Reconstruction of the Daily MODIS Land Surface Temperature Product Using the Two-Step Improved Similar Pixels Method

Spatiotemporal change patterns and driving factors of land surface temperature in the Yunnan-Kweichow Plateau from 2000 to 2020

In this study, the spatiotemporal change patterns and driving factors of land surface temperature (LST) on the Yunnan-Kweichow Plateau (YKP) during 2000-2020 are investigated by using the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST dataset provided by National Tibetan Plateau Data Center. The YKP LST spatiotemporal change patterns are revealed at annual, seasonal, monthly, and daily scales. Furthermore, seven driving factors such as air temperature, land cover types, normalized difference vegetation index, precipitation, solar radiation, elevation, and latitude are quantified the impacts on LST spatial heterogeneity at annual scale. The main findings are as follows: (1) Annual mean LST increases by 0.016 K/year. Annual mean daytime LST slightly decreases by 0.009 K/year. Annual mean nighttime LST significantly increases by 0.042 K/year. (2) The trend and seasonal components of the daily, daily mean daytime, and daily mean nighttime LST have five and four breakpoints respectively, indicating that the variation of LST is unstable during 2000-2020 on the YKP. (3) The LST lapse rates at nighttime are generally higher than those at daytime on the YKP at the annual, seasonal, and monthly scales. The LST maximum lapse rate is 0.59 K/100 m in summer nighttime, and the LST minimum lapse rate is 0.18 K/100 m in winter daytime. (4) The controlling effects of seven factors are generally stronger in the nighttime than those in the daytime. The factors of elevation and air temperature dominate the LST spatial distribution on the YKP, with a contribution rate of >70 %. In addition, the interactions among the seven factors are all enhancing the effects on the spatial distribution of annual mean LST, including bivariate enhancement and nonlinear enhancement. This study contributes to the mitigation and adaptation to climate change of LST in the plateau and plays a theoretical reference role in formulating corresponding policies for environmental protection.

Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model

Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily mean Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1 × 1 km². The ensemble model incorporated four base models, including a generalized additive model (GAM), a generalized additive mixed model (GAMM), and two machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. Various spatial predictors were included as adjustment variables in all the base models, including land cover type, normalized difference vegetation index (NDVI), and elevation. The ensemble model showed high performance with an overall R² of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Although each base model performed well, the two machine learning models (RF [R² = 0.97], XGBoost [R² = 0.98]) had better performance than the two regression models (GAM [R² = 0.95], GAMM [R² = 0.96]). In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, NDVI, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment in future epidemiological studies.

MODIS Land Surface Temperature Product Reconstruction Based on the SSA-BiLSTM Model

Land surface temperature (LST) is an important parameter indispensable for studying the substance and energy exchanges between the land surface and the atmosphere, climate changes, and other related aspects. However, due to cloud cover, there are many null values in MODIS (Moderate Resolution Imaging Spectroradiometer) LST data, which prevents such data from being widely used. Therefore, an LST reconstruction method is proposed by combining data decomposition with data prediction—SSA (Singular Spectrum Analysis) and BiLSTM (Bidirectional Long Short-Term Memory). This method consists of two major processes, namely, rough LST reconstruction based on the SSA model and refined LST reconstruction based on the BiLSTM model. The accuracy of the proposed method is verified through “removal–reconstruction–comparison” using remote sensing data and measured data. The verification results show that when the rate of original missing values in the LST time series for the study area is lower than 10%, the RMSE is smaller than 1.1 K, and the correlation coefficient is more significant than 0.98. Even when the rate of missing data is 40% and 50%, the proposed method remains accurate, the values of RMSE are 1.8331 K and 2.2929 K, and the importance of R2 are 0.9856 and 0.9800, respectively. The proposed method is compared with other existing LST reconstruction methods. The results of the comparative analysis indicate that the proposed method is superior to other methods in terms of reconstruction accuracy and stability. Moreover, the LST data reconstructed using the proposed method are highly consistent with the measured data, which further proves the accuracy of this method in LST reconstruction. The research findings provide a new technique and idea for accurate LST reconstruction.

