Improved surface temperature estimates with MASTER/AVIRIS sensor fusion

This paper presents a new temperature and emissivity separation (TES) algorithm for retrieving land surface temperature (LST) from the MEdium Resolution Spectral Imager-LL (MERSI) onboard Fengyun-3E (FY-3E), the first satellite of the FY-3 series placed in a dawn-dusk, sun-synchronous orbit. An optimized Water Vapor Scaling (WVS) method was proposed for atmospheric correction, scaling reanalysis water vapor profiles on a pixel-by-pixel basis for both graybody and nongraybody pixels without the need for external land surface emissivity (LSE) data as input. In situ validation showed the retrieved FY-3E/MERSI LST achieved a bias of −0.61 K and an RMSE of 2.24 K at the SURFace RADiation budget (SURFRAD) network sites. Cross-comparison with the LST product of Geostationary Operational Environmental Satellite-16 (GOES-16) showed a high degree of spatial distribution consistency, with an average bias and RMSE of −0.21 K and 1.64 K, respectively. Simulation experiments showed that for nongraybodies, the optimized WVS method more effectively improved the accuracy of the TES-derived LST compared to the traditional interpolation-based WVS method. This study holds the potential to offer valuable insights into improving atmospheric correction for remotely sensed TIR data.

Land surface temperature (LST) is a key parameter for a wide number of applications. The Advanced Himawari Imager (AHI) onboard Himawari-8 has four thermal infrared (TIR) bands in the atmospheric window, which provides the possibility of separating LST and LSE from AHI TIR data. In this paper, the TES algorithm and water vapor scaling (WVS) method were combined to retrieve LST and LSE from the AHI TIR data. The retrieval results are evaluated by the ground measurements collected from one site in the Baseline Surface Radiation Network (BSRN) network. The average Bias and RMSE of the estimated LST are -0.096K and 0.705K, respectively. This study demonstrates the capability of the combinations of TES algorithm and WVS method in retrieving LST and LSE from AHI TIR data.

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

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

Accurate estimation of land surface temperature (LST) is crucial for ecological environment monitoring and climate change studies in mountainous areas. The current LST retrieval algorithms were developed without accounting for the topographic effect, which can only be used to retrieve LST over relatively flat surfaces. Due to the impact of 3-D structure of mountainous surfaces, rugged terrain makes the processes of thermal radiation more complex. In this study, a radiative transfer equation (RTE)-based single-channel algorithm was proposed to retrieve LST with topographic effect correction from the Landsat 8 thermal infrared (TIR) data in mountainous areas. This algorithm accounts for the changes in the thermal radiation components in the TIR RTE caused by the topographic effect. According to the analysis of simulation data, sky-view factor (SVF), atmospheric water vapor content, surface emissivity of target pixel, and average LST of the surrounding terrain have significant influence on the magnitude of the topographic effect. The differences between the LST retrieved without/with topographic effect correction from the Landsat 8 TIR data are related to SVF. The topographic effect should be taken into account in the LST retrieval algorithm when SVF is smaller than 0.7. The largest LST difference of approximately 1 K occurs in the deep valley. The results indicate that LST without topographic effect correction could be overestimated to be as high as 1 K. Due to a lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> LST measurements, the performance of the LST retrieval algorithm in mountainous areas was only evaluated by comparing the brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at three subregions. There is a good consistency between BT at the TOA simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at the three subregions, with a root-mean-squared error (RMSE) of less than 0.23 K.

Land surface temperature is an important driving force in the exchange of water, heat, and even CO 2 at the surface-atmosphere interface in the desert regions. The rapid and continuous measurements of land surface temperature are meaningful to the ecological and environmental researches. A physically based single-frequency and double-polarization algorithm for retrieving land surface temperature is developed in this study. The 18.7 GHz vertically polarized emissivities are firstly estimated from the Polarization Ratio (PR, defined as the ratio of the horizontal to vertical brightness temperature at the same frequency) at 18.7 GHz. And then the estimated emissivities can be directly used to retrieve land surface temperature without considering the atmospheric effect. A preliminary validation is done in the Taklimakan desert. The retrieved land surface temperatures are compared to the infrared land surface temperature products for all the year of 2007 with a Root Mean Square Error (RMSE) of 3.05 K.

