Land Surface Temperature Model Research Articles

Urban growth’s implications on land surface temperature in a medium-sized European city based on LCZ classification

The study determined the influence of changes in land use and land cover (LULC) on land surface temperature (LST) over a 33-year period based on a medium-sized European city (Poznań, Poland). The LST was estimated from Landsat 5, 8 and Terra (MOD11A2v6) satellites. The local estimation of climate patterns was based on the Local Climate Zones (LCZ) classification utilised with the methodology proposed by the World Urban Database and Access Portal Tools (WUDAPT). Moreover, the Copernicus’ imperviousness density product (IMD) was used. Between 2006 and 2018 the area with IMD of 41–100% increased by 6.95 km2, 0–20% decreased by 7.03 km2. The contribution of built-up LCZs increased by 7.4% (19.21 km2) between 1988 and 2021 reaching 13% (34 km2) within open mid-rise LCZ. Due to urbanisation and reforestation, low plants LCZ shrunk by 12.7%. For every 10% increase in IMD, LST increases by up to 0.14 °C. Between 1988 and 2021 the LSTm in specific LCZs rose from 1.52 up to 2.97 °C. As per LST models LCZ change from natural to built-up led up to 1.19 °C LST rise. The increase of the LSTm was registered even when the LCZ remained unchanged.

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.

The effect of pixel heterogeneity on surface heat and water vapor flux estimated by the remote sensing-based model coupled with deep learning

Accurate quantification of sensible (H) and latent (LE) heat fluxes are desperately crucial for agricultural irrigation strategy, drought monitoring, water resource management, and climate change. Regional-scale H and LE were derived generally from satellite remote sensing data using relevant modeling methods. However, one of the challenges of modeling methods is to deal with sub-pixel heterogeneity, because the model usually assumes homogeneous conditions within a pixel. In this study, a hybrid model, which coupled deep neural network (DNN) and the remote sensing-based two-source energy balance (TSEB) model, was developed to estimate H and LE at multiple spatial resolutions to further quantify the influence of pixel size on the modeled fluxes. First, the DNN-based land surface temperature (LST) estimation model was developed and validated. LST at multiple spatial resolutions (i.e., 1, 5, 10, 30, 100, and 1000 m) were generated by the DNN method using multi-source satellite data including the GaoFen-2 (GF-2, a Chinese high-resolution optical remote-sensing satellite), Sentinel-2, and Landsat-8 images, respectively. Then, the hybrid DNN-TSEB model was used to estimate H and LE at multi-pixel sizes. Finally, modeled H and LE at 1 ∼ 1000 m were validated by eddy covariance (EC) and optical-microwave scintillometer (OMS) flux observation. Results revealed that the DNN-based LST model was a potential approach to generate LST at multiple pixel sizes. The hybrid DNN-TSEB model can produce more accurate H and LE when different land cover types were discriminated over heterogeneous farmland (e.g., the average bias for H and LE at a spatial resolution of 1 m were −9 and −6 W/m−2, respectively). Besides, the deviation of modeled fluxes from the observations increased as the spatial resolution became coarser, particularly the pixel size was greater than 30 m (average bias for H and LE were −34 and 30 W m−2, respectively). However, the magnitude of bias of fluxes estimates remained unchanged when the pixel sizes of satellite data were between 100 and 1000 m. The more important finding was that the hybrid model was a promising approach, which can obtain accurate estimates of H and LE at a high spatial resolution. In addition, the performance of the model based on GF-2 data was better than that based on Sentinel-2 and Landsat-8 data. The results suggest that pixel heterogeneity should be considered when estimating H and LE over heterogeneous landscapes with various surface types.

