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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]

It is fundamental to obtain accurate land surface temperature (LST) to study surface energy process. Infrared thermal imagers are commonly used for deriving LST on the basis of radiance measurements. However, when deriving LST from brightness temperature of a blackbody in thermal imagers, thermal imagers only allow setting a fixed land surface emissivity (LSE). This causes uncertainty in retrieving thermal infrared (TIR) temperature from heterogeneous surfaces with varied LSE, such as those covered in vegetation. Corrections can be made using the Normalized Vegetation Index (NDVI). However, commercial thermal imagers provide only red (R), green (G), and blue (B) bands without a near infrared band so NDVI cannot be calculated on the same instrument and is commonly not available. We propose an alternative method to estimate LSE using RGB-based vegetation index. Thereafter the estimated LSE was used to correct the TIR temperature derived from the fixed LSE. An experiment was conducted to validate the proposed correcting method. The results show that 1) the corrected LST values were closer to the ground truth, with a mean absolute error (MAE) of 0.41 ± 0.34 °C, whereas the MAE was 0.75 ± 0.56 °C for the uncorrected LST; 2) the more heterogeneous the surface, the greater the difference between the corrected and uncorrected LST values, indicating the necessity of LST correction when applying thermal imagers over heterogeneous surface.

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products. [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.

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products.

Natural surfaces are mostly anisotropic emitters, contributing to the anisotropic behavior of Land Surface Temperature (LST). This characteristic of thermal infrared emissivity is well known and several studies have tried to simulate this behavior either with physical or empirical models. However, given the high heterogeneity of land surfaces, the translation of the angular dependence of emissivity as provided from measurements or models into satellite pixel scale anisotropy is generally very difficult. Here we propose a reformulation of the Mean Minimum-Maximum Difference (MMD) curve of the Temperature-Emissivity Separation (TES) algorithm to allow a correct adjustment of the TES retrievals by taking into account the emissivity angular distribution. For that purpose, the Multi-Sensor method is used to obtain directional emissivities at different sites in the Saharan and Namib desert. The data is then used to calibrate the view-angle dependence of the new MMD formulation. The TES retrievals obtained with the new formulation show a good agreement with the Multi-Sensor data. Results also suggest that the new coefficients of the MMD can be applied to other sensors with similar spectral channels. The new angle-dependent emissivities may lead to a reduction of LST bias as high as 2 K for view angles above 50o. The proposed formulation is currently only valid over barren surfaces. • We propose updating the TES algorithm to incorporate emissivity directionality. • The multi-sensor method is used to create a calibration dataset. • The MMD equation was revised to include a view angle dependence. • The new formulation can be applied to sensors with similar spectral configurations. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a crucial biophysical variable for remote sensing vegetation studies. LAI estimation through remote sensing data has mostly been investigated using visible and near-infrared (0.4–1.3 μm, VNIR) and Shortwave Infrared (1.4–3 μm, SWIR) data. However, Thermal Infrared (3–14 μm, TIR) data for LAI retrieval has rarely been explored. This study aims to predict LAI by integrating VNIR and TIR data from Unmanned Aerial Systems (UAS) in a mixed temperate forest, the Haagse Bos, Enschede, the Netherlands. The VNIR and TIR images were acquired in September 2020, in conjunction with fieldwork to collect LAI in situ data for 30 plots. TIR images were acquired at two heights (i.e., 85 m and 120 m above ground) to investigate the effect of flight height on the LAI prediction accuracy by means of UAS data. Land Surface Temperature (LST) and Land Surface Emissivity (LSE) were computed and extracted from the collected images. LAI was estimated using seven vegetation indices and Partial Least Squares Regression (PLSR). LAI prediction accuracy using VNIR reflectance spectra was compared to the accuracy achieved by integrating VNIR data with LST or LSE applying vegetation indices as well as PLSR. Among the applied vegetation indices, the Reduced Simple Ratio (RSR) gained the highest prediction accuracy using VNIR data (R2 = 0.5815, RMSE = 0.6972); the prediction accuracy was not improved when LST was integrated with VNIR data but increased when LSE was included (RSR: R2 = 0.7458, RMSE = 0.5081). However, when LST from 85 m altitude and VNIR data was applied as an input using PLSR (R2 = 0.5565, RMSECV = 0.7998), the LAI prediction accuracy was slightly increased compared to when only VNIR data was used (R2 = 0.4452, RMSECV = 0.8668). Integrating VNIR data with LSE significantly improved the LAI retrieval accuracy (R2 = 0.7907, RMSECV = 0.8351). These findings corroborate prior research indicating that combining LSE with VNIR data can increase the prediction accuracy of LAI. However, LST was found to be ineffective for this purpose. The relationship between LAI and LSE should be the subject of more investigation through various approaches to bridge the existing scientific gap.

