Radiometric surface temperature calibration effects on satellite based evapotranspiration estimation

Agriculture on the Texas High Plains (THP) uses approximately 89% of groundwater withdrawals from the Ogallala Aquifer, leading to steady decline in water table levels. Therefore, efficient water management is essential for sustaining agricultural production in the THP. Accurate evapotranspiration (ET) maps provide critical information on actual spatio‐temporal crop water use. METRIC (Mapping Evapotranspiration at High Resolution using Internalized Calibration) is a remote sensing based energy balance method that uses radiometric surface temperature (T _s) for mapping ET. However, T _s calibration effects on satellite based ET estimation are less known. Further, METRIC has never been applied for the advective conditions of the semi‐arid THP. In this study, METRIC was applied and predicted ET was compared with measured values from five monolithic weighing lysimeters at the USDA‐ARS Conservation and Production Research Laboratory in Bushland, Texas, USA. Three different levels of calibration were applied on a Landsat 5 Thematic Mapper's thermal image acquired on 23 July 2006 to derive T _s. Application of METRIC on a MODTRAN calibrated image improved the accuracy of distributed ET prediction. In addition, ET estimates were further improved when a THP‐specific model was used for estimating leaf area index. Results indicated that METRIC performed well with ET mean bias error±root mean square error of 0.4±0.7 mm d^?1.     

Evaluation of the DART 3D model in the thermal domain using satellite/airborne imagery and ground-based measurements

This work provides an evaluation of the discrete anisotropy radiative transfer (DART) three-dimensional (3D) model in assessing the simulation of directional brightness temperatures (T _b) at both sensor and surface levels. Satellite imagery acquired with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), airborne imagery acquired with the Airborne Hyperspectral Scanner (AHS) sensor and ground-based measurements collected over an agricultural area were used to evaluate the DART model at nadir views. Directional radiometric temperatures measured with a goniometric system at ground level were also used to evaluate modelling results at different view angles. The DART model was evaluated over three homogeneous plots: bare soil (BS), green grass (GG) and sand (NS). The results show good agreement between the simulations and the satellite, airborne and ground-based measurements, with root mean square errors (RMSEs) less than 2.0 K. However, three major discrepancies were found: (1) differences greater than 4.0 K over BS when comparing DART and ASTER, attributed to turbulence-induced temperature fluctuations, (2) higher differences in sensor-level than in surface-level comparisons when using AHS due to thermal heterogeneity of the selected regions of interest in the image and also to differences in atmospheric correction performed over the imagery and the correction included in the DART model, especially for bands located in the lowest atmospheric transmissivity regions and (3) RMSEs greater than 2.0 K when comparing DART results and ground measurements over the NS plot, due to the strong emissivity correction in the 8.0–9.0 μm bands, where the measured emissivity was below 0.75. Despite these discrepancies, we show that the DART model is a useful tool for simulating remotely sensed thermal images over different landscapes. Finally, new versions of this model are continuously being released to solve technical problems and improve the simulation results.     

Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX

A sequence of five high-resolution satellite-based land surface temperature (T_s) images over a watershed area in Iowa were analyzed. As a part of the SMEX02 field experiment, these land surface temperature images were extracted from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM) thermal bands. The radiative transfer model MODTRAN 4.1 was used with atmospheric profile data to atmospherically correct the Landsat data. NDVI derived from Landsat visible and near-infrared bands was used to estimate fractional vegetation cover, which in turn was used to estimate emissivity for Landsat thermal bands. The estimated brightness temperature was compared with concurrent tower based measurements. The mean absolute difference (MAD) between the satellite-based brightness temperature estimates and the tower based brightness temperature was 0.98 °C for Landsat 7 and 1.47 °C for Landsat 5, respectively. Based on these images, the land surface temperature spatial variation and its change with scale are addressed. The scaling properties of the surface temperature are important as they have significant implications for changes in land surface flux estimation between higher-resolution Landsat and regional to global sensors such as MODIS.     