Assimilation of surface‐sensitive bands' clear‐sky radiance data using retrieved surface temperatures from geostationary satellites

AbstractClear‐sky radiances (CSRs) derived from observations made by imager sensors on board geostationary satellites are widely used in most operational numerical weather prediction systems. CSRs have data on tropospheric water vapour and temperatures, and the products at water vapour bands are generally assimilated into global data assimilation systems. In another band, known as the CO2 band (13.3–13.4 μm), CSRs are not used widely yet, despite having a wealth of information about temperatures in the mid‐ and low troposphere. This is mainly because of the high surface sensitivity of this band, which makes it difficult to accurately simulate brightness temperatures when there are non‐negligible errors in the surface parameters in the models. This article quantitatively investigated the surface sensitivities of CO2 and water vapour bands, which have the sensitivity under dry atmospheric conditions, and developed retrieval of land surface temperature (LST) from window band CSRs to obtain a more accurate simulated brightness temperature. Additionally, it was discovered that the retrieved LST outperformed that from the model in short‐range forecasts for the low‐water‐vapour band (7.3 μm) CSR data assimilation, and throughout the forecasting period, especially in the tropics, for the CO2 band CSR data assimilation. We also examined an unexpected improvement in the low troposphere in the model's LST trials, and we concluded that it was related to the relationship between the LST and atmospheric temperature biases.

ASSESSING LAND SURFACE TEMPERATURE IN URBAN AREAS USING OPEN-SOURCE GEOSPATIAL TOOLS

Abstract. Land surface temperature (LST) in urban areas can be traditionally observed by thermal remote sensors. The increasing availability of 3D city models provides an alternative approach based on geospatial modeling. Using solar radiation tools and scripting in GRASS GIS, we have developed a physically-based LST model that can be used to estimate LST in urban areas represented by vector 3D city models. It uses standard input parameters such as solar irradiance, albedo or convection heat transfer coefficient for urban surfaces. The solar irradiance is estimated using the v.sun solar radiation add-on module in GRASS GIS. The LST values are calculated using map algebra operations using a Python script. The suggested methodology was applied to the study area in the city of Košice, Slovakia. Results indicate that urban morphology has a strong impact on spatial distribution of LST during the daylight hours. The accurate parameterization of urban surfaces can increase the accuracy of the model that can be used for urban planning, optimization of energy use in buildings or mitigation of urban heat island effects.

Step-By-Step Downscaling of Land Surface Temperature Considering Urban Spatial Morphological Parameters

Land surface temperature (LST) is one of the most important parameters in urban thermal environmental studies. Compared to natural surfaces, the surface of urban areas is more complex, and the spatial variability of LST is higher. Therefore, it is important to obtain a high-spatial-resolution LST for urban thermal environmental research. At present, downscaling studies are mostly performed from a low spatial resolution directly to another high resolution, which often results in lower accuracy with a larger scale span. First, a step-by-step random forest downscaling LST model (SSRFD) is proposed in this study. In our work, the 900-m resolution Sentinel-3 LST was sequentially downscaled to 450 m, 150 m and 30 m by SSRFD. Then, urban spatial morphological parameters were introduced into SSRFD, abbreviated as SSRFD-M, to compensate for the deficiency of remote-sensing indices as driving factors in urban downscaling LST. The results showed that the RMSE value of the SSRFD results was reduced from 2.6 °C to 1.66 °C compared to the direct random forest downscaling model (DRFD); the RMSE value of the SSRFD-M results in built-up areas, such as Gulou and Qinhuai District, was reduced by approximately 0.5 °C. We also found that the underestimation of LST caused by considering only remote-sensing indices in places such as flowerbeds and streets was improved in the SSRFD-M results.

High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China

Satellite-derived rugged land surface temperature (LST) is an important parameter indicating the status of the Earth’s surface energy budget and its seasonal/temporal dynamic change. However, existing LST products from rugged areas are more prone to error when supporting applications in mountainous areas and Earth surface processes that occur at high spatial and temporal resolutions. This research aimed to develop a method for generating rugged LST with a high temporal and spatial resolution by using an improved ensemble LST model combining three regressors, including a random forest, a ridge, and a support vector machine. Different combinations of high-resolution input parameters were also considered in this study. The input datasets included Moderate Resolution Imaging Spectroradiometer (MODIS) LST datasets (MxD11A1) for nighttime, temporal Sentinel-2 Multispectral Instrument (MSI) datasets, and digital elevation model (DEM) datasets. The 30 m rugged LST datasets derived were compared against an in situ LST dataset obtained at Saihanba Forest Park (SFP) sites and an ASTER-derived 90 m LST, respectively. The results with in situ measurements demonstrated significant LST details, with an R2 higher than 0.95 and RMSE around 3.00 K for both Terra/MOD- and Aqua/MYD-based LST datasets, and with slightly better results being obtained from the Aqua/MYD-based LST than that from Terra/MOD. The inter-comparison results with ASTER LST showed that over 80% of the pixels of the difference image for the two datasets were within 2 K. In light of the complex topography and distinct atmospheric conditions, these comparison results are encouraging. The 30 m LST from the method proposed in this study also depicts the seasonality of rugged surfaces.