East Asia is the second-largest mineral dust source in the world, after the Sahara. When dispersed in the atmosphere, mineral dust can alter the Earth's radiation budget by changing the atmosphere's absorption and scattering properties. Therefore, the mineralogical composition of dust is key to understanding the impact of mineral dust on the atmosphere. This paper presents new information on mineralogical dust during East Asian dust events that were obtained from laboratory dust measurements combined with satellite remote sensing dust detections from the Infrared Atmospheric Sounding Interferometer (IASI). However, the mineral dust in this region is lifted above the continent in the lower troposphere, posing constraints due to the large variability in the Land Surface Emissivity (LSE). First, a new methodology was developed to correct the LSE from a mean monthly emissivity dataset. The results show an adjustment in the IASI spectra by acquiring aerosol information. Then, the experimental extinction coefficients of pure minerals were linearly combined to reproduce a Gobi dust spectrum, which allowed for the determination of the mineralogical mass weights. In addition, from the IASI radiances, a spectral dust optical thickness was calculated, displaying features identical to the optical thickness of the Gobi dust measured in the laboratory. The linear combination of pure minerals spectra was also applied to the IASI optical thickness, providing mineralogical mass weights. Finally, the method was applied after LSE optimization, and mineralogical evolution maps were obtained for two dust events in two different seasons and years, May 2017 and March 2021. The mean dust weights originating from the Gobi Desert, Taklamakan Desert, and Horqin Sandy Land are close to the mass weights in the literature. In addition, the spatial variability was linked to possible dust sources, and it was examined with a backward trajectory model. Moreover, a comparison between two IASI instruments on METOP-A and -B proved the method's applicability to different METOP platforms. Due to all of the above, the applied method is a powerful tool for exploiting dust mineralogy and dust sources using both laboratory optical properties and IASI detections. [ABSTRACT FROM AUTHOR]

Monitoring Land Surface Temperature (LST) from Landsat satellites has been shown to be effective in the estimation of crop water needs and modeling water use efficiency. Accurate LST estimation becomes critical in semiarid areas under water scarcity scenarios. This work shows the assessment of some well-known Single-Channel (SC) and Split-Window (SW) algorithms, adapted to Landsat 8/TIRS, under the conditions of a high-contrast semiarid agroecosystem. The recently released Landsat 8 Level-2 LST product (L8_ST) has also been included in the performance analysis. Ground measurements of surface temperature were taken for the evaluation during the summers of 2018–2019 in the cropland area of the Barrax test site, Spain. A dataset of 44 ground samples and 11 different L8/TIRS dates/scenes was gathered, covering a variety of crop fields and surface conditions. In addition, a simplified Single Band Atmospheric Correction (L-SBAC) was introduced based on a linearization of the atmospheric correction parameters with the water vapor content (w) and a redefinition of the emissivity threshold for the emissivity correction in the study site. The best results show differences within ±4.0 K for temperatures ranging 300–325 K. Statistics for the L-SBAC result in a RMSE of ±1.8 K with negligible systematic deviation. Similar results were obtained for the other SC and SW algorithms tested, whereas an overestimation of 1.0 K was observed for the L8_ST product because of inappropriate assignment of emissivity values. These results show the potential of the proposed linearization approach and set the uncertainty for LST estimates in high-contrast semiarid agroecosystems. [ABSTRACT FROM AUTHOR]

Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD (Surface Radiation Budget Network) sites, 6 ARM (Atmospheric Radiation Measurement) sites, and 9 NDBC (National Data Buoy Center) sites. The results indicate that a relatively consistent performance among Landsat 5, 7, and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity (LSE) correction methods for Landsat 5, 7, and 8 sensors. Large bias and root mean square error (RMSE) of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index (NDVI). Except for the sites with incorrect LSE estimation, a mean bias (RMSE) of the differences between Landsat LST and in situ LST is 1.0 K (2.1 K) over snow-free land surfaces, −1.1 K (1.6 K) over snow surfaces, and −0.3 K (1.1 K) over water surfaces.

The study proposed a methodology for the retrieval of precise Land Surface Temperature (LST) in Jharia Coalfield from night-time ASTER multispectral thermal infrared (TIR) data by split-window algorithm (SWA) using atmospheric transmittance and band-specific Land Surface Emissivity (LSE). For deriving night-time atmospheric transmittance, water vapor content was retrieved from night-time ASTER TIR data by modified split-window covariance and variance ratio approach. Improved LSE was retrieved by the proposed modified LSE model by integrating refined thermal emission-vegetation cover model, modified normalized difference water index and bandwidth-weighted red band reflectivity model. The retrieved SWA LST was compared with LST obtained by single-channel algorithm (SCA) across three coal fire test sites to demonstrate significant improvement in temperature contrast between coal fire and background pixels. Besides, SWA LST based coal fire thermal anomalies are significantly comparable (including substantially reduced false alarms) with in-situ observations than that of SCA LST. [ABSTRACT FROM AUTHOR]

