectra yields an average bias between two radiometric measurements by two individual spectrometers of 8%, with a larger variability in measurements of downwelling radiance (25%) compared to upwelling radiance (6%). Spectral shifts in the spectrometer relevant for SIF retrievals are consistently below 1 spectral pixel (up to 0.75). Found spectral shifts appear to be mostly dependent on temperature (as measured by a temperature probe in the instrument). Retrieved SIF shows a low variability of 1.8% compared with a noise reduced SIF estimate based on APAR. A combination of airborne imaging and in situ non-imaging fluorescence spectroscopy highlights the importance of a homogenous sampling surface and holds the potential to further uncover SIF retrieval issues as here shown for early evening acquisitions. Our experiments clearly indicate the need for careful site selection, measurement protocols, as well as the need for harmonized processing. This work thus contributes to guiding cal/val activities for the upcoming FLEX mission.

A Machine Learning Snowfall Retrieval Algorithm for ATMS

This article describes the development of a machine learning (ML)-based algorithm for snowfall retrieval (Snow retrievaL ALgorithm fOr gpM–Cross Track, SLALOM-CT), exploiting ATMS radiometer measurements and using the CloudSat CPR snowfall products as references. During a preliminary analysis, different ML techniques (tree-based algorithms, shallow and convolutional neural networks—NNs) were intercompared. A large dataset (three years) of coincident observations from CPR and ATMS was used for training and testing the different techniques. The SLALOM-CT algorithm is based on four independent modules for the detection of snowfall and supercooled droplets, and for the estimation of snow water path and snowfall rate. Each module was designed by choosing the best-performing ML approach through model selection and optimization. While a convolutional NN was the most accurate for the snowfall detection module, a shallow NN was selected for all other modules. SLALOM-CT showed a high degree of consistency with CPR. Moreover, the results were almost independent of the background surface categorization and the observation angle. The reliability of the SLALOM-CT estimates was also highlighted by the good results obtained from a direct comparison with a reference algorithm (GPROF).

Emission from human skin in the sub THz frequency band

Recently published Radiometric measurements of human subjects in the frequency range 480–700 GHz, demonstrate the emission of blackbody radiation from the body core, rather than the skin surface. We present a detailed electromagnetic simulation of the dermis and epidermis, taking into account the presence of the sweat duct. This complex structure can be considered as an electromagnetic bio-metamaterial, whereby the layered structure, along with the topology of the sweat duct, reveals a complex interference pattern in the skin. The model is capable of accurately representing the skin greyness factor as a function of frequency and this is confirmed by radiometry of living human skin.

Optical alignment and radiative flux characterization of a multi-source high-flux solar simulator

High-flux solar simulators are research-grade sources of artificial radiation mimicking optical characteristics of concentrating solar systems. They allow for experimental evaluation of high-temperature solar thermal devices and materials under controlled and reproducible conditions. In this study, we demonstrate the application of close-range photogrammetry in tandem with radiometry to optically align radiation modules of a multi-source high-flux solar simulator and to characterize its radiative output. The photogrammetric setup consists of photogrammetry targets, a digital camera and photogrammetry software. The radiometric measurements are conducted using mobile Lambertian targets, a heat flux gauge, and a complementary metal-oxide semi-conductor camera equipped with neutral density filters. An iterative procedure for adjusting lamp positions allows obtaining optical configurations of radiative modules meeting experimental requirements of high and ultra-high temperature solar thermal and thermochemical research. Close-range photogrammetry is demonstrated to be a convenient and effective method to obtain the as-built geometric configuration of the solar simulator setup. We report characteristics of an example radiative module configuration for a subset of six radiation modules. The peak and mean radiative fluxes over a 60-mm diameter flat target located in the focal plane are 3080kWm−2 and 1135kWm−2, respectively. The mean aiming error, defined as the average distance between theoretical and actual aiming points in the focal plane, is reduced from 37.20mm to 4.50mm for the pre- and post-alignment configurations, respectively.

