Search Results (15)

Nighttime near-surface air temperature is a critical input for ecological, hydrological, and meteorological models and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived instantaneous nighttime near-surface air temperature (T_a) and daily minimum temperatures (T_min) can provide spatially continuous monitoring. The MOD07 Level-2 and MYD07 Level-2 atmospheric profile product provides air temperature at various altitude levels, facilitating a more direct estimation of T_a and T_min. However, previous validations mainly focused on daytime, with a lack of validation for nighttime. Therefore, this study comprehensively evaluated the MOD07 Level-2 and MYD07 Level-2 derived T_a by 2168 hourly meteorological measurements over 5000 m altitude spanning in China. Furthermore, a detailed evaluation of their capability to estimate T_min was also compared with MOD11 Level-2 and MYD11 Level-2 land surface temperature. Our results show that the highest available pressure method (HAP) estimated that, in instantaneous nighttime T_a, there was severe underestimation especially in high-altitude areas for both MOD07 (r = 0.95, Bias = −0.27 °C, and RMSE = 4.53 °C) and MYD07 data (r = 0.96, Bias = −0.17 °C, and RMSE = 3.73 °C). The adiabatic lapse rate (ALR) correction effectively reduced these errors, achieving optimal accuracy with MYD07 data (r = 0.97, Bias = −0.05 °C, and RMSE = 3.29 °C). However, the underestimation by the HAP method was still insufficient compared to T_min estimation by land surface temperature (LST). The LST method offers improved accuracy (r = 0.98, Bias = −0.16 °C, RMSE = 2.89 °C). In general, MYD-based estimations consistently outperformed MOD-based estimations. However, seasonal and elevational variability was observed in all methods, with errors increasing notably in mountainous areas (RMSE rapidly increases to 5 °C and above when the altitude exceeds 2000 m). These findings can provide practical guidance for selecting appropriate inversion methods according to terrain and season and support the development of more accurate air temperature products for a range of climate- and environmental-related applications. Full article

Near-surface air temperature (T_a) estimation by geostationary meteorological satellites is mainly carried out under clear-sky conditions. In this study, we propose an all-weather T_a estimation method utilizing FY-4A Advanced Geostationary Radiation Imager (AGRI) and the Global Forecast System (GFS), along with additional auxiliary data. The method includes two neural-network-based T_a estimation models for clear and cloudy skies, respectively. For clear skies, AGRI LST was utilized to estimate the T_a (T_a,clear), whereas cloud top temperature and cloud top height were employed to estimate the T_a for cloudy skies (T_a,cloudy). The estimated T_a was validated using the 2020 data from 1211 stations in China, and the RMSE values of the T_a,clear and T_a,cloudy were 1.80 °C and 1.72 °C, while the correlation coefficients were 0.99 and 0.986, respectively. The performance of the all-weather T_a estimation model showed clear temporal and spatial variation characteristics, with higher accuracy in summer (RMSE = 1.53 °C) and lower accuracy in winter (RMSE = 1.88 °C). The accuracy in southeastern China was substantially better than in western and northern China. In addition, the dependence of the accuracy of the T_a estimation model for LST, CTT, CTH, elevation, and air temperature were analyzed. The global sensitivity analysis shows that AGRI and GFS data are the most important factors for accurate T_a estimation. The AGRI-estimated T_a showed higher accuracy compared to the ERA5-Land data. The proposed models demonstrated potential for T_a estimation under all-weather conditions and are adaptable to other geostationary satellites. Full article

