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The Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) Version 2 (V002) has been available since March 2019 from the NASA LP DAAC (Land Processes Distributed Active Archive Center) and provides global, monthly infrared land surface emissivity and uncertainty at 0.05 degrees (~5 km) resolution. A climatology of the CAMEL V002 product is now available at the same spatial, temporal, and spectral resolution, covering the CAMEL record from 2000 to 2016. Characterization of the climatology over case sites and IGBP (International Geosphere-Biosphere Programme) land cover categories shows the climatology is a stable representation of the monthly CAMEL emissivity. Time series of the monthly CAMEL V002 product show realistic seasonal changes but also reveal subtle artifacts known to be from calibration and processing errors in the MODIS MxD11 emissivity. The use of the CAMEL V002 climatology mitigates many of these time dependent errors by providing an emissivity estimate which represents the complete 16-year record. The CAMEL V002 climatology's integration into RTTOV (Radiative Transfer for TOVS) v12 is demonstrated through the simulation of IASI (Infrared Atmospheric Sounding Interferometer) radiances. Improved stability in CAMEL Version 3 is expected in the future with the incorporation of the new MxD21 and VIIRS VNP21 emissivity products in MODIS Collection 6.1. [ABSTRACT FROM AUTHOR]

Validation of emissivity (ε) retrievals from spaceborne thermal infrared (TIR) sensors typically requires spatial extrapolations over several orders of magnitude for a comparison between centimeter-scale laboratory ε measurements and the common decameter and lower resolution of spaceborne TIR data. In the case of NASA's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) temperature and ε separation algorithm (TES), this extrapolation becomes especially challenging because TES was originally designed for the geologic surface of Earth, which is typically heterogeneous even at centimeter and decameter scales. Here, we used the airborne TIR hyperspectral Mako sensor with its 2.2 m/pixel resolution, to bridge this scaling issue and robustly link between ASTER TES 90 m/pixel emissivity retrievals and laboratory ε measurements from the Algodones dune field in southern California, USA. The experimental setup included: (i) Laboratory XRD, grain size, and TIR spectral measurements; (ii) radiosonde launches at the time of the two Mako overpasses for atmospheric corrections; (iii) ground-based thermal measurements for calibration, and (iv) analyses of ASTER day and night ε retrievals from 21 different acquisitions. We show that while cavity radiation leads to a 2% to 4% decrease in the effective emissivity contrast of fully resolved scene elements (e.g., slipface slopes and interdune flats), spectral variability of the site when imaged at 90 m/pixel is below 1%, because at this scale the dune field becomes an effectively homogeneous mixture of the different dune elements. We also found that adsorption of atmospheric moisture to grain surfaces during the predawn hours increased the effective ε of the dune surface by up to 0.04. The accuracy of ASTER's daytime emissivity retrievals using each of the three available atmospheric correction protocols was better than 0.01 and within the target performance of ASTER's standard emissivity product. Nighttime emissivity retrievals had lower precision (<0.03) likely due to residual atmospheric effects. The water vapor scaling (WVS) atmospheric correction protocol was required to obtain accurate (<0.01) nighttime ASTER emissivity retrievals. [ABSTRACT FROM AUTHOR]

An autonomous system for field land surface temperature (LST) measurements taken at different observation angles was developed to be deployed easily at any conventional meteorological tower station. The system permits ground-truth data to be acquired on a continuous basis, and angularly scans land and sky hemispheres with a single thermal-infrared (TIR) radiometer. This paper describes the autonomous angular system and the methodology to assess ground-truth LST and relative-to-nadir emissivity data from system measurements. Ground-truth LSTs were used to validate satellite-retrieved LST products at two experimental sites (rice crop and shrubland areas). The relative-to-nadir emissivity values were used to analyze the anisotropy of surface emissive properties over thermally-homogeneous covers. The EOS-MODIS MOD11-L2/MYD11-L2 LST product was evaluated and shown to work within expected uncertainties (<2.0 K) when tested against the system data. A slight underestimation of around -0.15 K was observed, which became greater for the off-nadir observation angles at the shrubland site. The system took angular measurements for the different seasonal homogeneous covers at the rice crop site. These measurements showed emissivity angular anisotropies, which were in good agreement with previously published data. The dual-view ENVISAT-AATSR data reproduced them, and revealed that the system data collected for thermally-homogeneous surfaces could be used to test future satellite TIR sensors with multi-angular or bi-angular capabilities, like the forthcoming SLSTR on board Copernicus Sentinel-3A. [ABSTRACT FROM AUTHOR]