Progress in the remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data

The extensive requirement of landsurface temperature (LST) for environmental studies and management activities of the Earth's resources has made the remote sensing of LST an important academic topic during the last two decades. Many studies have been devoted to establishing the methodology for the retrieval of LST from channels 4 and 5 of Advanced Very High Resolution Radiometer (AVHRR) data. Various split-window algorithms have been reviewed and compared in the literature to understand their differences. Different algorithms differ in both their forms and the calculation of their coefficients. The most popular form of split-window algorithm is T _s=T ₄+A(T ₄-T ₅)+B , where T _s is land surface temperature, T ₄ and T ₅ are brightness temperatures of AVHRR channels 4 and 5, A and B are coefficients in relation to atmospheric effects, viewing angle and ground emissivity. For the actual determination of the coefficients, no matter the complexity of their calculation formulae in various algorithms, only two ways are practically applicable, due tothe unavailability of many required data on atmospheric conditions and ground emissivities in situ satellite pass. Ground data measurements can be used to calibrate the brightness temperature obtained by remote sensing into the actual LST through regression analysis on a sample representing the studied region. The other way is standard atmospheric profile simulationusing computer software such as LOWTRAN7. Ground emissivity has a considerable effect on the accuracy of retrieving LST from remote sensing data. Generally, it is rational to assume an emissivity of 0.96 for most ground surfaces. However, the difference of ground emissivity between channels 4 and 5 also has a significant impact on the accuracy of LST retrieval. By combining the data of AVHRR channels 3, 4 and 5, the difference can be directly calculated from remote sensing data. Therefore, much more study is required on how to accurately determine the coefficients of split-window algorithms in the application of remote sensing to examine LST change and distribution in the real world.     

Processing and accuracy of Landsat Thematic Mapper data for lake surface temperature measurement

Processing of Landsat-5 TM thermal images for lake surface temperature determination is addressed. A specific preprocessing algorithm to reduce sensor noise is presented and calibration and atmospheric correction is discussed. The atmospheric impact on thermal radiation measurements is modelled using Lowtran-7 utilizing radiosonde data. Comparing ground truth measurements acquired for 21 images between 1987 and 1994 with satellite derived temperatures yielded a mean square error of 0.53 deg K. A systematic overestimation or underestimation of Landsat derived temperatures was not found. The emissivity effect upon the accuracy of the derived surface temperature is discussed as well as effects of using alternate atmospheric profile data.     

Intercomparison of methods for estimating land surface temperature from a Landsat-5 TM image in an arid region with low water vapour in the atmosphere

This study compares the methods for retrieving the land surface temperature (LST) (T _s) from Landsat-5 TM (Thematic Mapper) data, including the radiative transfer equation (RTE) method, the mono-window algorithm (MWA) and the generalized single-channel (GSC) method in an arid region with low atmospheric water vapour content. In addition, T _s calculated without atmospheric correction of TM band 6 is also assessed. The intercomparison is divided into two parts. The first part is applying the methods at the Biandukou site (100° 58′ E, 38° 16′ N, elevation?=?2690 m) and the second part is applying them at Binggou (100° 13′ E, 38° 42′ N, elevation?=?3400 m) and Arou (100° 27′ E, 38° 36′ N, elevation?=?2960 m) sites. Results demonstrate that these methods provide acceptable accuracies at the Biandukou site. At this site, GSC generates nearly the same accuracy as RTE; MWA estimations are slightly less accurate than RTE and GSC; estimations without atmospheric correction of TM band 6 exhibit the largest errors. On the other hand, MWA is a good choice for retrieving the LST at Binggou and Arou sites. In cases where the meteorological parameters are unavailable, it is an alternative option to calculate T _s directly from TM band 6 image without atmospheric correction at these two sites.     

Remote sensing based estimation of evapotranspiration rates

A remote sensing based method is presented for calculating evapotranspiration rates (λE) using standard meteorological field data and radiometric surface temperature recorded for bare soil, maize and wheat canopies in Denmark. The estimation of λE is achieved using three equations to solve three unknowns; the atmospheric resistance (r_ae ), the surface resistance (r_s ) and the vapour pressure at the surface (e_s ) where the latter is assessed using an empirical expression. The method is applicable, without modification, to dense vegetation and moist soil, but for a dry bare soil, where the effective source of water vapour is below the surface, the temperature of the evaporating front (T_s *) can not be represented by the measured surface temperature (T_s ). In this case (T_s -T_s *) is assessed as a linear function of the difference between surface temperature and air temperature. The calculated λE is comparable to latent heat fluxes recorded by the eddy covariance technique.     

