Land Surface Temperature Measurements Research Articles

Development and validation of algorithms for LST measurement from NOAA-11/AVHRR satellite data

The purpose of this project was to develop the algorithms and characterise measurement errors of land surface temperature (LST). An initial investigation of the accuracy of retrieval of LST has employed synthetic radiances, produced using Lowtran7, for a series of land and atmospheric situations representative of the Australian continental climate. The synthetic data have been modelled to incorporate variation of surfaces emissivity, atmospheric temperature, water vapour profile, and the surface or boundary temperature. The study indicated that the success of the scheme depended on the validity of the modelled radiances, particularly with respect to the formulation of the radiative role of the water vapour constituent of the atmosphere. Whirls, the use of regression in deriving the coefficients for LST equation minimised the bias errors in the radiative transfer calculation that may arise from air mass dependence. However, our algorithms derived using statistical regression and synthetic atmospheres indicated good accuracy for LST measurement with and rms error of order 1°C when validated using data for an instrumented field site.

Validation of Physical Radiative Transfer Equation-Based Land Surface Temperature Using Landsat 8 Satellite Imagery and SURFRAD in-situ Measurements

Land Surface Temperature (LST) is a key criterion in the physics of the Earth surface that controls the interactions between the land and atmosphere. The objective of this study is to evaluate the performance of physics-based Radiative Transfer Equation (RTE) method on LST retrieval using Landsat 8 satellite imagery and simultaneous in-situ LST data. In order to validate the satellite-based LST, in-situ LST measurements were obtained from Surface Radiation Budget Network (SURFRAD) stations simultaneous with satellite data acquisitions. In the study, four SURFRAD stations (BND, FPK, TBL and GWN) and five images for each SURFRAD station, totally twenty cloud-free images, were used for RTE-based LST validation. RTE method uses the atmospheric parameters acquired from radiosounding data simultaneous with satellite pass; however, these parameters were retrieved from NASA's atmospheric correction parameter calculator since radiosounding data are not available every time. Thus, this situation is another contribution of this study. As a result of the validation process of all data, the statistical measures, namely, coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and RMSE-observations standard deviation ratio (RSR) were calculated as 0.96, 3.12 K, 2.30 K and 0.33, respectively. However, the accuracy of RTE method on LST retrieval increased (R2 = 0.97, RMSE = 2.17 K, MAE = 1.44 K and RSR = 0.25) after removing TBL station from the analysis, since LST differences in this station were high for all scenes. RSR (ranging from 0 to high positive vlues) is an important measure for model evaluation, and the lower RSR value means high performance of the model. The obtained results revealed that physics-based RTE method is an effective and practical way for LST retrieval from Landsat 8 data despite using interpolated atmospheric parameters instead of radiosounding data.

RELATIONSHIPS BETWEEN LAND USE/LAND COVER AND LAND SURFACE TEMPERATURE IN TABRIZ FROM 2000 TO 2017

Abstract. Urban Heat Island (UHI) refers to the development of higher urban temperatures of an urban area compared to the temperatures of surrounding suburban and rural areas. Highly reflective urban materials to solar radiation present a significantly lower surface temperature and contribute to reducing the sensible heat released in the atmosphere and mitigating the urban heat island. Many studies of the UHI effect have been based on Land Surface Temperature (LST) measurements from remote sensors. The remotely sensed UHI has been termed the surface urban heat island (SUHI) effect. This study examines Tabriz city land use/land cover (LULC) and LST changes using Landsat satellite images between 2000 and 2017. Maximum likelihood classification and single channel methods were used for LULC classification and LST retrieval respectively. Results show that impervious surface has increased 13.79% and bare soil area has decreased 16.2%. The results also revealed bare soil class LST after a constant trend become increasing. It also revealed the impervious surface LST has a decreasing trend between 2000 and 2011 and has a little change. Using materials that have low absorption and high reflectance decrease the effect of heat island considerably.