Temperature analysis is of special interest in polar areas because temperature is an essential variable in the energy exchange between the Earth’s surface and atmosphere. Although land surface temperature (LST) obtained using satellites and air temperature (T_a) have different physical meanings and are measured with different techniques, LST has often been successfully employed to estimate T_a. For this reason, in this work, we estimated T_a from LST MODIS collection 6 (C6) and used other predictor variables. Daily mean T_a was calculated from Spanish State Meteorological Agency (AEMET) stations data on the Livingston and Deception Islands, and from the PERMASNOW project stations on Livingston Island; both islands being part of the South Shetland Islands (SSI) archipelago. In relation to our previous work carried out in the study area with collection 5 (C5) data, we obtained higher R² values (R²_CV = 0.8, in the unique model with Terra daytime data) and lower errors (RMSE_CV = 2.2 °C, MAE_CV = 1.6 °C). We corroborated significant improvements in MODIS C6 LST data. We analyzed emissivity as a possible factor of discrepancies between C5 and C6, but we did not find conclusive results, therefore we could not affirm that emissivity is the factor that causes differences between one collection and another. The results obtained with the applied filters indicated that MODIS data can be used to study T_a in the area, as these filters contribute to the reduction of uncertainties in the modeling of T_a from satellites. Full article

It is known that changes in temperature could cause changes in the Antarctic Ice Sheet, which would have an immediate and long-term impact on the global mean sea level. For this reason, the monitoring of air temperature (T_a) is of great interest to the scientific community. On the other hand, Antarctica constitutes an area of difficult access, which makes it difficult to obtain in situ data. Because of this, Land Surface Temperature (LST) remote sensing data have become an important alternative for estimating T_a. In this work, we estimated T_a from daytime and nighttime LST data at maritime Antarctic sites in the South Shetland Archipelago using empirical models, based on the addition of spatiotemporal variables. We used T_a data from the Spanish Antarctic stations and from the PERMASNOW project stations. MOD11A1 and MYD11A1 (Collection 6) Moderate Resolution Imaging Spectroradiometer (MODIS) LST products were downloaded from the Google Earth Engine platform and only the highest quality data were selected. Outliers associated with clouds were removed with filters. Two different multilinear regression models were tested: models for each individual station and global models based on the data from all the stations. The simple regression analysis LST against T_a showed that a better fit is always achieved with daytime LST data (R² average = 0.73) than with nighttime LST data (R² average = 0.56). The performance of the models was improved with the addition of spatiotemporal variables as predictive variables, with which we obtained an average R² = 0.75 for daytime data and an average R² = 0.60 for nighttime data. The global models allowed for improving the correlation and reducing the errors with respect to the models obtained using individual stations. Global models provide a precise description of the behavior of the temperature in maritime Antarctica, where it is not possible to install and maintain a dense network of weather stations. Full article

The Urban Heat Island (UHI) effect is of critical concern for cities’ adaptation to climate change. The UHI effect shows substantial intra-urban variation at the city microscale, causing disparities in thermal comfort and energy consumption. Therefore, air temperature assessment should be prioritized for effective heat mitigation and climate adaptation. However, meteorological stations’ spatial distribution is far from meeting the scale that the UHI and its driving parameters operate. This limitation hampers demonstrating the intra-city variability of UHI and its origin of sources; for example, most studies employ Land Surface Temperature (LST), usually without demonstrating the relationship between UHI and LST. The current body of knowledge on urban climate implies a much better understanding and more detailed information on the spatial pattern of UHI and the driving factors to provide decision-makers with tools to develop effective UHI mitigation and adaptation strategies. In an attempt to address the adequacy of the use of LST and UPs in describing the intra-city variability of UHI, this study investigates the relationship between LST daytime and nighttime, and air temperature (Ta) daytime and nighttime, and driving urban parameters (UPs) of UHI together. Although it is well recognized that the intensity of the UHI is characterized by Ta, particularly at night, so-called nocturnal UHI, the use of remotely sensed LST is common, owing to the lack of spatially detailed Ta data in cities. Our findings showed that nocturnal UHI is weakly correlated with nighttime LST with a Pearson correlation (r) of 0.335 at p > 0.05 and that it is not correlated with daytime LST for the case study, highlighting the need for Ta observations for representing the intra-urban variation of nocturnal UHI. Among UPs, Sky View Factor (SVF), Building Volume Density (BVD), and Road Network Density (RND) explained 69% of the variability of Ta nighttime that characterizes nocturnal UHI. Therefore, UPs that performed well in estimating nocturnal UHI may be used in the absence of densely distributed Ta measurements. In a further investigation of the urban cooling phenomenon based on UHI diurnal changes, a particular region with high nighttime temperatures spoiled the Ta daytime and nighttime coherence. This region is characterized by high Mean Building Height (MBH), BFD, and BVD that re-emits heat, low SVF that prevents urban cooling, and high RND that releases extra heat at night. These particular UPs can be of prior interest for urban cooling. The present study, exploring the relationships of LST and Ta in a diurnal context, offers a further understanding of the preference of LST, Ta, or UPs to characterize UHI. Ta, in relation to major causative factors (UPs), provides insights into addressing the localities most vulnerable to the UHI effect and possible strategies targeting heat mitigation for sustainability and climate change resilience. Full article

Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements due to spatial heterogeneity. Based on in situ measurements in Guangdong Province from 2012 to 2018, this study applied three machine learning (ML) models and fused multi-source datasets to evaluate the performance of four data combinations in Ta estimation. Correlations of covariates were compared, focusing on rice planting areas (RA). The results showed that (1) The fusion of multi-source data improved the accuracy of model estimations, where the best performance was achieved by the random forest (RF) model combined with the ERA5 combination, with the highest R² reaching 0.956, the MAE value of 0.996 °C, and the RMSE of 1.365 °C; (2) total precipitation (TP), wind speed (WD), normalized difference vegetation index (NDVI), and land surface temperature (LST) were significant covariates for long-term Ta estimations; (3) Rice planting improved the model performance in estimating Ta, and model accuracy decreased during the crop rotation in summer. This study provides a reference for the selection of temperature estimation models and covariate datasets. It offers a case for subsequent ML studies on remote sensing of temperatures over agricultural areas and the impact of agricultural cultivation on global warming. Full article

In this article, we present empirical models for estimating daily mean air temperature (T_a) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained and validated using the daily mean T_a from three Spanish in situ meteorological stations (AEMET stations), Juan Carlos I (JCI), Johnsons Glacier (JG), and Hurd Glacier (HG), and three stations in our team’s monitoring sites, Incinerador (INC), Reina Sofía (SOF), and Collado Ramos (CR), as well as daytime and nighttime Terra-MODIS LST and Aqua-MODIS LST data between 2000 and 2016. Two types of multiple linear regression (MLR) models were obtained: models for each individual station (for JCI, INC, SOF, and CR—not for JG and HG due to a lack of data) and global models using all stations. In the study period, the JCI and INC stations were relocated, so we analyzed the data from both locations separately (JCI1 and JCI2; INC1 and INC2). In general, the best individual T_a models were obtained using daytime Terra LST data, the best results for CR being followed by JCI2, SOF, and INC2 (R² = 0.5–0.7 and RSE = 2 °C). Model cross validation (CV) yielded results similar to those of the models (for the daytime Terra LST data: R²_CV = 0.4–0.6, RMSE_CV = 2.5–2.7 °C, and bias = −0.1 to 0.1 °C). The best global T_a model was also obtained using daytime Terra LST data (R² = 0.6 and RSE = 2 °C; in its validation: R²_CV = 0.5, RMSE_CV = 3, and bias = −0.03), along with the significant (p < 0.05) variables: linear time (t) and two time harmonics (sine-cosine), distance to the coast (d), slope (s), curvature (c), and hour of LST observation (H). T_a and LST data were carefully corrected and filtered, respectively, prior to its analysis and comparison. The analysis of the T_a time series revealed different cooling/warming trends in the locations, indicating a complex climatic variability at a spatial scale in the Hurd Peninsula. The variation of T_a in each station was obtained by the Locally Weighted Regression (LOESS) method. LST data that was not “good quality” usually underestimated T_a and were filtered, which drastically reduced the LST data (<5% of the studied days). Despite the shortage of “good” MODIS LST data in these cold environments, all months were represented in the final dataset, demonstrating that the MODIS LST data, through the models obtained in this article, are useful for estimating long-term trends in T_a and generating mean T_a maps at a global level (1 km² spatial resolution) in the Hurd Peninsula of Livingston Island. Full article

