Empirical Estimation of Near-Surface Air Temperature in China from MODIS LST Data by Considering Physiographic Features

A method for improving the estimation of extreme air temperature by satellite

Air temperature (Ta) data obtained from meteorological stations were spatially discontinuous. Some satellite data have complete spatial coverage and strong relationships with Ta (e.g., elevation and land surface temperature). Therefore, Ta can be mapped using in situ Ta and satellite data. However, this method may have a large bias when estimating the extreme Ta. In this study, the error prediction and correction (EPC) method, incorporating Cubist machine learning algorithm, was proposed to improve the estimation of extreme Ta. The accuracy of the EPC method was compared with that of the widely used method in previous studies in east China from 2003 to 2012. The mean absolute errors (MAEs) of the estimated daily Ta using the EPC method ranged from 0.75-1.01 °C, which were 0.57-0.96 °C lower than that of the method in the literature. The biases of the estimated Ta obtained using the two methods were close to zero. However, the biases can be as high as 7.10 °C when Ta is extremely low and as low as -3.09 °C when Ta is extremely high. Compared with the method in the literature, the EPC method can reduce the MAE by 1.41 °C, root mean square error by 1.49 °C, and bias by 1.61 °C of the estimated extreme Ta. Additionally, the EPC method produced satisfactory accuracy (MAEs <0.9 °C) of the estimated heat and cold wave magnitudes. Finally, a 1 km resolution daily Ta map in east China from 2003 to 2012 was developed, which will be useful data in multiple research fields.

Merging framework for estimating daily surface air temperature by integrating observations from multiple polar-orbiting satellites

Reconstructing spatially continuous surface air temperature (SAT) is of great significance to climate and environmental studies. Substantial efforts have been made to estimate daily SAT based on land surface temperature (LST) derived from polar-orbiting satellites. However, previous studies are nearly all limited to estimating daily SAT based on MODIS LST from NASA's Terra or Aqua by applying different statistical learning methods. Various satellites from earth observation missions, particularly the missions for meteorological satellites, are capable of acquiring thermal infrared observations, but its implications for SAT estimation are significantly ignored. In this study, for the first time, we proposed a merging framework for estimating daily mean SAT by integrating LST datasets from multiple polar-orbiting satellites, including Metop-B from EUMETSAT's Polar System (EPS), SNPP and JPSS-1 from NOAA's Joint Polar Satellites System (JPSS), and Terra and Aqua from NASA's EOS. This study is also the first to explore the estimating of daily SAT based on LST derived from the meteorological satellites in EPS and JPSS. The framework integrates 10 estimation models based on different LST from the five satellites and generates daily merged SAT by averaging the daily SAT estimates from the models. Here we show that the framework significantly improves the spatial coverage of daily SAT estimates for cloud-free areas by an overall increase of 39% with respect to the mean coverage of the LST datasets from the five satellites. Daily coverage of the merged SAT from the framework is nearly all above 75% with an average of 91%. Compared to the SAT estimated from MODIS LST, overall increases in the coverage of daily SAT are 37%-51%. Estimation models in the framework all achieved comparable and satisfactory predicative performances with an average RMSE of 1.7-1.9 K for sample-based cross-validation, and 1.9-2.2 K for site-based cross-validation.

Evaluation and analysis on suitability of human settlement environment in Qingdao