Variation of the land surface temperature field in rare-Earth ore mining areas based on temperature downscaling

During the mining process of ionic rare earth elements (REE), mining disturbances, such as land surface excavation, stripped vegetation, injection holes, and tailings discharge, have led to large-scale vegetation degradation and land desertification, ultimately resulting in land surface temperature (LST) differentiation. The surface thermal anomaly is an important ecological parameter, and its spatiotemporal variation has become a powerful tool for identifying surface ecological disturbance. Because the REE mining areas have the characteristics of scattered ore and small areas, it is of great value to obtain LST data with strong practicality and high spatial resolution for sustainable ecological environment monitoring. In this study, the Lingbei mining area in Dingnan County, Ganzhou, China was selected as the study area. Via the use of Landsat and GaoFen-1 (GF-1) remote sensing images, and based on data fusion and the mixed-pixel decomposition method, the temperature downscaling with image fusion and spectral unmixing (TDIFSU) model and the temperature downscaling with data fusion and spectral unmixing (TDDFSU) model were constructed. In addition, they were compared with the commonly used temperature-downscaling disaggregation procedure for radiometric LST (DisTrad) model to explore the accuracy and applicability of different downscaling models for REE mining areas. Finally, based on remote sensing data from the past 20 years, a suitable downscaling model was selected and the temperature difference index (TDI) was used to analyze the spatiotemporal evolutions of ecological disturbances caused by REE mining. The results showed TDDFSU model had the highest accuracy and the smallest mean absolute error (MAE) and root mean square error (RMSE), which were 1.062 K and 1.386 K in February and 0.877 K and 1.094 K in October, respectively, but it had limitations in spatiotemporal study. The accuracy of TDIFSU model was the second, and TDIFSU model based on Landsat data has more advantages in long time series research. DisTrad model has the lowest accuracy. The most suitable model can be selected according to the time span and accuracy; The LST obtained by downscaling can reflect the actual land surface in more detail, and the boundaries between different ground objects were clearer; The high-temperature areas were mainly concentrated in the vicinity of long-term ore occurrences covered with a large amount of tailings. Medium-temperature areas were mainly bare soil and in situ leaching vegetation areas, and low-temperature areas were mainly natural and reclaimed vegetation areas; During the monitoring of the LST in Lingbei mining area for nearly 20 years, the temperature differences caused by different mining activities were revealed, and the land surface disturbance situation has improved after the state actively carried out REE mining environmental control.

Estimation of all-sky 1 km land surface temperature over the conterminous United States