Infrared surface emissivity is needed for the calculation of net longwave radiation, a critical parameter in weather and climate models and Earth's radiation budget. Due to a prior lack of spatially and temporally variant global broadband emissivity (BBE) measurements of the surface, it is common practice in land surface and climate models to set BBE to a single constant over the globe. This can lead to systematic biases in the estimated net and longwave radiation for any particular location and time of year. Under the National Aeronautics and Space Administration's (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, a new global, high spectral resolution land surface emissivity dataset has recently been made available at monthly at 0.05 degree resolution since 2000. Called the Combined ASTER MODIS Emissivity over Land (CAMEL), this dataset is created by the merging of the MODIS baseline-fit emissivity database developed at the University of Wisconsin-Madison and the ASTER Global Emissivity Dataset (GED) produced at the Jet Propulsion Laboratory. CAMEL has 13 hinge points between 3.6–14.3 µm which are expanded to cover 417 infrared spectral channels within the same wavelength region using a principal component regression approach. This work presents the method for calculating BBE using the new CAMEL dataset. BBE is computed via numerical integration over the CAMEL High Spectral Resolution product for two different wavelength ranges—3.6–14.3 µm which takes advantage of the full, available CAMEL spectra and 8.0–13.5 µm which has been determined to be an optimal range for computing the most representative all wavelength, longwave net radiation. CAMEL BBE uncertainty estimates are computed, and comparisons are made to BBE computed from lab validation data for selected case sites. Variations of BBE over time and land cover classification schemes are investigated and converted into flux to demonstrate the equivalent error in longwave radiation which would be made by the use of a single, constant BBE value. Misrepresentations in BBE by 0.05 at 310 K corresponds to potential errors in longwave radiation of over 25 W/m2. [ABSTRACT FROM AUTHOR]

The surface effective emissivities of Arctic sea ice are calculated using Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements. These emissivities are analyzed for stable winter conditions during the months of January–May and November and December of 2020 for several main sea ice types defined with the sea ice maps of the Arctic and Antarctic Research Institute (AARI). The sea ice emissivities are derived from the AMSR2 data using the radiation transfer model for a non-scattering atmosphere and ERA5 reanalysis data. The emissivities are analyzed only for areas of totally consolidated sea ice of definite types. Probability distribution functions are built for the emissivities and their functions for such sea ice types as nilas, young ice, thin first-year (FY) ice, medium FY ice, thick FY ice and multi-year ice. The emissivity variations with frequency are estimated for each of the considered sea ice type for all seven months. The variations are calculated both for the emissivities and for their gradients at the AMSR2 channel frequencies. Obtained emissivities turned out to be generally lower than reported previously in scientific studies, whereas the emissivity variability values proved to be much larger than was known before. For all FY ice types, at all the frequencies, an increase in the emissivity at the beginning of winter and its decrease by the end of May are observed. The emissivity gradients demonstrate noticeable decreases with sea ice age, and their values may be used in sea ice classification algorithms based on the AMSR2 data. [ABSTRACT FROM AUTHOR]

Microwave surface emissivity is complex and variable, leading to increased difficulty in accurately retrieving atmospheric parameters and assimilating satellite microwave observations over land. The Community Radiative Transfer Model (CRTM) land emissivity model is a useful tool for providing microwave emissivity over complex surfaces. By combining the model with ground measurements from a mobile multi-surface observing system at the Xianghe site, China, the performance of the land emissivity model is evaluated over grass and sand surfaces. The simulated and measured emissivity agrees at both polarizations over the grassland surface but a more significant difference is observed at the horizontal polarization over the sand surface. To solve this problem, the Q/H module for soil reflectance roughness correction in the CRTM emissivity model was replaced with the Qp module for the sand surface. This results in a significant improvement in the horizontal polarization simulation, with the corresponding mean bias error (MBE) reducing from 0.08 in the Q/H module to less than 0.03. The adjustment demonstrates that the Qp module more effectively corrects the roughness effect on horizontally polarized emissivity for bare soil surfaces. For grassland, the CRTM emissivity model with the Q/H module demonstrates accurate simulations, showing its suitability for vegetated land surfaces. [ABSTRACT FROM AUTHOR]

