Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

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

Land Surface Temperature (LST) is an important input parameter for many land surface models. The accuracy of satellite LST products directly affect its application; therefore, it is necessary to evaluate LST products. In this study, two satellite remotely sensed LST products, i.e. AMSR2 LST and MODIS LST, were evaluated against the in-situ LSTs at 17 ground sites in Heihe River Basin in 2014. Results show that both AMSR2 and MODIS LSTs have good correlations with the in-situ LST, with R2 from 0.80 to 0.98 except at AR2 site at daytime. However, both of these two products have large systematic errors compared with the in-situ LST. The possible main reason is the scale mismatch between the FOV of the longwave radiometer and the AMSR2 and MODIS pixels.

Land surface temperature (LST) is a key parameter in ecological and farm environment studies. The study area is located in Zhangye of Gansu province, mainly was covered by crops and desert. To retrieve LST from ASTER thermal infrared (TIR), split window algorithm was used. Surface emissivity and atmospheric transmittance was estimated previously. To evaluate the estimated result, the ASTER and MODIS LST production was collected and compared in both visual method and spatial distributions of LST profiles derived from typical transects. The maps showed that the general distribution tendency of ASTER LST was consistent with MODIS LST data and corresponded to the NDVI image in an inverse fashion. To gain an insight into the negative relationship between LST and NDVI, empirical statistics was conducted and the results showed that there was a strong negative relationship between LST and NDVI (R2=0.508). Further, the mean temperature and standard deviation of each land cover types for two standard LST productions and LST estimated in our method were collected to make a comparison. For the three LST data, the sequence of temperature values for land use/land cover (LULC) from high to low was same: sand, desert, impervious, vegetation and water. However, ASTER LST retrieval in our method was lower than the other two LST data. It may be caused by the estimated parameters or the coarse resolution of MODIS. In our study, a relative comparison approach was adopted to verify the result, which proved LST images retrieved from only two ASTER thermal channels using our developed algorithms were reliable and easily realized.

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are −0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI.

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 ASCAT (Advanced SCATterometer) soil moisture product with 10-km spatial resolution was retrieved based on the soil water index (SWI) algorithm from the data acquired by the scatterometer on board the Meteorological OPerational (MetOP) satellites (MetOP-A, MetOP-B). In this study, the ASCAT product was downscaled from 10-km to 1-km spatial resolution based on the Apparent Thermal Inertia (ATI) estimated from MODIS Land Surface Temperature (LST) and Albedo retrievals in 54 grids (1 degree ∗1 degree) around 54 FLUXNET stations. First, the ATI was estimated at 1-km spatial resolution by using MODIS LST and Albedo data at the same spatial resolution and then resampled to 10-km. Second, the relationship between ASCAT soil moisture and ATI at 10-km spatial resolution was established. Finally, the spatiotemporally continuous soil moisture at 1-km spatial resolution was retrieved using the obtained relationship between ATI and ASCAT at 10-km spatial resolution, and the ATI data at 1-km spatial resolution. However, there were many missing values in the MODIS LST maps leading to spatiotemporal discontinuity in LST and calculated ATI data. To obtain spatiotemporal continuous ATI data, this study first reconstructed the MODIS LST data by finding similar points that had the same land cover type and similar NDVI (the Normalized Difference Vegetation Index) value. In this study, we found that the LST data of similar points in a pair of temporal adjacent LST images had a linear relationship. The LST data of these similar points in a pair of temporal adjacent LST images were used to establish a linear relationship and then used to reconstruct the pair of temporally adjacent LST images. The reconstructed LST data were used to obtain the spatiotemporal continuous ATI data at 1-km and 10-km spatial resolutions. In this study, downscaled 1-km spatial resolution soil moisture product within the 54 grids around the FLUXNET sites were obtained in 2013. Results indicated that the spatial distribution of the downscaled soil moisture using the reconstructed MODIS LST data is better than that using original MODIS LST data. Additionally, the downscaled soil moisture was evaluated against in-situ soil moisture measurements at 54 FLUXNET stations. The average of RMSE (the Root Mean Square Error) was 0.098 m3m−3 and the average of MAE (the Mean Absolute Error) was 0.08 m3m−3.

