ASTER Land Surface Temperature Research Articles

Geothermal target detection integrating multi-source and multi-temporal thermal infrared data

Thermal infrared remote sensing (TIRS) technology is intriguing for geothermal anomaly detection due to its time efficiency and cost-effectiveness. Land surface temperature (LST) derived from TIRS indicates potential geothermal anomalies. However, LST on different natural features, exhibiting relatively cold/hot anomalies in daytime and nighttime, affected the identification of abnormal areas. The aim of this study was to highlight geothermal anomalous areas by integrating multi-view daytime and nighttime LST data. Specifically, the winter LST time series of drillings on 2013–2023 was processed based on Landsat-8 TIRS in diurnal scenarios. An information aggregation classification method about Dempster-Shafer evidence theory was presented to target recognition at nighttime by the ASTER LST product. This day-night information fusion analysis effectively highlighted potential areas with geothermal anomalies. The results demonstrated that the geothermal anomalies were mainly distributed along the Ruili-Longling Fault and Wanding Fault in a Northeast (NE) – Southwest (SW) direction, and the NE side was more significant than the SW side. These findings suggested that the east side was closer to the magma heat source, and the presumed magma heat source location aligned closely with the detected geothermal anomalies. Geological data were employed for geological interpretation of the LST anomaly area, eliminating non-geothermal influences. Along with drilling data verification, this method was successfully used to identify geothermal anomaly areas in Ruili City, Yunnan Province. Overall, this study provided valuable insights into the detection of geothermal anomalies through TIRS, contributing to the successful development of geothermal resources.

A Spatial Downscaling Approach for Land Surface Temperature by Considering Descriptor Weight

Acquiring the satellite land surface temperature (LST) with high spatiotemporal resolutions is pressing in the land surface biophysical process. However, most current LST products hardly satisfy this requirement. LST Downscaling provides an effective way to solve this issue by introducing driving factors, but existing methods usually ignore the weights of descriptors. In this letter, based on the Geographically Weighted Regression (GWR) and Random Forest (RF), a new downscaling method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., WGWR) considering the weights of LST descriptors is proposed. To examine the performance of WGWR, the 100-m Landsat-8 TIRS and Terra ASTER LSTs are aggregated to 1000 m as the simulated coarse LSTs, and then the coarse LSTs are downscaled to 100 m using WGWR, RF, and GWR. Meanwhile, the original 100-m LSTs are used as validation references. Results indicate that the proposed WGWR outperforms RF and GWR: for RF (GWR), the RMSEs can be reduced by 0.34 K (0.26 K) in Zhangye and 0.22 K (0.1 K) in Beijing. Compared to RF and GWR, WGWR also yields better image quality: the downscaled LST images have neither obvious smoothing effect nor boundary effect and maintain the details of the image at high spatial resolution. Validation based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> LST indicates that the downscaled LST based on WGWR has better agreement with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> LST, and the RMSE is reduced by 0.57 K. The proposed WGWR contributes to obtain high spatio-temporal resolution LSTs and promote hydrological, meteorological, and ecological studies.

Assessment of the TsHARP method for spatial downscaling of land surface temperature over urban regions

Nowadays, acquiring high spatial and temporal land surface temperature (LST) is one of the significant factors in urban climate studies. This study aims to assess the sharpening thermal imagery (TsHARP) method, which is developed for determining the LST in urban regions. Whereas the original TsHARP method correlates the Normalized Difference Vegetation Index (NDVI) with the LST, the current study seeks to evaluate the performance of the TsHARP method by correlating between the LST and NDVI over urban areas. Additionally, it is intended to investigate the relationship between the impervious surfaces (IS) and LST. At this point, the Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER image of the year 2003 was used to retrieve the IS as well as the LST of the Kuala Lumpur city, Malaysia. To retrieve the main LST, IS and NDVI, ASTER Level1B data were used. On the other hand, ASTER land surface temperature products (AST08) was utilized to evaluate the downscaled LST (DLST) image. With selection of the ~1/4 of the pixels with lowest coefficient of variation (CV), the LST has more correlation with the IS in comparison with the NDVI, while the correlation between the IS and LST was increased in this study. The IS index illustrates the most optimal correlation for the urban LST (R2 = 0.59). Furthermore, the TsHARPIS-based method depicted a good correlation between the DLST and the ASTER LST product (RMSE = 1.5 °C, R2 = 0.83) compared to the NDVI (RMSE = 4 °C, R2 = 0.73). Consequently, it is found that the application of TsHARPIS-based method to ASTER remote sensing data is a robust/low-cost approach for spatial downscaling of the LST over urban regions.