Examining the Relationship Between Spatial Configurations of Urban Impervious Surfaces and Land Surface Temperature

The urban heat island (UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area (ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature (LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper (TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas (i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas (i.e.,<25%). The results suggest that, in addition to the abundance of ISAs, their spatial association has a significant effect on UHIs.

Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures

Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or on generalization ability and a flexible ATC modelling framework for different numbers of thermal observations is lacking. Here, we propose a hybrid ATC model (ATCH) that considers both prediction accuracy and generalization ability; our approach combines multiple harmonics with a linear function of LST-related factors, including surface air temperature (SAT), NDVI, albedo, soil moisture, and relative humidity. Based on the proposed ATCH, various parameter-reduction approaches (PRAs) are designed to provide model derivatives which can be adapted to different scenarios. Using Terra/MODIS daily LST products as evaluation data, the ATCH is compared with the original sinusoidal ATC model (termed the ATCO) and its variants, and with two frequently-used gap-filling methods (Regression Kriging Interpolation (RKI) and the Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST)), under clear-sky conditions. In addition, under overcast conditions, the LSTs generated by ATCH are directly compared with in-situ LST measurements. The comparisons demonstrate that the ATCH increases the prediction accuracy and the overall RMSE is reduced by 1.8 and 0.7 K when compared with the ATCO during daytime and nighttime, respectively. Moreover, the ATCH shows better generalization ability than the RKI and behaves better than the RSDAST when the LST gap size is spatially large and/or temporally long. By employing LST-related controls (e.g., the SAT and relative humidity) under overcast conditions, the ATCH can better predict the LSTs under clouds than approaches that only adopt clear-sky information as model inputs. Further attribution analysis implies that incorporating a sinusoidal function (ASF), the SAT, NDVI, and other LST-related factors, provides respective contributions of around 16%, 40%, 15%, and 30% to the improved accuracy. Our analysis is potentially useful for designing PRAs for various practical needs, by reducing the smallest contribution factor each time. We conclude that the ATCH is valuable for further improving the quality of LST products and can potentially enhance the time series analysis of land surfaces and other applications.

Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures

Hydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which upscaled point-scale measurements to the grid-scale (1 km) by retrieving SM information using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and topography data (including aspect and elevation data) and in situ measurements from a wireless sensor network (WSN). First, the time series of pixel-scale (1 km) representative SM information was retrieved from in situ measurements of SM, topography data, and LST. Second, Bayesian linear regression was used to calibrate the relationship between the representative SM and the WSN measurements. Last, the calibrated relationship was used to upscale a network of in situ measured SM to map spatially continuous SM at a high resolution. The upscaled SM data were evaluated against ground-based SM measurements with satisfactory accuracy—the overall correlation coefficient (r), slope, and unbiased root mean square difference (ubRMSD) values were 0.82, 0.61, and 0.025 m3/m3, respectively. Moreover, when accounting for topography, the proposed upscaling algorithm outperformed the algorithm based only on SM derived from LST (r = 0.80, slope = 0.31, and ubRMSD = 0.033 m3/m3). Notably, the proposed upscaling algorithm was able to capture the dynamics of SM under extreme dry and wet conditions. In conclusion, the proposed upscaled method can provide accurate high-resolution SM estimates for hydro-agricultural applications.

An Integrated Method for Reconstructing Daily MODIS Land Surface Temperature Data

