A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering

A stepwise framework for interpolating land surface temperature under cloudy conditions based on the solar-cloud-satellite geometry

All-sky 1 km land surface temperature (LST) data are urgently needed. Two widely applied approaches to derive such LST data are merging thermal infrared remote sensing (TIR)-passive microwave remote sensing (PMW) observations and merging TIR reanalysis data. However, as only the Moderate Resolution Imaging Spectroradiometer (MODIS) is adopted as the TIR source for merging, current 1 km all-sky LST products are limited to the MODIS observation time. Therefore, a gap still remains in terms of all-sky LST data with a higher temporal resolution or at other times (e.g., dawn-dusk time). Under this background, this study merged the observations of the Medium Resolution Spectrum Imager (MERSI-LL) on board the dusk-dawn-orbit Fengyun (FY)-3E satellite and Global Land Data Assimilation System (GLDAS) data to estimate dawn-dusk 1 km all-sky LST using a random forest-based method (RFRTM). The results showed that the model had good robustness, with an STD of 0.62-0.86 K of the RFRTM LST, compared with the original MERSI-LL LST. Validation against in situ LST showed that the estimated LST had an accuracy of 1.34-3.71 K under all-sky conditions. In addition, compared with the dawn-dusk LST merged from MERSI-LL and the Special Sensor Microwave Imager/Sounder (SSMI/S), the RFRTM LST showed better performance in accuracy and image quality. This study's findings are beneficial for filling the gap in all-sky LST at high spatiotemporal resolutions for associated applications.

수문학, 기상학 및 기후학 등에서 필수적인 자료중의 하나인 지상기온 자료는 최근 보건, 생물, 환경 등의 다양한 분야로까지 활용영역이 확대되고 있어 그 중요성이 커지고 있으나 지상관측을 통한 지상기온자료의 취득은 시공간적인 제약이 크기 때문에 실측된 기온자료는 시공간 해상도가 낮아 높은 해상도가 요구되는 연구 분야에서는 활용성에 큰 제약을 갖게 된다. 이를 극복하기 위한 하나의 대안으로 상대적으로 높은 시공간 해상도를 가지고 있는 위성영상자료에서 얻을 수 있는 지표면온도 자료를 이용하여 지상기온을 추정하는 많은 연구들이 수행되어 왔다. 본 연구는 이러한 연구의 일환으로써 기상청에서 제공하고 있는 AWS(Automatic Weather Station)에서 취득된 2010년 지상 온도 자료(AWS data)를 바탕으로 대표적인 지표면 온도자료인 MODIS Land Surface temperature(LST data:MOD11A1)와 지상기온에 영향을 미칠 수 있는 Land Cover Data, DEM(digital elevation model) 등의 보조 자료와 함께 다양한 지구통계 기법들을 이용하여 남한 지역의 지상기온을 추정하였다. 추정 전 2010년 전체(365일) LST자료와 AWS자료와의 차이에 대한 RMSE(Root Mean Square Error)값의 계절별 피복별 분석결과 계절에 따른 RMSE값의 변동계수는 0.86으로 나타났으나 피복에 따른 변동계수는 0.00746으로 나타나 계절별 차이가 피복별 차이보다 큰 것으로 분석 되었다. 계절별 RMSE 값은 겨울철이 가장 낮은 것으로 나타났으며 AWS자료와 LST자료와 보조자료를 이용한 선형 회귀분석결과에서도 겨울철의 결정 계수가 가장 높은 0.818로 나타났으며, 여름철의 경우에는 0.078로 나타나 계절별 차이가 매우 크게 나타났다. 이러한 결과를 바탕으로 지구통계 기법들의 대표적인 방법론인 크리깅 방법 중 일반적으로 많이 사용되고 있는 정규 크리깅, 일반 크리깅, 공동 크리킹, 회귀 크리깅을 이용하여 지상기온을 추정한 후 모델의 정확도를 판단할 수 있는 교차 검증을 실시한 결과 정규 크리깅과 일반 크리깅에 의한 RMSE 값은 1.71, 공동 크리깅과 회귀 크리깅에 의한 RMSE 값은 각각 1.848, 1.63으로 나타나 회귀 크리깅 방법에 의한 추정의 정확도가 가장 높은 것으로 분석되었다. Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