Observations in thermal infrared (IR) contribute substantially to the understanding of the global fluxes of energy and matter between Earth’s surface, ocean and atmosphere. Key parameters derived from such observations are Sea Surface Temperature (SST), Land Surface Temperature (LST) and Land Surface Emissivity (LSE). These variables are important for weather forecasting and climate modelling. However, satellite systems currently in orbit provide only a small number of spectral bands in the thermal region, and consequently cannot be used for temperature emissivity separation (TES) to accurately derive LST and LSE. Hence, capacities to investigate processes or phenomena where LST in high temporal and high spatial resolution (

Generating a long-time-series, high-spatial-resolution land surface temperature (LST) product has considerable applications in monitoring water stress, surface energy and water balance at the field scale. This paper proposes an operational method to generate 30-m LSTs from thermal infrared (TIR) observations of Landsat series. Two key issues were addressed in the proposed method: one involved determining the land surface emissivity (LSE) by developing different LSE retrieval methods for specific land cover types; the other involved choosing an optimal reanalysis atmospheric profile for implementing the atmospheric correction of TIR data. After LSE determination and atmospheric correction, LST was resolved by inverting the radiative transfer equation. In situ measured LST and LSE data were used to validate the proposed method. The validation results based on the measurements from 24 sites showed that the absolute average bias of the LSE data estimated from Landsat 5/7/8 was generally within 0.01, and the standard deviations were all less than 0.002. The average biases of the retrieved LST at SURFRAD sites were 1.11/1.54/1.63 K, whereas the RMSEs were 2.72/3.21/3.02 K for Landsat 5/7/8, respectively. The average biases (RMSEs) of the retrieved LST at the BSRN and Huailai sites were 0.08 K (3.69 K) and 0.90 K (3.42 K) for Landsat 7 and Landsat 8, respectively. Furthermore, the validation results at the SURFRAD sites show that the precision and uncertainty of the retrieved Landsat 5/7/8 LSTs were all better than those of the USGS LSTs. Finally, we produced monthly composited LST maps for the Chinese landmass and continental United States using the retrieved Landsat 5/7/8 LSTs. This study provides guidance on how to estimate large-scale LSTs from satellite sensors with only one TIR channel. We will massively produce global LSTs from Landsat series TIR data and release them to the public in the next stage.

Most major cities worldwide are affected Urban Heat Islands – a condition of relatively higher temperatures being observed in one area compared to another that can be caused by a decrease in greenspace. One of the major reasons attributed to this increase in the warming of urban landscapes is the decrease in green space. This concept has received a lot of attention due to the destruction of vegetation for urban development and has prompted long-term spatial-temporal studies of Urban Heat Islands to understanding local climates. The objective of this study is to use Landsat data to examine the temporal intensification of UHIs and their variability from 1984–2016 for the cities of Chicago and Minneapolis-St. Paul. Landsat L4-5 TM), L7 ETM+), OLI and TIRS from 1984 to 2016 was used to examine land surface temperature (LST). Firstly, we converted the digital number (DN) to spectral radiance (L) and to temperature in Kelvin and from kelvin to Celsius and a conversion from Radiance to Top of the Atmosphere Reflectance and estimation of land surface emissivity. Finally, LST was estimated and Urban Heat Island retrieval and anomalies computed to help examine inconsistencies in our data. Our analysis showed year-to-year fluctuations in surface temperature, intensification of UHIs for both metro areas. Using a defined deductive index to identify environmentally critical areas, estimates of UHIs based on LST showed that both metropolitan areas are UHIs with LST > µ + 0.5 × δ. Higher intensification values were observed in 1988 and 2010 for Chicago and 1984, 1999 and 2016 for Minneapolis-St. Paul from analysis. While both areas have the similar climatic conditions, our analysis show differences in UHIs intensification as observed in their urban growth patterns. Chicago experiences a higher UHI intensity compared to Minneapolis-St. Paul and this could be explained by higher number of tall buildings than Minneapolis-St. Paul. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth's land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD (Surface Radiation Budget Network) sites, 6 ARM (Atmospheric Radiation Measurement) sites, and 9 NDBC (National Data Buoy Center) sites. The results indicate that a relatively consistent performance among Landsat 5, 7, and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity (LSE) correction methods for Landsat 5, 7, and 8 sensors. Large bias and root mean square error (RMSE) of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index (NDVI). Except for the sites with incorrect LSE estimation, a mean bias (RMSE) of the differences between Landsat LST and in situ LST is 1.0 K (2.1 K) over snow-free land surfaces, −1.1 K (1.6 K) over snow surfaces, and −0.3 K (1.1 K) over water surfaces. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important parameter in global climate change and urban thermal environmental studies. The significance of land surface temperature is being acknowledged gradually and interest is increasing in developing methodologies for the retrieval of LST from Satellite Remote Sensing (SRS) data. Thermal Infrared Sensor (TIRS) of Landsat-8 is the newest TIR sensor for the Landsat Data Continuity Mission (LDCM), offering two adjacent thermal infrared bands (10, 11), having significant beneficiary for the land surface temperature inversion. The spectral radiance can be estimated through TIR bands 10 and 11 of Landsat-8 OLI_TIRS satellite image. In the present study, the radiative transfer equation-based method has been employed in estimating LST of Lahore and the analysis demonstrated that estimated LST has the highest accuracy from the radiative transfer method through band 10. Land Surface Emissivity (LSE) was derived with the aid of the NDVI’s threshold technique. The present study results show that as the built-up area increases and vegetation cover decreases in urban surface, they are linked to increase in urban land surface temperature and conversely larger vegetation cover associated with lower urban temperature. The output exposed that LST was high in built-up and barren land, whereas it was low in the area where there were more vegetation cover and water

