Estimation Of Surface Emissivity Research Articles

A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021

Abstract. Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. In this study, we developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05∘, using Advanced Very High Resolution Radiometer (AVHRR) Level-1b Global Area Coverage (GAC) data from 1981 to 2021. The GT-LST product was generated using four main processes: (1) GAC data reading, calibration, and preprocessing using open-source Python libraries; (2) cloud detection using the AVHRR-Phase I algorithm; (3) land surface emissivity estimation using an improved method considering annual land cover changes; (4) LST retrieval based on a nonlinear generalized split-window algorithm. Validation with in situ measurements from Surface Radiation Budget (SURFRAD) sites and Baseline Surface Radiation Network sites showed that the overall root-mean-square errors (RMSEs) of GT-LST varied from 1.6 to 4.0 K, and nighttime LSTs were typically better than daytime LSTs. Intercomparison with the Moderate Resolution Imaging Spectroradiometer LST products (MYD11A1 and MYD21A1) revealed that the overall root-mean-square difference (RMSD) was approximately 3.0 K. Compared with MYD11A1 LST, GT-LST was overestimated, and relatively large RMSDs were obtained during the daytime, spring, and summer, whereas the significantly smaller positive bias was obtained between GT-LST and MYD21A1 LST. Furthermore, we compared our newly generated dataset with a global AVHRR daytime LST product at the selected measurements of SURFRAD sites (i.e., measurements of these two satellite datasets were valid), which revealed similar accuracies for the two datasets. However, GT-LST can additionally provide nighttime LST, which can be combined with daytime observations estimating relatively accurate monthly mean LST, with an RMSE of 2.7 K. Finally, we compared GT-LST with a regional twice-daily AVHRR LST product over continental Africa in different seasons, with RMSDs ranging from 2.1 to 4.3 K. Considering these advantages, the proposed dataset provides a better data source for a range of research applications. GT-LST is freely available at https://doi.org/10.5281/zenodo.7113080 (1981–2000) (Li et al., 2022a), https://doi.org/10.5281/zenodo.7134158 (2001–2005) (Li et al., 2022b), and https://doi.org/10.5281/zenodo.7813607 (2006–2021) (J. H. Li et al., 2023).

Downwelling longwave radiation and sensible heat flux observations are critical for surface temperature and emissivity estimation from flux tower data

Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. In contrast, plot-scale LST estimation at flux tower sites is commonly based on the inversion of upwelling longwave radiation captured by tower-mounted radiometers, whereas the role of the downwelling longwave radiation component is often ignored. We found that neglecting the reflected downwelling longwave radiation leads not only to substantial bias in plot-scale LST estimation, but also have important implications for the estimation of surface emissivity on which LST is co-dependent. The present study proposes a novel method for simultaneous estimation of LST and emissivity at the plot-scale and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both upwelling and downwelling longwave radiation components are 0.5–1.5 K lower than estimates using only upwelling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the upwelling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. The correspondence between plot-scale LST and landscape-scale LST depends on site-specific characteristics, such as canopy density, sensor locations and viewing angles. Here we also quantify the uncertainty in plot-scale LST estimates due to uncertainty in tower-based measurements using the different methods. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.

Augmenting the spatial resolution of climate-change temperature projections for city planners and local decision makers