Downscaling MODIS nighttime land surface temperatures in urban areas using ASTER thermal data through local linear forest

• A new nighttime LST downscaling method using local linear forest (LLF) was proposed. • Feature-selected ASTER nighttime LSTs were identified as important input kernels. • The LLF-based model predicted LST with high linearity, especially for both extremes. • DLST revealed more distinct characteristics of urban climate than MODIS LST. Spatial downscaling effectively produces high spatiotemporal resolution land surface temperature (LST) in urban areas. Although nighttime LST is an essential indicator in urban thermal research, few LST downscaling studies have focused on nighttime in fine resolution. This study proposed a novel approach using local linear forest (LLF) to downscale 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) nighttime LSTs to 250 m spatial resolution in three cities: Rome, Madrid, and Seoul. First, we used Least Absolute Shrinkage and Selection Operator (LASSO) to select a set of past clear-sky ASTER LSTs (ALST) which showed a high spatial correlation with the target MODIS LST. Downscaling models were then developed using input kernels of the selected ALSTs and eight auxiliary variables: normalized difference vegetation index (NDVI), elevation, slope, built-up area percentage, road density, population density, wind speed, and distance from the built-up weighted center of the study area. Three schemes were evaluated: scheme 1 (S1) using only auxiliary variables as input kernels with a random forest (RF) model; scheme 2 (S2) using selected ALSTs and auxiliary variables as input kernels with an RF model; and scheme 3 (S3) using input kernels as in S2 but with the LLF model. Validation was performed using bias-corrected ALSTs for seven reference dates in the three cities. LLF-based S3 showed the highest accuracy with an average correlation coefficient (R) ∼ 0.94 and Root Mean Square Error (RMSE) ∼ 0.64 K while maintaining the dynamic range of the original LST at the finer resolution. The downscaled LST (DLST) based on S3 effectively depicted the nocturnal thermal spatial pattern in greater detail than the other two schemes did. The S3-based DLST also showed a relatively high spatial correlation with the in-situ nighttime air temperature within the cities. When compared to the original 1 km LST, S3-based DLST showed larger surface urban heat island intensity for the urban-type surfaces and a higher temporal correlation with nighttime air temperature.

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.

Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

Validation of Collection 6 MODIS land surface temperature product using in situ measurements

Land surface temperature (LST) is an important physical quantity at the land-atmosphere interface. Since 2016 the Collection 6 (C6) MODIS LST product is publicly available, which includes three refinements over bare soil surfaces compared to the Collection 5 (C5) MODIS LST product. To encourage the use of the C6 MODIS LST product in a wide range of applications, it is necessary to evaluate the accuracy of the C6 MODIS LST product. In this study, we validated the C6 MODIS LST product using temperature-based method over various land cover types, including grasslands, croplands, cropland/natural vegetation mosaic, open shrublands, woody savannas, and barren/sparsely vegetated. In situ measurements were collected from various sites under different atmospheric and surface conditions, including seven SURFRAD sites (BND, TBL, DRA, FPK, GCM, PSU, and SXF) in the United States, three KIT sites (EVO, KAL, and GBB) in Portugal and Namibia, and three HiWATER sites (GBZ, HZZ, and HMZ) in China. The spatial representativeness of the in situ measurements at each site was separately evaluated during daytime and nighttime using all available clear-sky ASTER LST products at 90 m spatial resolution. Only six sites during daytime are selected as sufficiently homogeneous sites despite the usually high spatial thermal heterogeneity, whereas during nighttime most sites can be considered to be thermally homogeneous and have similar LST and air temperature. The C6 MODIS LST product was validated using in situ measurements from the selected homogeneous sites during daytime and nighttime: except for the GBB site, large RMSE values (>2 K) were obtained during daytime. However, if only satellite LST with a high spatial thermal homogeneity on the MODIS pixel scale are used for LST validation, the best daytime accuracy (RMSE <1.3 K) for the C6 MODIS LST product is achieved over the BND and DRA sites. Except for the DRA site, the RMSE values during nighttime are <2 K at the selected homogeneous sites. Furthermore, the accuracy of the C6 MODIS LST product was compared with that of the C5 MODIS LST product during nighttime at the selected homogeneous sites. Except for the GBB site, there are only small differences (<0.4 K) between the RMSE values for the C5 and C6 MODIS LST products.