Monitoring Land Surface Temperature (LST) from Landsat satellites has been shown to be effective in the estimation of crop water needs and modeling water use efficiency. Accurate LST estimation becomes critical in semiarid areas under water scarcity scenarios. This work shows the assessment of some well-known Single-Channel (SC) and Split-Window (SW) algorithms, adapted to Landsat 8/TIRS, under the conditions of a high-contrast semiarid agroecosystem. The recently released Landsat 8 Level-2 LST product (L8_ST) has also been included in the performance analysis. Ground measurements of surface temperature were taken for the evaluation during the summers of 2018–2019 in the cropland area of the Barrax test site, Spain. A dataset of 44 ground samples and 11 different L8/TIRS dates/scenes was gathered, covering a variety of crop fields and surface conditions. In addition, a simplified Single Band Atmospheric Correction (L-SBAC) was introduced based on a linearization of the atmospheric correction parameters with the water vapor content (w) and a redefinition of the emissivity threshold for the emissivity correction in the study site. The best results show differences within ±4.0 K for temperatures ranging 300–325 K. Statistics for the L-SBAC result in a RMSE of ±1.8 K with negligible systematic deviation. Similar results were obtained for the other SC and SW algorithms tested, whereas an overestimation of 1.0 K was observed for the L8_ST product because of inappropriate assignment of emissivity values. These results show the potential of the proposed linearization approach and set the uncertainty for LST estimates in high-contrast semiarid agroecosystems.

Land surface emissivity retrieval is important for the remote identification of natural materials and can be used to identify the presence of silicate minerals. However, its estimation from passive sensors involves an undetermined function related to radiance data, which is influenced by the atmosphere. We tested three methods for temperature emissivity retrieval in a dune field composed of 99.53% quartz (SiO2) using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. The tested methods were the reference channel method (RCM), emissivity normalization method (ENM), and temperature emissivity separation (TES) method. An average quartz reference spectrum for the dune samples was calculated from an emissivity database based on temperature and used to evaluate the emissivity products of four ASTER images. In general, the three tested methods had a good approximation when analysed the emissivity reference curve, especially for longer wavelengths that ranged between 2 and 4% of emissivity. The RCM and ENM produced very similar results with the coefficients of determination (R2) as 0.9960 (RMSE 0.0184) and 0.9959 (RMSE 0.0185), respectively. RCM method presented superior results (R2: 0.9960, RMSE: 0.0184), compared to the TES method (R2: 0.9947, RMSE: 0.0197). The TES method showed good results only for shorter wavelengths and, hence, to identify specific targets using ASTER data, such as silicate minerals, it is better to use the RCM method. The emissivity value selected at the saturation point of the spectral library based on temperature is fundamental in acquiring more reliable data. [ABSTRACT FROM AUTHOR]

Predicting land surface energy budgets requires precise information of land surface emissivity (LSE) and land surface temperature (LST). LST is one of the essential climate variables as well as an important parameter in the physics of land surface processes at local and global scales, while LSE is an indicator of the material composition. Despite the fact that there are numerous publications on methods and algorithms for computing LST and LSE using remotely sensed data, accurate prediction of these variables is still a challenging task. Among the existing approaches for calculating LSE and LST, particular attention has been paid to the normalised difference vegetation index threshold method (NDVITHM), especially for agriculture and forest ecosystems. To apply NDVITHM, knowledge of the proportion of vegetation cover (PV) is essential. The objective of this study is to investigate the effect of the prediction accuracy of the PV on the estimation of LSE and LST when using NDVITHM. In August 2015, a field campaign was carried out in mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany, coinciding with a Landsat-8 overpass. The PV was measured in the field for 37 plots. Four different vegetation indices, as well as artificial neural network approaches, were used to estimate PV and to compute LSE and LST. The results showed that the prediction accuracy of PV improved using an artificial neural network (R2CV = 0.64, RMSECV = 0.05) over classic vegetation indices (R2CV = 0.42, RMSECV = 0.06). The results of this study also revealed that variation in the accuracy of the estimated PV affected calculation results of the LSE. In addition, our findings revealed that, though LST depends on LSE, other parameters should also be taken into account when predicting LST, as more accurate LSE results did not increase the prediction accuracy of LST.