Land surface temperature (LST) is a critical parameter in land surface process. The Moderate Resolution Imaging Spectroradiometer (MODIS) can be used to generate various LST data products, and these data have been widely applied in many studies. Unfortunately, cloud contamination brings about numerous missing or abnormal values, which negatively affect the application of LST data. To reconstruct missing values and improve data quality, this paper proposes an integrated method for reconstructing LST data under two conditions: Clear sky and cloudy sky. For the clear-sky condition, the MODIS eight-day LST (MOD11A2) product is used to be interpolated into the low-quality daily LST dataset using the harmonic analysis of time series (HANTS) algorithm. And then the linear regression algorithm is implemented on the original good-quality pixels of the MODIS daily LST (MOD11A1) product. After that, seamless processing on the reconstructed low-quality daily LST dataset is carried using the Poisson image editing method, and finally the high-quality daily LST dataset under clear-sky condition are then obtained. For the cloudy-sky conditions, the revised neighboring-pixel (NP) algorithm that originates from the surface energy balance theory is used to reconstruct the real LST data. To evaluate the reconstruction performance under clear-sky condition, a simulated dataset is generated from simulated missing pixels with good quality that are randomly chosen from 98 available LST images in the year 2010. Meanwhile, the real LST measurements collected from ground sites are used to assess the reconstructed results under cloudy-sky condition. Satisfactory validation results show that the proposed integrated method effectively reconstructs the missing information and low-quality pixels caused by cloud cover and other factors. The filled data can seamlessly preserve the temporal and spatial consistence of the daily LST data, which do promote the practical utility of the MODIS LST product.

Laboratory calibration and field measurement of land surface temperature and emissivity using thermal infrared multiband radiometers

Accurate ground measurements of land surface temperature (LST) are necessary for validating satellite LST products. In order to provide reliable data, ground radiometers must be calibrated with reference to an international standard, and radiometric temperatures must be corrected for land surface emissivity. As opposed to water, land surface emissivity is not usually known for many ground covers, so an emissivity value has to be assumed, assigned from spectral emissivity libraries or measured for each land cover and spectral band considered. The aim of this study is to show the laboratory calibration and the methodology for simultaneous field measurements of LST and emissivity employed in the comparison experiment held in the project Fiducial Reference Measurements for validation of Surface Temperature from Satellites (FRM4STS) funded by the European Space Agency. We used multiband CE-312 radiometers (five narrow bands plus one broad band in the 8–13 μm window) to simultaneously retrieve LST and band emissivities by means of the temperature-emissivity separation (TES) method for different ground covers (clover, tarmac, soil, gravel and sand). The TES method requires near-simultaneous measurements of ground-leaving radiances and sky downwelling radiances; the latter being measured in the field using a gold reflectance panel. For each surface cover, TES provided band emissivities in the CE-312 bands and LST from continuous radiance measurements performed over time. As a result of the experiment, we present the laboratory calibration of the CE-312 radiometers carried out against a traceable, reference blackbody, and the LST series and band emissivity values for the ground covers considered, together with a detailed LST uncertainty analysis including the uncertainties associated to the calibration of ground radiometers, the emissivity estimation by means of the TES method, and the sky radiance measurements, among others. According to these results, the total LST uncertainty was estimated as 0.4-0.6 K for the ground covers measured during the experiment.

ACCURACY EVALUATION OF STRUCTURE FROM MOTION THERMAL MOSAICING IN THE CENTER OF TOKYO

In the airborne and high-resolution measurement of Land Surface Temperature (LST) over large area, capturing and synthesizing of many images are necessary. In the conventional method, the process of georeferencing a large number of LST images is necessary to make one large image. Structure from Motion (SfM) technique was applied to automized the georeferencing process. We called it “SfM Thermal Mosaicing”. The objective of this study is to evaluate the accuracy of SfM thermal mosaicing in making an orthogonal LST image. By using airborne thermal images in the center of Tokyo, the LST image with the 2m resolution was created by using SfM thermal mosaicing. Its accuracy was then analyzed. The result showed that in the whole examined area, the mean error distance was 4.22m and in the small parts of the examined area, the mean the error distance was about 2m. Considering the image resolution, the error was minimal indicating good performance of the SfM thermal mosaicing. Another advantage of SfM thermal mosaicing is that it can make precise orthogonal LST image. With the progress of UAV and thermal cameras, the proposed method will be a powerful tool for the environmental researches on the LST.

Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land

AbstractField‐scale surface soil moisture (SSM, 0–10 cm), which is closely linked with land surface temperature (LST), is particularly important to agricultural water resource management. Active and passive microwave remote sensing‐based SSM retrievals on the order of kilometer squared resolutions are difficult to apply to heterogeneous agricultural land surfaces that may need SSM data at a resolution of 30 m. In this study, the High‐resolution Urban Thermal Sharpener and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model were applied to downscale optical and thermal remote sensing data simultaneously by blending Landsat and MODIS red‐near infrared‐LST data, with the ultimate goal to generate field‐scale SSM values from the trapezoidal approach. To evaluate the performance of the downscaled LSTE (based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model method) and SSM, an irrigation district (Area 1) in Inner Mongolia and an irrigation district in the North China Plain (Area 2) with varying spatial heterogeneity were selected as the testbeds. Results indicated that the downscaled LSTE was highly consistent with synchronous Landsat LSTH and in situ LST measurements in Area 1, with the root‐mean‐square error ranging from 0.73 to 2.75 K. Compared with the MODIS SSM, the average root‐mean‐square error of the downscaled SSM improved from 0.048 to 0.038 cm3/cm3 for both areas. The downscaled LSTE and SSM developed in this study enhance the spatiotemporal resolutions of the SSM estimates, maximizing the potential of remotely sensed information for agricultural water resource management.

Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels

Remotely sensed Land Surface Temperature (LST) is of paramount importance in numerous environmental applications. Although, coarse spatial resolution sensors provide frequent LST measurements, their applicability is rather limited for many applications. Downscaling methods are therefore applied to improve the spatial resolution of LST products. A number of Machine Learning (ML) methods have already been used in the LST downscaling studies. Nevertheless, the literature lacks a suitable inter-comparison of different ML methods, as well as the impact of the feature selection process on downscaling results. This study aims at comparing downscaled LST from 1 km daily MODIS LST product (MOD11A1) to 240 m using Random Forest Regression (RFR), Support Vector Regression (SVR), Extreme Learning Machine (ELM) and Temperature Sharpening (TsHARP) approaches with 11 predictor variables at a heterogeneous area in different seasons. In addition, by implementing a feature selection with the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) method, the most important variables were selected and used as inputs of the models. The results were evaluated against the LST derived from Landsat-8 thermal imageries using a split window method, showing that all the ML methods perform well in LST downscaling (with an average RMSE = 2.5 and MAE = 1.74) with marginal differences, outperforming the TsHARP method (RMSE∼ 3.02, MAE∼ 2.19). Among all the methods, ELM required the least computational effort, and when it was combined with SVM-RFE, general efficiency of the downscaling procedure was increased substantially.

Spatiotemporal characteristics of urban land surface temperature and UHI formation: a case study of Tehran, Iran

Urban heat island (UHI) is one of the adverse effects of densely populated cities. Therefore, studying the causes of temperature variations and UHI formation in urban area has become more important as a significant stage in improving urban health and development. Toward this end, impacts of urban Land Use/Land Cover (LULC) characteristics and spatiotemporal pattern of Land Surface Temperature (LST) under different weather conditions were investigated for determining the area with high potential of UHI formation in Tehran. Furthermore, LST of six different land covers was recorded with 15-min time intervals using 2channel-thermocouples with six PT100 sensors. In addition, SHRP model that can effectively predict LST using air temperature were developed for different weather conditions. Then, the accuracy of the model was determined by actual measured LST. Results indicate that temperature of land surface corresponds exactly to the distinct LULC types. The temperature of water bodies and vegetated area ranges between 12–14 °C and 8–10 °C lower than the surface temperature of roads and built-up areas in summer and winter times, respectively. Hence, bare lands, built-up areas and roads with average surface temperature of 48.5, 47.2, and 46.1 °C are the hottest places of the city with high potential of UHI formation. Also, results illustrate that SHRP model has high degree of accuracy for estimating the LST using air temperature when direct observations and measurement of LST are unavailable (r = 0.95).