Estimating midday near-surface air temperature by weighted consideration of surface and atmospheric moisture conditions using COMS and SPOT satellite data

The measurement of near-surface air temperature (T_a) is critically important for understanding the Earth’s energy and water circulation system and for diverse modelling applications. T_a data obtained from meteological ground stations are basically available but not suitable for large-scale areas, because of their spatial limitation. Remote-sensing techniques can provide a spatially well-distributed T_a map. However, the current remote-sensing methodology for T_a mapping has accuracy inferior to common expectations in terms of the region of various terrestrial ecosystems and climatic conditions. Our aim was to develop a midday T_a retrieval algorithm with reasonable accuracy over Northeast Asia during one seasonal year. In consideration of the various environmental conditions in our study area, T_a was calculated using land surface temperature and the normalized difference vegetation index in the nine cases derived from the combination of surface and atmospheric moisture conditions, and a weighting factor was applied to reduce the bias error among T_a results from nine cases. The reasonable pixel window size was established as 13 × 13. The validation process yielded a coefficient of determination (R²), root mean square error, and bias values of 0.9401, 2.8865 K, and 0.4920 K, respectively. Although the study area includes diverse land-cover and climatic conditions, our satellite-derived T_a data provided better results compared with a previous study of only four cases with no weighting function in the Korean peninsula. Our suggested methodology will be useful in estimating T_a using satellite data, particularly over complex land surfaces.     

Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data

In this paper, a theoretical study complementary to others given in the literature about the errors committed on the land surface temperature retrieved from the radiative transfer equation in the thermal infrared region by remote sensing techniques has been analysed. For this purpose, the MODTRAN 3.5 code has been used in order to simulate different conditions and evaluate the influence of several parameters on the land surface temperature accuracy: atmospheric correction, noise of the sensor, land surface emissivity, aerosols and other gaseous absorbers, angular effects, wavelength uncertainty, full‐width half‐maximum of the sensor and band‐pass effects. The results show that the most important error source is due to atmospheric effects, which leads to an error on surface temperature between 0.2 K and 0.7 K, and land surface emissivity uncertainty, which leads to an error on surface temperature between 0.2 and 0.4 K. Hence, assuming typical uncertainties for remote sensing measurements, a total error for land surface temperature between 0.3 K and 0.8 K has been found, so it is difficult to achieve an accuracy lower than these values unless more accurate in situ values for emissivity and atmospheric parameters are available.     

Comparison of MODIS land surface temperature and ground-based observed air temperature in complex topography

Near-ground air temperature (T _a) and land surface temperature (T _s) are important parameters in studies related to variations in hydrology, biodiversity and climate change. However, complicated mountainous terrain tends to hinder observations in such areas. The scarce observations from mountainous areas can be augmented with data from a 1 km high spatial resolution data set. This data set is obtained from the land surface temperature element of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments installed on the Aqua and Terra Earth observation satellites from NASA. This study used regional monthly mean T _a data for Taiwan as a reference to assess the monthly mean T _s data set. The results showed that the two sets of data had correlation coefficients of 0.91–0.96, and the standard deviations of the differences between the two sets were 1.25–1.77°C. These results could serve as a reference for research related to climate and ecology. Further analysis indicated some possible sources of bias between T _s and T _a: (1) the significant influences caused by soil moisture between wet and dry seasons; (2) the difference between ground-based weather station elevation and 1 km grid-averaged elevation; and (3) interaction among the satellite view, solar zenith angle and terrain gradient. When the T _s product (V005) is used directly in ecological study and application, it is essential to have a clear knowledge of the bias and its possible causes.     