Land surface temperature (LST) is a crucial parameter for hydrology, climate monitoring, and ecological and environmental research. LST products from thermal infrared (TIR) satellite data have been widely used for that. However, TIR information cannot provide LST data under cloudy-sky conditions. All-sky LST can be estimated from microwave measurements, but their coarse spatial resolution, narrow swaths, and short temporal range make it impossible to generate a long-term, high-resolution, accurate global all-sky LST global. This study proposes a methodology for generating the all-sky LST product by combining multiple data from Moderate Resolution Imaging Spectroradiometer (MODIS), reanalysis, and ground in situ measurements using a random forest. Field measurements from the AmeriFlux and Surface Radiation Budget (SURFRAD) networks were used for model training and validation. Cloudy-sky and clear-sky LST models were developed separately. To further improve the accuracy of the cloudy-sky LST model, the conventional RF model was extended to incorporate temporal information. The models were validated using in situ LST measurements from 2010, 2011, and 2017 that were not used for the model training. For the cloudy-sky and clear-sky models, root-mean-square-error (RMSE) = 2.767 and 2.756 K, R2 = 0.943 and 0.963, and bias = −0.143 and − 0.138 K, respectively. The same validation samples were used to validate both the MODIS LST product under clear-sky conditions and all-sky Global Land Data Assimilation System (GLDAS) LST product at 0.25° spatial resolution, with RMSE = 3.033 and 4.157 K, bias = −0.362 and − 0.224 K, and R2 = 0.904 and 0.955, respectively. Additionally, the 10-fold cross-validation results using all the training datasets further indicate the model stability. The models were applied to generate the all-sky LST product from 2000 to 2015 over the conterminous United States (CONUS). Our product shows similar spatial patterns to the MODIS and GLDAS LST products, but it is more accurate. Both validation and product comparisons demonstrated the robustness of our proposed models in generating the all-sky LST product.

Development of numerical land surface temperature model of Jeju Island, South Korea based on finite element method

Owing to the importance of land-surface temperature (LST) and its variability in ecosystems, long-term LST monitoring is necessary. Because direct field measurements and satellite images have drawbacks, an appropriate LST simulation model is required. Therefore, in this study, we developed an hourly land-surface temperature model for Jeju Island and evaluated the variability in the predicted LST. The LST model was developed based on numerical analysis using the finite element method and calibrated and validated by comparing the simulated and measured temperature data obtained from four weather stations on Jeju Island. The simulated and measured LST have coefficients of determination of 0.96 and root mean square errors of 2.29 °C in validation. The model was used to predict LST data for a representative day of every month in 2018 using the mean monthly air temperature, and the variability of the data was calculated using the information entropy-based disorder index (DI). Linear relationships among the geographic coordinates, terrain attributes, mean LST, and DI were analyzed. Latitude and shaded relief were significantly linearly related with the overall mean LST and mean DI of the entire period, whereas longitude, elevation, and slope were not. The overall mean LST and mean DI for the entire period were negatively linearly related, but significant linear relationship did not appear in the case of individual months.

Multi-layer perceptron-Markov chain-based artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature

Reliable and accurate environmental state prediction can help in long-term sustainable planning and management. Enormous land-use/ land-cover (LULC) transformation has been increasing the carbon emissions (CEs) and land surface temperature (LST) around the world. The study aimed to (i) examine the influences of land specific CEs on LST dynamics and (ii) simulate future potential LULC, CEs and LST pattern of Khulna City Corporation. Landsat satellite images of the year 2000, 2010 and 2020 were used to derive LULC, LST and CEs pattern and change. The correlation between land-use indices (NDBI, NDVI, NDWI) and LST was examined to explore the impacts of LULC change on LST. Unplanned urbanization has increased 11.79 Km2(26.10%) buildup areas and 25,268 tons of CEs during 2000–2020. The calculated R2 value indicates the strong positive correlation between CEs and LST. To simulate the future LULC, CEs and LST pattern for the year 2030 and 2040, multi-layer perceptron-Markov chain (MLP-MC)-based artificial neural network model was utilized with the accuracy rate of 94.12%, 99% and 98.48% for LULC, LST and CEs model, respectively. The simulation shows that by 2040, buildup area will increase to 87.33%, net CEs will increase by 19.82 × 104tons, and carbon absorptions will decrease by 23. 55 × 104tons and 69.54% of the total study area's LST will be above 390C. Such predictions signify the necessity of implementing a sustainable urban development plan immediately for the sustainable, habitable and sound urban environment.

