Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Yıl 2024, Cilt: 9 Sayı: 2, 233 - 246, 28.07.2024

Jagadish Kumar Mogaraju

https://doi.org/10.26833/ijeg.1394111

Öz

Remote sensing (RS), Geographic information systems (GIS), and Machine learning can be integrated to predict land surface temperatures (LST) based on the data related to carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), absorbing aerosol index (AAI), and Aerosol optical depth (AOD). In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier (ET), Logistic regressors (LR), and Random Forests (RF). The accuracy of the LR classifier (0.89 or 89%) is higher than ET (82%) and RF (82%) classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve (AUC), Recall, Precision, F1 score, Kappa, and MCC (Matthew’s correlation coefficient). Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales (2019 to 2022). In order to evaluate the model graphically, ROC (Receiver operating characteristic) curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE (t-distributed stochastic neighbour embedding) plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

Anahtar Kelimeler

Machine learning, Geographic information systems, Sentinel-5-P, MODIS, Land surface temperature

Etik Beyan

Not applicable

Destekleyen Kurum

Not applicable

Proje Numarası

NA

Teşekkür

The authors sincerely thank the Government of India, NASA, USGS, ISRO, Government of Andhra Pradesh, District administration of Annamayya district for helping in this research work through data supply.

Kaynakça

• Hulley, G. C., Ghent, D., Göttsche, F. M., Guillevic, P. C., Mildrexler, D. J., & Coll, C. (2019). Land surface temperature. Taking the Temperature of the Earth, 57-127. https://doi.org/10.1016/B978-0-12-814458-9.00003-4
• Tran, D. X., Pla, F., Latorre-Carmona, P., Myint, S. W., Caetano, M., & Kieu, H. V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119-132. https://doi.org/10.1016/j.isprsjprs.2017.01.001
• Pal, S., & Ziaul, S. K. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 125-145. https://doi.org/10.1016/j.ejrs.2016.11.003
• Li, Z. L., Wu, H., Duan, S. B., Zhao, W., Ren, H., Liu, X., ... & Zhou, C. (2023). Satellite remote sensing of global land surface temperature: Definition, methods, products, and applications. Reviews of Geophysics, 61(1). https://doi.org/10.1029/2022RG000777
• Mumtaz, F., Tao, Y., de Leeuw, G., Zhao, L., Fan, C., Elnashar, A., ... & Wang, D. (2020). Modeling spatio-temporal land transformation and its associated impacts on land surface temperature (LST). Remote Sensing, 12(18), 2987. https://doi.org/10.3390/rs12182987
• Walker, J. C., Hays, P. B., & Kasting, J. F. (1981). A negative feedback mechanism for the long‐term stabilization of Earth's surface temperature. Journal of Geophysical Research: Oceans, 86(C10), 9776-9782. https://doi.org/10.1029/JC086iC10p09776
• Xiang, Y., Ye, Y., Peng, C., Teng, M., & Zhou, Z. (2022). Seasonal variations for combined effects of landscape metrics on land surface temperature (LST) and aerosol optical depth (AOD). Ecological Indicators, 138, 108810. https://doi.org/10.1016/j.ecolind.2022.108810
• Roy, S. S. (2008). Impact of aerosol optical depth on seasonal temperatures in India: a spatio‐temporal analysis. International Journal of Remote Sensing, 29(3), 727-740. https://doi.org/10.1080/01431160701352121
• Singh, R. P., Kumar, J. S., Zlotnicki, J., & Kafatos, M. (2010). Satellite detection of carbon monoxide emission prior to the Gujarat earthquake of 26 January 2001. Applied Geochemistry, 25(4), 580-585. https://doi.org/10.1016/j.apgeochem.2010.01.014