Evaluation of the Weak Constraint Data Assimilation Approach for Estimating Turbulent Heat Fluxes at Six Sites

A number of studies have estimated turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into the strong constraint-variational data assimilation (SC-VDA) approaches. The SC-VDA approaches do not account for the structural model errors and uncertainties in the micrometeorological variables. In contrast to the SC-VDA approaches, the WC-VDA approach (the so-called weak constraint-VDA) accounts for the effects of structural and model errors by adding a model error term. In this study, the WC-VDA approach is tested at six study sites with different climatic and vegetative conditions. Its performance is also compared with that of SC-VDA at the six study sites. The results show that the WC-VDA produces 10.16% and 10.15% lower root mean square errors (RMSEs) for sensible and latent heat flux estimates compared with the SC-VDA approach. The model error term can capture errors in the turbulent heat flux estimates due to errors in LST and micrometeorological measurements, as well as structural model errors, and does not allow those errors to adversely affect the turbulent heat flux estimates. The findings also indicate that the estimated model error term varies reasonably well, so as to capture the misfit between predicted and observed net radiation in different hydrological and vegetative conditions. Finally, synthetically generated positive (negative) noises are added to the hydrological input variables (e.g., LST, air temperature, air humidity, incoming solar radiation, and wind speed) to examine whether the WC-VDA approach can capture those errors. It was found that the WC-VDA approach accounts for the errors in the input data and reduces their effect on the turbulent heat flux estimates.

Mapping regional turbulent heat fluxes via variational assimilation of land surface temperature data from polar orbiting satellites

Estimation of turbulent heat fluxes by assimilating sequences of land surface temperature (LST) measurements into variational data assimilation (VDA) frameworks has been the subject of several studies. The VDA approaches estimate turbulent heat fluxes by minimizing the difference between LST observations and estimations from the heat diffusion equation. The VDA methods have been tested only with high temporal resolution LST observations (e.g., from geostationary satellites) when applied at regional scales. Geostationary satellites can capture the diurnal cycle of LST, but they have a relatively low spatial resolution and mainly focus on low latitudes. To overcome these shortcomings, this study assimilates high spatial resolution LST data from polar orbiting satellites (e.g., Moderate Resolution Imaging Spectroradiometer, MODIS) into the combined-source (CS) and dual-source (DS) VDA schemes. An expression is developed to obtain an a priori evaporative fraction (EF) estimate from leaf area index (LAI) or apparent thermal inertia (ATI). The a priori EF estimate is used as an initial guess in the VDA approach. The results indicate that the VDA method is able to find the optimal value of EF by assimilating the low-temporal resolution MODIS LST data. The predicted turbulent heat fluxes from VDA are compared with the measurements from the large-aperture scintillometer at three sites (Arou, Daman, and Sidaoqiao) in the Heihe River Basin (located in northwest China). The findings indicate that the CS and DS VDA models perform well in various hydrological and vegetative conditions. The three-site-average root mean square errors (RMSEs) of sensible and latent heat fluxes estimates from the CS scheme are 37.44 W m−2 and 94.30 W m−2, respectively. The DS model reduces the abovementioned RMSEs by 19.82% and 21.37%, respectively. Overall, the results show that using the a priori EF estimate from the proposed expression in the VDA approach eliminates the need for the high resolution LST data from geostationary satellites, and allows the VDA method to estimate turbulent heat fluxes by assimilating LST data from polar orbiting satellites. Finally, several numerical tests are conducted to assess the effect of LST temporal sampling on the turbulent heat fluxes estimates. The results show that the LST measurement at 1400 Local Time (LT) has the most amount of information for partitioning the available energy into sensible and latent heat fluxes.

Scaling Effect of Fused ASTER-MODIS Land Surface Temperature in an Urban Environment.