Evaluation of satellite land surface temperature (LST) is one of the most difficult tasks in LST retrieval algorithm development, because of spatial and temporal variability of land surface temperature and surface emissivity variations. A large number of high quality "match-up" satellite and ground LST data is needed for the evaluation process. In developing a LST algorithm for the GOES-R Advanced Baseline Imager, we produced a set of "match-up" dataset from SURFace RADiation (SURFRAD) budget network ground measurements and GOES-8 and -10 satellite measurements. The dataset covers one-year GOES Imager data over six SURFRAD sites in the United States. A stringent cloud filtering procedure was applied to minimize cloud contamination in the match-up dataset. Each of the SURFRAD sites contains enough match-up data pairs for ensuring significance of statistical analyses of the LST algorithm. The evaluation was performed by directly and indirectly comparing the SURFRAD and satellite LSTs of each site. The direct comparison was illustrated using scatter plots and histogram plots of the ground and the satellite LSTs, while the indirect comparison was performed using a matrix analysis model developed by Flynn (2006)[1]. We demonstrated that LST measurements from the SURFRAD instrument can be used in our evaluation of the GOES-R LST algorithm development and the precision of the GOES-R LST algorithm can be fairly well estimated.

Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data

서울을 포함한 수도권의 지표면 온도를 분석하기 위하여 Landsat과 MODIS의 지표면 온도, AWS의 기온, 지표면 고도 및 토지이용도를 이용하였다. Landsat과 MODIS 위성의 지표면 온도와 AWS 기온의 분석은 상관계수, 평방근 오차(Root Mean Squared Error, RMSE), 선형회귀분석 등의 통계분석방법을 적용하였다. Landsat과 MODIS 지표면 온도의 상관계수는 0.32이고 RMSE는 4.61 K였다. 그리고 Landsat과 MODIS 지표면 온도와 AWS 기온의 상관성은 각각 0.83과 0.96이며 RMSE는 3.28 K, 2.25 K이었다. Landsat과 MODIS 지표면 온도는 비교적 높은 상관성을 보였으나 각각의 선형회귀의 기울기는 0.45와 1.02이었다. Landsat 5의 경우 전체 관측소에 대하여 0.5이하의 낮은 상관성을 보였고 Landsat 8의 경우는 일치되는 지점이 다른 위성에 비하여 적었으나 0.5이상의 상관성을 나타냈다. Landsat 7은 대부분 0.8이상의 높은 상관성을 보였고 대체적으로 서울중심부에서 높은 상관성이 나타났다. 위성의 지표면 온도와 지표유형에 따른 AWS 기온사이의 상관성은 0.8이상의 높은 상관성을 보였다. Landsat 위성의 지표면 온도의 상관성은 0.84이었고 RMSE는 3.1 K이상이었으며 MODIS 위성의 상관계수는 0.96이상이고 RMSE는 2.6 K이하였다. 결과적으로 두 위성의 지표온도의 차이는 관측시각 차이에 의한 것으로 위성의 해상도에 따라 복사량을 탐지하는 지표면의 면적 차이에 의하여 발생되는 것으로 사료된다. In order to analyze the Land Surface Temperature (LST) in metropolitan area including Seoul, Landsat and MODIS land surface temperature, Automatic Weather Station (AWS) temperature, digital elevation model and landuse are used. Analysis method among the Landsat and MODIS LST and AWS temperature is basic statistics using by correlation coefficient, root-mean-square error and linear regression etc. Statistics of Landsat and MODIS LST are a correlation coefficient of 0.32 and Root Mean Squared Error (RMSE) of 4.61 K, respectively. And statistics of Landsat and MODIS LST and AWS temperature have the correlations of 0.83 and 0.96 and the RMSE of 3.28 K and 2.25 K, respectively. Landsat and MODIS LST have relatively high correlation with AWS temperature, and the slope of the linear regression function have 0.45 (Landsat) and 1.02 (MODIS), respectively. Especially, Landsat 5 has lower correlation about 0.5 or less in entire station, but Landsat 8 have a higher correlation of 0.5 or more despite of lower match point than other satellites. Landsat 7 have highly correlation of more than 0.8 in the center of Seoul. Correlation between satellite LSTs and AWS temperature with landuse (urban and rural) have 0.8 or higher. Landsat LST have correlation of 0.84 and RMSE of more than 3.1 K, while MODIS LST have correlation of more than 0.96 and RMSE of 2.6 K. Consequently, the difference between the LSTs by two satellites have due to the difference in the optical observation and detection the radiation generated by the difference in the area resolution.

