Abstract
Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, multilayer perceptron (MLP), long short-term memory (LSTM) and an integrated machine learning model, namely Convolutional LSTM (ConvLSTM), were utilized for one step ahead LST prediction. Data were gathered from 1-day (MYD11A1) and 8-day composite (MYD11A2) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, which have 1-km × 1-km spatial resolution. Considering the inability of MODIS sensors to provide LST data under cloudy conditions, Inverse DISTANCE WEIGHTING (IDW), natural neighbor (NN), and cubic spline (C) methods were used to overcome the missing pixel problem. The proposed methods were tested over the Northern part of Adana province, Turkey, and the performances of the models were quantitatively evaluated through performance measures, namely, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected datasets range from 01 January 2017 to 01 November 2020 and from 01 January 2015 to 01 November 2020 for daily LST and 8-day composite LST, respectively. While 60% of the datasets were used as training set, the remaining 40% were used as validation (20%) and test (20%) sets. RMSE maps were generated to evaluate the pixelwise performance of the proposed method. On the other hand, the best average RMSE and MAE for the daily test set were obtained from the combination of ConvLSTM and NN (NN-ConvLSTM) as 3.62 °C and 2.85 °C, respectively, while they were acquired 3.57 °C and 2.69 oC from the combination of MLP and NN (NN-MLP) for the 8-day composite LST test set. The results revealed that the proposed hybrid models could be used for one step ahead spatiotemporal prediction of LST data.
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The datasets and codes used and/or analyzed in this study can be shared by the corresponding author based on a reasonable request.
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The authors would like to thank the US Geological Union (USGS) and MODIS team for enabling the LST products freely available.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. Serkan Kartal. The first draft of the manuscript was written by Dr. Aliihsan Sekertekin and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Kartal, S., Sekertekin, A. Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models. Environ Sci Pollut Res 29, 67115–67134 (2022). https://doi.org/10.1007/s11356-022-20572-9
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DOI: https://doi.org/10.1007/s11356-022-20572-9