Surface Temperature Retrieval Research Articles

Cloud detection for HY-1C/COCTS over the ocean based on spectral-and-textural-information-guided deep neural network

AbstractGiven that clouds can absorb and scatter radiation signals in the visible and infrared bands, cloud detection is a key preprocessing step for ocean color and sea surface temperature retrievals. In this research, a Spectral-and-Textural-Information-Guided deep neural Network (STIGNet) is designed for cloud detection in global ocean data from the Haiyang-1C (HY-1C)/Chinese Ocean Color and Temperature Scanner (COCTS). Considering the spectral and textural properties of clouds, the model incorporates HY-1C/COCTS spectral data, differences in brightness temperature (BT), local statistical characteristics of BT, and geographical location information–all of which are closely related to cloud features. Notably, an edge learning module is implemented to emphasize edge features during the training process. We construct a HY-1C/COCTS cloud detection dataset to train and test the cloud detection model. In the dataset, labels are generated by combining the Bayesian cloud detection method with a manual mask. Analysis of the resulting cloud detection images indicates that STIGNet exhibits accurate performance across various types of clouds while showing minimal overestimated errors in areas such as ocean fronts or sun glints, where they tend to occur frequently. The ablation experiments performed on physical-based input features and edge learning modules show enhancements in cloud detection accuracy. Evaluation results demonstrate an overall accuracy of 96.64%, with a cloud overestimated error of 1.61% and a cloud missed error of 1.76%. These findings highlight the effectiveness of STIGNet in generating precise cloud masks for HY-1C/COCTS data.

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025.

Assessment and Validation of Land Surface Temperature Retrieval Algorithms Using Landsat 8 TIRS Data in Antarctic Ice-Free Areas

This study evaluates the effectiveness of different Land Surface Temperature (LST) retrieval algorithms applied to Landsat 8 Thermal Infrared Sensor (TIRS) data in the ice-free regions of the Antarctic Peninsula. The primary objective is to determine the most accurate algorithm for LST estimation in these environments. Three algorithms, namely radiative transfer equation (RTE), single channel (SC), and mono window (MW), were utilised and compared to in-situ measurements at two locations in the northern part of James Ross Island (JRI), Antarctic Peninsula. The study considered various factors influencing LST accuracy, including land surface emissivity, atmospheric conditions, and sun elevation angles. The findings reveal that all three algorithms demonstrate significant sensitivity to emissivity. The MW algorithm emerged as the most suitable, showing the lowest root mean square error (RMSE) of 3.06 °C, followed by the SC and RTE algorithms with RMSE values of 3.68 and 3.98 °C, respectively. The study also underscores a strong positive correlation between LST retrieval accuracy and sun elevation angle, with more accurate results obtained from satellite images acquired in February, characterised by lower sun elevation angles. No significant relationship with water vapour content in the atmosphere was identified during the investigated period.

Data-driven approach for land surface temperature retrieval with machine learning and sentinel-2 data

This research endeavors to advance land surface temperature (LST) prediction accuracy through the development of a sophisticated machine learning model. Leveraging the potential of Sentinel 2 data and atmospheric parameters, we augment Landsat-based LST with MODIS-based LST, enriching the temporal dimensions of our dataset. A distinctive feature of our study is the pioneering use of Sentinel 2 data as inputs for LST prediction, a facet scarcely explored in the existing literature. Our investigation delves into the correlation dynamics between LST and atmospheric parameters. Notably, the study employs a diverse set of machine learning models, including Extra Trees, Random Forests, LightGBM, XGBoost, and Support Vector Regressor. These models collectively exhibit superior performance, with Extra Trees emerging as a standout performer, with a minimal mean absolute error (MAE) of 0.423, a root mean square error (RMSE) of 1.340 °C, and an impressive coefficient of determination (R2) of 0.984. The exploration of Sentinel 2 data as an input source for LST prediction not only refines predictive accuracy but also opens novel research avenues in the realm of LST dynamics. This study contributes to the existing body of knowledge by introducing innovative methodologies and providing a comprehensive understanding of the intricate correlations influencing LST.

