Land Surface Emissivity Products Research Articles

Land surface emissivity retrieval from SDGSAT-1: comparison of LSE products with different spatial resolutions

ABSTRACT Land surface emissivity (LSE) represents an essential parameter for understanding land change and surface energy balance. While numerous algorithms have been proposed to retrieve LSE, they are seldom applied to remote sensing data with high spatial resolution. The SDGSAT-1 satellite provides an opportunity to retrieve LSE with high spatial resolution that can be regarded as references for validating LSE products generated from coarse-spatial-resolution satellite data. However, no study has yet explored LSE-retrieval based on SDGSAT-1. This research seeks to fill this gap through evaluating the radiometric calibration accuracy of SDGSAT-1, using five methods to retrieve pixel-level emissivity of Guangzhou, and comparing such results with emissivity generated from other satellites (e.g. Landsat 8, ECOSTRESS, and MODIS) that transited Guangzhou during the similar period. Our results indicated that SDGSAT-1 performed well in radiometric calibration. Heterogeneous vegetation and man-made surfaces registered significant emissivity differences in varying spatial resolutions, whereas relatively homogeneous water bodies registered smaller differences. Decreasing spatial resolution amplifies the emissivity differences of the same land cover among distinctive sensors. The five emissivity-retrieval methods performed exceptionally well based on SDGSAT-1, registering average absolute differences less than 0.026 as compared with Landsat 8; with decreasing spatial resolutions, the maximum difference between SDGSAT-1 and MODIS LSE product reached 0.124.

Land Surface Emissivity Product for NOAA JPSS and GOES-R Missions: Methodology and Evaluation

Land surface emissivity (LSE) is a key parameter for the determination of land surface temperature (LST) from thermal remotely sensed data. A new LSE product has been developed at the National Oceanic and Atmospheric Administration (NOAA), College Park, MD, USA, to enhance the LST product for the Joint Polar Satellite System (JPSS) and the Geostationary Operational Environmental Satellite R-Series (GOES-R) missions as well as to support the forecasting models. A 1-km resolution bare ground emissivity was derived from the historical Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset (ASTER-GED) and Moderate Resolution Imaging Spectroradiometer (MODIS) LSE product. It is then spectrally adjusted to the split window (SW) channels of Visible/Infrared Imager Radiometer Suite (VIIRS) onboard JPSS and Advanced Baseline Imager (ABI) from GOES-R. VIIRS daily green vegetation fraction and snow fraction products are subsequently used to account for the dynamic variations. Validation results indicate that the product has a good agreement with in situ observations, with an emissivity difference less than 0.006 at the bare surface sites and a difference less than 0.007 at one cropland site with three growth stages. The intercomparison with NASA VIIRS daily LSE product proves a good agreement with a standard deviation of less than 0.012. To evaluate the LSE performance in LST retrieval, a whole year VIIRS LST is produced and validated over the SURFace RADiation budget observing network (SURFRAD) sites. The results indicate that the LSE works well with LST RMSE of 1.78 (daytime) and 1.58 K (nighttime). The new LSE algorithm has been integrated to NOAA-20 LST with the status of provisional maturity and will be applied on GOES-16/17 LST product in the near future.

Estimation of Land Surface Temperature Using FengYun-2E (FY-2E) Data: A Case Study of the Source Area of the Yellow River

Land surface temperature (LST) is a key variable used for studies of water cycles and energy budgets of land–atmosphere interfaces. This study addresses the theory of LST retrieval from data acquired by the Chinese operational geostationary meteorological satellite FengYun-2E (FY-2E) in two thermal infrared channels (IR1: 10.29–11.45 μm and IR2: 11.59–12.79 μm) using a generalized split-window algorithm. Specifically, land surface emissivity (LSE) in the two thermal infrared channels is estimated from the LSE in channels 31 and 32 of the moderate-resolution imaging spectroradiometer (MODIS) product. In addition, an eight-day composition MODIS LSE product (MOD11A2) and the daily MODIS LSE product (MOD11A1) are used in the algorithm to estimate FY-2E emissivities. The results indicate that the LST derived from MOD11A1 is more accurate and, therefore, more appropriate for daily cloud-free LST estimation. Finally, the estimated LST was validated using the MODIS LST product for the heterogeneous source area of the Yellow River. The results show a significant correlation between the two datasets, with a correlation coefficient ( R ) varying from 0.60 to 0.94 and a root mean square error ranging from 1.89 to 3.71 K. Moreover, the estimated LST agrees well with ground-measured soil temperatures, with an R of 0.98.