Spatial Downscaling of MODIS Land Surface Temperature: Recent Research Trends, Challenges, and Future Directions

Satellite-based land surface temperature (LST) has been used as one of the major parameters in various climate and environmental models. Especially, Moderate Resolution Imaging Spectroradiometer (MODIS) LST is the most widely used satellite-based LST product due to its spatiotemporal coverage (1 km spatial and sub-daily temporal resolutions) and longevity (> 20 years). However, there is an increasing demand for LST products with finer spatial resolution (e.g., 10-250 m) over regions such as urban areas. Therefore, various methods have been proposed to produce high-resolution MODIS-like LST less than 250 m (e.g., 100 m). The purpose of this review is to provide a comprehensive overview of recent research trends and challenges for the downscaling of MODIS LST. Based on the recent literature survey for the past decade, the downscaling techniques classified into three groups―kernel-driven, fusion-based, and the combination of kernel-driven and fusion-based methods―were reviewed with their pros and cons. Then, five open issues and challenges were discussed: uncertainty in LST retrievals, low thermal contrast, the nonlinearity of LST temporal change, cloud contamination, and model generalization. Future research directions of LST downscaling were finally provided.

Attribution of Land‐Use/Land‐Cover Change Induced Surface Temperature Anomaly: How Accurate Is the First‐Order Taylor Series Expansion?

AbstractLand surface temperature (LST) responds to land‐use/land‐cover change (LULCC), which modifies surface properties that control the surface energy balance (SEB). Quantifying changes in LST due to individual perturbations caused by LULCC is an attribution problem. Most attribution methods are based on the first‐order Taylor series expansion (FOTSE) of a linearized SEB equation. The accuracy of these methods is affected by the use of FOTSE at two places. The first is to linearize the SEB equation and to obtain an analytical solution for LST (the LST model), and the second is to obtain LST changes as the linear sum of concurrent changes in multiple factors (the attribution model). In this study, we systematically assess the importance of non‐linear effects lost in these linearization processes using the second‐order Taylor series expansion (SOTSE). Results show that while the SOTSE LST model outperforms the FOTSE LST model, the order of Taylor series expansion in the LST model does not significantly influence the attribution of LST changes. However, the SOTSE attribution model is considerably more accurate than the FOTSE attribution model, especially when the magnitude of perturbations is large. Results suggest that contributions from higher‐order and cross‐order terms in the attribution model can be as large as 50%. Sensitivity analysis further shows that non‐linear effects associated with changing surface resistance for LULCC scenarios with large perturbations (e.g., deforestation and urbanization) are particularly strong. In conclusion, we recommend using the FOTSE LST model and the SOTSE attribution model.

Simulation of green roofs and their potential mitigating effects on the urban heat island using an artificial neural network: A case study in Austin, Texas

The growing number of densely populated cities has resulted in a decrease in vegetation coverage, which in turn, has resulted in a temperature increase in urban areas. This phenomenon is known as urban heat island (UHI). Several strategies have been proposed to mitigate the effect of urban heat islands in recent years, including usage of green roof coverage. However, there is a need for a rigorous analysis of the relationship between UHI and different urban characteristics using advanced models for urban planners to make policy-decisions to mitigate the UHI effect. In this study, the cooling effect of the green roof strategy in the city of Austin, considering 2D/3D urban characteristic parameters was investigated. To begin with, land surface temperature (LST) was estimated from Landsat 8 TIRS data in July 2016. Also, 3D urban morphology parameters derived from light detection and ranging (LiDAR) data. To simulate the green roof strategy, Sentinel 2A satellite images were used to calculate the normalized difference vegetation index and the normalized difference built-up index because of higher spatial resolution than Landsat 8 OLI. Then a multilayer feed-forward neural network was applied as a nonlinear model to find a relationship between LST and various urban characteristic parameters simultaneously. Furthermore, the importance of the variables in LST modeling was evaluated using sensitivity analysis. After that, some downtown residential and office buildings, which had the potential to become a green roof, were selected to implement the green roof strategy. Finally, by analyzing the relationship between LST reduction due to green roof simulation and urban indicators, the best buildings for green roof implementation were determined. Results showed that the accuracy of the LST modeling was reached to R2 = 0.786 and RMSE = 0.956 °C. In addition, by greening 3.2% of total building roofs, the average of LST decreased by 1.96 °C. Moreover, the results indicated that the building green roofs with (i) heights of 15–25 m, (ii) the highest values of sky view factor and solar radiation, and (iii) the lowest distance to the water body, had the greatest cooling effects on LST. Consequently, these findings indicated that the green roof has a significant effect on temperature reduction, especially by selecting the buildings with the above mentioned characteristics.