Sampling Error of Mean and Trend of Nighttime Air Temperature at Weather Stations Over China Using Satellite Data as Proxy

AbstractMeteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land‐atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under‐sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under‐sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station.

Study on the contributions of 2D and 3D urban morphologies to the thermal environment under local climate zones

With the changes in global climate and rapid urbanization, identifying the contribution of urban morphology to the thermal environment under different scenarios could be helpful in proposing differentiated optimization responses of different scenarios for improving the thermal environment by means of natural cooling. This study calculated 2D and 3D metrics and retrieved surface temperature based on building vector data, land use, electronic map, the remote sensing images. Then, the study of spatial scale sensitivity was developed to build a nested multi-scale Local Climate Zone (LCZ) (NMSLCZ) map. Finally, the eXtreme Gradient Boost (XGBoost) regression model was performed to explore the relative contributions of 2D and 3D morphologies of different LCZ scenarios to surface temperatures. The results of scale sensitivity analysis showed the size of the optimal spatial scale decreased with the increase in building height. The 3D morphologies show more significant effects on LST variations than 2D morphologies. The surface temperature mainly by built type is significantly higher than that mainly by land cover type. Scenarios representing dense trees, shrubs, low vegetation, and water have significant cooling effects. In the scenarios of built types, the surface temperature of compact and large low-rise buildings is higher than that of the other building forms. Our findings provide a new perspective for LCZ mapping of scale optimization of the thermal environment. Meanwhile, the contributions of 2D and 3D morphologies on LSTs could help urban planners and managers understand the impacts of climate change and propose relevant strategies toward sustainable cities.

Correction of Thin Cirrus Absorption Effects in Landsat 8 Thermal Infrared Sensor Images Using the Operational Land Imager Cirrus Band on the Same Satellite Platform.

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth's surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4-2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes.

Analysis of Urban Expansion and Heat-Island Effect of Hefei Based on ENVI

Urbanization is one of the most significant features of current social progress. Using Landsat TM/OLI images from 1995, 2005, and 2018, the land-use change, vegetation coverage, and land surface temperature retrieval of Hefei are studied, and the driving force of Hefei’s expansion is analyzed. The influence of urban expansion and vegetation coverage on the intensity of the urban heat-island effect is discussed. As the city develops, various factors, such as natural conditions, economic growth, demographic changes, and policy decisions, are driving the expansion of construction land in Hefei. The overall performance shows expansion to the southwest of the main urban area, the surface temperature rises as the city expands, and the area of the heat-island effect also increases, showing the trend of multi-center distribution. There is a clear negative correlation between land surface temperature and vegetation coverage. Therefore, increasing the city’s green infrastructure can effectively alleviate the severe heat-island effect.

Comment on Yu et al. Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Remote Sens. 2014, 6, 9829–9852

Accurate land surface temperature (LST) retrieval from satellite data is pivotal in environmental monitoring and scientific research. This study addresses the impact of variability in the effective wavelengths used for LST retrieval from the Thermal Infrared Sensor (TIRS) data of Landsat 8. We conduct a detailed analysis comparing the effective wavelengths reported by Yu et al. (2014) and those derived from data provided by the USGS. Our analysis reveals significant variability in the effective wavelengths for bands 10 and 11 of Landsat 8. By applying Planck’s Law and utilizing the K1 and K2 coefficients available in the metadata of Landsat 8 products, we derive the effective wavelengths for bands 10 and 11. We also rederive the effective wavelength by integrating the spectral response function of the TIRS1 sensor. Our findings indicate that the effective wavelength for band 10 is 10.814 μm, aligning with the USGS data, while the effective wavelength for band 11 is 12.013 μm. We discuss the implications of these corrected effective wavelengths on the accuracy of LST retrieval algorithms, particularly the single channel algorithm (SC) and the radiative transfer equation (RT) employed by Yu et al. The importance of using precise effective wavelengths in satellite-based temperature retrieval is emphasized, to ensure the reliability and consistency of results. This analysis underscores the critical role of accurate spectral calibration parameters in remote sensing studies and provides valuable insights in the field of land surface temperature retrieval from Landsat 8 TIRS data.