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.

Urban environment imposes challenges due to its dynamics and thermodynamic characteristics of the built environment. The present study aims to study the effect of lockdown during COVID-19 on the spatio-temporal land surface temperature (LST) patterns in Dehradun city. The TIRS sensor data of 14 April 2020 (post-lockdown), 28 April 2019, 25 April 2018 and 08 May 2017 were downloaded, and LST was retrieved using radiative transfer equation. The wardwise change in LST, urban hot spots and thermal comfort was studied as a function of built-up density. It was observed that there was an overall decrease in LST values in Dehradun city in post-COVID lockdown period. Wards with high built-up density had minimum decrease in LST; on the contrary, wards with large proportion of open spaces and having low, medium built-up density had the maximum decrease in LST. Hot spot analysis was carried out using Getis Ord GI* statistic, and the level of thermal comfort was found using the urban thermal field variance index. It was observed that there was an increase in number of hot spots accompanied by a decrease in thermal comfort level post-lockdown. The methodology proposed in the present study can be applied to other Indian cities which exhibit similar growth patterns and will provide a tool for rational decision making. [ABSTRACT FROM AUTHOR]

Observations in thermal infrared (IR) contribute substantially to the understanding of the global fluxes of energy and matter between Earth's surface, ocean and atmosphere. Key parameters derived from such observations are Sea Surface Temperature (SST), Land Surface Temperature (LST) and Land Surface Emissivity (LSE). These variables are important for weather forecasting and climate modelling. However, satellite systems currently in orbit provide only a small number of spectral bands in the thermal region, and consequently cannot be used for temperature emissivity separation (TES) to accurately derive LST and LSE. Hence, capacities to investigate processes or phenomena where LST in high temporal and high spatial resolution (<100 m) is required, such as agricultural applications or urban heat island monitoring, are limited. Additionally, the measurement of radiative energy released from active large and small fires, which contribute significantly to greenhouse gas emissions, is still challenging with current IR systems. Here, we introduce the proposed multispectral sensor system DIEGO (Dynamic Infrared Earth Observation on the ISS Orbit) with 11 spectral bands and a ground sampling distance of less than 60 m, which aims to reduce the observation gap in the thermal infrared significantly. [ABSTRACT FROM AUTHOR]

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Urbanization and human activity within an urban system produce many destructive and irreversible effects on natural environment such as air pollution and climate changes. One of the important effects of climate change is the formation of surface urban heat island (SUHI) which is an area with higher temperature than surroundings. It is important to study the surface urban heat islands to understand the complexity of the climate systems and to lessen their impact on the environment. In this paper, an approach for detecting SUHIs based on the combination between a set of Landsat 8's Thermal Infrared Sensor (TIRS) night vision images and Spot5 data was proposed. To accurately detect SUHIs over Jeddah City, it is important to determine the land surface temperature (LST). To achieve this goal, pixel values of Landsat images were converted to represent at sensor temperature. The spot image was classified using supervised classification techniques to determine feature types in the scene, the emissivity value for each pixel was assigned using classification-based emissivity and NDVI-based emissivity. Then, the two values of at sensor temperature and feature emissivity were linked together to retrieve an accurate LST. Based on the results of this study, the SUHIs over Jeddah City appeared as small boundaries in the South area of the city, as a result of the land use patterns. The difference between urban and non-urban areas ranges from 4 to 7 °C. The SUHIs over Petromin neighborhood and Almohajer neighborhood were presented. Night vision Landsat 8 offers an effective framework to delineate and monitor behavior, movement, and size of SUHIs. The early detection of SUHIs by remote sensing data contributes in discovering environmental imbalance and helps to identify problems and developing solutions. [ABSTRACT FROM AUTHOR]

Land emissivity sensed within a suitable range of wavelengths by microwave radiometer is useful to deduce land surface temperature and land surface emissivity. Brightness temperature measurements by space-borne microwave sensors have been utilized to determine land surface emissivity for the study of climatology, hydrological and agricultural applications. Currently, application of sounder data is gaining attention, in analyzing land surface characteristic features especially due to the higher spatial resolution, in general. In the present work, an attempt is made to obtain land surface emissivity from the six channels of SAPHIR (Sondeur Atmospherique du Profil d Humidit Intertropicale par Radiometrie), sensor with special reference to bare land and south Western Ghats of India during pre-monsoon and post-monsoon seasons. The innovation of the present analysis is to demonstrate the possibility of estimating and retrieving surface parameters from emissivity values retrieved from brightness temperature measured from SAPHIR sounder with channel 6 of 183.31 ± 11.0 GHz over selected study area with various surface conditions. Further, the present analysis relates to application of emissivity values retrieved from brightness temperature measured from SAPHIR sounder with channel 6, i.e., 183.31 ± 11.0 GHz for the study of vegetation, climatology and agricultural applications. [ABSTRACT FROM AUTHOR]