• Marbach, T., Beirle, S., Liu, C., Platt, U., & Wagner, T. (2008). Biomass burning emissions from satellite observations: synergistic use of formaldehyde (HCHO), fire counts, and surface temperature. In Remote Sensing of Fire: Science and Application, 7089, 131-140. https://doi.org/10.1117/12.793654
• Morfopoulos, C., Müller, J. F., Stavrakou, T., Bauwens, M., De Smedt, I., Friedlingstein, P., ... & Regnier, P. (2022). Vegetation responses to climate extremes recorded by remotely sensed atmospheric formaldehyde. Global Change Biology, 28(5), 1809-1822. https://doi.org/10.1111/gcb.15880
• Zheng, Y., Unger, N., Barkley, M. P., & Yue, X. (2015). Relationships between photosynthesis and formaldehyde as a probe of isoprene emission. Atmospheric Chemistry and Physics, 15(15), 8559-8576. https://doi.org/10.5194/acp-15-8559-2015
• Ramanathan, V., Callis, L. B., & Boughner, R. E. (1976). Sensitivity of surface temperature and atmospheric temperature to perturbations in the stratospheric concentration of ozone and nitrogen dioxide. Journal of the Atmospheric Sciences, 33(6), 1092-1112. https://doi.org/10.1175/1520-0469(1976)033<1092:SOSTAA>2.0.CO;2
• Schumann, U., & Huntrieser, H. (2007). The global lightning-induced nitrogen oxides source. Atmospheric Chemistry and Physics, 7(14), 3823-3907. https://doi.org/10.5194/acp-7-3823-2007
• Conley, A. J., Westervelt, D. M., Lamarque, J. F., Fiore, A. M., Shindell, D., Correa, G., ... & Horowitz, L. W. (2018). Multimodel surface temperature responses to removal of US sulfur dioxide emissions. Journal of Geophysical Research: Atmospheres, 123(5), 2773-2796. https://doi.org/10.1002/2017JD027411
• Ward, P. L. (2009). Sulfur dioxide initiates global climate change in four ways. Thin Solid Films, 517(11), 3188-3203. https://doi.org/10.1016/j.tsf.2009.01.005
• Abidin, M. R., Nur, R., Mayzarah, E. M., & Umar, R. (2021). Estimating and monitoring the land surface temperature (LST) using Landsat OLI 8 TIRS. International Journal of Environment, Engineering and Education, 3(1), 17-24. https://doi.org/10.55151/ijeedu.v3i1.43
• Kafy, A. A., Shuvo, R. M., Naim, M. N. H., Sikdar, M. S., Chowdhury, R. R., Islam, M. A., ... & Kona, M. A. (2021). Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using machine learning algorithms in a fastest-growing megacity of Bangladesh. Remote Sensing Applications: Society and Environment, 21, 100463. https://doi.org/10.1016/j.rsase.2020.100463
• Roberts, D. A., Dennison, P. E., Roth, K. L., Dudley, K., & Hulley, G. (2015). Relationships between dominant plant species, fractional cover and land surface temperature in a Mediterranean ecosystem. Remote Sensing of Environment, 167, 152-167. https://doi.org/10.1016/j.rse.2015.01.026
• Sekertekin, A., Kutoglu, S. H., & Kaya, S. (2016). Evaluation of spatio-temporal variability in Land Surface Temperature: A case study of Zonguldak, Turkey. Environmental Monitoring and Assessment, 188, 1-15. https://doi.org/10.1007/s10661-015-5032-2
• Wan, Z., Zhang, Y., Zhang, Q., & Li, Z. L. (2004). Quality assessment and validation of the MODIS global land surface temperature. International Journal of Remote Sensing, 25(1), 261-274. https://doi.org/10.1080/0143116031000116417
• Zaitunah, A., Silitonga, A. F., & Syaufina, L. (2022). Urban greening effect on land surface temperature. Sensors, 22(11), 4168. https://doi.org/10.3390/s22114168
• Ziaul, S., & Pal, S. (2018). Analyzing control of respiratory particulate matter on Land Surface Temperature in local climatic zones of English Bazar Municipality and Surroundings. Urban Climate, 24, 34-50. https://doi.org/10.1016/j.uclim.2018.01.006