A new approach for land surface emissivity estimation using LDCM data in semi-arid areas: exploitation of the ASTER spectral library data set

ABSTRACTIn this research, a new approach called non-vegetated based emissivity estimation method (NV-method) for estimating land surface emissivity (LSE) on Landsat-8 (known as Landsat Data Continuity Mission, LDCM) data has been proposed for semi-arid areas. At first, a simulation of channel emissivities and reflective bands of basic classes in vegetation and non-vegetated areas is accomplished based on convolving Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral Library with LDCM spectral response functions. Then, four main classes in non-vegetated areas are defined to determine separate emissivity estimate model as a function of reflective bands from basic spectra associated with the main class. The LSEs in mixed and vegetation areas are adopted from the simplified normalized difference vegetation index (NDVI)-based emissivity threshold method (N-methodTHM), namely SN-methodTHM and improved N-methodTHM (IN-methodTHM) methods, respectively. The NV-method is empirically tested using LDCM data and the obtained LSEs were compared with two scenes of LSE product of the ASTER. The root mean square error (RMSE) values of computed LSEs by NV-method are 0.46% and 0.81%, for band 10 and 11, respectively, in the first examined scene. While, for the second scene, the RMSE are 0.36% and 0.56% for band 10 and 11, respectively. Moreover, the NV-method were compared with N-methodTHM, SN-methodTHM, and IN-methodTHM in non-vegetated areas. Generally, the obtained results of LSEs by NV-method are better than that of results from the compared methods in non-vegetated areas in terms of statistical measures. Except in rocky class, for which N-methodTHM provides better results, the NV-method achieved superior results in soil texture and man-made classes, which are dominating classes in the study area.

Fusion Methods for Land Surface Emissivity and Temperature Retrieval of the Landsat Data Continuity Mission Data

In this paper, a new approach based on two fusion schemes is proposed to overcome the uncertainties in land surface emissivity (LSE) estimation and, consequently, land surface temperature (LST) retrieval. The fusion schemes are called image-based weighted methods and knowledge-based weighted methods, in which each of them includes two LSE estimation methods. The effectiveness of the two proposed fusion schemes is empirically tested over two scenes of Landsat-8 (known as Landsat Data Continuity Mission) data sets, and the obtained LSEs by individual and proposed methods were compared to the LSE product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) by image-based and class-based cross-comparison. In both scenes, the adjusted normalized emissivity method (ANEM) and NDVI-based emissivity method (NBEM) provide appropriate results among five individual methods. In contrast, weighted to median (WMED) achieves superior results among the proposed fusion methods for both scenes. In addition, the root-mean-square error (rmse) values of LSE obtained by ANEM and WMED are 1.48% and 0.87%, which lead to 1.25 K and 0.73 K errors in the LST retrieval by the single-channel algorithm in the first scene, respectively. For the second scene, the error values of NBEM and WMED are 1.10% and 0.52%, which lead to 0.93 K and 0.44 K errors in the LST, respectively. Moreover, the error ranges and rmse of cross-comparison for the obtained LSE in the proposed methods were remarkably decreased. Also, in this research, for LST cross-comparison, an alternative scaling method based on LST products of the Moderate Resolution Imaging Spectroradiometer was proposed. The LST validation results demonstrated that the proposed methods provide better estimates in terms of three accuracy measures in both examined data sets.

Assessment of the consistency among global microwave land surface emissivity products

Abstract. The goal of this work is to intercompare four global land surface emissivity products over various land-cover conditions to assess their consistency. The intercompared land emissivity products were generated over a 5-year period (2003–2007) using observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E), the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and WindSat. First, all products were reprocessed in the same projection and spatial resolution as they were generated from sensors with various configurations. Then, the mean value and standard deviations of monthly emissivity values were calculated for each product to assess the spatial distribution of the consistencies/inconsistencies among the products across the globe. The emissivity products were also compared to soil moisture estimates and a satellite-based vegetation index to assess their sensitivities to changes in land surface conditions. Results show the existence of systematic differences among the products. Also, it was noticed that emissivity values in each product have similar frequency dependency over different land-cover types. Monthly means of emissivity values from AMSR-E in the vertical and horizontal polarizations seem to be systematically lower than the rest of the products across various land-cover conditions which may be attributed to the 01:30/13:30 LT overpass time of the sensor and possibly a residual skin temperature effect in the product. The standard deviation of the analyzed products was lowest (less than 0.01) in rain forest regions for all products and highest at northern latitudes, above 0.04 for AMSR-E and SSM/I and around 0.03 for WindSat. Despite differences in absolute emissivity estimates, all products were similarly sensitive to changes in soil moisture and vegetation. The correlation between the emissivity polarization differences and normalized difference vegetation index (NDVI) values showed similar spatial distribution across the products, with values close to the unit except over densely vegetated and desert areas.