The Altiplano-Puna Volcanic Complex (APVC) of the Central Andes is an arid region with extensive volcanism, possessing various geological features comparable to those of other solar system objects. The unique features of the APVC, e.g., hydrothermal fields and evaporite salars, have been used as planetary analogs before, but the complexity of the APVC presents a wealth of opportunities for more analog studies that have not been exploited previously. Motivated by the potential of using the APVC as an analog of the volcanic terrains of solar system objects, we mapped the mineralogy and silica content of the APVC up to ~100,000 km2 in northern Chile based on a combination of remote sensing data resembling those of the Moon and Mars. The band ratio indices of Landsat 8 Operational Land Imager multispectral images and mineral classifications based on spectral hourglass approach using Earth Observing-1 Hyperion hyperspectral images (both in the visible to shortwave infrared wavelengths) were used to map iron-bearing and alteration minerals. We also used Hyperion imagery to detect feldspar spectral signatures and demonstrated that feldspar minerals can be detected on non-anorthosites, which may influence interpretations of feldspar spectral signatures on Mars. From the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset, we derived the silica percentage of non-evaporite rocks within errors of approximately 2–3 wt.% SiO2 for those in the 60–70 wt.% range (about 8 wt.% errors for the 50–60 wt.% range). Based on an integrated assessment of the three datasets, we highlighted three regions of particular interest worthy of further field investigation. We also evaluated the benefits and limitations of all three remote sensing methods for mapping key minerals and capturing rock diversity, based on available samples and existing geological maps. [ABSTRACT FROM AUTHOR]

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin. [ABSTRACT FROM AUTHOR]

This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm's implementation to any area of interest. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), as higher-level products based on the temperature-emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 °C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated "on-the-fly" and are available on-line via a web application. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of −0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product. [ABSTRACT FROM AUTHOR]

The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958). [ABSTRACT FROM AUTHOR]

On 19th September 2021, a protracted eruption of the Cumbre Vieja Volcano on the Canary Island of La Palma commenced and continued for a duration of 12 weeks. Lava flows starting from the rift zone at the mid-western flank of Cumbre Vieja advanced toward the western coast of the island. The eruption was monitored by different remote sensing satellites, including the Copernicus Sentinel missions and DESIS. The Sentinel-2 Copernicus satellites acquired multispectral data from 15th September onward. On September 30th, and with a difference of ∼2 h with respect to Sentinel-2 A, the DESIS hyperspectral sensor also acquired data from the volcano and then again on 15th October 2021. Typically, mid-infrared (around 3.8 μ m) data are used for the thermal analysis of active lava flows. However, neither Sentinel-2 nor DESIS possesses mid-infrared bands and the Sentinel-2 high-wavelengths bands (∼2 μ m) have some limitations. Nevertheless, the hyperspectral character of DESIS enables the analysis of active erupting volcanoes in near-infrared wavelengths. The results of this analysis find fluid lava temperatures of about 1100–1200 K but there are problems associated with the high-temperature lava spectral emissivity. [ABSTRACT FROM AUTHOR]

Monitoring changes in vegetation cover over time is crucial for understanding the spatial distribution of rainfall, as well as the dynamics of plants and animals in the Namib desert. Traditional vegetation indices have limitations in capturing changes in vegetation cover within water-limited ecosystems like the Namib gravel plains. Spectral emissivity derived from thermal infrared remote sensing has recently emerged as a promising tool for distinguishing between bare ground and non-green vegetation in arid environments. This study investigates the potential of satellite-derived emissivities for mapping changes in fractional vegetation cover across the Namib gravel plains. Analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) band 29 (λ = 8.55 µm) emissivity time series from 2001 to 2021, our findings demonstrate the ability of both Normalized Difference Vegetation Index (NDVI) and emissivity to detect sudden vegetation growth on the gravel plains. Emissivity additionally allows monitoring the extent of desiccated grass over several years after a rainfall event. Our results support a relationship between the change in fractional vegetation cover, the amount of rainfall and emissivity change magnitude. Information from NDVI and emissivity therefore provide complementary information for assessing vegetation in arid environments. [ABSTRACT FROM AUTHOR]