Cross-satellite comparison of operational land surface temperature products derived from MODIS and ASTER data over bare soil surfaces

The collection 6 (C6) MODIS land surface temperature (LST) product is publicly available for the user community. Compared to the collection 5 (C5) MODIS LST product, the C6 MODIS LST product has been refined over bare soil pixels. Assessing the accuracy of the C6 MODIS LST product will help to facilitate the use of the LST product in various applications. In this study, we present a cross-satellite comparison to evaluate the accuracy of the C6 MODIS LST product (MOD11_L2) over bare soil surfaces under various atmospheric and surface conditions using the ASTER LST product as a reference. For comparison, the C5 MODIS LST product was also used in the analysis. The absolute biases (0.2–1.5K) of the differences between the C6 MODIS LST and ASTER LST over bare soil surfaces are approximately two times less than those (0.6–3.8K) of the differences between the C5 MODIS LST and ASTER LST. Furthermore, the RMSEs (0.7–2.3K) over bare soil surfaces for the C6 MODIS LST are significantly smaller than those (0.9–4.2K) for the C5 MODIS LST. These results indicate that the accuracy of the C6 MODIS LST product is much better than that of the C5 MODIS LST product. We recommend that the user community employs the C6 MODIS LST product in their applications.

Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE

Land surface temperature (LST) is an important parameter that is highly responsive to surface energy fluxes and has become valuable to many disciplines. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between them. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of thermal infrared (TIR) data or LST, but rarely both. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. The aim of this study is to conduct a comprehensive evaluation of the ESTARFM algorithm for generating ASTER-like daily LST by three approaches: simulated data, ground measurements and remote sensing products, respectively. The datasets of LST ground measurements, MODIS, and ASTER images were collected in an arid region of Northwest China during the first thematic HiWATER-Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012 from May to September. Firstly, the results of the simulation test indicated that ESTARFM could accurately predict background with temperature variations, even coordinating with small ground objects and linear ground objects. Secondly, four temporal ASTER and MODIS data fusion LSTs (i.e., predicted ASTER-like LST products) were highly consistent with ASTER LST products. Here, the four correlation coefficients were greater than 0.92, root mean square error (RMSE) reached about 2 K and mean absolute error (MAE) ranged from 1.32 K to 1.73 K. Finally, the results of the ground measurement validation indicated that the overall accuracy was high (R2 = 0.92, RMSE = 0.77 K), and the ESTARFM algorithm is a highly recommended method to assemble time series images at ASTER spatial resolution and MODIS temporal resolution due to LST estimation error less than 1 K. However, the ESTARFM method is also limited in predicting LST changes that have not been recorded in MODIS and/or ASTER pixels.

Improved surface temperature estimates with MASTER/AVIRIS sensor fusion

Land surface temperature (LST) is an important parameter in many ecological studies. The current Root Mean Square Error (RMSE) in standard MODIS and ASTER LST products is greater than 1K, and for ASTER can be as large as 4K for graybody pixels such as vegetation. Errors of 3 to 8K have been observed for ASTER in humid conditions, making knowledge of atmospheric water vapor content critical in retrieving accurate LST. For this reason improved accuracy in LST measurements through the synthesis of visible-to-shortwave-infrared (VSWIR) derived water vapor maps and Thermal-Infrared (TIR) data is one goal of the Hyperspectral Infrared Imager, or HyspIRI, mission. The 2011 ER-2 Delano/Lost Hills flights acquired data with both the MODIS/ASTER Simulator (MASTER) and Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) instruments flown concurrently. This study compares LST retrieval accuracies from the standard JPL MASTER temperature products produced using the temperature–emissivity separation (TES) algorithm, and the water vapor scaling (WVS) atmospheric correction method proposed for HyspIRI. The two retrieval methods are run both with and without high spatial resolution AVIRIS-derived water vapor maps to assess the improvement from VSWIR synthesis. We find improvement using VSWIR derived water vapor maps, with the WVS method being most accurate overall. For closed canopy agricultural vegetation we observed temperature retrieval RMSEs of 0.49K and 0.70K using the WVS method on MASTER data with and without AVIRIS derived water vapor, respectively.