Validation of the moderate-resolution imaging spectroradiometer land surface emissivity products over the Taklimakan Desert

Land surface emissivity is a key parameter in estimating the land surface radiation budget. The validation of the moderate-resolution imaging spectroradiometer (MODIS) land sur- face emissivity with field measurements is rarely performed.In this study, a field measurement was performed over the central part of the Taklimakan Desert for the validation of the MODIS land surface emissivity products (MOD11B1) Version 4 (V4.1) and Version 5 (V5). The homogeneity of two validation sites was verified using the advanced spaceborne thermal emission and reflection radiometer (ASTER) land surface temperature and emissivity acquired closely before and after the overpass of MODIS. MOD11B1 V4.1 and V5 emissivity data for bands 29, 31, and 32 were compared to the emissivity calculated from the field measured emissivity spectra convolved with the filter function of the MODIS bands 29 (8.52 μm), 31 (11.03 μm), and 32 (12.04 μm). The comparison results indicate that the V4.1 emissivity data agree well with the fieldmeasurements, with meanabsolute differences of 0.017 and 0.007 for site 1 and site 2,respec- tively, and the mean absolute differences of the V5 emissivity data were 0.034 and 0.033 for site 1 and site 2, respectively. The data version used must be considered when MOD11B1 is used in real applications, especially for time series analysis. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.8.083675)

Land Surface Temperature and Emissivity Retrieval From Time-Series Mid-Infrared and Thermal Infrared Data of SVISSR/FY-2C

This work addressed the retrieval of land surface emissivity (LSE) and land surface temperature (LST) by using Middle Infra-Red (MIR) and Thermal Infra-Red (TIR) channels from the data acquired by the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) onboard Chinese geostationary meteorological satellite FengYun 2C (FY-2C). SVISSR/FY-2C sensor acquires image covering the full disk with a temporal resolution of 30 minutes. The LST and LSE retrieval procedures can be shown as follows. Firstly, taking into the fact that land surface is non-lambertian characteristics, the time-series bi-directional reflectances in SVISSR/FY-2C MIR channel 4 (3.8 μm ) were estimated from the combined MIR and TIR channels with day-night SVISSR/FY-2C data. A diurnal temperature cycle (DTC) model was used to correct for the atmospheric effects. The atmospheric profile data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were adopted with the aid of the radiative transfer code (MODTRAN 4.0). Secondly, a Bidirectional Reflectance Distribution Function (BRDF) model named as RossThick-LiSparse-R model was used to estimate the hemispherical directional reflectance in MIR channel from the time-series bi-directional reflectance data. Then, the LSE in MIR channel can be retrieved according to Kirchhoff's law. The LSEs in TIR channels can be estimated based on the Temperature Independent Spectral Indices (TISI) concept. And the LST can be retrieved using the split-window algorithm. Finally, a cross-validation method was used to evaluate the retrieval accuracies with the Moderate-resolution Imaging Spectroradiometer (MODIS) MOD11B1 LST/LSE V5 product. The results demonstrated that the emissivities in 11 μm and 12 μm were underestimated approximately 0.003 and 0.004 compared with MOD11B1 LSE product over the study area. The FY-2C LST were overestimated approximately 1.65 K and 2.87 K during the night-time and day-time, respectively, compared with MOD11B1 LST product over the study area.

Validation of six satellite-retrieved land surface emissivity products over two land cover types in a hyper-arid region

This study compares six satellite-retrieved land surface emissivity (LSE) products over gravel plains and sand dunes of the hyper-arid Namib desert in Namibia and validates them with in-situ measurements performed with the ‘emissivity box method’. The following products are compared: LSE derived by the Land Surface Analysis — Satellite Application Facility (LSA-SAF) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), LSE products MOD11A2.C5, MOD11B1.C4.1, and MOD11B1.C5 derived for the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS-Terra, LSE derived with the Temperature Emissivity Separation (TES) algorithm for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard EOS-Terra, and LSE derived with the TES algorithm for EOS-Terra/MODIS data. The LSA-SAF, MOD11A2.C5, and MOD11B1.C5 algorithms directly or indirectly utilize land cover classification and vegetation cover fraction data with the result that for arid regions their LSE are practically identical to the bare ground emissivities assigned to those classes. Over the gravel plains, mean LSA-SAF, ASTER-TES, and MODTES LSE are about 0.950 in the 11μm range, whereas mean MOD11A2.C5 and MOD11B1.C5 are about 1.5% (~1K) higher. The LSA-SAF algorithm misclassifies the sand dunes as ‘open & closed shrubland’, which results in an overestimated mean LSE (0.969). Since MOD11A2.C5 and MOD11B1.C5 utilize a similar classification and similar emissivity library data, their LSE estimates for the sand dunes are also too high (mean of 0.972 and 0.980, respectively). In contrast, the physics-based ASTER-TES and MODTES algorithms estimate mean sand dune LSE as 0.952 and 0.948, respectively. The physics-based MOD11B1.C4.1 algorithm produced noisy LSE estimates with frequent outliers at 5km resolution: spatial averaging yielded mean LSE of 0.950 and 0.954 for the gravel plains and the sand dunes, respectively. Based on a combined analysis of in-situ LSE and TES retrieved LSE, and also accounting for uncertainty in the fraction of dry grass (only gravel plains), for future work it is recommended to use SEVIRI ch10.8 emissivities of 0.941±0.004 for the sand dunes and 0.944±0.015 for the gravel plains, respectively. The results suggest that split window algorithms would benefit significantly from using physically based MODTES LSE.