The widespread availability of Land Surface Temperature (LST) data from various sources presents a contemporary challenge for urban climate studies: how to efficiently compare these data with the results of traditional methods of temperature monitoring, which typically assume measurements at 2 m under sheltered conditions. In this line, the current study is based primarily on data extracted from a network of 31 points of hourly temperature monitoring at the 2 m level (Tair2m), in use between 2019 and 2021, in the city of Suceava in north-eastern Romania. These data allowed a detailed mapping for each hourly time step through multiple regression, adjusted by IDW, which was identified as the best interpolation method of Tair2m. These data were analyzed in parallel with LST data derived from Landsat imagery available in the analyzed period for 35 summer days with no or low cloud cover. The mapping results of both the Tair2m and LST data describe the main characteristics of the Suceava urban agglomeration (SvMA) heat island, which presents polynuclear features with intensities—as expressed by the temperature difference between the cores of the heat island and the surrounding rural areas—spanning during the summer noontime between 3.0 °C based on Tair2m and 7.1 °C on LST, respectively. The values of the Tair2m–LST differences were 0.68 °C on average, ranging from 5.33 to −19.17 °C, directly proportional to the imperviousness ratio (IMD) values, reaching the highest values in the local climate zones (LCZs) with a high built-up ratio (up to −19.17 °C) and the lowest (0.5 ÷ −0.5 °C) for those with bare soils, with isolated bushes and trees, with few or no buildings. The study results could serve as a tool to downscale the LST data to the level of Tair2m, which is useful for interpretation of the data derived from these commonly used tools in urban climate monitoring.

With the continuous improvement of urbanization levels in the Lhasa area, the urban heat island effect (UHI) has seriously affected the ecological environment of the region. However, the satellite-based thermal infrared land surface temperature (LST), commonly used for UHI research, is affected by cloudy weather, resulting in a lack of continuous spatial and temporal information. In this study, focusing on the Lhasa region, we combine simulated LST data obtained by the Weather Research and Forecasting (WRF) model with remote sensing-based LST data to reconstruct the all-weather LST for March, June, September, and December of 2020 at a resolution of 0.01° while using the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST as a reference (in terms of accuracy). Subsequently, based on the reconstructed LST, an analysis of the UHI was conducted to obtain the spatiotemporal distribution of UHI in the Lhasa region under all-weather LST conditions. The results demonstrate that the reconstructed LST effectively captures the expected spatial distribution characteristics with high accuracy, with an average root mean square error of 2.20 K, an average mean absolute error of 1.51 K, and a correlation coefficient consistently higher than 0.9. Additionally, the heat island effect in the Lhasa region is primarily observed during the spring and winter seasons, with the heat island intensity remaining relatively stable in winter. The results of this study provide a new reference method for the reconstruction of all-weather LST, thereby improving the research accuracy of urban thermal environment from the perspective of foundational data. Additionally, it offers a theoretical basis for the governance of UHI in the Lhasa region.

The relationship between remotely sensed land surface temperature (LST) data and ground-measured air temperature is important for a number of applications. This article details a pilot project over the summer (June, July, August) 2010 using Moderate Resolution Imaging Spectroradiometer (MODIS) LST data and air temperature data from a custom network of data loggers across the conurbation of Birmingham, UK. The results show that at night-time air temperature is consistently higher than the satellite-measured LST, but significant station-specific variability exists.

Based on the Beijing Climate Center’s land surface model BCC_AVIM (Beijing Climate Center Atmosphere-Vegetation Interaction Model), the ensemble Kalman filter (EnKF) algorithm has been used to perform an assimilation experiment on the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product to study the influence of satellite LST data frequencies on surface temperature data assimilations. The assimilation results have been independently tested and evaluated by Global Land Data Assimilation System (GLDAS) LST products. The results show that the assimilation scheme can effectively reduce the BCC_AVIM model simulation bias and the assimilation results reflect more reasonable spatial and temporal distributions. Diurnal variation information in the observation data has a significant effect on the assimilation results. Assimilating LST data that contain diurnal variation information can further improve the accuracy of the assimilation analysis. Overall, when assimilation is performed using observation data at 6-hour intervals, a relatively good assimilation result can be obtained, indicated by smaller bias (<2.2K) and root-mean-square-error (RMSE) (<3.7K) and correlation coefficients larger than 0.60. Conversely, the assimilation using 24-hour data generally showed larger bias (>2.2K) and RMSE (>4K). Further analysis showed that the sensitivity of assimilation effect to diurnal variations in LST varies with time and space. The assimilation using observations with a time interval of 3 hours has the smallest bias in Oceania and Africa (both<1K); the use of 24-hour interval observation data for assimilation produces the smallest bias (<2.2K) in March, April and July.