Sea and lake surface temperature retrieval from Landsat thermal data in Northern Germany

Landsat thermal data are employed to derive lake and sea surface temperatures. The limitations of this approach are obvious, since the calculation of surface temperatures based solely on image data requires at least two thermal bands to compensate the atmospheric influence which is mainly caused by water vapour absorption. However, the 1 km spatial resolution of currently available multi‐band thermal satellite sensors (NOAA‐AVHRR, MODIS) is often not appropriate for lake and coastal zone applications. Therefore, it is worthwhile investigating the accuracy which can be obtained with single‐band thermal data using radiosonde information of the atmospheric water vapour column from meteorological stations in the study area. In addition, standard atmospheres from the MODTRAN code were considered that are based on seasonal climatologic values of water vapour, e.g. mid‐latitude summer, mid‐latitude winter, etc.

The study area of this investigation comprises various lakes and coastal zones of the Baltic Sea in NE Germany. Landsat‐7 ETM+ imagery of nine acquisition dates was selected covering the time span from February to November 2000. Results of derived lake and sea surface temperatures were compared with in situ measurements and with an empirical model of the Deutscher Wetterdienst (Germany's National Meteorological Service, DWD). RMS deviations of 1.4 K were obtained for the satellite‐derived lake surface temperatures with respect to in situ measurements and 2.2 K with respect to the empirical DWD model. RMS deviations of 1.6 K were obtained with respect to in situ bulk temperatures in coastal zones of the Baltic Sea. This level of agreement can be considered as satisfactory given the principal constraints of this approach. A better accuracy can only be obtained with high spatial resolution (<100 m) multi‐band thermal instruments delivering imagery on an operational basis.     

Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Air temperature (T_a) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (T_max), mean (T_mean), and minimum air temperature (T_min) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily T_a and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best T_mean and T_max models were satisfactory (coefficient of determination (R²) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the T_min models (R² = 0.80–0.81 and RSE = 2.83–3.00 K). In this article T_min models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best T_min models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved T_min models yield R² = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R² and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the T_min models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.     

On-board radiometric calibration for thermal emission band of FY-3C/MERSI

On-board radiometric calibration is the most efficient method to improve the measurement accuracy of satellite-borne sensors. Chinese Medium Resolution Spectral Imager (MERSI), loaded on the Fengyun-3C (FY-3C) satellite, uses one satellite-borne fixed-temperature blackbody (BB) for its thermal emission band’s (TEB) radiometric calibration, but its optimal calibration algorithm is not determined. By using MERSI’s on-board calibration data taken on November 2013, this article investigates two algorithms of linear and semi-nonlinear calibration for MERSI TEB’s on-board radiometric calibration and finds that the linear calibration is more reasonable than semi-nonlinear calibration because linear coefficients’ variation tendency can reflect MERSI’s inherent systematic properties better. The relative difference between linear properties and inherent properties for pixel variability being 9.5%, mirror-side variability being 21.5% and scan variability being 17.8% are all smaller than those between semi-nonlinear case and inherent case. All of those suggest that the linear calibration is coincident with the inherent systematic properties. By using MERSI’s calibration data at June 2014, the performance of those two algorithms is validated by comparing the difference between inferred BB radiance L_I and standard BB radiance L_S (0.01%), and between inferred BB brightness temperature T_I and standard BB temperature T_S (0.25 K).     

Estimation of instantaneous air temperature using remote sensing data

In arid areas, the variation of air temperature can be considerable, so instantaneous air temperature (T_ai) estimation is needed in different environmental researches. In this research, two different remote sensing data are used for estimating T_ai for clear sky days in 2009 in Fars Province, Iran, including atmospheric temperature profile and land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer. The T_ai from a number of surface weather sites is used to judge the best T_ai estimation. Stations’ elevation, latitude, and land cover type are considered to show their effect on T_ai estimation. The estimated T_ai evaluation focuses on daily and seasonal timescales in the daytime and night time separately. Both LST and vertical temperature profile data produced relatively high coefficient of determination values and small root mean square error value for T_ai estimation, especially during the night time. Land cover and elevation vary the error values in T_ai estimation more, when LST data is used. In comparison atmospheric temperature profile indicates a smaller error in T_ai estimation in spring and summer and in urban land cover type, while using LST data presents a better result in fall and winter especially at night time.     