Assessing the land use dynamics and thermal environment using geospatial techniques in the industrial city of Chotanagpur Plateau Region, India.

The phenomenon of urban heat island (UHI) is characterized by industrial, economic development, unplanned and unregulated land use as well as a rapid increase in urban population, resulting a warmer inner core in contrast to the surrounding natural environment, thus requiring immediate attention for a sustainable urban environment. This study examined the land use/land cover (LULC) change, pattern of spectral indices (Normalized Difference Vegetation Index, NDVI; Normalized Difference Water Index, NDWI; Normalized Difference Built-up Index, NDBI and Normalized Difference Bareness Index, NDBaI), retrieval of land surface temperature (LST) and Urban Thermal Field Variance Index (UTFVI) as well as identification of UHI from 2000 to 2022. The relationship among LST and LULC spectral indices was estimated using Pearson's correlation coefficient. The Landsat-5 (TM) and Landsat-8 (OLI/TIRS) satellite data have been used, and all tasks were completed through various geospatial tools like ArcGIS 10.8, Google Earth Engine (GEE), Erdas Imagine 2014 and R-Programming. The result of this study depicts over the period that built-up area and water bodies increased by 119.78 and 35.70%, respectively. On the contrary, fallow and barren decreased by 55.33 and 32.31% respectively over the period. The mean and maximum LST increased by 3.61 °C and 2.62 °C, and the study reveals that a high concentration of UTFVI and UHI in industrial areas, coal mining sites and their surroundings, but the core urban area has observed low LST and intensity of UHI than the peripheral areas due to maintained vegetation cover and water bodies. An inverse relationship has been found among LST, NDVI and NDWI, while adverse relationships were observed among LST, NDBI and NDBaI throughout the period. Sustainable environment planning is needful for the urban area, as well as the periphery region and plantation is one of the controlling measures of LST and UHI increment. This work provides the scientific base for the study of the thermal environment which can be one of the variables for planning of Asansol City and likewise other cities of the country as well as the world.

Solar zenith angle-based calibration of Himawari-8 land surface temperature for correcting diurnal retrieval error characteristics

The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which can arise from variations in viewing geometries and inherent differences in sensor types and LST algorithms. Despite this, previous research has primarily focused on analysing the directionality of LST without thoroughly exploring systematic differences between platforms. Hence, we presented a Solar Zenith Angle-based Calibration (SZAC) method to harmonise the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline) with the MODerate-resolution Imaging Spectroradiometer (MODIS) LST. SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different platforms, which is anticipated to complement typical directionality analyses. We evaluated the harmonised LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparison. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and −0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. SZAC can also reduce LST angular effects due to its capability of quantifying spatial inhomogeneity of surface heat dynamics. Furthermore, it is possible to implement SZAC using LST data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas. This could improve our understanding of the error characteristics of coincident imageries from geostationary and polar-orbiting platforms, allowing for targeted refinements and global harmonisations to further enhance applicability.

Soil Moisture Profiles of Ecosystem Water Use Revealed With ECOSTRESS

AbstractWhile remote sensing has provided extensive insights into the global terrestrial water, carbon, and energy cycles, space‐based retrievals remain limited in observing the belowground influence of the full soil moisture (SM) profile on ecosystem function. We show that this gap can be addressed when coupling 70 m resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station retrievals of land surface temperature (LST) with in‐situ SM profile measurements. These data sets together reveal that ecosystem water use decreases with depth with 93% of sites showing significant LST coupling with SM shallower than 20 cm while 34% of sites have interactions with SM deeper than 50 cm. Furthermore, the median depth of peak ecosystem water use is estimated to be 10 cm, though forests have more common peak interactions with deeper soil layers (50–100 cm) in 37% of cases. High spatial resolution remote sensing coupled with field‐level data can thus elucidate the role of belowground processes on land surface behavior.