Based on the community radiative transfer model, ensemble tree-based random forest algorithm, and extreme gradient boosting tree algorithm, this study established a random forest retrieval model (RF) and an extreme gradient boosting tree retrieval model (XGBoost) for the microwave land surface emissivity by using ERA5 reanalysis data and the observed brightness temperature of 10.65 GHz vertical polarization from FY3C Microwave Radiation Imager-I. In addition, an optimized Bayesian regularized neural network retrieval model (M2_30NN) has also been established on the basis of the original neural network land surface emissivity retrieval model (M1_20NN). The results show that compared with the simulated brightness temperature of the original land surface emissivity, the simulated brightness temperature of the land surface emissivity from each retrieval model is not only significantly improved in the correlation coefficient with the observed brightness temperature (5.92% (M1_20NN), 4.23% (M2_30NN), 14.21% (RF), 13.07% (XGBoost)), but also in the evaluation indexes of root mean square error, mean absolute error and explained variance regression score in the training datasets. Furthermore, in terms of testing datasets and spatiotemporal independence test datasets, the retrieval results of RF and XGBoost models can capture the spatial distribution patterns that are consistent with observations well, and also show great numerical improvement compared with the original model. In general, the XGBoost retrieval model is the best, followed by the RF retrieval model. [ABSTRACT FROM AUTHOR]

Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR window bands (i.e., surface-sensitive channels) that can span into the far-infrared (FIR) region under dry polar conditions. To model the satellite observed radiance within these bands, an accurate a priori emissivity is necessary for the surface in question, usually provided in the form of a physical or empirical model. To address the needs of hyperspectral TIR satellite radiance assimilation, this paper discusses the research, development, and preliminary validation of a physically based snow/ice emissivity model designed for practical implementation within operational fast-forward models such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Community Radiative Transfer Model (CRTM). To accommodate the range of snow grain sizes, a hybrid modeling approach is adopted, combining a layer scattering model based on the Mie theory (viz., the Wiscombe–Warren 1980 snow albedo model, its complete derivation provided in the Appendices) with a specular facet model. The Mie-scattering model is valid for the smallest snow grain sizes typical of fresh snow and frost, whereas the specular facet model is better suited for the larger sizes and welded snow surfaces typical of aged snow. Comparisons of the model against the previously published spectral emissivity measurements show reasonable agreement across zenith observing angles and snow grain sizes, and preliminary observing system experiments (OSEs) have revealed notable improvements in snow/ice surface window channel calculations versus hyperspectral TIR satellite observations within the NOAA NWP radiance assimilation system. [ABSTRACT FROM AUTHOR]

Quantifying roughness effects on ground surface emissivity is an important step in obtaining high-quality soil moisture products from large-scale passive microwave sensors. In this study, we used a semi-empirical method to evaluate roughness effects (parameterized here by the Hr parameter) on a global scale from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) observations. AMSR-E brightness temperatures at 6.9 GHz obtained from January 2009 to September 2011, together with estimations of soil moisture from the SMOS (Soil Moisture and Ocean Salinity) L3 products and of soil temperature from ECMWF's (European Centre for Medium-range Weather Forecasting) were used as inputs in a retrieval process. In the first step, we retrieved a parameter (referred to as the Hr parameter) accounting for the combined effects of roughness and vegetation. Then, global MODIS NDVI data were used to decouple the effects of vegetation from those of surface roughness. Finally, global maps of the Hr parameters were produced and discussed. Initial results showed that some spatial patterns in the Hr values could be associated with the main vegetation types (higher values of Hr were retrieved generally in forested regions, intermediate values were obtained over crops and grasslands, and lower values were obtained over shrubs and desert) and topography. For instance, over the USA, lower values of Hr were retrieved in relatively flat regions while relatively higher values were retrieved in hilly regions. [ABSTRACT FROM AUTHOR]

Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spectral emissivity measurement standards for TIR hyperspectral imagers in the field. In this paper, we address the problems encountered when measuring emissivity spectra in the field and propose a practical data acquisition and processing framework for a Fourier transform (FT) TIR hyperspectral imager—the Hyper-Cam LW—to obtain high-quality emissivity spectra in the field. This framework consists of three main parts. (1) The performance of the Hyper-Cam LW sensor was evaluated in terms of the radiometric calibration and measurement noise, and a data acquisition procedure was carried out to obtain the useful TIR hyperspectral imagery in the field. (2) The data quality of the original TIR hyperspectral imagery was improved through preprocessing operations, including band selection, denoising, and background radiance correction. A spatial denoising method was also introduced to preserve the atmospheric radiance features in the spectra. (3) Three representative temperature-emissivity separation (TES) algorithms were evaluated and compared based on the Hyper-Cam LW TIR hyperspectral imagery, and the optimal TES algorithm was adopted to determine the final spectral emissivity. These algorithms are the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm, the improved Advanced Spaceborne Thermal Emission and Reflection Radiometer temperature and emissivity separation (ASTER-TES) algorithm, and the Fast Line-of-sight Atmospheric Analysis of Hypercubes-IR (FLAASH-IR) algorithm. The emissivity results from these different methods were compared to the reference spectra measured by a Model 102F spectrometer. The experimental results indicated that the retrieved emissivity spectra from the ISSTES algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer. The root-mean-square error (RMSE) between the retrieved emissivity and the standard spectra was 0.0086, and the spectral angle error was 0.0093. [ABSTRACT FROM AUTHOR]

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature. [ABSTRACT FROM AUTHOR]

Synergies of optical, thermal and laser scanning remotely sensed data provide valuable information to study the structure and functioning of terrestrial ecosystems. One of the few fully operational airborne multi-sensor platforms for ecosystem research in Europe is the Flying Laboratory of Imaging Systems (FLIS), operated by the Global Change Research Institute of the Czech Academy of Sciences. The system consists of three commercial imaging spectroradiometers. One spectroradiometer covers the visible and near-infrared, and the other covers the shortwave infrared part of the electromagnetic spectrum. These two provide full spectral data between 380–2450 nm, mainly for the assessment of biochemical properties of vegetation, soil and water. The third spectroradiometer covers the thermal long-wave infrared part of the electromagnetic spectrum and allows for mapping of surface emissivity and temperature properties. The fourth instrument onboard is the full waveform laser scanning system, which provides data on landscape orography and 3D structure. Here, we describe the FLIS design, data acquisition plan and primary data pre-processing. The synchronous acquisition of multiple data sources provides a complex analytical and data framework for the assessment of vegetation ecosystems (such as plant species composition, plant functional traits, biomass and carbon stocks), as well as for studying the role of greenery or blue-green infrastructure on the thermal behaviour of urban systems. In addition, the FLIS airborne infrastructure supports calibration and validation activities for existing and upcoming satellite missions (e.g., FLEX, PRISMA). [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach to subpixel infrared (IR) emitter detection in VIIRS mid-wave (MWIR) infrared bands M12–M13 at night, based on the presence of a tightly clustered background diagonal present in full granule scattergrams of M12 versus M13 radiances. This diagonal is found universally in night-time VIIRS data collected worldwide. The diagonal feature is absent during the day due to solar reflectance. The existence of the diagonal is attributed to close spacing in the bandpass centers of the VIIRS' two MWIR bands. Apparently, the M12 and M13 emissivities are highly correlated to background objects, such clouds, ocean and land surfaces. The VIIRS Nightfire (VNF) algorithm detects pixels containing IR emitters based on their departure from the background diagonal. This paper outlines the method and compares VNF results with those from MODIS and VIIRS hotspot detection. [ABSTRACT FROM AUTHOR]

The Community Microwave Emission Modelling platform (CMEM) has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the forward operator radiative transfer model for low frequency passive microwave brightness temperatures (TB). It is used at ECMWF for L-band TB monitoring over snow free areas. In this paper, upgrades to CMEM are presented in order to explore forward modelling in snow-covered areas for coupled land-atmosphere numerical weather prediction systems. The upgrades enable to use CMEM on an extended range of frequencies and the Helsinki University of Technology multi-layer snow emission model is implemented. Offline CMEM experiments are evaluated against AMSR2 (Advanced Microwave Scanning Radiometer 2) observations showing that simulated TB is improved when using a multi-layer snow scheme, compared to a single-layer scheme. The improvements mainly result from a better representation of snow characteristics in the multi-layer snowpack model. CMEM is also evaluated in the Integrated Forecasting System and coupled to RTTOV (Radiative Transfer for TOVS). The numerical results show improved simulated TB at low frequency V polarization over snow-covered area compared to a configuration using emissivity atlas. Degradations at frequencies higher than 20 GHz indicate that further improvements are required in the emissivity and snowpack properties modelling. [ABSTRACT FROM AUTHOR]