A new directional canopy emissivity model (CE-P) based on spectral invariants is proposed in this paper. First, we prove the existence of the spectral invariant properties in the thermal infrared (TIR) band using a Monte Carlo model. Based on it, the equation of the new model is derived from the perspective of absorption. In this expression, single-scattering and multiscattering effects are separated analytically in the TIR band. We find that the overall contribution of multiple scatterings is less than 0.005 when the component emissivities are over 0.90, and the overall contribution decreases with increasing leaf or soil emissivity. Furthermore, the new model can avoid the logical difficulty encountered when using the traditional cavity effect factor to simulate the emissivity of a sparse vegetation canopy. The results of 4SAIL and Discrete Anisotropic Radiative Transfer (DART) are selected to do cross validation. The CE-P can achieve a high accuracy compared with 4SAIL and DART, with an absolute bias less than 0.002 when the leaf (soil) emissivity is equal to 0.98 (0.94). Four widely used analytical models are selected for comparison. The resulting accuracies of these models are ordered from CE-P to REN15, FR97, FR02, and VALOR96 with the most serious error up to 0.002, 0.002, 0.007, 0.013, and 0.014, respectively. Three main conclusions are obtained through the sensitivity analysis: the multiscattering between vegetation and the background can be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), the second and higher order scattering within the vegetation can also be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), and the single-scattering effect within the canopy should be considered which can be calculated using three view factors. [ABSTRACT FROM AUTHOR]

Correct specification of a target’s longwave infrared (LWIR) surface emissivity has been identified as one of the greatest sources of uncertainty in the remote sensing of land surface temperature (LST). Field and laboratory emissivity measurements are essential for improving and validating LST retrievals, but there are differing approaches to making such measurements and the conditions that they are made under can affect their performance. To better understand these impacts we made measurements of fourteen manmade and natural samples under different environmental conditions, both in situ and in the laboratory. We used Fourier transform infrared (FTIR) spectrometers to deliver spectral emissivities and an emissivity box to deliver broadband emissivities. Field- and laboratory-measured spectral emissivities were generally within 1–2% in the key 8–12 micron region of the LWIR atmospheric window for most samples, though greater variability was observed for vegetation and inhomogeneous samples. Differences between laboratory and field spectral measurements highlighted the importance of field methods for these samples, with the laboratory setup unable to capture sample structure or inhomogeneity. The emissivity box delivered broadband emissivities with a consistent negative bias compared to the FTIR-based approaches, with differences of up to 5%. The emissivities retrieved using the different approaches result in LST retrieval differences of between 1 and 4 °C, stressing the importance of correct emissivity specification.

This paper uses the refined Generalized Split-Window (GSW) algorithm to derive the land surface temperature (LST) from the data acquired by the Visible and Infrared Radiometer on FengYun 3B (FY-3B/VIRR). The coefficients in the GSW algorithm corresponding to a series of overlapping ranges for the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The GSW algorithm is applied to retrieve LST from FY-3B/VIRR data in an arid area in northwestern China. Three emissivity databases are used to evaluate the accuracy of different emissivity databases for LST retrieval, including the ASTER Global Emissivity Database (ASTER_GED) at a 1-km spatial resolution (AG1km), an average of twelve ASTER emissivity data in the 2012 summer and emissivity spectra extracted from spectral libraries. The LSTs retrieved from the three emissivity databases are evaluated with ground-measured LST at four barren surface sites from June 2012 to December 2013 collected during the HiWATER field campaign. The results indicate that using emissivity extracted from ASTER_GED can achieve the highest accuracy with an average bias of 1.26 and −0.04 K and an average root mean square error (RMSE) of 2.69 and 1.38 K for the four sites during daytime and nighttime, respectively. This result indicates that ASTER_GED is a useful emissivity database for generating global LST products from different thermal infrared data and that using FY-3B/VIRR data can produce reliable LST products for other research areas.

This work estimated and validated the land surface temperature (LST) from thermal-infrared Channels 4 (10.8 µm) and 5 (12.0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A) meteorological satellite. The LST, mean emissivity and atmospheric water vapor content (WVC) were divided into several tractable sub-ranges with little overlap to improve the fitting accuracy. The experimental results showed that the root mean square errors (RMSEs) were proportional to the viewing zenith angles (VZAs) and WVC. The RMSEs were below 1.0 K for VZA sub-ranges less than 30° or for VZA sub-ranges less than 60° and WVC less than 3.5 g/cm2, provided that the land surface emissivities were known. A preliminary validation using independently simulated data showed that the estimated LSTs were quite consistent with the actual inputs, with a maximum RMSE below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST product MOD11_L2 showed that the minimum RMSE was 1.68 K for grass, and the maximum RMSE was 3.59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1.82 K.