There is limited research in land surface temperatures (LST) simulation using image fusion techniques, especially studies addressing the downscaling effect of LST image fusion. LST simulation and associated downscaling effect can potentially benefit the thermal studies requiring both high spatial and temporal resolutions. This study simulated LSTs based on observed Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST imagery with Spatial and Temporal Adaptive Reflectance Fusion Model, and investigated the downscaling effect of LST image fusion at 15, 30, 60, 90, 120, 250, 500, and 1000 m spatial resolutions. The study area partially covered the City of Los Angeles, California, USA, and surrounding areas. The reference images (observed ASTER and MODIS LST imagery) were acquired on 04/03/2007 and 07/01/2007, with simulated LSTs produced for 4/28/2007. Three image resampling methods (Cubic Convolution, Bilinear Interpolation, and Nearest Neighbor) were used during the downscaling and upscaling processes, and the resulting LST simulations were compared. Results indicated that the observed ASTER LST and simulated ASTER LST images (date 04/28/2007, spatial resolution 90 m) had high agreement in terms of spatial variations and basic statistics based on a comparison between the observed and simulated ASTER LST maps. Urban developed lands possessed higher LSTs with lighter tones and mountainous areas showed dark tones with lower LSTs. The Cubic Convolution and Bilinear Interpolation resampling methods yielded better results over Nearest Neighbor resampling method across the scales from 15 to 1000 m. The simulated LSTs with image fusion can be used as valuable inputs in heat related studies that require frequent LST measurements with fine spatial resolutions, e.g., seasonal movements of urban heat islands, monthly energy budget assessment, and temperature-driven epidemiology. The observation of scale-independency of the proposed image fusion method can facilitate with image selections of LST studies at various locations.

Utility of the two-source energy balance (TSEB) model in vine and interrow flux partitioning over the growing season

For monitoring water use in vineyards, it becomes important to evaluate the evapotranspiration (ET) contributions from the two distinct management zones: the vines and the interrow. Often the interrow is not completely bare soil but contains a cover crop that is senescent during the main growing season (nominally May–August), which in Central California is also the dry season. Drip irrigation systems running during the growing season supply water to the vine plant and re-wet some of the surrounding bare soil. However, most of the interrow cover crop is dry stubble by the end of May. This paper analyzes the utility of the thermal-based two-source energy balance (TSEB) model for estimating daytime ET using tower-based land surface temperature (LST) estimates over two Pinot Noir (Vitis vinifera) vineyards at different levels of maturity in the Central Valley of California near Lodi, CA. The data were collected as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Local eddy covariance (EC) flux tower measurements are used to evaluate the performance of the TSEB model output of the fluxes and the capability of partitioning the vine and cover crop transpiration (T) from the total ET or T/ET ratio. The results for the 2014–2016 growing seasons indicate that TSEB output of the energy balance components and ET, particularly, over the daytime period yield relative differences with flux tower measurements of less than 15%. However, the TSEB model in comparison with the correlation-based flux partitioning method overestimates T/ET during the winter and spring through bud break, but then underestimates during the growing season. A major factor that appears to affect this temporal behavior in T/ET is the daily LAI used as input to TSEB derived from a remote sensing product. An additional source of uncertainty is the use of local tower-based LST measurements, which are not representative of the flux tower measurement source area footprint.

Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

After Landsat 8 was launched in 2013, it was observed that for Thermal Infrared sensor (TIRS) bands, radiance from outside of an instrument’s field-of-view produced a non-uniform ghost signal across the focal plane that varied depending on the out-of-scene content (i.e., the stray light effect). A new stray light correction algorithm (SLCA) is currently operational and has been implemented into the United States Geological Survey (USGS) ground system since February 2017. The SLCA has also been applied to reprocess historical Landsat 8 scenes. After approximately two years of SLCA implementation, more land surface temperature (LST) validation studies are required to check the effect of correction in the estimation of LST from different retrieval algorithms. For this purpose, three different LST estimation method algorithms (i.e., the radiative transfer equation (RTE), single-channel algorithm (SCA), and split-window algorithm (SWA)) have been assessed. The study site is located on the campus of the University of Balearic Islands on the island of Mallorca (Spain) in the western Mediterranean Sea. The site is considered a heterogeneous area that is composed of different types of surfaces, such as buildings, asphalt roads, farming areas, sloped terrains, orange fields, almond trees, lawns, and some natural vegetation regions. Data from 21 scenes, which were acquired by the Landsat 8-TIRS sensor and extracted from a 100 × 100 m2 pixel, were used to retrieve the LST with different algorithms; then, they were compared with in situ LST measurements from a broadband thermal infrared radiometer located on the same Landsat 8 pixel. The results show good performances of the three methods, with the SWA showing the lowest observed RMSE (within 1.6–2 K), whereas the SCA applied to the TIRS band 10 (10 µm) was also appropriate, with a RMSE ranging within 2.0–2.3 K. The LST estimates using the RTE algorithm display the highest observed RMSE values (within 2.0–3.6 K) of all of the compared methods, but with an almost unbiased value of −0.1 K for the case of techniques applied to band 10 data. The SWAs are the preferred method to estimate the LST in our study area. However, further validation studies around the world are required.

Monitoring of Thermal Activity at the Hatchobaru–Otake Geothermal Area in Japan Using Multi-Source Satellite Images—With Comparisons of Methods, and Solar and Seasonal Effects

The Hatchobaru–Otake (HO) geothermal field is proximal to the Kuju volcano on Kyushu, Japan. There are currently three geothermal power plants operating within this geothermal field. Herein, we explore the thermal status of the HO geothermal area using ASTER thermal infrared data to monitor heat losses from 2009 to 2017. We assessed the solar effects and seasonal variation on heat losses based on day- and night-time Landsat thermal infrared images, and compared three conventional methods of land surface temperature (LST) measurements. The normalized difference vegetation index threshold method of emissivity, the split window algorithm for LST, and the Stefan–Boltzmann equation for radiative heat flux (RHF) were used to determine the heat loss within the study area. The radiative heat loss (RHL) was 0.36 MW, 38.61 MW, and 29.14 MW in 2009, 2013, and 2017, respectively, from the HO geothermal field. The highest anomaly in RHF was recorded in 2013, while the lowest was in 2009. The RHLs were higher from Otake than from the Hatchobaru thermal area in the year of 2013 (~31%) and 2017 (~78%). The seasonal variation in the RHLs based on all three LST estimation methods had a similar pattern, with the highest RHL (about 383–451 MW) in spring and the lowest (about 10–222 MW) in autumn for the daytime images from the HO geothermal field. In the nighttime images, the highest RHL was about 35–67 MW in autumn and the lowest was about 1–3 MW in spring, based on the three LST methods for RHFs. The highest RHL was about 35–42 MW in spring (day) and 3–7 MW in autumn (night) from the Hatchobaru thermal area, analyzed separately. Similarly, the highest RHL was about 22–25 MW in spring (day) and 4–5 MW in winter (night) from the Otake thermal area. The seasonal variation was greatly influenced by the regional ambient temperature. We also observed that clouds had a huge effect, with the highest values for both LST and RHF recorded below clouds on an autumn day. Overall, we obtained higher LSTs at nighttime and lower LSTs during the day from the improved mono-window algorithm than the split window algorithms for all of the seasons. The heat losses were also higher for the improved mono-window algorithm than the split window algorithms, based on the LST nighttime thermal infrared data. Considering the error level of the LST methods and Landsat 8 band 11, this study recommends the IWM method for LST using the Landsat 8 band 10 data. This study also suggests that both the nighttime ASTER and Landsat 8 thermal infrared data could be effective for monitoring the thermal status of the HO geothermal area, given that data is available for the entire period.