• Mahdavifard, M., Ahangar, S. K., Feizizadeh, B., Kamran, K. V., & Karimzadeh, S. (2023). Spatio-Temporal monitoring of Qeshm mangrove forests through machine learning classification of SAR and optical images on Google Earth Engine. International Journal of Engineering and Geosciences, 8(3), 239-250. https://doi.org/10.26833/ijeg.1118542
• Ebrahimy, H., & Azadbakht, M. (2019). Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels. Computers & Geosciences, 124, 93-102. https://doi.org/10.1016/j.cageo.2019.01.004
• Srivastava, P. K., Han, D., Ramirez, M. R., & Islam, T. (2013). Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resources Management, 27, 3127-3144. https://doi.org/10.1007/s11269-013-0337-9
• Sun, Y., Gao, C., Li, J., Wang, R., & Liu, J. (2019). Quantifying the effects of urban form on land surface temperature in subtropical high-density urban areas using machine learning. Remote Sensing, 11(8), 959. https://doi.org/10.3390/rs11080959
• Li, W., Ni, L., Li, Z. L., Duan, S. B., & Wu, H. (2019). Evaluation of machine learning algorithms in spatial downscaling of MODIS land surface temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2299-2307. https://doi.org/10.1109/JSTARS.2019.2896923
• Wang, S., Ma, Y., Wang, Z., Wang, L., Chi, X., Ding, A., ... & Zhang, Y. (2021). Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: impacts of COVID-19 pandemic lockdown. Atmospheric Chemistry and Physics, 21(9), 7199-7215. https://doi.org/10.5194/acp-21-7199-2021
• Tan, J., NourEldeen, N., Mao, K., Shi, J., Li, Z., Xu, T., & Yuan, Z. (2019). Deep learning convolutional neural network for the retrieval of land surface temperature from AMSR2 data in China. Sensors, 19(13), 2987. https://doi.org/10.3390/s19132987
• Mohammad, P., Goswami, A., Chauhan, S., & Nayak, S. (2022). Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India. Urban Climate, 42, 101116. https://doi.org/10.1016/j.uclim.2022.101116
• Pandey, A., Mondal, A., Guha, S., Singh, D., & Kundu, S. (2023). Analysis of the Variability in Land Surface Temperature Due to Land Use/Land Cover Change for a Sustainable Urban Planning. Journal of Landscape Ecology, 16(3), 20-35. https://doi.org/10.2478/jlecol-2023-0015
• Pandey, A., Mondal, A., Guha, S., Upadhyay, P. K., & Singh, D. (2023). A long-term analysis of the dependency of land surface temperature on land surface indexes. Papers in Applied Geography, 9(3), 279-294. https://doi.org/10.1080/23754931.2023.2187314
• Pandey, A., Mondal, A., Guha, S., Upadhyay, P. K., Rashmi, & Kundu, S. (2023). Analysis of spectral indices-based downscaled land surface temperature in a humid subtropical city. International Journal of Image and Data Fusion, 14(4), 336-358. https://doi.org/10.1080/19479832.2023.2252818
• Pandey, A., Mondal, A., Guha, S., & Upadhyay, P. K. (2022). A seasonal investigation on land surface temperature and spectral indices in Imphal City, India. Journal of Landscape Ecology, 15(3), 1-18. https://doi.org/10.2478/jlecol-2022-0015
• Pandey, A., Mondal, A., Guha, S., Upadhyay, P. K., & Singh, D. (2022). Land use status and its impact on land surface temperature in Imphal city, India. Geology, Ecology, and Landscapes, 1-15. https://doi.org/10.1080/24749508.2022.2131962
• https://earthexplorer.usgs.gov/
• https://giovanni.gsfc.nasa.gov/giovanni/
• https://developers.google.com/earth-engine/datasets/catalog/sentinel-5p
• https://developers.google.com/earth-engine/datasets/catalog/modis