Before the 2010, studies in climate change (CC) projections embracing scales below 3° were difficult to find. This has changed dramatically over the past ten years, with literature addressing high resolution grids for climate studies, allowing a better understanding and forecasting of CC at finer scales. However, downscaling methods remain poorly explored in urban planning. Research shows that the main difficulties relate to mismatches between data needs and data availability, terminology, constraints of information technology and maps that inform spatial planning decision-making processes. Based on dynamic downscaled maps for RCP 4.5 and RCP 8.5 at 10 km resolution published by Ecuador’s Ministry of Environment and Water (MAAE), we develop a method for augmenting the resolution scale at 30 m. We use digital elevation models and Landsat 4/5/7/8 satellite imagery for land surface temperature (LST) and present a series of steps and equations before applying Stefan Bolzman’s law. We present the necessary equations between the filling-in of LST outliers, and their projection onto air temperature at 2 m height, taking surface emissivity estimates based on (Alves et al 2017 J. Hyperspectral Remote Sens. 7 91–100). We extrapolate the resulting air temperature in time with Fourier’s series, and for the purpose of coherence among scales, we upscale air temperature maps at 30 m to those at 10 km resolution. The resulting CC projection maps are validated with the temporal series of air temperature (max, min, mean) from the meteorological station in the Ecuadorian city of Portoviejo (Student’s t-test) for the period between 1981 and 2005, with Portoviejo city facing temperature increases of up to 2 °C under RCP 4.5 scenario in the period 2011–2040 vs 1981–2005. The final CC maps have an augmented resolution of 30 m, are compatible with those of MAAE, and offer a low-cost procedure for informing land-use and urban planners, as well as local development decision makers, of temperature anomalies due to climate change.

A New Description of Small-Scale and Large-Scale Roughness in the Fast Ocean Surface Emissivity Model

AbstractThe widely used Fast Microwave Ocean Surface Emissivity Model (FASTEM) does not include the interaction between small-scale and large-scale roughness, which seems to induce errors in the ocean surface emissivity estimation. In this study, we attempt to develop a new model that might be included in the FASTEM-like model. In the developed model, the large-scale roughness is expressed as a function of the local incidence angle (LIA) within the context of Fresnel reflection theory, incorporating the interactions between the small-scale and large-scale roughness into the fast ocean surface emissivity model, as done in the two-scale approach. With the new expression of the large-scale roughness, we also provide a more physically based form of the equation for the fast ocean surface emissivity calculation that includes the small-scale scattering over a geometrically rough surface. In addition, an algorithm for estimating two-scale roughness from the measured or modeled polarized emissivities in conjunction with the proposed fast ocean surface emissivity equation is provided. The results demonstrate that the interactions between two-scale roughness should be considered in order to estimate accurate two-scale roughness influences on the ocean surface emissivity.

Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, Landsat and AggieAir optical data.

Microbolometer thermal cameras in UAVs and manned aircraft allow for the acquisition of high-resolution temperature data, which, along with optical reflectance, contributes to monitoring and modeling of agricultural and natural environments. Furthermore, these temperature measurements have facilitated the development of advanced models of crop water stress and evapotranspiration in precision agriculture and heat fluxes exchanges in small river streams and corridors. Microbolometer cameras capture thermal information at blackbody or radiometric settings (narrowband emissivity equates to unity). While it is customary that the modeler uses assumed emissivity values (e.g. 0.99-0.96 for agricultural and environmental settings); some applications (e.g. Vegetation Health Index), and complex models such as energy balance-based models (e.g. evapotranspiration) could benefit from spatial estimates of surface emissivity for true or kinetic temperature mapping. In that regard, this work presents an analysis of the spectral characteristics of a microbolometer camera with regard to emissivity, along with a methodology to infer thermal emissivity spatially based on the spectral characteristics of the microbolometer camera. For this work, the MODIS UCBS Emissivity Library, NASA HyTES hyperspectral emissivity, Landsat, and Utah State University AggieAir UAV surface reflectance products are employed. The methodology is applied to a commercial vineyard agricultural setting located in Lodi, California, where HyTES, Landsat, and AggieAir UAV spatial data were collected in the 2014 growing season. Assessment of the microbolometer spectral response with regards to emissivity and emissivity modeling performance for the area of study are presented and discussed.