Sensitivity study of soil moisture on the temporal evolution of surface temperature over bare surfaces

Land surface soil moisture (SSM) is a fundamental variable in the hydrological cycle and is an important parameter in investigations on water and energy balances at the Earth's surface. Many efforts have been made to derive SSM from remotely sensed thermal infrared data. Using the Noah land surface model (LSM) and the Gaussian emulation machine for sensitivity analysis (GEM-SA) software, a sensitivity study was conducted for bare soil to investigate the interrelationship between the evolution of land surface temperature (LST) and SSM. Based on the diurnal cycles of LST and net surface shortwave radiation, eight parameters intuitively related to SSM were defined, and a sensitivity analysis (SA) was performed in the presence and absence of atmospheric variation. The results provided insight into the relationships between the eight parameters and various environmental factors such as soil physical parameters, soil moisture, albedo, and atmospheric parameters. For instance, the results suggested that the surface air temperature had a significant effect on the LST, especially the maximum, minimum, and average daytime temperatures. For a given atmospheric forcing data set, the LST rising rate normalized by the difference in the net surface shortwave radiation during the mid-morning (T _N) was the parameter most sensitive to the SSM, contributing 80.72% to the total variance. In addition, the time at which the daily maximum temperature occurred (t _d), the daily minimum temperature, and the LST nocturnal decay coefficient were strongly related to the soil type. Using a linear combination of T _N and t _d, a method was proposed to retrieve the SSM, and the coefficients of the linear model were found to be independent of the soil type for a given atmospheric condition. Compared with the actual SSM values used in the Noah LSM simulation, the root mean square error (RMSE) of the SSM retrieved from our proposed method was within 0.04 m³ m^?3 for all the 20 clear days evaluated in the present study.     

Estimation of spatially enhanced soil moisture combining remote sensing and artificial intelligence approaches

The main objective of this study is to combine remote-sensing and artificial intelligence (AI) approaches to estimate surface soil moisture (SM) at 100 m spatial and daily temporal resolution. The two main variables used in the Triangle method, that is, land-surface temperature (LST) and vegetation cover, were downscaled and calculated at 100 m spatial resolution. LSTs were downscaled applying the Wavelet-Artificial Intelligence Fusion Approach (WAIFA) on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imageries. Vegetation fractions were also estimated at 100 m spatial resolution using linear spectral un-mixing and Wavelet–AI models. Vegetation indices (VIs) were replaced with the vegetation fractions obtained from sub-pixel classification in the T_s–VI triangle space. The downscaled data were then used for calculating the evaporative fraction (EF), temperature-vegetation-dryness index (TVDI), vegetation temperature condition index (VTCI), and temperature-vegetation index (TVX) at 100 m spatial resolution. Thereafter, surface SM modelling was performed using a combination of Particle Swarm Optimization with Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and Support Vector Regression (PSO-SVR) modelling approaches. Results showed that the best input data set to estimate SM includes EF, TVDI, T_s, F_vegetation, F_soil, temperature (T), precipitation at time t (P_t, P_{t – 1}, P_{t – 2}), and irrigation (I). It was also confirmed that PSO-SVR outperformed the PSO-ANFIS modelling approach and could estimate SM with a coefficient of determination (R²) of 0.93 and a root mean square error (RMSE) of 1.29 at 100 spatial resolution. Range of error was limited between ?2.64% and 2.8%. It was also shown that the method proposed by Tang et al., (2010) improved the final SM estimations.     

AVHRR split window temperature differences and total precipitable water over land surfaces

Measurements from the thermal infrared split window channels of the AVHRR sensor were investigated for their relationship to the total atmospheric water vapour amount over land surfaces. The difference in brightness temperature between the AVHRR channel 4 and 5 (10·3–11·3μm and 11·4–12·3μm respectively) was found to be a linear function of total precipitable water, for several stations in differing climatic regimes. For each individual location the total precipitable water was estimated with a standard error ranging from 0·22 to 0·48 cm for the complete range of conditions from wet to dry season or summer to winter. For mid-latitude continental locations there is very little influence of atmospheric aerosols on the relationship while for the African Sahel region the effect of large airborne particulates with a silicate component introduces a significant effect at large values of aerosol optical depth due to absorption. The influence of spectral emissivity variation in the split window region was also observed for arid regions where there is a significant quartz component to the soil. It is concluded that for regional retrieval of precipitable water, this technique provides sufficient accuracy for application to correction of near-infrared satellite data such as AVHRR channel 2 (0·71 –0·98 μm), however the site specific relation between T ₄-T ₅ and PW needs to be established with independent PW measurements.     