The Arctic has experienced the most rapid warming in the world in recent decades. Complex topography combines with low solar elevation to create distinct microclimates in Arctic regions, and for many applications such as ecological response and cryospheric change it is critical to obtain reliable temperature trends at the local scale. Due to lack of weather stations, satellite land surface temperature (LST) is increasingly important as a proxy for air temperature (Tair), but how accurately it can represent microclimates is unknown. For the first time, we compare 10 years (2007–2017) of Tair recorded over a dense network of 65 sites (~25 km2) around Kevo Subarctic Research Station in Finland with equivalent moderate resolution imaging spectroradiometer (MODIS) LST at 1 km resolution from MOD11A2/MYD11A2 8‐day products. We assess whether LST can pick up the extreme local gradients in air temperature (>20 °C/km) caused by cold air drainage. Although there is a high correspondence between LST and Tair anomalies on a synoptic timescale, small‐scale patterns in Tair (lapse rates, aspect contrasts) are not picked up by LST. Temperature gradients in Tair become positive (temperature inversions) in winter, and at night, but LST gradients show almost the reverse. Aspect contrasts in Tair peak in spring and autumn during the day, but LST shows biggest differences in the evening. Land cover has a large influence on LST, tundra heating up/cooling down more than birch or pine forest. The conflation between land cover and elevation means that differential land‐cover response dominates the elevational LST signal. Contrasts between Tair and LST cannot be explained by the number of stations measuring Tair in a pixel, elevation error, timing differences or the frequency of cloud cover within the 8‐day composite. Important features of the Arctic climate such as microscale cold air drainage are thus potentially obscured by land‐cover effects.

Long‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured.

<p>Remote Sensing (RS) information has progressively found, in recent years, more and more applications in hydrological modelling as a valuable tool for easy and frequent collection of geophysical data. However, this kind of data should be handled carefully, minding its characteristics, spatial resolution and the heterogeneity of the target area.</p><p>In this work, a scale analysis on evapotranspiration estimates over heterogeneous crops is performed combining a distributed energy-water balance model (FEST-EWB) and high-resolution remotely-sensed Land Surface Temperature (LST) and vegetation data.</p><p>The FEST-EWB model is calibrated on measured LST, based on a procedure where every single pixel is modified independently one from the other; hence in each pixel of the analysed domain the minimum of the pixel difference between modelled RET and satellite observed LST is searched over the period of calibration.</p><p>The case study is a Sicilian vineyard, with test dates in the summer of 2008. Meteorological and energy fluxes data are available from an eddy-covariance station, while LST and vegetation data are obtained from low-altitude flights at the high resolution of 1.7 metres.</p><p>After a preliminary calibration on LST data and validation on energy fluxes, the scale analysis is performed in two ways: model input aggregation and model output aggregation. Four coarser scales are selected in reference to some common satellite products resolution: 10.2 m (in reference to Sentinel’s 10 m), 30.6 m (Landsat, 30 m), 244.8 m (MODIS visible, 250 m) and 734.4 m (MODIS, 1000 m). First, modelled surface temperature and evapotranspiration are aggregated to each scale by progressive averaging. Then, model inputs are upscaled to the same spatial resolutions and the model is calibrated anew, obtaining independent results directly at the target scale.</p><p>The results of the two procedures are found to be quite similar, testifying to the capacity of the model to provide accurate products for a heterogeneous area even at low resolutions. The robustness of the analysis is strengthened by a further comparison with two well-established energy-balance algorithms: the one source Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) model.</p>