Super-Resolution for Land Surface Temperature Retrieval Images via Cross-Scale Diffusion Model Using Reference Images

Geothermal resources are efficient, clean, and renewable energy sources. Using high-resolution images captured by remote sensing satellites for temperature retrieval and searching for geothermal anomaly areas is an efficient method. However, obtaining land surface temperature retrieval images requires multiple steps of calculation, which can result in a great loss of image information and resolution. Therefore, the super-resolution reconstruction of LST retrieval images is currently a challenge in geothermal resource exploration. Although the current super-resolution methods for LST retrieval images can appropriately restore image quality, the overall restoration of the surface temperature information in the region is still not ideal. We propose a cross-scale reference image super-resolution model based on a diffusion model using deep learning technology. First, we propose the Pre-Super-Resolution Network (PreNet), which can improve both indices and the visual effect of images. Second, to reduce the white noise in the super-resolution images, we propose the Cross-Scale Reference Image Attention Mechanism (CSRIAM). The introduction of this mechanism greatly reduces noise in the images and improves the overall image quality. Compared to previous methods, we improved both experimental indices such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), etc., and vision quality, and optimized the recovery of geothermal anomalies. Through our experimental results, we found that the CS-Diffusion model has a very strong ability to restore the image quality of the LST retrieval. After restoring its image quality, we can make a positive contribution to subsequent geothermal resource exploration.

Research on Landsat 8 land surface temperature retrieval and spatial–temporal migration capabilities based on random forest model

Land surface temperature (LST) plays a crucial role in characterizing land surface processes and the energy balance. Accurate monitoring of spatial–temporal variations in LST holds great significance for global and regional climate change research. However, with the increasing of multi-source remote sensing data, it has become a challenging task to construct an LST model that reduces dependence on auxiliary data (such as land surface emissivity (LSE) and atmospheric water vapor content (WVC) synchronized with satellite observations. This study proposed an LST retrieval model based on random forest model (RFLSTR). The results of 10-fold cross-validation showed that the RFLSTR model had high modelling accuracy, the root mean square error (RMSE) and mean absolute error (MAE) were lower than 2 K. Landsat 8 images from the Beijing area and South Korea, were utilized to assess the spatial–temporal migration performance of the RFLSTR model. Additionally, Landsat 9 images from South Korea were employed to analyse the sensor migration performance of the RFLSTR model. In conclusion, the findings highlight that the proposed RFLSTR model achieve high retrieval accuracy without relying on auxiliary data (such as LSE and WVC) synchronized with satellite observations. Moreover, the RFLSTR model exhibits robust spatial–temporal capabilities, enhancing the efficiency of LST retrieval and facilitating the utilization of satellite data.

Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods

Land Surface Temperature (LST) measurements using remote sensing data are conducted using various methods, including the Single Channel Method (SCM) and the Multi-Channel Method (MCM). Emissivity, which significantly affects LST, is determined using methods such as the Correction-Based Emissivity Method (CBEM) and the Non-Linear Based Emissivity Method (NBEM). Given these factors, validating LST measurements is crucial for assessing the accuracy of data processing results. This study aims to compare LST data retrieved using SCM and MCM with various emissivity calculation methods and assess their accuracy compared to single measurements with an infrared thermometer, measurements using a grid plot, and measurements using a thermal camera with the assistance of an Unmanned Aerial Vehicle (UAV). The results show that SCMJM&S and SCMAC did not produce optimal accuracy (>2.5K) when using the emissivity estimates from CBEM and NBEM. In contrast, MCM that adopted MCMSko, MCMQin, and MCMMao generally produced high-accuracy LST with an average difference of <1 K from actual temperatures, specifically in shrub pixels. MCM with the NDVI threshold method (NDVITHM) showed excellent results in distinguishing between vegetation and non-vegetation objects. In conclusion, MCM provides more accurate LST than SCM. Nevertheless, selecting the appropriate method should consider heterogeneity, homogeneity, and regional physiography.