Modeling permafrost distribution over the river basins of Mongolia using remote sensing and analytical approaches

The spatial distribution of permafrost and associated mean annual ground temperature (MAGT) and active layer thickness (ALT) are crucial data for hydrological studies. In this paper, we present the current state of knowledge on the spatial distribution of the permafrost properties of 29 river basins in Mongolia. The MAGT and ALT values are estimated by applying TTOP and Kudryavtsev methods. The main input of both methods is the spatially distributed surface temperature. We used the 8-day land surface temperature (LST) data from the day- and night-time Aqua and Terra images of the moderate resolution imaging spectroradiometer (MODIS). The gaps of the MODIS LST data were filled by spatial interpolation. Next, an LST model was developed based on 34 observational borehole data using a panel regression analysis (Baltagi, Econometric analysis of panel data, 3 edn, Wiley, New York, 2005). The model was applied for the whole country and covered the period from August 2012 to August 2013. The results show that the permafrost covers 26.3% of the country. The average MAGT and ALT for the permafrost region is − 1.6 °C and 3.1 m, respectively. The MAGT above -2 °C (warm permafrost) covers approximately 67% of the total permafrost area. The permafrost area and distribution in cold and warm permafrost varies highly over the country, in particular in regions where the river network is highly developed. High surface temperatures associated with climate change would result in changes of permafrost conditions, and, thus, would impact the surface water availability in these regions. The data on permafrost conditions presented in this paper can be used for further research on changes in the hydrological conditions of Mongolia.

Mapping regional evapotranspiration in cloudy skies via variational assimilation of all-weather land surface temperature observations

Recently, a number of studies estimated evapotranspiration (ET) via variational assimilation of land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS). Their unknown parameters are neutral bulk heat transfer coefficient (CHN) (that scales the sum of sensible and latent heat fluxes), and evaporative fraction (EF) (that represents the partitioning of available energy between sensible and latent heat fluxes). The variational data assimilation (VDA) approaches estimate CHN and EF by minimizing the difference between the MODIS LST observations and model estimates. The applicability of these studies is limited only to clear-sky conditions where MODIS LST data are available. Even when the sky is clear, they cannot robustly update the initial guess of EF (i.e., the a priori EF value) because of the low temporal resolution of MODIS LST data. This study overcomes these shortcomings by 1) using the random forest (RF) method to obtain a reasonable the a priori EF value, and 2) assimilating the all-weather LST data (which are obtained by merging thermal infrared and passive microwave observations) into the VDA approach. The VDA approach is applied to the Source Regions of Rivers (SRR) in southwest China with heavy cloud covers. Results show that the RF method obtains a reasonably accurate the a priori EF value. Compared to assimilating the MODIS LST product, assimilation of all-weather LST data lead to an improvement in the ET estimates, especially in regions with dense clouds. Comparison of ET estimates with the measurements at four sites (i.e., Dangxiong, Linzhi, Naqu, and Qomolangma) in the SRR shows that the VDA approach can accurately estimate ET in cloudy conditions. Finally, the three-cornered hat (TCH) method is employed to assess the relative uncertainty of ET estimates over the SRR.

Evaluation of spatially heterogeneous driving forces of the urban heat environment based on a regression tree model

The urban heat environment, represented by land surface temperature (LST), is highly heterogeneous. Previous studies have investigated the universal physical laws during LST modeling. However, spatial patterns of the driving forces and their temporal differences have not been well examined. In this study, spatially heterogeneous effects of two important urban biophysical compositions, Normalized Difference Vegetation Index (NDVI) and impervious surfaces, on LST are determined by a regression tree model. The results showed that LST patterns varied on different dates. Additionally, nonlinear relationships existed between LST, NDVI, and impervious surfaces. The regression tree model outperformed the multiple linear regression in modeling LST variations. Furthermore, the "rules" derived from regression tree indicated the forces that are driving LST locally. These results suggest that the "rules" can be used as urban planning indicators and provide insights into the variations in driving forces linked to the urban landscape. We found that the regression tree is an important approach to adopt for conducting studies on urban heat and provides insight into complex and hierarchical LST patterns and processes, thus facilitating urban heat island mitigation in terms of reducing energy use and air pollution in urban areas.