Unique NISTAR-Based Climate GCM Diagnostics of the Earth’s Planetary Albedo and Spectral Absorption Through Longitudinal Data Slicing

Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar and emitted thermal radiation permit a unique model/data comparison perspective that is not readily available from other satellite data. The key factor is the unique Lissajous orbital viewing geometry from the Lagrangian L1 point, which enables a continuous view of Earth’s sunlit hemisphere. The National Institute of Standards and Technology Advanced Radiometer (NISTAR) is the DSCOVR Mission energy budget instrument, which views the reflected and emitted radiation of the Earth’s sunlit hemisphere by means of single pixel active cavity full-spectrum (Band-A, 0.2–100 μm) and filtered solar wavelength (Band-B, 0.2–4.0 μm; and Band-C, 0.7–4.0 μm) radiometer measurements. An additional solar wavelength photodiode channel (0.3–1.1 μm) provides a calibration reference. The objective of this study is the assessment of climate GCM performance via direct model/data comparisons. Such comparisons are difficult due to quasi-chaotic natural variability present in real-world observational data and in climate GCM simulations. This is where the unique DSCOVR viewing geometry makes possible the longitudinal data slicing methodology for more direct model/data comparison. The key point of the longitudinal slicing approach is that data integration over the entire sunlit hemisphere eliminates the quasi-chaotic meteorological weather-scale noise, while preserving intra-seasonal and planetary-scale variability. The rotation of the Earth that retrieves this climate-style, large-scale longitudinal and seasonal variability. The hemispheric averaging is accomplished automatically in NISTAR measurements with its single-pixel view of the Earth. For climate GCMs, this requires implementing the Sunlit Hemisphere Sampling (SHS) scheme to operate on the GCM run-time output data, utilizing the DSCOVR Satellite Ephemeris data to assure precise viewing geometry between NISTAR measurements and GCM output data, while averaging out the meteorological weather noise. However, GCM generated data are radiative fluxes, while NISTAR (and EPIC) measurements are near-backscattered radiances. Conversing NISTSR measurements into radiative fluxes cannot be accomplished using NISTAR data alone, even with detailed support from conventional satellite data. But the identical viewing geometry of Earth’s sunlit hemisphere, and synergistic analyses of EPIC data make it feasible for this conversion of NISTAR near-backscatter radiances into radiative fluxes.

Monitoring Directional Dynamics of Growing Wheat Crop Canopy Using Ground based Time Series Remote Sensing Radiative Measurements

Agricultural crop monitoring is an issue of extreme importance under global climate change, increased natural disasters and population explosion threatening global food security. In this paper, dynamic behaviour of spectro-directional reflectance properties of wheat canopy has been studied using radiometric measurements performed over a wheat field during the entire crop life cycle under varying viewing geometries. The study reveals that biological growth rhythm of wheat crop associated with continuous alteration in canopy condition particularly in terms of optical and morphological properties during the life cycle results in a distinct and systematic changing pattern of bidirectional responses in red and near Infrared (NIR). Analysis shows appreciable sensitivity of radiometric measurements both in the red and NIR regions to crop phenological transformation and changes. Soil background influenced the overall angular anisotropy pattern and manifested relatively high surface reflectance specifically at the early growth stage. At this stage, changes in viewing direction give rise to additional variability in directional reflectance due to varying proportion of soil-vegetation and enhanced contrast between wheat crop and its soil background. Both amplitude and angular pattern of directional response of the canopy undergo appreciable changes with time. Asymmetry in directional reflectance has been noticed in both the spectral regions on either side of nadir in the principal plane. In this connection, time varying angular characteristics of normalized difference vegetation index (NDVI) has also been studied in relation to crop growth.