The coming years will see the launch of several missions (TRISHNA, LSTM, SBG), which will acquire images in four or more spectral bands in thermal infrared (TIR) at high spatial resolution (~50–60 m) and with high temporal revisit (~2–3 days). The derivation of surface temperature and emissivity values from top-of-atmosphere radiances is not straightforward, as it is a non-deterministic process requiring additional information. In this paper, we propose the algorithm DirecTES to efficiently separate surface temperature and emissivity. This algorithm is based on the use of a comprehensive spectral database of emissivity, resulting in a well-posed deterministic problem while not assuming strong hypotheses. The algorithm can also benefit from non-TIR information, such as the acquisitions from the same satellite but in the visible and near-infrared domains, or exogenous data—land/sea mask or soil-occupation map. These would help identify the nature of the surface and therefore improve the temperature and emissivity retrievals. After the complete description of the method, we evaluate the performances of DirecTES on theoretical landscapes in TRISHNA's context under a large range of atmospheric conditions. The retrievals of surface temperature reach RMSEs of 0.8 K over vegetation and 0.5 K over water, including both sensor and atmospheric uncertainties. We then evaluate DirecTES on ECOSTRESS images on sites where the ECOSTRESS Land Surface Temperature (LST) performance has been documented; DirecTES surface temperature retrievals are consistent with the ECOSTRESS LST product and the in-situ data. [ABSTRACT FROM AUTHOR]

An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites. [ABSTRACT FROM AUTHOR]

The RST (Robust Satellite Techniques)-FLARE algorithm is a satellite-based method using a multitemporal statistical analysis of nighttime infrared signals strictly related to industrial hotspots, such as gas flares. The algorithm was designed for both identifying and characterizing gas flares in terms of radiant/emissive power. The Val d'Agri Oil Center (COVA) is a gas and oil pre-treatment plant operating for about two decades within an anthropized area of Basilicata region (southern Italy) where it represents a significant potential source of social and environmental impacts. RST-FLARE, developed to study and monitor the gas flaring activity of this site by means of MODIS (Moderate Resolution Imaging Spectroradiometer) data, has exported VIIRS (Visible Infrared Imaging Radiometer Suite) records by exploiting the improved spatial and spectral properties offered by this sensor. In this paper, the VIIRS-based configuration of RST-FLARE is presented and its application on the recent (2015-2019) gas flaring activity at COVA is analyzed and discussed. Its performance in gas flaring characterization is in good agreement with VIIRS Nightfire outputs to which RST-FLARE seems to provide some add-ons. The great consistency of radiant heat estimates computed with both RST-FLARE developed configurations allows proposing a multi-sensor RST-FLARE strategy for a more accurate multi-year analysis of gas flaring. [ABSTRACT FROM AUTHOR]

Aiming at low spectral contrast materials, the Optimized Smoothing for Temperature Emissivity Separation (OSTES) method was developed to improve the Temperature and Emissivity Separation (TES) algorithm based on the linear relationship between brightness temperature and emissivity features, but there was little smoothing improvement for higher spectral contrast materials. In this paper, a new nonlinear-relationship based algorithm is presented, focusing on improving the performance of the OSTES method for materials with middle or high spectral contrast. This novel approach is a two-step procedure. Firstly, by introducing atmospheric impact factor, the nonlinear relationship is mathematically proved using first-order Taylor series approximation. Moreover, it is proven that nonlinear model has stronger universality than linear model. Secondly, a new method named Temperature and Emissivity Separation with Nonlinear Constraint (TESNC) is proposed based on the nonlinear model for smoothing temperature and emissivity retrieval. The key procedure of TESNC is the lowest emissivity smoothing estimation based on nonlinear model and retrieved by minimizing the reconstruction error of the Planck radiance. TESNC was tested on a series of synthetic data with different kinds of natural materials representing several multispectral and hyperspectral infrared sensors. It is shown that, especially for materials with higher spectral contrast, the proposed method is less sensitive to changes in atmospheric conditions and sample temperatures. Furthermore, the standard Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) products in different kind of atmospheric conditions were used for verifying the improvement. TESNC is more accurate and stable with the decrease of emissivity and changes of atmospheric conditions compared with TES, Adjusted Normalized Emissivity Method (ANEM), and OSTES methods. [ABSTRACT FROM AUTHOR]