A Physics-Based Unmixing Method to Estimate Subpixel Temperatures on Mixed Pixels

This paper presents a new algorithm for the analysis of linear spectral mixtures in the thermal infrared domain, with the goal to jointly estimate the abundance and the subpixel temperature in a mixed pixel, i.e., to estimate the relative proportion and the temperature of each material composing the mixed pixel. This novel approach is a two-step procedure. First, it estimates the emissivity and the temperature over pure pixels using the standard temperature and emissivity separation (TES) algorithm. Second, it estimates the abundance and the subpixel temperature using a new unmixing physics-based model, called Thermal Remote sensing Unmixing for Subpixel Temperature (TRUST). This model is based on an estimator of the subpixel temperature obtained by linearizing the black body law around the mean temperature of each material. The abundance is then retrieved by minimizing the reconstruction error with the estimation of the subpixel temperatures. The TRUST method is benchmarked on simulated scenes against the fully constrained least squares unmixing applied on the radiance and on the estimation of surface emissivity using the TES algorithm. The TRUST method shows better results on pure and mixed pixels composed of two materials. TRUST also shows promising results when applied on thermal hyperspectral data acquired with the Thermal Airborne Spectrographic Imager during the Detection in Urban scenario using Combined Airborne imaging Sensors campaign and estimates coherent localization of mixed-pixel areas.

Rotation period of Venus estimated from Venus Express VIRTIS images and Magellan altimetry

The 1.02 μm wavelength thermal emission of the nightside of Venus is strongly anti-correlated to the elevation of the surface. The VIRTIS instrument on Venus Express has mapped this emission and therefore gives evidence for the orientation of Venus between 2006 and 2008. The Magellan mission provided a global altimetry data set recorded between 1990 and 1992. Comparison of these two data sets reveals a deviation in longitude indicating that the rotation of the planet is not fully described by the orientation model recommended by the IAU. This deviation is sufficiently large to affect estimates of surface emissivity from infrared imaging. A revised period of rotation of Venus of 243.023 ± 0.002 d aligns the two data sets. This period of rotation agrees with pre-Magellan estimates but is significantly different from the commonly accepted value of 243.0185 ± 0.0001 d estimated from Magellan radar images. It is possible that this discrepancy stems from a length of day variation with the value of 243.023 ± 0.002 d representing the average of the rotation period over 16 years.

Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by Day/Night LST algorithm

Surface emissivity estimation is a significant factor for the land surface temperature estimation from remotely sensed data. For fully vegetated surfaces, the emissivity estimation is performed in a simple manner since the emissivity is relatively uniform. However, for arid land with sparse vegetation, the estimation is more complicated since the emissivity of the exposed soil and rock is highly variable. In this study, mean and difference emissivity for bands 31 and 32 of MODIS sensor have been derived based on NDVI values. First, the NDVI thresholds have been determined to separate bare soil, partially vegetated soil and fully vegetated land. Then regression relations have been derived to estimate mean and difference emissivity of the bare soil samples and partially vegetated surfaces. A constant emissivity is also used for fully vegetated area. Along with the correlations, standard deviations of the regression relations have been examined for a set of representative soil types. Standard deviations smaller than 0.003 in mean emissivity and smaller than 0.004 in difference emissivity are resulted in regression linear relations. Evaluation of the NDVI derived regression relations has been performed using the results of MODIS Day/Night Land Surface Temperature (LST) algorithm on a pair of MODIS images. Using around 45,500 pixels with different soil and land cover types, emissivity of each pixel in bands 31 and 32 have been estimated. The calculated emissivities have been compared with emissivities calculated by MODIS Day/Night LST algorithm. Biases and standard deviations of NDVI-based relations show relatively high agreement for mean and difference emissivity relations with Day/Night method results. It may be concluded that the proposed algorithm can be used as a rather simple alternative to complex emissivity estimation algorithms.

The performance of the multispectral thermal imager (MTI) surface temperature retrieval algorithm at three sites