Evaluation of Ground-Based Models for Estimating Surface Albedo with In-Situ Radiometric Measurements across China

Surface albedo is an essential parameter in many solar radiation applications. Although several models are available, it remains debatable whether they are applicable to other locations. Using long-term daily measurements of radiation acquired by observational networks in China, this study examined the applicability of six existing albedo models: Ineichen model (IeM), Gueymard model (GM), Dong model (DeM), Iziomon-Mayer model (IMM), Morton model (MM), and Zhou model (ZeM). The evaluation results of model performance through statistical analysis showed that among the available ground albedo models, ZeM had the best overall performance at 12 selected stations, IeM was shown to provide acceptable estimations for locations where albedo records are readily available. The statistical results of individual station have shownthat the number of input parameters is not the only key factor for determining the predictive performance of ground albedo models. In other words, a simple model has potential for accurate estimation of ground albedo with appropriate model parameters. Therefore, the simple two-parameter DeM was selected to re-calibrate with in-situ radiometric measurements, which can be adopted as a surrogate for ZeM to predict surface albedo in China.

MAPPING THE MINERAL ZONATION AT THE ERNEST HENRY IRON OXIDE COPPER-GOLD DEPOSIT: VECTORING TO Cu-Au MINERALIZATION USING MODAL MINERALOGY

Abstract Iron oxide copper-gold (IOCG) deposits form in spatial and genetic relation to hydrothermal iron oxide-alkali-calcic-hydrolytic alteration and thus show a mappable zonation of mineral assemblages toward the orebody. The mineral zonation of a breccia matrix-hosted orebody is efficiently mapped by regularly spaced samples analyzed by the scanning electron microscopy-integrated mineral analyzer technique. The method results in quantitative estimates of the mineralogy and allows the reliable recognition of characteristic alteration as well as mineralization-related mineral assemblages from detailed mineral maps. The Ernest Henry deposit is located in the Cloncurry district of Queensland and is one of Australia’s significant IOCG deposits. It is known for its association of K-feldspar altered clasts with iron oxides and chalcopyrite in the breccia matrix. Our mineral mapping approach shows that the hydrothermal alteration resulted in a characteristic zonation of minerals radiating outward from the pipe-shaped orebody. The mineral zonation is the result of a sequence of sodic alteration followed by potassic alteration, brecciation, and, finally, by hydrolytic (acid) alteration. The hydrolytic alteration primarily affected the breccia matrix and was related to economic mineralization. Alteration halos of individual minerals such as pyrite and apatite extend dozens to hundreds of meters beyond the limits of the orebody into the host rocks. Likewise, the Fe-Mg ratio in hydrothermal chlorites changes systematically with respect to their distance from the orebody. Geochemical data obtained from portable X-ray fluorescence (p-XRF) and petrophysical data acquired from a magnetic susceptibility meter and a gamma-ray spectrometer support the mineralogical data and help to accurately identify mineral halos in rocks surrounding the ore zone. Specifically, the combination of mineralogical data with multielement data such as P, Mn, As, P, and U obtained from p-XRF and positive U anomalies from radiometric measurements has potential to direct an exploration program toward higher Cu-Au grades.

Arbitrary estimation of uranium ore reserves in Qattar–V, North Eastern Desert, Egypt using geographic information system

The qattar - V (G-V) uranium occurrence occurs at the contact areas between Gabal Qattar (G - Qattar) granite and Hammamat sedimentary rocks. The aim of this study is to estimate uranium ore reserves using a geographic information system by analyzing all the exploration data sets at G-V. Several geological investigations, including field and mineralogical observations, geochemical and radiometric surveys, have been performed for this uranium occurrence. Integrate these investigations with new radiometric and geochemical measurements to cover all the investigated areas has been done in this study. Using GIS it is possible to visualize the results and to extract the spatial features indicative of uranium mineralization's over G-V uranium occurrence. Our methodology involves the generation of prediction maps for the favorable host rock, structure control of uranium mineralization, alterations in intimate association with uranium, radiometrical and geochemical measurements. Combining all these spatial features leads to the differentiation between the areas with economical and for grades of uranium and the generation of a potential uranium map. From this map, the estimation of uranium ore quantity and quality at G-V uranium occurrences can be calculated using cuboids of constant widths, different lengths, and heights. This method gave arbitrary but reasonable estimates of uranium in selected blocks.