A confident knowledge of land surface emissivity at viewing zenith angles far from nadir is of prime interest to perform an accurate correction of the anisotropy effect in the measurements made by orbiting thermal infrared (TIR) sensors. It is also important for the correct treatment of angular measurements carried out by remote sensors such as AATSR/ENVISAT, MODIS/Terra–Aqua, or the recently launched SLSTR/Sentinel-3, which can also be used for the angular normalization of land surface temperature due to viewing geometry effect. In this letter, the anisotropy of TIR emissivity predicted by two analytical, Warren–Wiscombe–Dozier and Hapke, models based on Mie diffraction theory was compared with field-measured values under dry conditions. The results showed good agreement between models and measurements (RMSEs ranging from ±0.004 to ±0.030 depending on the sample, with an average value of ±0.016) if the compactness of the grains soil is taken into account, demonstrating the good performance of the cosine term of the zenith angle included in the expressions of both models. A significant disagreement between models and measurements is, however, obtained for some samples at high zenith angles, suggesting the necessity of a fudge factor to include in the compactness correction in that condition. [ABSTRACT FROM AUTHOR]

The regional-scale consistency between four precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the Arabian Peninsula was assessed. Their macroscale relationships were inter-compared with soil moisture and total water storage (TWS) estimates from AMSR-E and GRACE. The consistency analysis was studied with multivariate statistical hypothesis testing and Pearson correlation metrics for the period from January 2000 to December 2010. The products and GRACE estimates were assessed over a representative sub-domain (United Arab Emirates) with available in situ well observations. Next, geographically temporally weighted regression (GTWR) was employed to examine the interdependencies among the peninsula’s hydrological components. The results showed GPCC-TRMM recording the highest correlation (0.85) with insignificant mean differences over more than 90% of the peninsula. The highest GTWR predictive performance of TWS (R2 = 0.84) was achieved with TRMM forcing, which indicates its potential to monitor changes in TWS over the arid peninsular region. [ABSTRACT FROM AUTHOR]

This paper presents a method for estimating the land surface temperature (LST) from Himawari-8 data. The Advanced Himawari Imager onboard Himawari-8 has three thermal infrared bands in the spectral range of 10 - 12.5 µm. We developed a nonlinear three-band algorithm (NTB) that makes the best use of these bands to estimate the LST. The formula of the algorithm includes 10 coefficients. The optimum values of these coefficients were derived using a statistical regression method from the simulated data, as obtained by a radiative transfer model. The simulated data sets correspond to a variety of values of LST, as well as surface emissivity, type and season of temperature and water vapor profiles. Viewing zenith angles (VZAs) from 0° to 60° were considered. For the coefficients obtained in this way, we verified the root-mean-square error (RMSE) in terms of the VZA, LST and precipitable water dependence. We showed that the NTB can accurately estimate the LST with an RMSE less than 0.9 K compared with the nonlinear split-window algorithm developed by Sobrino and Romaguera (2004). Moreover, we evaluated the sensitivities of the LST algorithms to the uncertainties in input data by using the dataset independent of the dataset used to obtain coefficients. Consequently, we showed that the NTB has the highest robustness against the uncertainties in input data. Finally, the stepwise LST retrieval method was constructed. This method includes a simple cloud mask procedure and the land surface emissivity estimation. The LST product was evaluated using in-situ data over the Tibetan Plateau, and the validity was confirmed. [ABSTRACT FROM AUTHOR]

Land surface emissivity (LSE) in the thermal infrared (TIR) is an essential parameter in the retrieving land surface temperature (LST) from space. This paper describes the LSE maps in three TIR bands (centered at 10.4, 11.2 and 12.4 µm) used for retrieving the LST from Himawari-8. Himawari-8, a next-generation geostationary satellite has high spatial and temporal resolutions compared to previous geostationary satellites. Because of these improvements, the Himawari-8 LST product is expected to contribute to the observation of small-scale environments in high-frequency. In this study, the LSE is estimated by a semi-empirical method, which is a combination of the classification based method and the normalized difference vegetation index (NDVI) thresholds method. The land cover classification information is taken from the Global Land Cover by National Mapping Organizations version3 (GLCNMO 2013). Material emissivities of soil, vegetation and others are taken from the MODIS UCSB emissivity library and the ASTER spectral library. This method basically follows the semi-empirical methods developed by the previous studies, but advanced considerations are added. These considerations are the phenology of vegetation, flooding of paddy fields, snow/ice coverage, and internal reflections (cavity effect) in urban areas. The average cavity effect on LSE in urban canopies is approximately 0.01, but it reaches 0.02 in built-up areas. The sensitivity analysis shows that the total LSE errors for the three bands are less than 0.02. The LSE estimation is especially stable at the vegetation area, where the error is less than 0.01. [ABSTRACT FROM AUTHOR]