• Khan, P. W., Byun, Y. C., & Jeong, O. R. (2023). A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels. Scientific Reports, 13(1), 10256. https://doi.org/10.1038/s41598-023-35476-y
• Mogaraju, J. K. (2023). Application of machine learning algorithms in the investigation of groundwater quality parameters over YSR district, India. Turkish Journal of Engineering, 7(1), 64-72. https://doi.org/10.31127/tuje.1032314
• Gong, X., Huang, Y., Duong, J., Leng, S., Zhan, F. B., Guo, Y., ... & Luo, L. (2023). Industrial air pollution and low birth weight in New Mexico, USA. Journal of Environmental Management, 348, 119236. https://doi.org/10.1016/j.jenvman.2023.119236
• Mogaraju, J. K. (2024). Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. Turkish Journal of Engineering, 8(1), 31-45. https://doi.org/10.31127/tuje.1223779
• Raju, R. S., Raju, G. S., & Rajasekhar, M. (2023). A study on hydro-geological characterization through Dar-Zarrouk parameters in hard rock terrain of Mandavi River Basin, Andhra Pradesh, India. Arabian Journal of Geosciences, 16(8), 453. https://doi.org/10.1007/s12517-023-11573-0
• Naidu, G., Zuva, T., & Sibanda, E. M. (2023, April). A review of evaluation metrics in machine learning algorithms. Computer Science On-line Conference, 15-25. https://doi.org/10.1007/978-3-031-35314-7_2
• Psaros, A. F., Meng, X., Zou, Z., Guo, L., & Karniadakis, G. E. (2023). Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons. Journal of Computational Physics, 477, 111902. https://doi.org/10.1016/j.jcp.2022.111902
• Islam, S. M., & Talukder, K. H. (2023). Exploratory Analysis of Smartphone Sensor Data for Human Activity Recognition. IEEE Access, 99481 – 99498. https://doi.org/10.1109/ACCESS.2023.3314651
• Hughes, B. K., Wallis, R., & Bishop, C. L. (2023). Yearning for machine learning: applications for the classification and characterisation of senescence. Cell and Tissue Research, 394(1), 1-16. https://doi.org/10.1007/s00441-023-03768-4
• Annapurna, S. M., Anitha, M., & Kumar, L. S. (2024). Composition and source based aerosol classification using machine learning algorithms. Advances in Space Research, 73(1), 474-497. https://doi.org/10.1016/j.asr.2023.09.068
• Choi, M., Zhang, J., Zhang, Y., Fan, J., Li, X., & Ying, Q. (2024). Impact of wildfires on regional ozone and PM2. 5: Considering the light absorption of Brown carbon. Atmospheric Environment, 316, 120196. https://doi.org/10.1016/j.atmosenv.2023.120196
• Zhen, Z., Chen, S., Yin, T., & Gastellu-Etchegorry, J. P. (2023). Globally quantitative analysis of the impact of atmosphere and spectral response function on 2-band enhanced vegetation index (EVI2) over Sentinel-2 and Landsat-8. ISPRS Journal of Photogrammetry and Remote Sensing, 205, 206-226. https://doi.org/10.1016/j.isprsjprs.2023.09.024
• vom Scheidt, F., & Staudt, P. (2024). A data-driven Recommendation Tool for Sustainable Utility Service Bundles. Applied Energy, 353, 122137. https://doi.org/10.1016/j.apenergy.2023.122137
• Moghadam, S. M., Yeung, T., & Choisne, J. (2023). A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors. Scientific Reports, 13(1), 5046. https://doi.org/10.1038/s41598-023-31906-z
• Javed, A. R., Saadia, A., Mughal, H., Gadekallu, T. R., Rizwan, M., Maddikunta, P. K. R., ... & Hussain, A. (2023). Artificial intelligence for cognitive health assessment: state-of-the-art, open challenges and future directions. Cognitive Computation, 15(6), 1767-1812. https://doi.org/10.1007/s12559-023-10153-4
• Singh, P. (2023). Systematic review of data-centric approaches in artificial intelligence and machine learning. Data Science and Management, 6(3), 144-157. https://doi.org/10.1016/j.dsm.2023.06.001