The origin of the potassium‐rich annular zones at the Bosumtwi impact structure, Ghana, investigated by field study, radiometric analysis, and first cosmogenic nuclide data

AbstractThe 10.5‐km‐diameter, 1 Ma Bosumtwi impact structure in Ghana is one of the youngest, large impact structures known on Earth. The preservation of the morphology of its ejecta deposits, with an annular moat and outer ridge resembling those of rampart impact craters on Mars, makes Bosumtwi a remarkable impact structure on the African continent. An airborne radiometric survey of the southwestern part of Ghana reveals enigmatic circular feature enriched in potassium, coinciding with the crater rim and an outer ejecta ridge at Bosumtwi. The goal of this study is to investigate possible origins of these features, by impact processes (shock metamorphic effects, impact‐induced hydrothermal systems) or postimpact surficial processes (erosion, weathering). The origin of these features is discussed here based on field observations, ground‐based radiometric measurements, and first cosmogenic nuclide analyses (10Be). The data indicate that the rim and outer ridge were eroded more rapidly than the rest of the impact structure. Accordingly, the downward advance of the weathering fronts in the annular moat, after ejecta emplacement, are responsible for leaching of K from the lateritic residual observed at the surface. The Bosumtwi impact structure is, therefore, a valuable natural laboratory to investigate the factors controlling erosion and weathering processes in the Ashanti belt since impact 1 Ma ago. Simulations of vertical profiles of 10Be concentration further constrain local variations of the erosion rate. In light of this study, circular K anomalies in radiometric surveys might be indicative of potential impact structures in tropical regions.

On the detection of a cosmic dawn signal in the radio background

The astrophysics of the cosmic dawn, when star formation commenced in the first collapsed objects, is predicted to be revealed by spectral and spatial signatures in the cosmic radio background at long wavelengths. The sky-averaged redshifted 21 cm absorption line of neutral hydrogen is a probe of the cosmic dawn. The line profile is determined by the evolving thermal state of the gas, radiation background, Lyman α radiation from stars scattering off cold primordial gas, and relative populations of the hyperfine spin levels in neutral hydrogen atoms. We report a radiometer measurement of the spectrum of the radio sky in the 55–85 MHz band, which shows that the profile found by Bowman et al. in data taken with the Experiment to Detect the Global Epoch of Reionization Signature (EDGES) low-band instrument is not of astrophysical origin; their best-fitting profile is rejected with 95.3% confidence. The profile was interpreted to be a signature of the cosmic dawn; however, its amplitude was substantially higher than that predicted by standard cosmological models. Our non-detection bears out earlier concerns and suggests that the profile found by Bowman et al. is not evidence for new astrophysics or non-standard cosmology.

Assessing synergistic radar and radiometer capability in retrieving ice cloud microphysics based on hybrid Bayesian algorithms

Abstract. The 2017 National Academy of Sciences Decadal Survey highlighted several high-priority objectives to be pursued in the decadal timeframe, and the next-generation Cloud, Convection and Precipitation (CCP) observing system is thereby contemplated. In this study, we develop a suite of hybrid Bayesian algorithms to evaluate two CCP remote sensor candidates including a W-band cloud radar and a (sub)millimeter-wave radiometer with channels in the 118–880 GHz frequency range for capability in constraining ice cloud microphysical quantities. The algorithms address active-only, passive-only, and synergistic active–passive retrievals. The hybrid Bayesian algorithms combine the Bayesian Monte Carlo integration and optimization process to retrieve quantities with uncertainty estimates. The radar-only retrievals employ the optimal estimation methodology, while the radiometer-involved retrievals employ ensemble approaches to maximize the posterior probability density function. A priori information is obtained from the Tropical Composition, Cloud and Climate Coupling (TC4) in situ data and CloudSat radar observations. End-to-end simulation experiments are conducted to evaluate the retrieval accuracies by comparing the retrieved parameters with known values. The experiment results suggest that the radiometer measurements possess high sensitivity to ice cloud particles with large water content. The radar-only retrievals demonstrate capability in reproducing ice water content profiles, but the performance in retrieving number concentration is poor. The synergistic observations enable improved pixel-level retrieval accuracies, and the improvements in ice water path retrievals are significant. The proposed retrieval algorithms could serve as alternative methods for exploring the synergistic active and passive concept, and the algorithm framework could be extended to the inclusion of other remote sensors to further assess the CCP observing system in future studies.

