Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria

This study develops a general method to evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. The method is based on the understanding that long-term annual ULST is controlled by three factors: (1) localized urbanization, (2) global climate change, and (3) interannual climate variation. Then the method removes the interannual climate fluctuations on long-term observed LST time series via linear regression and separates the contributions of urbanization and climate change to the impacts on long-term ULST via urban–rural comparison. The method is applied to Lagos, a fast-growing metropolis in the tropical West Africa, as an example for reference. Combined time-series daily daytime and nighttime MODIS Land Surface Temperature (LST) data over the years of 2003–2021 are used as the representation of land surface temperature. To avoid the potentioal interannual data biase due to uneven availability of data in the rainy seasons over years, only MODIS LST data from dry seasons are used in the study. The results are summarized as follows for Lagos: (1) long-term annual ULST is confirmed to be controlled by the three factors; (2) the proposed method can separate the contribution of the three factors to the ULST; (2) both localized urbanization and global warming are verified to contribute to the ULST increase with positive trends; (3) daytime ULST increased the most in the afternoon time at a mean rate of 1.429 °C per decade, with 0.985 °C (10 year)⁻¹ contributed by urbanization and 0.444 °C (10 year)⁻¹ contributed by climate warming; (4) nighttime ULST in Lagos increased the most after midnight at a rate of 0.563 °C (10 year)⁻¹, with 0.56 °C (10 year)⁻¹ contributed by urbanization and 0.003 °C (10 year)⁻¹ contributed by climate warming; and (5) urbanization is generally responsible for around 60.97% of the urban warming in Lagos. Therefore, the increasing urbaniztion-induced urban heat island effect is the major cause for more heat-related health risks and climate extremes that many urban residents are suffering. The results of this study are of useful reference for both urbanization and climate change related issues in the geo-science field.

Estimation of Daily and Instantaneous Near-Surface Air Temperature from MODIS Data Using Machine Learning Methods in the Jingjinji Area of China

Meteorologically observed air temperature (Ta) 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 Ta on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous Ta when satellites overpass. This study aims to produce both daily and instantaneous Ta 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 Ta 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 Ta. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous Ta 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.

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.

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.

A High-Temperature Risk Assessment Model for Maize Based on MODIS LST

Currently, high-temperature risk assessments of crops at the regional scale are usually conducted by comparing the observed air temperature at ground stations or via the remote sensing inversion of canopy temperature (such as MODIS (moderate-resolution imaging spectroradiometer) land surface temperature (LST)) with the threshold temperature of the crop. Since this threshold is based on the absolute temperature value, it is difficult to account for changes in environmental conditions and crop canopy information between different regions and different years in the evaluation model. In this study, MODIS LST products were used to establish an evaluation model (spatiotemporal deviation mean (STDM)) and a classification method to determine maize-growing areas at risk of high temperatures at the regional scale. The study area was the Huang-Huai-Hai River plain of China where maize is grown and high temperatures occur frequently. The spatiotemporal distribution of the high-temperature risk of summer maize was determined in the study area from 2003 to 2018. The results demonstrate the applicability of the model at the regional scale. The distribution of high-temperature risk in the Huang-Huai-Hai region was consistent with the actual temperature measurements. The temperatures in the northwestern, southwestern, and southern parts were relatively high and the area was classified as a stable zone. Shijiazhuang, Jiaozuo, Weinan, Xi’an, and Xianyang city were located in a zone of increasing high temperatures. The regions with a stable high-temperature risk were Xiangfan, Yuncheng, and Luoyang city. Areas of decreasing high temperatures were Handan, Xingtai, Bozhou, Fuyang, Nanyang, Linfen, and Pingdingshan city. Areas that need to focus on preventing high-temperature risks include Luoyang, Yuncheng, Xianyang, Weinan, and Xi’an city. This study provides a new method for the detailed evaluation of regional high-temperature risk and data support.

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 μg/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.