Capturing the Diurnal Cycle of Land Surface Temperature Using an Unmanned Aerial Vehicle

Characterizing the land surface temperature (LST) and its diurnal cycle is important in understanding a range of surface properties, including soil moisture status, evaporative response, vegetation stress and ground heat flux. While remote-sensing platforms present a number of options to retrieve this variable, there are inevitable compromises between the resolvable spatial and temporal resolution. For instance, the spatial resolution of geostationary satellites, which can provide sub-hourly LST, is often too coarse (3 km) for many applications. On the other hand, higher-resolution polar orbiting satellites are generally infrequent in time, with return intervals on the order of weeks, limiting their capacity to capture surface dynamics. With recent developments in the application of unmanned aerial vehicles (UAVs), there is now the opportunity to collect LST measurements on demand and at ultra-high spatial resolution. Here, we detail the collection and analysis of a UAV-based LST dataset, with the purpose of examining the diurnal surface temperature response: something that has not been possible from traditional satellite platforms at these scales. Two separate campaigns were conducted over a bare desert surface in combination with either Rhodes grass or a recently harvested maize field. In both cases, thermal imagery was collected between 0800 and 1700 local solar time. The UAV-based diurnal cycle was consistent with ground-based measurements, with a mean correlation coefficient and root mean square error (RMSE) of 0.99 and 0.68 °C, respectively. LST retrieved over the grass surface presented the best results, with an RMSE of 0.45 °C compared to 0.67 °C for the single desert site and 1.28 °C for the recently harvested maize surface. Even considering the orders of magnitude difference in scale, an exploratory analysis comparing retrievals of the UAV-based diurnal cycle with METEOSAT geostationary data yielded pleasing results (R = 0.98; RMSE = 1.23 °C). Overall, our analysis revealed a diurnal range over the desert and maize surfaces of ~20 °C and ~17 °C respectively, while the grass showed a reduced amplitude of ~12 °C. Considerable heterogeneity was observed over the grass surface at the peak of the diurnal cycle, which was likely indicative of the varying crop water status. To our knowledge, this study presents the first spatially varying analysis of the diurnal LST captured at ultra-high resolution, from any remote platform. Our findings highlight the considerable potential to utilize UAV-based retrievals to enhance investigations across multi-disciplinary studies in agriculture, hydrology and land-atmosphere investigations.

Analysis of the long‐term high‐resolution infrared radiation sounder land surface temperature against ground measurements during 1980–2009 in the Poyang Lake basin, China

Land surface temperature (LST) plays an essential role in the energy exchanges between land and atmosphere. It reflects the changes of available terrestrial energy in the context of global warming, and thus can serve as an indicator of regional climate change. The recently developed HIRS (High‐resolution infrared radiation sounder onboard NOAA satellites) LST product is among the longest LST records. We have analysed the HIRS LST against ground measurements from 66 stations in the Poyang Lake basin, China, considering the temporal variations at daily and interannual time scales during 1980–2009. The error metrics of correlation coefficient, Bias and unbiased Root Mean Square Deviation (ubRMSD) are used, together with the long‐term linear trends. The two data sets have considerable agreement with respect to daily variations. Their correlation coefficients are higher than 0.95 for all stations; and those for daily anomalies range from 0.73 to 0.85 across the basin. The Bias and the ubRMSD have indicated that the HIRS data have generally underestimated the LST across the basin, mainly because of its large underestimation of the occurrences of temperature higher than 35 °C. Nevertheless, the HIRS LST has largely overestimated the occurrences of summer days with temperature ranging 20–30 °C. The Bias and the ubRMSD appear to be smaller in the cropped plains and much larger in the afforested mountain areas. As for the interannual variations, the HIRS LST shows highest correlations with ground measurement in spring; they are lowest in summer, possibly due to the interference of monsoonal precipitation. Both the HIRS LST and ground measurements show significant increasing trends with similar patterns in spring and winter. However, the long‐term trends are significantly overestimated across the basin in summer, but underestimated in autumn in the HIRS LST data.