Natural rough surfaces have inherent multiscale roughness. This article presents the modeling and analysis of microwave emission from a multiscale soil surface. Unlike the linear superposition of different correlation functions with various correlation lengths, we applied the frequency modulation concept to characterize the multiscale roughness, in which the modulation does not destroy the surface's curvature but only modifies it. The multiscale effect on emission under different observation geometries and surface parameters was examined using an AIEM model. The paper provides new insights into the dependence of polarized emissivity on multiscale roughness: V-polarized emissivity is much less sensitive to multiscale roughness across the moisture content from dry to wet (5–30%). The H-polarized is sensitive to multiscale roughness, especially at higher moisture content. The predicted emissivity will have considerable uncertainty, even for the same baseline correlation length, without accounting for the multiscale roughness effect. V-polarized emissivity is less sensitive to the multiscale effect than H-polarized and the higher modulation ratio indicates larger emissivity. The higher modulation ratio indicates larger emissivity. Multiscale roughness weakens the polarization difference, particularly in higher moisture conditions. In addition, ignoring the multiscale effect leads to underestimated emissivity to a certain extent, particularly at the larger RMS height region. Finally, when accounting for multiscale roughness, model predictions of emission from a soil surface are in good agreement with two independently measured data sets. [ABSTRACT FROM AUTHOR]

Research on grassland monitoring based on temperature/emissivity separation based on hyperspectral thermal infrared (HTIR) remote sensing is rare. Based on the longwave TIR instrument (Hyper-CAM), this study designed two experiments to collect HTIR datasets, separate the temperature and emissivity of different vegetation of grassland, and analyze the relationship between the emissivity of vegetation and soil moisture content. First, we collected the HTIR remotely sensed dataset of different kinds of vegetation and used the temperature/emissivity separation algorithm to separate the temperature and emissivity of seven types of vegetation. The temperature and emissivity of these types of vegetation were separated. Then, the absorption characteristics of the emissivity spectral curves of each type of grass were analyzed. The distribution and differences of the temperature and specific emissivity in different parts of these seven grassland vegetation types were quantitatively analyzed, and the relationship between their changes and vegetation leaf moisture and vegetation health status was also analyzed. Second, to monitor the drought of grassland vegetation, a second experiment was designed to measure the changes in the emissivity under different soil water contents. This observation experiment took Artemisia frigida as the research object. From the results of the separation of the temperature and emissivity, we found that the emissivity of Artemisia frigida has significantly changed with the increase in the water content, and the emissivity showed an overall increasing trend. We also quantitatively analyzed the differences in the temperature and specific emissivity between Artemisia frigida and Artemisia subulata Nakai, both belonging to the genus Artemisia, under different water content conditions. The overall waveform characteristics and their similarities and differences at 850–1280 cm−1 were compared and analyzed. The experimental results shows that Hyper-CAM can effectively obtain the emissivity of various types of grassland vegetation as the absorption characteristics of grassland vegetation in the thermal infrared spectral region were quite notable, which shows the significant potential ability of identification and discrimination of different types of grassland vegetation. [ABSTRACT FROM AUTHOR]

To investigate the microwave radiation characteristics of different vegetation types, the "pure pixels" of 12 typical vegetated land types were selected and corresponding emissivity was retrieved under clear sky based on L1C AMSR2 observed brightness temperatures (TBs). According to the retrieved values for the 12 types, the spectral features in summer from 10.65 to 89 GHz were analyzed first. Then, the temporal variations in emissivity at 10.65, 18.7, and 36.5 GHz H-polarized (hereinafter 10H, 18H and 36H) are shown for the period from January 2018 to September 2020. Finally, the responses of 10H emissivity to surface skin temperature (SKT), the normalized differential vegetation index (NDVI), and soil moisture content (SMC) were quantitatively evaluated using a step-by-step analysis method. The general results are as follows: H-polarized (H-pol) emissivity increases with frequency and vegetation biomass, while the polarization differences decrease with frequency and vegetation biomass. The responses of V-pol emissivity to frequency and biomass are different from those of H-pol emissivity, and there are negative correlations with frequency and unusually high low-frequency values in grasslands and open shrublands (OS). The temporal variation amplitude of emissivity seems to be negatively correlated with vegetation biomass, and evergreen broadleaf forests show little variation. In general, the seasonal changes in emissivity are consistent with those of NDVI for most vegetation types. Nevertheless, in some cases, the change in emissivity is obviously ahead or behind that of NDVI, revealing that NDVI and emissivity may be sensitive to different vegetation elements that do not change in sync. In addition, variations in emissivity at different frequencies also show different amplitudes and turning points. Generally, the response of the 10H emissivity to SKT is weak, regardless of whether the response is positive or negative. The relatively large negative responses can be attributed to other indirect causes. NDVI plays a positive role in emissivity of the low-biomass vegetation in drier environments and medium- or high-biomass vegetation with clear seasonal variation. SMC is a complex factor that can have a positive or negative effect on emissivity. [ABSTRACT FROM AUTHOR]