Near-surface air temperature and the underlying soil temperature are among the key components of the Earth's surface energy budget, and they are important variables for the comprehensive assessment of global climate change. Better understanding of the difference in magnitude between these two variables over high-latitude regions is also crucial for accurate detections of freeze and thaw (FT) states. However, these differences are not usually considered and included in current remote sensing-based FT detection algorithms. In this study, the difference between near-surface air temperature at the 2-m height and soil temperature at the 5-cm depth is assessed using ground-based observations that span a three-year period from 2013 to 2015. Results show noticeable differences between air and soil temperatures over temporal scales that range from diurnal to seasonal. The study also suggests that the ground-based upper layer soil temperature may be a better surrogate than the near-surface air temperature for the reliable detection of FT states at high-latitudes. Furthermore, the results from this study are particularly useful for better understanding the surface energy budget that ultimately drives the land surface processes that are embedded within weather and climate models. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter for a wide number of applications, including hydrology, meteorology and surface energy balance. In this study, we first proposed a new land surface emissivity (LSE) scheme, including a lookup table-based method to determine the vegetated surface emissivity and an empirical method to derive the bare soil emissivity from the Global LAnd Surface Satellite (GLASS) broadband emissivity (BBE) product. Then, the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data and the Feng Yun-3C/Medium Resolution Spectral Imager (FY-3C/MERSI) precipitable water vapor product were used to correct the atmospheric effects. After resolving the land surface emissivity and atmospheric effects, the LST was derived in a straightforward manner from the FY-3C/MERSI data by the radiative transfer equation algorithm and the generalized single-channel algorithm. The mean difference between the derived LSE and field-measured LSE over seven stations is approximately 0.002. Validation of the LST retrieved with the LSE determined by the new scheme can achieve an acceptable accuracy. The absolute biases are less than 1 K and the STDs (RMSEs) are less than 1.95 K (2.2 K) for both the 1000 m and 250 m spatial resolutions. The LST accuracy is superior to that retrieved with the LSE determined by the commonly used Normalized Difference Vegetation Index (NDVI) threshold method. Thus, the new emissivity scheme can be used to improve the accuracy of the LSE and further the LST for sensors with broad spectral ranges such as FY-3C/MERSI. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibetan plateau plays an important role in climate change with its unique characteristics, and the surface emissivity is an important parameter to describe the surface characteristics. It is also very important for the accurate retrieval of surface and atmospheric parameters. Different types of surface features have their own radiation characteristics due to their differences in structure, water content and roughness. In this study, the microwave land surface emissivity (10.65, 18.7, 23.8, 36.5 and 89 GHz) of the Qinghai-Tibetan Plateau was calculated using the simplified microwave radiation transmission equation under clear atmospheric conditions based on Level 1 brightness temperatures from the Microwave Radiation Imager onboard the FY-3B meteorological satellite (FY-3B/MWRI) and the National Centers for Environmental Prediction Final (NCEP-FNL) Global Operational Analysis dataset. Furthermore, according to the IGBP (International Geosphere-Biosphere Program) classified data, the spectrum and spatial distribution characteristics of microwave surface emittance in Qinghai-Tibetan plateau were further analyzed. The results show that almost all 16 types of emissivity from IGBP at dual-polarization (vertical and horizontal) increase with the increase of frequency. The spatial distribution of the retrieving results is in line with the changes of surface cover types on the Qinghai-Tibetan plateau, showing the distribution characteristics of large polarization difference of surface emissivity in the northwest and small polarization difference in the southeast, and diverse vegetation can be clearly seen in the retrieving results. In addition, the emissivity is closely related to the type of land surface. Since the emissivity of vegetation is higher than that of bare soil, the contribution of bare soil increases and the surface emissivity decreases as the density of vegetation decreases. Finally, the source of retrieval error was analyzed. The errors in calculating the surface emissivity might mainly come from spatiotemporal collocation of reanalysis data with satellite measurements, the quality of these auxiliary datasets and cloud and precipitation pixel discrimination scheme. Further quantitative analysis of these errors is required, and even standard procedures may need to be improved as well to improve the accuracy of the calculation.

Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A field campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. A normalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artificial neural network was used to predict the LAI using: (i) reflectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reflectance data from the OLI sensor and the land surface emissivity; and (iii) reflectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modified vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artificial neural network, the prediction accuracy of LAI increased (R2CV = 0.58, RMSECV = 0.83 m2m−2). The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2). For the first time, our results demonstrate that the combination of Landsat-8 reflectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This finding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.

Land surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs). To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W) and atmospheric temperature (T0) in the bottom layer. To parameterize the constant, an exponential approximation between Wand T0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NEDT = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017. [ABSTRACT FROM AUTHOR]