Land surface temperature (LST) is an important parameter for measuring the water-heat balance on the Earth's surface. Remote sensing technology provides a unique way to monitor LSTs over large spatial areas. Thermal infrared (TIR)-derived LST has a high spatial resolution and accuracy, but missing values caused by clouds hinder applications of this method. Passive microwave radiation can penetrate clouds, but the data have a relatively lower spatial resolution and accuracy. The Bayesian maximum entropy (BME) method was used for blending the Moderate Resolution Imaging Spectroradiometer (MODIS) LST and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) LST to produce a spatially complete, accurate, and high-spatial-resolution LST over the Tibetan Plateau (TP) and the Heihe River Basin (HRB). The accuracy of the BME method was validated using the adjusted soil temperature collected from the two verification regions. The root-mean-square errors (RMSEs) are less than 3.54 and 4.89 K over the relatively flat verification region during nighttime and daytime, respectively. In another rugged terrain verification region, the RMSE is less than 3 K during nighttime and ranges from 4.2 to 8.29 K during daytime. The RMSE of the blended LST is significantly better than that of a recent study of the AMSR-E LST retrieved during nighttime, and the RMSE is comparable to the AMSR-E LST retrieved during daytime. The BME method was adopted to integrate the MODIS LST and AMSR-E LST acquired on November 30, 2010 over the TP and HRB. The spatial completeness of the blended LST reached 100%, and the blended LST spatial pattern was generally consistent with the spatial patterns of the MODIS LST and AMSR-E LST. This paper demonstrates the utility of generating all-weather regional LSTs using the BME method from TIR LST and microwave LST products.

ABSTRACTThe MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data have been widely used for air temperature estimation in mountainous regions where station observations are sparse. However, the performance of MODIS LST in high-elevation glacierized areas remains unclear. This study investigates air temperature estimation in glacierized areas based on ground observations at four glaciers across the Tibetan Plateau. Before being used to estimate the air temperature, MODIS LST data are evaluated at two of the glaciers, which indicates that MODIS night-time LST is more reliable than MODIS daytime LST data. Then, linear models based on each of the individual MODIS LST products from two platforms (Terra and Aqua) and two overpasses (night-time and daytime) are built to estimate daily mean, minimum and maximum air temperatures in glacierized areas. Regional glacier surface (RGS) models (mean /-mean-square differences: 3.3, 3.0 and 4.8°C for daily mean, minimum and maximum air temperatures, respectively) show higher accuracy than local non-glacier surface models (mean root-mean-square differences: 4.2, 4.7 and 5.7°C). In addition, the RGS models based on MODIS night-time LST perform better to estimate daily mean, minimum and maximum air temperatures than using temperature lapse rate derived from local stations.