Land surface temperature retrieval from Landsat 8 OLI/TIRS images based on back-propagation neural network

Land surface temperature (LST) is an important parameter related to the environmental assessment concerning the surface energy, water balance, greenhouse effect, etc. at local and global scales. With the rapid development of the remote sensing technology, various methodologies have been developed to retrieve LST from space-based remote sensing images. Due to the ill-posed problem, the LST retrieval is still a challenge. In this research, a so-called multiple band reflectance (MBR)-LST model has been proposed based on the back-propagation neural (BPN) network, which can be employed to retrieve the LSTs from Landsat 8 Operational Land Imager (OLI)/TIRS images as well as produce continuous spatial LST distributions with a spatial resolution of 30 m. Experiments conducted in two randomly selected areas in mainland China proved that the proposed MBR-LST model has yielded a much better performance than the traditional radiative transfer equation (RTE) method with respect to both the accuracy and stability for the LST retrievals. Moreover, another significant advantage of the proposed MBR-LST is the generic nature – once trained by the sample data in the whole region of mainland China, the proposed MBR-LST model can be utilized for the accurate LST-retrievals in any area of mainland China.

Improving corn yield prediction across the US Corn Belt by replacing air temperature with daily MODIS land surface temperature

While canopy temperature has been extensively utilized for field-level crop health assessment, the application of satellite-based land surface temperature (LST) images for corn yield modeling has been limited. Furthermore, long term yield projections in the context of climate change have primarily employed air temperature (Tair) and precipitation, which may inadequately reflect crop stress. This study assessed potential benefits of satellite-derived LST for predicting annual corn yield across the US Corn Belt from 2010 to 2016. A novel killing degree day metric (LST KDD) was computed with daily LST images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and compared to the typically used Tair-based metric (Tair KDD). Our findings provide strong evidence that LST KDD is capable of predicting annual corn yield with less error than Tair KDD (R2/RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). Even while adjusting for seasonal temperature and precipitation parameters, the R2 and RMSE of the LST model were approximately 9% higher and 2.0 Bu/Acre lower than the Tair model, respectively. The superior performance of LST can be attributed to its ability to better incorporate evaporative cooling and water stress. We conclude that MODIS LST can improve yield forecasts several months prior to harvest, especially during extremely warm and dry growing seasons. Furthermore, the better performance of LST models over Tair and precipitation models suggest that subsequent long term yield projections should consider additional factors indicative of water stress.

A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

Land surface temperature (LST) is an important parameter in the study of the physical processes of land surface. Understanding the surface temperature lapse rate (TLR) can help to reveal the characteristics of mountainous climates and regional climate change. A methodology was developed to calculate and analyze land-surface TLR in China based on grid datasets of MODIS LST and digital elevation model (DEM), with a formula derived on the basis of the analysis of the temperature field and the height field, an image enhancement technique used to calculate gradient, and the fuzzy c-means (FCM) clustering applied to identify the seasonal pattern of the TLR. The results of the analysis through the methodology showed that surface temperature vertical gradient inversion widely occurred in Northeast, Northwest, and North China in winter, especially in the Xinjiang Autonomous Region, the northern and the western parts of the Greater Khingan Mountains, the Lesser Khingan Mountains, and the northern area of Northwest and North China. Summer generally witnessed the steepest TLR among the four seasons. The eastern Tibetan Plateau showed a distinctive seasonal pattern, where the steepest TLR happened in winter and spring, with a shallower TLR in summer. Large seasonal variations of TLR could be seen in Northeast China, where there was a steep TLR in spring and summer and a strong surface temperature vertical gradient inversion in winter. The smallest seasonal variation of TLR happened in Central and Southwest China, especially in the Ta-pa Mountains and the Qinling Mountains. The TLR at very high altitudes (>5 km) was usually steeper than at low altitudes, in all months of the year.