The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (AEEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of and , respectively. For Du et al.’s algorithm, the w range of to g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from AEEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

Land surface temperature (LST) is a key indicator of the land surface state and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. Split-window algorithms have been used with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to estimate instantaneous LST for nearly 20 years. However, the low accuracy of LST retrievals associated with intractable variability has often hindered its wide use. In this study, we developed a six-year daily (day and night) NOAA-14 AVHRR LST dataset over continental Africa. By combining vegetation structural data available in the literature and a geometric optics model, we estimated the fractions of sunlit and shaded endmembers observed by AVHRR for each pixel of each overpass. Although our simplistic approach requires many assumptions (e.g., only four endmember types per scene), we demonstrate through correlation that some of the AVHRR LST variability can be attributed to angular effects imposed by AVHRR orbit and sensor characteristics, in combination with vegetation structure. These angular effects lead to systematic LST biases, including "hot spot" effects when no shadows are observed. For example, a woodland case showed that LST measurements within the "hot-spot" geometry were about 9 K higher than those at other geometries. We describe the general patterns of these biases as a function of tree cover fraction, season, and satellite drift (time past launch). In general, effects are most pronounced over relatively sparse canopies (tree cover <60%), at wet season sun-view angle geometries (principal plane viewing) and early in the satellite lifetime. These results suggest that noise in LST time series may be strongly reduced for some locations and years, and that long-term LST climate data records should be normalized to a single sun-view geometry, if possible. However, much work remains before these can be accomplished.

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.

Abstract. Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution with global coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination, indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporal gap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020 based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the data pre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In the spatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in each pixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for each day. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values in the original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by large areas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation with different missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error (RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime and mid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling, and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhang et al., 2021).

The effect of emissivity on the measurement of land surface (LS) temperature from satellite radiances using the split-window method is investigated. Formulas are derived to relate the LS temperatures to two brightness temperatures measured from space in the AVHRR 4 and AVHRR 5 channels, and their accuracies are discussed. Results indicate that to determine LS temperatures from a satellite, the spectral emissivity must be known to an accuracy on the order of 0.005 for the average and 0.0007 for the difference in order to obtain an error of the order of 0.5 K.

Thermal infrared (TIR) observations from geostationary satellites enable the retrievals of the diurnal variations in land surface temperatures (LST) linked to the land-atmosphere energy exchange and water cycle. However, cloud cover obstructs TIR signals and leads to missing gaps that generally account for more than 50% of TIR LST maps. This study proposed an effective method to estimate hourly all-weather LST from the Advanced Geosynchronous Radiation Imager (AGRI) data under the framework of surface energy balance (SEB) theory. An improved temperature and emissivity separation algorithm was first used to obtain the high-quality clear-sky LST, which plays a decisive role in ensuring the accuracy of recovered cloudy-sky LST. Then we proposed a unique way to solve the temperature difference (ΔLST) between the cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects (CRE). The bias (RMSE) of the estimated AGRI hourly cloudy-sky LST is 0.10 K (3.71 K) during the daytime, and -0.20 K (2.73 K) during the nighttime, respectively. The overall bias (RMSE) of the estimated AGRI all-weather LST is 0.02 K (2.84 K). The estimated hourly all-weather LST not only captures the rapid variation in diurnal LST but is also promising for temporal upscaling. The temporally upscaled daily mean LST shows a bias (RMSE) of 0.03 K (1.35 K). This study provides a promising solution to generate diurnal hourly all-weather LST for AGRI and other geostationary satellites.

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

Evaluation of six land-surface diurnal temperature cycle models using clear-sky in situ and satellite data

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.