Land surface emissivity retrieval from airborne sensor over urban areas

In this paper we compare three different methodologies to retrieve land surface emissivity (LSE) over urban areas: the NDVI thresholds method, the temperature and emissivity separation (TES) algorithm and the temperature independent spectral indices (TISI) algorithm. The methodologies were applied to the Airborne Hyperspectral Scanner (AHS) imagery acquired during the Dual-use European Security IR Experiment 2008 (DESIREX 2008) experimental campaign over the city of Madrid (Spain). The images have a spatial resolution of 4m. The retrieved values are compared to in situ data measured during the campaign at 4 sites. Results show a good performance of the TISI and the TES algorithms over urban surfaces, while the NDVI threshold method does not appear to be capable to distinguish between different artificial surfaces. The land surface temperature (LST) was retrieved with a split window algorithm, using the three different LSE products. Differences of up to 3K were realized in some surfaces when different LSE maps were used. Over artificial urban surfaces TES and TISI showed better agreement with in-situ data than NDVI. Finally, the TES is the algorithm that best reproduces the LST over an urban area, without the requirement of high temporal resolution of the sensor.

Validation of the Atmospheric Infrared Sounder (AIRS) version 5 land surface emissivity product over the Namib and Kalahari deserts

Hyperspectral infrared sounders require accurate knowledge of the land surface emissivity (LSE) to retrieve important climate variables such as surface temperature, air temperature, and total water vapor from space. This study provides a method for validating and assessing the Atmospheric Infrared Sounder (AIRS) version 5 LSE product using high–spatial resolution data (90 m) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) which has five bands in the thermal infrared region (8–12 μm, 1250–833 cm−1) and high–spectral resolution laboratory measurements of sand samples collected over the Namib and Kalahari deserts in southern Africa. Results indicate that the mean, absolute daytime LSE difference between AIRS and the laboratory results for six wavelengths in window regions between 3.9 and 11.4 μm (2564–877 cm−1) was 2.3% over the Namib and 0.70% over the Kalahari, while the mean difference with ASTER was 2.3% over the Namib and 2.26% over the Kalahari for four bands between 8 and 12 μm. Systematic modeling and surface dependent AIRS LSE retrieval errors such as large discrepancies between day and nighttime shortwave LSE (up to 15%), unphysical values (LSE >1), and large daytime temporal variations in the shortwave region (up to 30%) are further discussed.

Development of a Global Infrared Land Surface Emissivity Database for Application to Clear Sky Sounding Retrievals from Multispectral Satellite Radiance Measurements

Abstract A global database of infrared (IR) land surface emissivity is introduced to support more accurate retrievals of atmospheric properties such as temperature and moisture profiles from multispectral satellite radiance measurements. Emissivity is derived using input from the Moderate Resolution Imaging Spectroradiometer (MODIS) operational land surface emissivity product (MOD11). The baseline fit method, based on a conceptual model developed from laboratory measurements of surface emissivity, is applied to fill in the spectral gaps between the six emissivity wavelengths available in MOD11. The six available MOD11 wavelengths span only three spectral regions (3.8–4, 8.6, and 11–12 μm), while the retrievals of atmospheric temperature and moisture from satellite IR sounder radiances require surface emissivity at higher spectral resolution. Emissivity in the database presented here is available globally at 10 wavelengths (3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 μm) with 0.05° spatial resolution. The wavelengths in the database were chosen as hinge points to capture as much of the shape of the higher-resolution emissivity spectra as possible between 3.6 and 14.3 μm. The surface emissivity from this database is applied to the IR regression retrieval of atmospheric moisture profiles using radiances from MODIS, and improvement is shown over retrievals made with the typical assumption of constant emissivity.