An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: A heatwave event in Naples

Southern European functional urban areas (FUAs) are increasingly subject to heatwave (HW) events, calling for anticipated climate adaptation measures. In the urban context, such adaptation strategies require a thorough understanding of the built-up response to the incoming solar radiation, i.e., the urban energy balance cycle and its implications for the Urban Heat Island (UHI) effect. Despite readily available, diurnal Land Surface Temperature (LST) data does not provide a meaningful picture of the UHI, in these midlatitudes FUAs. On the contrary, the mid-morning satellite overpass is characterized by the absence of a significant surface UHI (SUHI) signal, corresponding to the period of the day when the urban-rural air temperature difference is typically negative. Conversely, nocturnal high-resolution LST data is rarely available. In this study, an energy balance-based machine learning approach is explored, considering the Local Climate Zones (LCZ), to describe the daily cycle of the heat flux components and predict the nocturnal SUHI, during an HW event. While the urban and rural spatial outlines are not visible in the diurnal thermal image, they become apparent in the latent and storage heat flux maps - built-up infrastructures uptake heat during the day which is released back into the atmosphere, during the night, whereas vegetation land surfaces loose diurnal heat through evapotranspiration. For the LST prediction model, a random forest (RF) approach is implemented. RF results show that the model accurately predicts the LST, ensuring mean square errors inferior to 0.1 K. Both the latent and storage heat flux components, together with LCZ classification, are the most important explanatory variables for the nocturnal LST prediction, supporting the adoption of the energy balance approach. In future research, other locations and time-series data shall be trained and tested, providing an efficient local urban climate monitoring tool, where in-situ air temperature observations are not available.

Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method

Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, an improved LST reconstruction method for cloud-covered pixels was proposed by building a linking model for the moderate resolution imaging spectroradiometer (MODIS) LST with other surface variables with a random forest regression method. The accumulated solar radiation from sunrise to satellite overpass collected from the surface solar irradiance product of the Feng Yun-4A geostationary satellite was used to represent the impact of cloud cover on LST. With the proposed method, time-series gap-free LST products were generated for Chongqing City as an example. The visual assessment indicated that the reconstructed gap-free LST images can sufficiently capture the LST spatial pattern associated with surface topography and land cover conditions. Additionally, the validation with in situ observations revealed that the reconstructed cloud-covered LSTs have similar performance as the LSTs on clear-sky days, with the correlation coefficients of 0.92 and 0.89, respectively. The unbiased root mean squared error was 2.63 K. In general, the validation work confirmed the good performance of this approach and its good potential for regional application.

A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia

MODIS land surface temperature data (MODIS Ts) products are quantified from the earth surface’s reflected thermal infrared signal via sensors onboard the Terra and Aqua satellites. MODIS Ts products are a great value to many environmental applications but often subject to discrepancies when compared to the air temperature (Ta) data that represent the temperature measured at 2 m above the ground surface. Although they are different in their nature, the relationship between Ts and Ta has been established by many researchers. Further validation and correction on the relationship between these two has enabled the estimation of Ta from MODIS Ts products in order to overcome the limitation of Ta that can only provide data in a point form with a very limited area coverage. Therefore, this study was conducted with the objective to assess the accuracy of MODIS Ts products, i.e., MOD11A1, MOD11A2, MYD11A1, and MYD11A2 against Ta and to identify the performance of a modified Linear Scaling using a constant and monthly correction factor (LS-MBC), and Quantile Mapping Mean Bias Correction (QM-MBC) methods for lowland area of Peninsular Malaysia. Furthermore, the correction factor (CF) values for each MBC were adjusted according to the condition set depending on the different bias levels. Then, the performance of the pre- and post-MBC correction for by stations and regions analysis were evaluated through root mean square error (RMSE), percentage bias (PBIAS), mean absolute error (MAE), and correlation coefficient (r). The region dataset is obtained by stacking the air temperature (Ta_r) and surface temperature (Ts_r) data corresponding to the number of stations within the identified regions. The assessment of pre-MBC data for both 36 stations and 5 regions demonstrated poor correspondence with high average errors and percentage biases, i.e., RMSE = 3.33–5.42 °C, PBIAS = 1.36–12.07%, MAE = 2.88–4.89 °C, and r = 0.16–0.29. The application of the MBCs has successfully reduced the errors and bias percentages, and slightly increased the r values for all MODIS Ts products. All post-MBC depicted good average accuracies (RMSE and MAE < 3 °C and PBIAS between ±5%) and r between 0.18 and 0.31. In detail, for the station analysis, the LS-MBC using monthly CF recorded better performance than the LS-MBC using constant CF or the QM-MBC. For the regional study, the QM-MBC outperformed the others. This study illustrated that the proposed LS-MBC, in spite of its simplicity, managed to perform well in reducing the error and bias terms of MODIS Ts as much as the performance of the more complex QM-MBC method.