Spatial configuration of anthropogenic land cover impacts on urban warming

Anthropogenic land cover types greatly influence the urban heat island (UHI) effects. This study examined effects of composition and spatial pattern of anthropogenic land cover features on land surface temperature (LST) in Phoenix, Arizona, USA, using a land cover map derived from high resolution satellite data and ASTER LST data. The spatial pattern of land cover features was measured by local Moran's I—a continuous spatial autocorrelation index, which can effectively describe dispersed or clustered patterns of land cover features. Our results showed that the composition and spatial pattern of buildings have minimal impacts on LST, while those of paved surfaces alter LST more drastically. The local Moran's I of paved surfaces have a stronger positive correlation with nighttime (r2=0.38) than daytime (r2=0.17) temperatures, suggesting that clustered paved surfaces create stronger warming effects at night. We further controlled for land cover compositions to minimize their effects on LST, and found that the magnitude of warming effects caused by clustered paved surfaces differed among landscapes of varying land cover compositions. Correlations between local Moran's I of paved surfaces and LST becomes stronger when paved surface fraction exceeds 50%. These results illustrated aggregate warming effects of clustered paved surfaces, and provide insights on UHI mitigation for land managers and urban planners.

Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing

Soil moisture (SM) plays a fundamental role in the land–atmosphere exchange process. Spatial estimation based on multi in situ (network) data is a critical way to understand the spatial structure and variation of land surface soil moisture. Theoretically, integrating densely sampled auxiliary data spatially correlated with soil moisture into the procedure of spatial estimation can improve its accuracy. In this study, we present a novel approach to estimate the spatial pattern of soil moisture by using the BME method based on wireless sensor network data and auxiliary information from ASTER (Terra) land surface temperature measurements. For comparison, three traditional geostatistic methods were also applied: ordinary kriging (OK), which used the wireless sensor network data only, regression kriging (RK) and ordinary co-kriging (Co-OK) which both integrated the ASTER land surface temperature as a covariate. In Co-OK, LST was linearly contained in the estimator, in RK, estimator is expressed as the sum of the regression estimate and the kriged estimate of the spatially correlated residual, but in BME, the ASTER land surface temperature was first retrieved as soil moisture based on the linear regression, then, the t-distributed prediction interval (PI) of soil moisture was estimated and used as soft data in probability form. The results indicate that all three methods provide reasonable estimations. Co-OK, RK and BME can provide a more accurate spatial estimation by integrating the auxiliary information Compared to OK. RK and BME shows more obvious improvement compared to Co-OK, and even BME can perform slightly better than RK. The inherent issue of spatial estimation (overestimation in the range of low values and underestimation in the range of high values) can also be further improved in both RK and BME. We can conclude that integrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take better advantage of the auxiliary information compared to Co-OK, and BME outperforms RK by integrating the auxiliary data in a probability form.

Quantifying the cool island intensity of urban parks using ASTER and IKONOS data

Urban parks can help mitigate urban heat island (UHI) effects and decrease cooling energy consumption in summer. However, it is unclear how park characteristics affect the formation of a park cool island (PCI). In this study, PCI intensity values for 92 parks in Nagoya, Japan were obtained from ASTER land surface temperature (LST) products and then correlated to detailed and use information derived from high-spatial-resolution IKONOS satellite data. The results indicate that (1) the cooling effect depends on the park size and seasonal radiation condition, and park size is non-linearly correlated to PCI intensity; (2) PCI intensity is mainly determined by the area of tree and shrub inside the park as well as the park shape, and grass has negative impact on PCI formation. The park vegetation and shape index (PVSI) proposed here well predicted PCI intensity of selected parks. These findings can help urban planners to understand PCI formation and design cool parks to counteract UHI effects.