Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye

Halil İbrahim Gündüz

doi:10.51489/tuzal.2014535

Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye

Abstract

This study evaluated three tree-based ensembles—Random Forest, XGBoost, and LightGBM—for seasonal modeling of land surface temperature (LST) in the Menteşe district of Muğla, Türkiye, a Mediterranean landscape with pronounced seasonality. Three nested feature sets—a topographic baseline extended first with hydrometeorological predictors and then with spectral indices—were compared for summer 2025 and winter 2025-2026, yielding 18 configurations across the three algorithms. Accuracy was assessed on a held-out random sample with bootstrap confidence intervals and Holm-corrected paired tests and feature attributions were quantified using Shapley additive explanations (SHAP) for each season. LightGBM with the full feature set was selected in both seasons (test R2 = 0.955, RMSE = 0.768 °C in summer; 0.932 and 0.525 °C in winter). Accuracy depended considerably more on the feature sets than on the algorithms: all feature-set increments remained significant after the Holm correction whereas the corrected tests detected no difference between LightGBM and XGBoost. Although all three feature groups contributed in each season, their relative contributions differed between seasons. The spectral indices dominated in summer, the topographic predictors held a nearly equal share in winter and the SHAP contribution of evapotranspiration reversed direction. The selected configurations were then applied across the district, and the resulting maps show a spatially heterogeneous summer pattern without a dominant gradient but a coherent winter gradient. These results suggest that, in landscapes with pronounced seasonality, LST should be modeled separately for each season and feature importance should be interpreted on a seasonal basis.