<p>Each experimental data measured by an instrument has an associated spatial (and temporal) support to which the measurement is assigned. In this sense a logger provides the temperature at a particular spatial location and has a point-support while satellite derived temperatures have an areal support equal to the size of the pixel of the satellite image (i.e. the spatial resolution of the image). Thus, when combining or merging both types of measurement, their support must be taken into account. In fact, in nature there is a continuous temperature field that is only accessible from empirical data with its associated support. In this work three sources of data have been considered to model the variability of temperature at two scales in the Southern Rocky Mountains across the northern Front Range of Colorado (NFRC). The coarse scale uses the NRCS SNOTEL stations across the NFRC and the fine scale uses iButton sensors at the Colorado State University Mountain Campus (CSUMC) located within the NFRC. The MODIS-based land surface temperature (LST), which has a spatial resolution of about 1 km, has been considered for both scales. The SNOTEL stations and the iButton sensors have a point support while satellite LST has an areal support. The main goal of this work is to assess the variability of the temperature field at both scales, taking into account the support effect of each set of experimental data, by using a geostatistical approach.</p><p>This research has been partially supported by the SIGLO-AN project from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad).</p>

Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) LST data products are usually desired for many applications. Instead of developing and launching new high-resolution satellite sensors for LST observations, a more economical and practical way is to develop proper methodologies to derive high-resolution LSTs from available Low-Resolution (LR) datasets. This study explores different algorithms to downscale low-resolution LST data to a high resolution. The existing regression-based downscaling methods usually require simultaneous observations and ancillary data. The Super-Resolution Reconstruction (SRR) method developed for traditional image enhancement can be applicable to high-resolution LST generation. For the first time, we adapted the SRR method for LST data. We specifically built a unique database of LSTs for the example-based SRR method. After deriving the LST data from the coarse-resolution passive microwave observations, the AMSR-E at 25 km and/or AMSR-2 at 10 km, we developed an algorithm to downscale them to a 1 km spatial resolution with the SRR method. The SRR downscaling algorithm can be implemented to obtain high-resolution LSTs without auxiliary data or any concurrent observations. The high-resolution LSTs are validated and evaluated with the ground measurements from the Surface Radiation (SURFRAD) Budget Network. The results demonstrate that the downscaled microwave LSTs have a high correlation coefficient of over 0.92, a small bias of less than 0.5 K, but a large Root Mean Square Error (RMSE) of about 4 K, which is similar to the original microwave LST, so the errors in the downscaled LST could have been inherited from the original microwave LSTs. The validation results also indicate that the example-based method shows a better performance than the self-similarity-based algorithm.

The need for cross-comparison and validation of all-weather Land Surface Temperature (LST) products has arisen due to the release of multiple such products aimed at providing comprehensive all-weather monitoring capabilities. In this study, we focus on validating two well-established all-weather LST products (i.e. MLST-AS and TRIMS LST) against in-situ measurements obtained from four high-quality LST validation sites: Evora, Gobabeb, KIT-Forest, and Lake Constance. For the land sites, MLST-AS exhibits better accuracy, with RMSEs ranging from 1.6 K to 2.1 K, than TRIMS LST, the RMSEs of which range from 1.9 K to 3.1 K. Because MLST-AS pixels classified as “inland water” are masked out, the validation over Lake Constance is limited to TRIMS LST: it yields a RMSE of 1.6 K. Furthermore, the validation results show that MLST-AS and TRIMS LST exhibit better accuracy under clear-sky conditions than unclear-sky conditions across all sites. Since the accuracy of the all-weather LST products is considerably affected by the input clear-sky LST products, we further compare the all-weather LST with the corresponding input clear-sky LST to conduct an error source analysis. Considering the clear-sky pixels on MLST-AS directly using the estimates from MLST, the error source analysis is limited to examining TRIMS LST and its input (i.e. MODIS LST). The findings indicate that TRIMS LST is highly correlated with MODIS LST. The investigation and validation of the two selected all-weather LST products objectively evaluate their accuracy and stability, which provides important information for applications of these all-weather LST products.