The recently launched Landsat-9 has an important mission of working together with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires the data of Landsat-9 to be highly consistent with that of Landsat-8 to avoid bias caused by data inconsistency when the two satellites are simultaneously used. Therefore, this study evaluated the consistency of the surface reflectance (SR) and land surface temperature (LST) data between Landsat-8 and Landsat-9 based on five test sites from different parts of the world using synchronized underfly image pairs of both satellites. Previous cross-comparisons have demonstrated high consistency between the spectral bands of Landsat-8 and Landsat-9, with differences of around 1%. However, it is unclear whether this low deviation will be amplified in subsequent multiband calculations. It is also necessary to determine whether the difference is consistent across different land cover types. Therefore, this study used a three-level cross-comparison approach to specifically examine these concerns. Besides the commonly used band-by-band comparison, which served as the first-level comparison in this study, this approach included a second-level comparison based on the calculations of several indicators and a third-level comparison based on a composite index calculated from the indicators obtained in the second-level comparison. This three-level approach will examine whether the difference found in the first-level per-band comparison would change after the subsequent calculations in the second- and third-level comparisons. The Remote Sensing based Ecological Index (RSEI) was used for this approach because it is a composite index integrating four indicators. The results of this three-level comparison show that the first-level per-band comparison exhibited high consistency between the two satellites’ SR data, with an average absolute percent change (PC) of 1.88% and an average R 2 of 0.957 across six bands in the five test sites. This deviation increased to 2.21% in the third-level composite index-based comparison, with R 2 decreasing to 0.956. This indicates that after complex calculations, the deviation between the bands of the two satellites was amplified to some extent. However, when analyzing specific land cover types, notable differences emerged between the two satellites for the water category, with an average absolute PC ranging from 18% to 35% and an R 2 of lower than 0.6. Additionally, there were also nearly 5% differences for the built-up land category, with an average R 2 value of lower than 0.7. The comparison of LST data between both satellites also reveals that the Landsat-9 LST is on average 0.24°C lower than Landsat-8 LST across the five test areas but can be 0.58°C lower in built-up land-dominated areas and 0.42°C higher in desert environments. Overall, the SR and LST data between Landsat-8 and Landsat-9 are consistent. However, their performance varies depending on different land cover types. Caution is needed particularly for water-related research when utilizing both satellites simultaneously. Significant discrepancies may also arise in the areas characterized by deserts and built-up lands.

Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors’ view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data.

High-resolution, all-weather land surface temperature (LST) data on a global scale are pivotal for accurately reflecting the thermal feedback from the underlying surface. We developed a novel physically constrained land surface model (LSM) simulated LST downscaling and polar-orbiting satellite LST for reconstructing a time-series framework that is particularly effective in addressing the challenges associated with frequent cloud cover and precipitation in mountainous regions. The framework employs observations from 11 stations and MODIS data, integrating the Noah-MP and the ensemble Kalman filter to construct the data assimilation downscaling framework (DADF). This approach provides a comprehensive solution for acquiring high-resolution, all-weather LST, with both temporal (hourly) and spatial (1 km) precision. To improve the accuracy of LSMs and DADF, we corrected the momentum roughness lengths for each of the six surface categories and soil emissivity based on site observations. Six underlying surface parameters derived from measured data serve as a valuable reference for investigating land surface processes in this region. In a validation of MODIS day and night LST with data from 11 measurements, we observed high precision in high-altitude alpine terrains (Kalasai and Arou) and undulating desert terrains (Tazhong sites A–E). The sand–air admixture in desert terrain, caused by wind, introduces errors in site-based observations. The average RMSE of DADF-LST is 3.85 K (compared to the RMSE of 4.72 K for MODIS LST), and the R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.82 across six categories. The DADF incorporates data assimilation algorithms for LST downscaling, enabling accurate capture of actual LST under cloudy conditions. It provides a physically constrained solution for obtaining LST data with high spatial and temporal resolution globally.

Near real-time land surface temperature reconstruction from FY-4A satellite using spatio-temporal attention network

This study proposes a modified vegetation-dependent temperature-vegetation dryness index (TVDI) model for analyzing regional drought disasters in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) of China. First, MODIS monthly normalized difference vegetation index (NDVI), land surface temperature (LST) data and land use/cover data (Land cover type2) were pre-processed as a consistent big dataset. The land use/cover data were modified and integrated into six primary types. Then, these land types were used as the base data layer to calculate the TVDI by parameterizing the relationship between the MODIS NDVI and LST data. By emphasizing different types of land uses, this study was able to compare and analyze the differences of the TVDI indices between the entire study area (no consideration of the land types) and the six classified land uses. The soil moisture data were used to validate the modified TVDI values based on different land uses, which confirmed that the modified model more effectively reflected drought conditions. Finally, the aforementioned model was used to analyze the temporal and spatial variation of drought experienced by vegetation cover from 2000 to 2014. The results of the modified model were validated with the synchronized soil moisture and precipitation data. The case study clearly demonstrated that the modified TVDI model, which is based on different vegetation indexes, could better reflect the drought conditions of the study area.

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.