Meteorologically observed air temperature (T_a) is limited due to low density and uneven distribution that leads to uncertain accuracy. Therefore, remote sensing data have been widely used to estimate near-surface T_a on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous T_a when satellites overpass. This study aims to produce both daily and instantaneous T_a datasets at 1 km resolution for the Jingjinji area, China during 2018–2019, using machine learning methods based on remote sensing data, dense meteorological observation station data, and auxiliary data (such as elevation and normalized difference vegetation index). Newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 surface Downward Shortwave Radiation (DSR) was introduced to improve the accuracy of T_a estimation. Five machine learning algorithms were implemented and compared so that the optimal one could be selected. The random forest (RF) algorithm outperformed the others (such as decision tree, feedforward neural network, generalized linear model) and RF obtained the highest accuracy in model validation with a daily root mean square error (RMSE) of 1.29 °C, mean absolute error (MAE) of 0.94 °C, daytime instantaneous RMSE of 1.88 °C, MAE of 1.35 °C, nighttime instantaneous RMSE of 2.47 °C, and MAE of 1.83 °C. The corresponding R² was 0.99 for daily average, 0.98 for daytime instantaneous, and 0.95 for nighttime instantaneous. Analysis showed that land surface temperature (LST) was the most important factor contributing to model accuracy, followed by solar declination and DSR, which implied that DSR should be prioritized when estimating T_a. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous T_a at unprecedented accuracy and temporal scale with proper training and very dense station data. The estimated dataset could be very useful for local climate and ecology studies, as well as for nature resources exploration. Full article

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R²) > 0.96 and root mean square error (RMSE) < 1.96 °C and R² > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale. Full article

The objective of this research was to develop a robust statistical model to estimate climatologies (2002–2017) of monthly average near-surface air temperature (Ta) over Mongolia using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) time series products and terrain parameters. Two regression models were analyzed in this study linking automatic weather station data (Ta) with Earth observation (EO) images: Partial least squares (PLS) and random forest (RF). Both models were trained to predict Ta climatologies for each of the twelve months, using up to 17 variables as predictors. The models were applied to the entire land surface of Mongolia, the eighteenth largest but most sparsely populated country in the world. Twelve of the predictor variables were derived from the LST time series products of the Terra MODIS satellite. The LST MOD11A2 (collection 6) products provided thermal information at a spatial resolution of 1 km and with 8-day temporal resolution from 2002 to 2017. Three terrain variables, namely, elevation, slope, and aspect, were extracted using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and two variables describing the geographical location of weather stations were extracted from vector data. For training, a total of 8544 meteorological data points from 63 automatic weather stations were used covering the same period as MODIS LST products. The PLS regression resulted in a coefficient of determination (R²) between 0.74 and 0.87 and a root-mean-square error (RMSE) from 1.20 °C to 2.19 °C between measured and estimated monthly Ta. The non-linear RF regression yielded even more accurate results with R² in the range from 0.82 to 0.95 and RMSE from 0.84 °C to 1.93 °C. Using RF, the two best modeled months were July and August and the two worst months were January and February. The four most predictive variables were day/nighttime LST, elevation, and latitude. Using the developed RF models, spatial maps of the monthly average Ta at a spatial resolution of 1 km were generated for Mongolia (~1566 × 10⁶ km²). This spatial dataset might be useful for various environmental applications. The method is transparent and relatively easy to implement. Full article

Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery with large scale spatial coverage and fine resolution. Estimating spatially continuous Ta by integrating ground measurements and satellite data is an active research area. A variety of methods have been proposed and applied in this area. However, the existing studies primarily focused on daily Ta and failed to quantify uncertainties in model parameter and estimated results. In this paper, a Bayesian Kriging regression (BKR) method is proposed to model and estimate monthly Ta using satellite-derived land surface temperature (LST) as the only input. The BKR is a spatial statistical model with the capacity to quantify uncertainties via Bayesian inference. The BKR method was applied to estimate monthly maximum air temperature (Tmax) and minimum air temperature (Tmin) over the conterminous United States in 2015. An exploratory analysis shows a strong relationship between LST and Ta at the monthly scale, indicating LST has the great potential to estimate monthly Ta. 10-fold cross-validation approach was adopted to compare the predictive performance of the BKR method with the linear regression method over the whole region and the urban areas of the contiguous United States. For the whole region, the results show that the BKR method achieves a competitively better performance with averaged RMSE values $1.23$ K for Tmax and $1.20$ K for Tmin, which are also lower than previous studies on estimation of monthly Ta. In the urban areas, the cross-validation demonstrates similar results with averaged RMSE values $1.21$ K for Tmax and $1.27$ K for Tmin. Posterior samples for model parameters and estimated Ta were obtained and used to analyze uncertainties in the model parameters and estimated Ta. The BKR method provides a promising way to estimate Ta with competitively predictive performance and to quantify model uncertainties at the same time. Full article

Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins. Full article

Recently, several methods have been introduced and applied to estimate daily air surface temperature (T_a) using MODIS land surface temperature data (MODIS LST). Among these methods, the most common used method is statistical modeling, and the most applied algorithms are linear/multiple linear regression models (LM). There are only a handful of studies using machine learning algorithm models such as random forest (RF) or cubist regression (CB). In particular, there is no study comparing different combinations of four MODIS LST datasets with or without auxiliary data using different algorithms such as multiple linear regression, random forest, and cubist regression for daily T_a-max, T_a-min, and T_a-mean estimation. Our study examines the mentioned combinations of four MODIS-LST datasets and shows that different combinations and differently applied algorithms produce various T_a estimation accuracies. Additional analysis of daily data from three climate stations in the mountain area of North West of Vietnam for the period of five years (2009 to 2013) with four MODIS LST datasets (AQUA daytime, AQUA nighttime, TERRA daytime, and TERRA nighttime) and two additional auxiliary datasets (elevation and Julian day) shows that CB and LM should be applied if MODIS LST data is used solely. If MODIS LST is used together with auxiliary data, especially in mountainous areas, CB or RF is highly recommended. This study proved that the very high accuracy of T_a estimation (R² > 0.93/0.80/0.89 and RMSE ~1.5/2.0/1.6 °C of T_a-max, T_a-min, and T_a-mean, respectively) could be achieved with a simple combination of four LST data, elevation, and Julian day data using a suitable algorithm. Full article

This study aims to evaluate quantitatively the land surface temperature (LST) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) MOD11A1 and MYD11A1 Collection 5 products for daily land air surface temperature (T_a) estimation over a mountainous region in northern Vietnam. The main objective is to estimate maximum and minimum T_a (T_a-max and T_a-min) using both TERRA and AQUA MODIS LST products (daytime and nighttime) and auxiliary data, solving the discontinuity problem of ground measurements. There exist no studies about Vietnam that have integrated both TERRA and AQUA LST of daytime and nighttime for T_a estimation (using four MODIS LST datasets). In addition, to find out which variables are the most effective to describe the differences between LST and T_a, we have tested several popular methods, such as: the Pearson correlation coefficient, stepwise, Bayesian information criterion (BIC), adjusted R-squared and the principal component analysis (PCA) of 14 variables (including: LST products (four variables), NDVI, elevation, latitude, longitude, day length in hours, Julian day and four variables of the view zenith angle), and then, we applied nine models for T_a-max estimation and nine models for T_a-min estimation. The results showed that the differences between MODIS LST and ground truth temperature derived from 15 climate stations are time and regional topography dependent. The best results for T_a-max and T_a-min estimation were achieved when we combined both LST daytime and nighttime of TERRA and AQUA and data from the topography analysis. Full article

Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc. Full article