Human settlement environment is space places closely related to human production and life, and also surface spaces inseparable from human activities. As a coastal city in the east of China, Qingdao has a relatively high level of urbanization. However, it also along with many urban problems at the same time, among which the problem of human settlement environment has attracted more and more general attention from people. According to the characteristics of human settlement environment in Qingdao, the research constructs an index system with 10 index factors from natural factors and humanity factors, and proposes a comprehensive evaluation model. Evaluate and grade suitability of human settlement environment in Qingdao, explore the spatial aggregation and differentiation of the quality of human settlement environment, and reveal the internal connection of spatial evolution. The results indicate that the overall livability of Qingdao is relatively good, showing a multi-center and radial driving development. The distribution of livability is uneven, showing a decreasing spatial distribution law from the coast to the inland, and the quality of human settlement environment in Jiaozhou Bay and the coastal areas is relatively high. Qingdao is mainly based on natural livability, supplemented by humanity livability, compared with natural suitability, the spatio-temporal evolution characteristics of humanity livability have experienced three stages: rising-contradictory rising-harmonious rising. The quality of human settlement environment has obvious spatial correlation and is positively correlated with the degree of agglomeration, and the agglomeration of blocks with a higher quality of human settlement environment is higher than that of blocks with a lower level. The rule of human settlement environment changing over time is that areas with high quality of human settlement environment begin to shift from the city center to the north and the south, transforming into multi-point development, and overall environmental suitability has been improved. According to the results of the comprehensive evaluation, combined with its local development status and policies, the research puts forward developmental suggestions for the construction of human settlement environment in Qingdao, and provides decision-making basis for relevant departments to solve the problem of deterioration of human settlement environment.

8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

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.

The Trend Inconsistency between Land Surface Temperature and Near Surface Air Temperature in Assessing Urban Heat Island Effects

The credible urban heat island (UHI) trend is crucial for assessing the effects of urbanization on climate. Land surface temperature (LST) and near surface air temperature (SAT) have been extensively used to obtain UHI intensities. However, the consistency of UHI trend between LST and SAT has rarely been discussed. This paper quantified the temporal stability and trend consistency between Moderate Resolution Imaging Spectroradiometer (MODIS) LST and in situ SAT. Linear regressions, temporal trends and coefficients of variations (CV) were analyzed based on the yearly mean, maximum and minimum temperatures. The findings in this study were: (1) Good statistical consistency (R2 = 0.794) and the same trends were found only in mean temperature between LST-UHI and SAT-UHI. There are 54% of cities that showed opposite temporal trends between LST-UHI and SAT-UHI for minimum temperature while the percentage was 38% for maximum temperature. (2) The high discrepancies in temporal trends were observed for all cities, which indicated the inadequacy of LST for obtaining reliable UHI trends especially when using the maximum and minimum temperatures. (3) The larger uncertainties of LST-UHI were probably due to high inter-annual fluctuations of LST. The topography was the predominant factor that affected the UHI variations for both LST and SAT. Therefore, we suggested that SAT should be combined with LST to ensure the dependable temporal series of UHI. This paper provided references for understanding the UHI effects on various surfaces.

Developing a temporally accurate air temperature dataset for Mainland China

Spatially continuous satellite data have been widely used to estimate monthly air temperature (Ta). However, it is still not clear whether the estimated monthly Ta is temporally consistent with observed Ta or not. In this study, the accuracies of interannual variations and temporal trends in estimated monthly Ta were systematically analyzed for Mainland China during 2001-2018. The differences in accuracy among five ways to input data into the model were investigated. The Cubist algorithm and ten variables were used to estimate monthly Ta. It was found that inputting data for the same month into the model can generate more accurate results than inputting all data into the model. Using temporal variables (i.e., month and year) can significantly increase the accuracy of estimated Ta. These results can be explained by different relationships between Ta and auxiliary variables that appear at different times. Thus, using temporal variables can help distinguish between different relationships and improve accuracy levels of the estimated Ta. When applying the best method (inputting data for the same month into the model and using the year as a temporal variable), the coefficient of determination (R²) of estimated monthly mean Ta, interannual variations in monthly mean Ta and temporal trends in monthly mean Ta were recorded as 0.997, 0.731 and 0.848, respectively. The root mean squared errors (RMSEs) of estimated monthly mean Ta, interannual variations in monthly mean Ta and temporal trends in monthly mean Ta were recorded as 0.629 °C, 0.593 °C and 0.201 °C/decade, respectively. An accurate, national coverage, 1 km spatial resolution and long time series (2001-2018) monthly mean, maximum and minimum Ta dataset was finally developed. The dataset can be of great use to many fields such as climatology, hydrology and ecology.