Technical note Analysis of a simplified relation for estimating daily evapotranspiration from satellite thermal IR data

Abstract

Estimation of evapotranspiration from thermal infrared (IR)as been widely studied in recent years and in particular by methods using the simplified relation ETR ? R _n = A ? B(T _s?T _a) proposed by Jackson et al. (1977) and thermal IR data from the NOAA satellite obtained near midday (local time). The representativeness of instantaneous temperature for computing daily fluxes is discussed and a one-layer model is presented for a developed crop. A new theoretical expression of the simplified relation which can be applied to developed crops is presented and the sensitivity of the parameters in this relation is examined.     

Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand

We investigated the use of Landsat Enhanced Thematic Mapper (ETM) imagery to synoptically quantify chlorophyll a (chl a) concentrations. Two adjoining pairs of images of the central North Island were acquired on two different days in summer and spring 2002. 6sv atmospheric correction was compared to the cosine of the solar zenith angle correction (COST) dark object subtraction (DOS) atmospheric correction. The highest correlation between 6sv ln(Band 3) water surface reflectance and ln(chl a) was found in the 24 January 2002 image (r ² = 0.954). 6sv atmospheric correction was preferable to COST-DOS as it gave more realistic reflectance values at a clear-water reference site and produced the highest correlation coefficient. The results from this investigation suggest that remote sensing provides a valuable tool to assess temporal and spatial distributions of chl a in unmonitored areas within lakes and that predictions may also be extended to unmonitored lakes within the domain of satellite image capture.     

Improvements in land surface temperature retrieval based on atmospheric water vapour content and atmospheric temperature

Three methods are currently used to retrieve land surface temperatures (LSTs) from thermal infrared data supplied by the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors: the radiative transfer equation, mono-window, and generalized single-channel algorithms. Most retrieval results obtained using these three methods have an average error of more than 1 K. But if the regional mean atmospheric water vapour content and temperature are supplied by in situ radiosounding observations, the mono-window algorithm is able to provide better results, with a mean error of 0.5 K. However, there are no in situ radiosounding data for most regions. This article provides an improved method to retrieve LST from Landsat TM and ETM+ data using atmospheric water vapour content and atmospheric temperature, which can be obtained from remote-sensing data. The atmospheric water vapour content at the pixel scale was first calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The emissivities of various land covers and uses were then defined by Landsat TM or ETM+ data. In addition, the temperature–vegetation index method was applied to map area-wide instantaneous near-surface air temperatures. The parameters of mean atmospheric water vapour content and temperature and land surface emissivity were finally inputted to the mono-window algorithm to improve the LST retrieval precision. Our results indicate that this improved mono-window algorithm gave a significantly better retrieval of the estimated LST than that using the standard mono-window algorithm, not only in dry and elevated mountain regions but also in humid regions, as shown by the bias, standard deviation (σ), and root mean square deviation (RMSD). In Madoi County, the improved mono-window algorithm validated against the LST values measured in situ produced a bias and RMSD of –0.63 K and 0.91 K, respectively, compared with the mono-window algorithm’s bias and RMSD of –1.08 K and 1.27 K. Validated against the radiance-based method, the improved algorithm shows bias and RMSD values of –1.08 K and 1.27 K, respectively, compared with the initial algorithm’s bias and RMSD –1.65 K and 1.75 K. Additionally, the improved mono-window algorithm also appeared to be more accurate than the mono-window algorithm, with lower error values when validated against in situ measurement and the radiance-based method in the validation area in Zhangye City, Gansu Province, China. Remarkable LST accuracy improvements are shown by the improved mono-window algorithm, with better agreement not only with the in situ measurements but also with the simulated LSTs in the two validation areas, indicating the soundness and suitability of this method.