The contrast in the emissivity spectra of phosphorite and associated carbonate rock can be used as a guide to delineate phosphorite within dolomite. The thermal emissivity spectrum of phosphorite is characterized by a strong doublet emissivity feature with their absorption minima at 9 µm and 9.5 µm; whereas, host rock dolomite has relatively subdued emissivity minima at ~9 µm. Using the contrast in the emissivity spectra of phosphorite and dolomite, data obtained by the thermal bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor were processed to delineate phosphorite within dolomite. A decorrelation stretched ASTER radiance composite could not enhance phosphorite rich zones within the dolomite host rock. However, a decorrelation stretched image composite of selected emissivity bands derived using the emissivity normalization method was suitable to enhance large surface exposures of phosphorite. We have found that the depth of the emissivity minima of phosphorite gradually has increased from dolomite to high-grade phosphorite, while low-grade phosphate has an intermediate emissivity value and the emissivity feature can be studied using three thermal bands of ASTER. In this context, we also propose a relative band depth (RBD) image using selected emissivity bands (bands 11, 12, and 13) to delineate phosphorite from the host rock. We also propose that the RBD image can be used as a proxy to estimate the relative grades of phosphorites, provided the surface exposures of phosphorite are large enough to subdue the role of intrapixel spectral mixing, which can also influence the depth of the diagnostic feature along with the grade. We have validated the phosphorite pixels of the RBD image in the field by carrying out colorimetric analysis to confirm the presence of phosphorite. The result of the study indicates the utility of the proposed relative band depth image derived using ASTER TIR bands for delineating Proterozoic carbonate-hosted phosphorite. [ABSTRACT FROM AUTHOR]

Snow cover over the Qinghai-Tibetan Plateau (QTP) plays an important role in climate, hydrological, and ecological systems. Currently, passive microwave remote sensing is the most efficient way to monitor snow depth on global and regional scales; however, it presents a serious overestimation of snow cover over the QTP and has difficulty describing patchy snow cover over the QTP because of its coarse spatial resolution. In this study, a new spatial dynamic method is developed by introducing ground emissivity and assimilating the snow cover fraction (SCF) and land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive snow depth at an enhanced spatial resolution. In this method, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) brightness temperature and MODIS LST are used to calculate ground emissivity. Additionally, the microwave emission model of layered snowpacks (MEMLS) is applied to simulate brightness temperature with varying ground emissivities to determine the key coefficients in the snow depth retrieval algorithm. The results show that the frozen ground emissivity presents large spatial heterogeneity over the QTP, which leads to the variation of coefficients in the snow depth retrieval algorithm. The overestimation of snow depth is rectified by introducing the ground emissivity factor at 18 and 36 GHz. Compared with in situ observations, the snow cover accuracy of the new method is 93.9%, which is better than the 60.2% accuracy of the existing method (old method) which does not consider ground emissivity. The bias and root-mean-square error (RMSE) of snow depth are 1.03 cm and 7.05 cm, respectively, for the new method; these values are much lower than the values of 6.02 cm and 9.75 cm, respectively, for the old method. However, the snow cover accuracy with depths between 1 and 3 cm is below 60%, and snow depths greater than 25 cm are underestimated in Himalayan mountainous areas. In the future, the snow cover identification algorithm should be improved to identify shallow snow cover over the QTP, and topography should be considered in the snow depth retrieval algorithm to improve snow depth accuracy in mountainous areas. [ABSTRACT FROM AUTHOR]