Research on Extracting Multiple Feature Impervious Surface Based on Fusion of Optics and Radar

Impervious surface is an important indicator for analyzing urban expansion, measuring the degree of urbanization, and characterizing the urban ecological environment. Accurately extracting impervious surface data is of great significance to regional economic development, disaster prediction, ecological restoration, and environmental assessment. In this paper, using multi-spectral, synthetic aperture radar (SAR) and surface temperature retrieval (LST) data, from the four perspectives of spectral features, time series features, SAR texture features, and coherence features, an index feature that highlights impervious surface information is constructed. The model generates a 10m impervious surface product in the urban area of Shaoguan, and verifies the accuracy of the real sample based on the 0.8m GF-2 data in the same period. The results indicate that the overall accuracy of extracting impermeable surfaces in the study area is 94%, with a Kappa coefficient of 0.92. The multi feature fusion random forest model combining optical data and SAR data has high extraction accuracy and applicability in the mountainous areas of southwestern China, which improves the misclassification of bare land and other land types as impermeable surfaces.

Multi-scale remote sensing retrieval model for land surface emissivity and its impact on land surface temperature estimation

ABSTRACT As a fundamental parameter of the surface radiation process, land surface emissivity (LSE) is the most direct factor affecting the land surface temperature (LST) retrieval accuracy. Based on the measured data from 2017 to 2023, the characteristics of LSE changes were analysed, different timescale LSE retrieval models were developed, and the impact of LSE on pixel scale observational LST acquisition was evaluated by using unmanned aerial vehicle (UAV) remote sensing images. The results show that with changes in atmospheric environmental conditions, the LSE changes are relatively less pronounced at 3.99 µm and 4.02 µm in the mid-infrared band and at 10.8 µm and 11.8 µm in the thermal infrared band than in the other bands. At the timescale above day, the LSE is significant correlation with the normalized differential vegetation index (NDVI). Vegetation is the main parameter that dominates the LSE change under heterogeneous underlying surfaces. On a daily timescale, LSE exhibits typical diurnal variation characteristics, and the amplitude of diurnal variation is influenced mainly by shallow soil water and heat conditions. The developed LSE model based on Fengyun-4A (FY-4A) satellite remote-sensing data can effectively reproduce the daily variation characteristics of LST and avoid the ‘jumping’ phenomenon that may occur in the NDVI threshold method. The root-mean-square error (RMSE) between the retrieved LST and in situ data is reduced to 3.78°C, and the retrieval accuracy is better than that of the published LST product. When using UAVs to obtain pixel-scale LSTs, dry bare soil is more sensitive to LSE. When the LSE increases from 0.95 to 1.0, the average LST obtained by the UAV deviates by approximately 1.0°C. This study preliminarily provides some spatiotemporal variation patterns of LSE and lays a foundation for the later-modified LST retrieval algorithms.

Effects of landscape pattern on land surface temperature in Nanchang, China

The composition and configuration of landscapes are critical important to design effective approaches to mitigate urban thermal environment in the urbanization process. In this research, land use maps and land surface temperature (LST) retrieval were derived in Nanchang city of central China based on product datasets and the thermal infrared band of Landsat. The results showed that the thermal environment of Nanchang had become worse over the past two decades, that is, the proportion of area of the extremely low temperature zone (ELTZ) decreased from 4.39 to 0.77% from 2001 to 2020, and that of medium temperature zone (MTZ) reduced by 20%, whereas those of the high temperature zone (HTZ) and the extremely high temperature zone (EHTZ) increased sharply after 2001, and by 2020, the area ratio increased by 11% and 7.16%, respectively. The agricultural land (AL) area decreased from 68.44 to 49.69%, was gradually replaced by construction land (CL). The CL occupied the largest proportion in EHTZ, HTZ and slight high temperature zone (SHTZ); water landscape (WL) and green land (GL) occupied the largest proportion in ELTZ, low temperature zone (LTZ); and AL occupied the largest proportion in SHTZ, MTZ, and slight low temperature zone (SLTZ). Landscape configuration also obviously impacted on LST. The model fitting was well (R = 0.87) between land use area and LST by multiple regression analysis. The significant correlation between LST and six landscape pattern indices of CL (p < 0.01) indicated that the larger percent (PLANT, R = 0.78) and the more concentrate (LPI, R = 0.73) of CL implied the higher LST, while the more fragment (NP, R = − 0.45), dispersed and complex shape (R = − 0.35) were benefit to relieve LST. Contrastively, the larger percent and the more concentrated and complex shape distribution of AL, GL and WL, the lower LST (p < 0.01). In addition, LST had closely correlation with landscape level indices such as aggregation degree (AI, R = 0.44) and diversity (SHDI, R = − 0.60) (p < 0.01).