Development of a standard method for primary monitoring of exposure to environmental radiation

All living things are constantly exposed to background radiation. The presence of radioactive materials in the earth's crust and cosmic radiation are largely responsible for this exposure. The present study aimed to develop a new methodology of easy applicability and low cost to carry out measurements of environmental background radiation and to be able to establish a map of environmental radiation for primary monitoring of these exposures. For that, ionizing radiation measurements, Meteorological and Radiometric measurements were performed. Geiger Mueller Counter (GM) was used, as it is widely used in services that use radioactive sources and research centers. Measurements obtained showed that there were no statistically significant changes in the 24 hours of measurements with the GM, in relation to the variations presented in the Meteorological and Radiometric measurements that changed according to the time of day. The average value of the background radiation readings was 0.0194 mR/h, with an average variance of 1.8E-06 and an average standard deviation of 1.35E-03. GM proved to be useful for primary measurement of background radiation. The map was developed using open source libraries for the Python programming language. The map may be hosted on an open access website, so that professionals, students and researchers may contribute, sending new data from different locations. It is essential that the levels of radiation exposure are demonstrated and understood. Easy access to this information may contribute to health organizations, agriculture and research centers to establish a baseline for future research.

Modelling surface temperature and radiation budget of snow-covered complex terrain

Abstract. The surface temperature controls the temporal evolution of the snowpack, playing a key role in metamorphism and snowmelt. It shows large spatial variations in mountainous areas because the surface energy budget is affected by the topography, for instance because of the modulation of the short-wave irradiance by the local slope and the shadows and the short-wave and long-wave re-illumination of the surface from surrounding slopes. These topographic effects are often neglected in large-scale models considering the surface to be flat and smooth. Here we aim at estimating the surface temperature of snow-covered mountainous terrain in clear-sky conditions in order to evaluate the relative importance of the different processes that control the spatial variations. For this, a modelling chain is implemented to compute the surface temperature in a kilometre-wide area from local radiometric and meteorological measurements at a single station. The first component of this chain is the Rough Surface Ray-Tracing (RSRT) model. Based on a photon transport Monte Carlo algorithm, this model quantifies the incident and reflected short-wave radiation on every facet of the mesh describing the snow-covered terrain. The second component is a surface scheme that estimates the terms of the surface energy budget from which the surface temperature is eventually estimated. To assess the modelling chain performance, we use in situ measurements of surface temperature and satellite thermal observations (Landsat 8) in the Col du Lautaret area, in the French Alps. The results of the simulations show (i) an agreement between the simulated and measured surface temperature at the station for a diurnal cycle in winter within 0.2 ∘C; (ii) that the spatial variations in surface temperature are on the order of 5 to 10 ∘C in the domain and are well represented by the model; and (iii) that the topographic effects ranked by importance are the modulation of solar irradiance by the local slope, followed by the altitudinal variations in air temperature (lapse rate), the re-illumination by long-wave thermal emission from surrounding terrain, and the spectral dependence of snow albedo. The changes in the downward long-wave flux because of variations in altitude and the absorption enhancement due to multiple bounces of photons in steep terrain play a less significant role. These results show the necessity of considering the topography to correctly assess the energy budget and the surface temperature of snow-covered complex terrain.