Keywords

References

Adeyeri, O. E., Folorunsho, A. H., Ayegbusi, K. I., Bobde, V., Adeliyi, T. E., Ndehedehe, C. E., & Akinsanola, A. A. (2024). Land surface dynamics and meteorological forcings modulate land surface temperature characteristics. Sustainable Cities and Society, 101, 105072. https://doi.org/10.1016/j.scs.2023.105072
Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2623–2631). Association for Computing Machinery. https://doi.org/10.1145/3292500.3330701
Altıner, F., & Bingöl, F. (2025). Spatio-temporal assessment of land surface temperature, vegetation cover, and built-up areas using LST, NDVI, and NDBI in Balıkesir, Türkiye (1985–2025). Sustainability, 17(20), 9245. https://doi.org/10.3390/su17209245
Arca, D., & Keskin Citiroglu, H. (2024). Determining climate classifications and producing climate border maps with GIS of Muğla province on the southern Aegean Sea coast of Türkiye. Theoretical and Applied Climatology, 155(7), 5745–5757. https://doi.org/10.1007/s00704-024-04954-2
Arslan, N., & Sekertekin, A. (2019). Application of long short-term memory neural network model for the reconstruction of MODIS land surface temperature images. Journal of Atmospheric and Solar-Terrestrial Physics, 194, 105100. https://doi.org/10.1016/j.jastp.2019.105100
Arunab, K. S., & Mathew, A. (2024). Exploring spatial machine learning techniques for improving land surface temperature prediction. Kuwait Journal of Science, 51(3), 100242. https://doi.org/10.1016/j.kjs.2024.100242
Böhner, J., & Antonić, O. (2009). Land-surface parameters specific to topo-climatology. In T. Hengl & H. I. Reuter (Eds.), Geomorphometry: Concepts, software, applications (Developments in Soil Science, Vol. 33, pp. 195–226). Elsevier. https://doi.org/10.1016/S0166-2481(08)00008-1
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., & Tait, A. M. (2022). Dynamic World, near real-time global 10 m land use land cover mapping. Scientific Data, 9(1), 251. https://doi.org/10.1038/s41597-022-01307-4
Cetin, M., Ozenen Kavlak, M., Senyel Kurkcuoglu, M. A., Bilge Ozturk, G., Cabuk, S. N., & Cabuk, A. (2024). Determination of land surface temperature and urban heat island effects with remote sensing capabilities: The case of Kayseri, Türkiye. Natural Hazards, 120(6), 5509–5536. https://doi.org/10.1007/s11069-024-06431-5
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8(7), 1991–2007. https://doi.org/10.5194/gmd-8-1991-2015
Crawford, C. J., Roy, D. P., Arab, S., Barnes, C., Vermote, E., Hulley, G., Gerace, A., Choate, M., Engebretson, C., Micijevic, E., Schmidt, G., Anderson, C., Anderson, M., Bouchard, M., Cook, B., Dittmeier, R., Howard, D., Jenkerson, C., Kim, M., … Zahn, S. (2023). The 50-year Landsat collection 2 archive. Science of Remote Sensing, 8, 100103. https://doi.org/10.1016/j.srs.2023.100103
Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall. https://doi.org/10.1201/9780429246593
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2), RG2004. https://doi.org/10.1029/2005RG000183
Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Hughes, M. J., & Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379–390. https://doi.org/10.1016/j.rse.2017.03.026
Friedl, M., & Sulla-Menashe, D. (2022). MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD12Q1.061
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2(1), 150066. https://doi.org/10.1038/sdata.2015.66
GCOS. (2022). The 2022 GCOS implementation plan (GCOS-244). World Meteorological Organization. https://library.wmo.int/idurl/4/58104
Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems (Vol. 35, pp. 507–520). Curran Associates, Inc. https://doi.org/10.52202/068431-0037
Guha, S., Govil, H., Dey, A., & Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1), 667–678. https://doi.org/10.1080/22797254.2018.1474494
Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1–2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
Hoang, N.-D., Pham, P. A. H., Huynh, T. C., Cao, M.-T., & Bui, D.-T. (2025). Geospatial urban heat mapping with interpretable machine learning and deep learning: A case study in Hue City, Vietnam. Earth Science Informatics, 18(1), 64. https://doi.org/10.1007/s12145-024-01582-2
Hoang, N.-D., Tran, V.-D., & Huynh, T.-C. (2025). From data to insights: Modeling urban land surface temperature using geospatial analysis and interpretable machine learning. Sensors, 25(4), 1169. https://doi.org/10.3390/s25041169
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70. https://www.jstor.org/stable/4615733
Huang, J., & Huang, F. (2026). Unraveling the nonlinear drivers of urban land surface temperature in high-density cities: An explainable machine learning analysis of Wuhan, China. Frontiers in Environmental Science, 14, 1801455. https://doi.org/10.3389/fenvs.2026.1801455
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Huete, A. R., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
Karakoç, A. (2025). Long-term (1984–2024) land surface temperature (LST) trends at Iztuzu Beach (Dalyan, Türkiye): A decadal analysis. Ege Coğrafya Dergisi, 34(2), 235–251. https://doi.org/10.51800/ecd.1800155
Kartal, S., & Sekertekin, A. (2022). Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models. Environmental Science and Pollution Research, 29(44), 67115–67134. https://doi.org/10.1007/s11356-022-20572-9
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 30). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf
Kim, M., Kim, D., & Kim, G. (2022). Examining the relationship between land use/land cover (LULC) and land surface temperature (LST) using explainable artificial intelligence (XAI) models: A case study of Seoul, South Korea. International Journal of Environmental Research and Public Health, 19(23), 15926. https://doi.org/10.3390/ijerph192315926
Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., & Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14–37. https://doi.org/10.1016/j.rse.2012.12.008
Li, Z.-L., Wu, H., Duan, S.-B., Zhao, W., Ren, H., Liu, X., Leng, P., Tang, R., Ye, X., Zhu, J., Sun, Y., Si, M., Liu, M., Li, J., Zhang, X., Shang, G., Tang, B.-H., Yan, G., & Zhou, C. (2023). Satellite remote sensing of global land surface temperature: Definition, methods, products, and applications. Reviews of Geophysics, 61(1), e2022RG000777. https://doi.org/10.1029/2022RG000777