This work describes the surface temperature retrieval algorithm for the U.S. Department of Energy Multispectral Thermal Imager (MTI) satellite and its performance at three test sites. The MTI is a 15-band multispectral research and technology development satellite that provides high spatial resolution imagery of the earth's surface from a polar orbit. It has a capability for pixel-by-pixel retrieval of total atmospheric water vapor during the day. The surface temperature retrieval process uses atmospheric profiles of temperature and water vapor from numerical weather prediction data assimilation products available for the globe. The profiles of water vapor are scaled using the retrieved pixel-by-pixel water vapor during the day to improve atmospheric compensation within the image. The algorithm requires specification of a maximum emissivity or the acceptance of a default: here the maximum band emissivity is known and specified. The results of the algorithm are evaluated at three sites with a high degree of surface uniformity and known spectral emissivity: Lake Tahoe, NV/CA, Ivanpah Playa, NV/CA, and the Mauna Loa caldera, HI. At the first two sites, coincident measurements of surface temperature and emissivity are available. At Mauna Loa, surface emissivity measurements are available, but surface temperature measurements are not. Therefore, at Mauna Loa, the surface emissivity estimates derived from the surface temperature retrieval are compared with emissivity measurements. The results show that the surface temperature retrievals had a root mean square (RMS) difference with surface measurements of 0.6 K during the day and 1.0 K at night. This indicates, albeit weakly, that the use of pixel-by-pixel water vapor retrieved by the satellite improves the overall surface temperature retrievals. At Ivanpah Playa, all the data are daytime, and the surface temperature retrievals have an RMS difference with the in situ measurements of 0.8 K. The Mauna Loa emissivities derived from the surface temperature retrieval agreed excellently with in situ emissivity measurements. The approach taken to perform surface temperature retrieval, used operationally within the MTI research and development program, is seen to be simple and effective.

Studies on Surface Temperature using Remote Sensing Technique in the NW Part of Bangladesh

A simple and straightforward technique based on remote sensing was applied for the estimation of surface emissivity and surface temperature at different dates in the northwestern part of Bangladesh for the summer and autumn period (March through October). Surface temperature was estimated from Pathfinder AVHRR Land (PAL) daily data. For this estimation different split window algorithms were employed. Estimated results show a discrepancy ranging from 0.04°C to 4.0°C. Surface emissivity was calculated based on fractionation of vegetation cover and NDVI at pixel level calculated from TM data. TM data was corrected for atmospheric effect. PAL data set was provided after atmospheric and geometric correction. Both air temperature and estimated surface temperature shows a decreasing trend from south to farther north. However, no distinct trend could be recognized in the temporal variation in surface temperature and in measured air temperature. A general trend of negative relationship could be concluded between NDVI and surface temperature from this study.

Variability of sea ice emissivity estimated from airborne passive microwave measurements during FIRE SHEBA

Passive microwave radiometers with frequencies ranging from 37 GHz to 220 GHz were flown over the Surface Heat Budget of the Arctic (SHEBA) experimental site in May and July 1998. These measurements were motivated by the possibility of determining cloud liquid water path, ice water path, and precipitation over sea ice from these frequencies. The comprehensive cloud data set collected in this experiment offers a unique opportunity for improving and adapting passive microwave retrieval methods for application to arctic clouds. However, retrieval of cloud properties from a downward looking radiometer requires an estimate of the surface emissivity and its spectral, spatial, and temporal variation. In this study, brightness temperature measurements are used to calculate sea ice emissivity at each frequency using ancillary aircraft data to characterize the atmosphere and obtain surface temperature. Surface emissivities on clear sky days during the FIRE Arctic Cloud Experiment (FIRE ACE) aircraft campaign have been calculated and compared with previous estimates cited in the literature. Average emissivity at nadir for snow‐covered sea ice in the experimental region during late May is estimated as 0.89 at 37 GHz, 0.74 at 89 GHz, 0.72 at 90 GHz, 0.73 at 150 GHz, and 0.84 at 220 GHz. In early July the average nadir emissivity for melting sea ice is 0.86 for 37 GHz and 0.84 for 90 GHz. Estimates of emissivity at 50° off nadir are compared with previous satellite and ground‐based measurements of dual polarized emissivities at 37 GHz and 90 GHz. Significant variability exists in published emissivity values due to variations in dielectric and physical properties of snow and ice, but our results fall within previously observed ranges. Uncertainties in the emissivity calculations are estimated, and the accuracy required for use of surface emissivity estimates in cloud retrieval methods is discussed.