Detecting Supercooled Water Clouds Using Passive Radiometer Measurements

AbstractSupercooled water clouds (SWCs) have significant impacts on the Earth's radiation balance, aircraft ice accretion and precipitation augmentation. This study introduces an efficient algorithm to detect SWCs from passive radiometers, which combines information from the reflectance difference between 1.61 and 2.25 μm channels, the brightness temperature difference between the 8.5 and 11 μm channels, and the cloud top temperature. Validated by space radar and lidar measurements, our algorithm can correctly detect 91% of SWC pixels, better than current Visible Infrared Imager Radiometer Suite operational product. SWCs are found mostly over the mid‐ to high‐latitude oceans and have a global occurrence frequency of ∼8% in cloudy skies. Since the channels used for the detection are available in most current operational polar and geostationary satellite radiometers, this SWC detection algorithm can be easily implemented for operations such as cloud monitoring, aviation safety, and SWC‐related weather modification.

System vicarious calibration of GCOM-C/SGLI visible and near-infrared channels

Vicarious calibration coefficients (kv) of Second-generation GLobal Imager (SGLI) for ocean color processing were derived using in-situ radiometric buoy measurements from the Marine Optical BuoY (MOBY) and the BOUée pour l'acquiSition d'une Série Optique à Long termE (BOUSSOLE). Two aerosol-model look up tables (LUTs) used in the GCOM-C aerosol retrieval algorithm (LUT-A) and in the previous version of ocean color atmospheric correction algorithm (LUT-B) were tested in the procedures to calculate kv and retrieve remote sensing reflectance (Rrs) and aerosol optical thickness (AOT). Bias of the processed Rrs compared to AERONET-OC Rrs was reduced by applying the determined kv (i.e., corrected SGLI radiance = original SGLI radiance/kv). LUT-A yielded smaller AOT bias compared to AERONET-OC AOT; on the other hand, LUT-B gave smaller Rrs noise due to gentle slope of the aerosol reflectance even though it caused AOT overestimation. When kv was derived by adjusting to the AOT measurements, kv was about 1.1 by LUT-A and 1.2 by LUT-B in the near-infrared (NIR) channel. However, the kv in the NIR channel was close to 1.0 when AOT and land surface reflectance measurements of Radiometric Calibration Network (RadCalNet) were used. The LUT-A with kv from MOBY and BOUSSOLE are currently adopted for the SGLI standard ocean color processing. Improvement is needed, however, to design an optimal LUT suitable for both aerosol and ocean color purposes.

L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory

This study reports on four consecutive winter campaigns (2016 &#x2013; 2020) at the &#x201C;Davos-Laret Remote Sensing Field Laboratory&#x201D; in the Swiss Alps to gain insight into the L-band microwave emission of ground covered with seasonal snow. Close-range L-band Brightness temperatures <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula> were measured over the site scanning different observation nadir angles <inline-formula><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> and azimuth angles <inline-formula><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> at Horizontal and Vertical polarizations <i>p</i> = {H,V}. State parameters of the snowpack (e.g., height, density, SWE) and the subnivean soil (permittivity, temperature) were measured quasi-simultaneously using in-situ sensors and sampling, as well as meteorological data. In each campaign <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula> were measured over a &#x201C;natural area&#x201D; and a &#x201C;reflector area&#x201D; with a metal mesh reflector laid on the ground before snow accumulation. The radiometer measurements over &#x201C;reflector area&#x201D; allowed to retrieve the time-series of Snow liquid Water-Content <inline-formula><tex-math notation="LaTeX">$W_\mathrm{{S}}$</tex-math></inline-formula> and Snow liquid Water-Column <inline-formula><tex-math notation="LaTeX">$WC_\mathrm{{S}}$</tex-math></inline-formula> which are employed as &#x201C;derived measurements&#x201D; to support interpretation of <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula> measured over &#x201C;natural areas&#x201D; during different Winter phases. The detailed approach for the estimation of <inline-formula><tex-math notation="LaTeX">$W_\mathrm{{S}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$WC_\mathrm{{S}}$</tex-math></inline-formula> using L-band radiometer data is presented. The data and analyses in this paper address the following major points: i) Determination of the characteristic features of measured <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula> during different periods in each of the four winter campaigns; ii) effects of dry- and wet snow precipitation on L-band radiometer data compared to corresponding simulations; iii) effect of removal and compression of the snowpack on <inline-formula><tex-math notation="LaTeX">$T_\mathrm{{B}}^{p,\phi }(\theta)$</tex-math></inline-formula>; and iv) effects of spatial heterogeneity on brightness temperatures. Finally, the study is concluded with recommendations relevant for future close-range remote sensing campaigns.

Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks

Passive remote sensing is a crucial technology for climate studies and Earth science. NASA's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400-1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio-frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

In-Flight Calibration of Visible and Near-Infrared Imaging Spectrometer (VNIS) Onboard Chang’E-4 Unmanned Lunar Rover

The Visible and Near-infrared Imaging Spectrometer (VNIS) is one of the scientific payloads onboard the Yutu-2 lunar rover of the Chang’E-4 mission. Based on Acousto-Optic Tunable Filter (AOTF) dispersion, a moving platform and a calibration unit, the VNIS was designed for lunar surface observations of the far side of the Moon for composition exploration and spectral experiments in the wavelength range of 0.45–2.4μm. Large differences in radiance caused by the discrepancy between the laboratory and lunar environment could affect the spectral shape and features. Therefore, in-flight calibration of the VNIS is critical for the interpretation of the acquired spectra. To precisely map the digital output to the spectral radiance, a calibration unit was installed on the VNIS to facilitate radiometric calibration measurements on the lunar surface. After the elimination of stray light and the shadow effect, the measured radiance of the calibration unit was derived by digital multiplication of the pre-flight coefficients. Considering the observation geometry, Moon-Sun distance, and the bi-directional reflectance factor (BRF) of the calibration panel, the theoretical radiance at the entrance pupil of VNIS was calculated. Using linear regression, in-flight calibration of the coefficients was performed and used to correct the spectral data. The performance of the spectrometer was evaluated using 12 lunar-day data. It was determined that the coefficients gradually converged and the variation was less than 1%, indicating that the in-flight calibration was robust. The proposed in-flight calibration method facilitated the acquisition of reliable data for lunar composition interpretation.

A Nonparametric Tropical Cyclone Wind Speed Estimation Model Based on Dual-Polarization SAR Observations

The C-band synthetic aperture radar (SAR) observation is one of the most popular sources for high-resolution tropical cyclone (TC) wind speed estimation. The scarcity of high wind speed data with good quality restricts the inversion accuracy of high wind speed. It is a challenge to obtain a high-precision wind speed inversion model with sparse data points. In this paper, the TC wind speed is successfully estimated from the dual-polarization SAR signal. Firstly, the training dataset with a total of 327 data points is formed using the Sentinel-1A EW/IW mode images and their temporal-spatial matched Stepped Frequency Microwave Radiometer (SFMR) measurements. Then, a novel nonparametric TC wind speed estimation model (hereafter NWSE model) is proposed with this dataset by using the Bayesian nonparametric general regression method. The wind speed is interpreted as a function of the cross-polarized normalized radar cross-sections (VH-NRCS) and incident angle for the NWSE model. Moreover, the wind speed obtained from the co-polarized signal is used to improve the accuracy of NWSE model under low wind speed. Finally, the validation results show the excellent overall consistency between the model retrieved wind speed and the collocated SFMR and SMAP measurements. Specifically, considering all the wind speeds, the overall root-mean-square error and absolute bias of NWSE model are 2.85 m/s and 2.26 m/s compared with the SMAP wind speed. When considering the wind speeds larger than 30 m/s, the RMSE and bias of NWSE model are 3.75 m/s and 2.78 m/s, respectively.