Diurnal to Decadal Variability in Land Surface and Air Temperature Gradient from 2002 to 2022 over the Contiguous United States

Abstract The surface and air temperature gradient (TS00 − Tair) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in TS00 − Tair from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that TS00 − Tair reaches its maximum at noon with an average of 6.85°C over the contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of TS00 − Tair has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (TS05), to calculate TS05 − Tair, we find that TS00 − Tair and TS05 − Tair have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of TS00 − Tair has a significant decreasing trend (−0.50° ± 0.007°C decade−1) from 2002 to 2022 due to Tair increasing at a higher rate than TS00 during the nighttime. The occurrence frequency of near-surface stable conditions (TS00 − Tair &lt; 0) increases significantly, and the frequency of unstable conditions (TS00 − Tair &gt; 0) decreases notably throughout the year except for winter. When it is stable, the magnitude of TS00 − Tair tends to decrease while the TS00 − Tair tends to increase when it is unstable, which is consistent with the drying condition caused by the precipitation deficit. This study provides the first observational evidence on how TS00 − Tair responds to warming. Significance Statement The impact of global warming on surface-air temperature gradients is a crucial scientific issue that needs to be addressed. These gradients determine changes in cloud and precipitation, affecting water resources. However, traditional surface temperature measurements from weather stations are of high uncertainty due to direct exposure to the insolation. Satellite retrieval of surface temperature is limited by the availability of clear sky conditions, with low accuracy for temperature gradient calculations. Despite their importance, high-accuracy data of land surface temperature are still lacking. To address this issue, the U.S. Climate Reference Network (USCRN) uses infrared radiometers to continuously monitor surface temperature with high accuracy and sampling frequency. This study reports on surface-air temperature gradients at more than 100 U.S. stations, providing insight into diurnal to decadal variability over the contiguous United States. The study also highlights the significant difference between the land surface temperature–air temperature gradient and soil temperature–air temperature gradient.

A normal form for synchronous land surface temperature and emissivity retrieval using deep learning coupled physical and statistical methods

The innovative normal form, named deep learning-couple-physical and statistical methods (DL-C-PS), has been developed for synchronously retrieving land surface temperature and emissivity (LST&E) from Japan's geostationary meteorological satellite Himawari-8 carrying the Advanced Himawari Imager (AHI). First, geophysical logical reasoning (GLR) and expert knowledge were used to formulate radiative transfer equations (RTEs). Then, a hybrid approach integrating physical and statistical methods was employed to derive the solution, with deep learning (DL) optimizing the solution process. Three band combination schemes were designed to assess the effectiveness of the DL-C-PS normal form. Simulation data from the MODerate spectral resolution atmospheric TRANsmittance mode (MODTRAN) yielded promising results during validation. Root mean square error (RMSE) values were below 1 K for LST and below 0.008 for LSE when using band combinations of four thermal infrared (TIR) bands or at least three TIR bands combined with water vapor information. Cross-validation and in situ validation showed consistent findings with simulation validation. Compared to MODIS LST&E products (MYD21), in most cases, the RMSE values for LST&E were approximately 2 K and less than 0.015 during daytime, and below 1.3 K and 0.017 during night, respectively. Validated against in situ observations, nighttime RMSE values for LST were approximately 1.5 K with correlation coefficient (R) values better than 0.93. The higher RMSE observed in daytime compared to nighttime can be attributed to the influence of the sun's illumination angle on satellite scanning imaging. Overall, this research presented a normal form for multi-parameter estimation by leveraging the optimal calculation of DL and incorporating physical interpretations.