Liu, X., Zheng, L., & Wang, Y. (2025). Revealing the roles of climate, urban form, and vegetation greening in shaping the land surface temperature of urban agglomerations in the Yangtze River Economic Belt of China. Journal of Environmental Management, 377, 124602. https://doi.org/10.1016/j.jenvman.2025.124602
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 30). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
Ma, X., Yang, J., Zhang, R., Yu, W., Ren, J., Xiao, X., & Xia, J. (2024). XGBoost-based analysis of the relationship between urban 2-D/3-D morphology and seasonal gradient land surface temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 4109–4124. https://doi.org/10.1109/JSTARS.2023.3348476
Ma, Y., He, T., Li, A., & Li, S. (2021). Evaluation and intercomparison of topographic correction methods based on Landsat images and simulated data. Remote Sensing, 13(20), 4120. https://doi.org/10.3390/rs13204120
Malakar, N. K., Hulley, G. C., Hook, S. J., Laraby, K., Cook, M., & Schott, J. R. (2018). An operational land surface temperature product for Landsat thermal data: Methodology and validation. IEEE Transactions on Geoscience and Remote Sensing, 56(10), 5717–5735. https://doi.org/10.1109/TGRS.2018.2824828
Mansourmoghaddam, M., Rousta, I., Ghafarian Malamiri, H., Sadeghnejad, M., Krzyszczak, J., & Ferreira, C. S. S. (2024). Modeling and estimating the land surface temperature (LST) using remote sensing and machine learning (case study: Yazd, Iran). Remote Sensing, 16(3), 454. https://doi.org/10.3390/rs16030454
Masek, J. G., Wulder, M. A., Markham, B., McCorkel, J., Crawford, C. J., Storey, J., & Jenstrom, D. T. (2020). Landsat 9: Empowering open science and applications through continuity. Remote Sensing of Environment, 248, 111968. https://doi.org/10.1016/j.rse.2020.111968
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
Meng, Y., Luo, Q., Bai, B., Li, Y., Lu, J., & Ren, J. (2025). Analysis of spatial heterogeneity in Xi’an’s urban heat island effect using multi-source data fusion. PLOS ONE, 20(10), e0332885. https://doi.org/10.1371/journal.pone.0332885
Mildrexler, D. J., Zhao, M., & Running, S. W. (2011). A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. Journal of Geophysical Research: Biogeosciences, 116(G3), G03025. https://doi.org/10.1029/2010JG001486
Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800. https://doi.org/10.1016/j.rse.2011.02.019
Naserikia, M., Hart, M. A., Nazarian, N., Bechtel, B., Lipson, M., & Nice, K. A. (2023). Land surface and air temperature dynamics: The role of urban form and seasonality. Science of the Total Environment, 905, 167306. https://doi.org/10.1016/j.scitotenv.2023.167306
Orhan, O. (2021). Mersin ilindeki kentsel büyümenin yer yüzey sıcaklığı üzerine etkisinin araştırılması [Investigating the effect of urban growth on land surface temperature in Mersin Province]. Geomatik, 6(1), 69–76. https://doi.org/10.29128/geomatik.679858
Pande, C. B., Egbueri, J. C., Costache, R., Sidek, L. M., Wang, Q., Alshehri, F., Din, N. M., Gautam, V. K., & Chandra Pal, S. (2024). Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development. Journal of Cleaner Production, 444, 141035. https://doi.org/10.1016/j.jclepro.2024.141035
Pringle, M. J., Schmidt, M., & Muir, J. S. (2009). Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 654–664. https://doi.org/10.1016/j.isprsjprs.2009.06.001
Rahman, S. U., Sha, J., Hayat, W., Li, X., & Akhtar, W. (2026). Integrating gradient boost for analyzing land surface temperature and its influencing factors. International Journal of Image and Data Fusion, 17(1), 2618665. https://doi.org/10.1080/19479832.2026.2618665
Rao, P. K. (1972). Remote sensing of urban “heat islands” from an environmental satellite. Bulletin of the American Meteorological Society, 53, 647–648.
Rengma, N. S., & Yadav, M. (2024). Machine learning-based estimation of land surface temperature variability over a large region: A temporally consistent approach using single-day satellite imagery. Environmental Monitoring and Assessment, 196(8), 738. https://doi.org/10.1007/s10661-024-12856-z
Rezaee, A., Goodarzi, M. R., Alavizadeh, S. M., & Goldani, M. (2026). Machine learning insights into land surface temperature variability and prediction: A spatiotemporal approach with feature importance and uncertainty analysis. Environmental Monitoring and Assessment, 198(5), 480. https://doi.org/10.1007/s10661-026-15205-4
Rikimaru, A., Roy, P. S., & Miyatake, S. (2002). Tropical forest cover density mapping. Tropical Ecology, 43(1), 39–47.
Rouse, J. W., Jr., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium, Volume I: Technical presentations, Section A (pp. 309–317). NASA Goddard Space Flight Center. https://ntrs.nasa.gov/citations/19740022614
Running, S. W., Mu, Q., Zhao, M., & Moreno, A. (2021). User’s guide: MODIS global terrestrial evapotranspiration (ET) product (MOD16A2/A3 and year-end gap-filled MOD16A2GF/A3GF), NASA Earth Observing System MODIS land algorithm (for Collection 6.1) (Version 1.0). NASA Land Processes Distributed Active Archive Center. https://lpdaac.usgs.gov/documents/931/MOD16_User_Guide_V61.pdf
Samb, R., Jena, A., Manavvi, S., Roy, U. K., & Yadav, B. (2026). Machine learning driven land surface temperature prediction and urban heat risk assessment in the Gambia. Discover Cities, 3(1), 49. https://doi.org/10.1007/s44327-026-00230-2
Şenay, D., Saygıner, C., & Nurlu, E. (2026). Pattern-based hierarchical clustering analysis of spatial drivers influencing land surface temperature in a basin of western Türkiye. International Journal of Environmental Science and Technology, 23(3), 186. https://doi.org/10.1007/s13762-025-06990-5
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., & Teuling, A. J. (2010). Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3–4), 125–161. https://doi.org/10.1016/j.earscirev.2010.02.004
Soenen, S. A., Peddle, D. R., & Coburn, C. A. (2005). SCS+C: A modified Sun-canopy-sensor topographic correction in forested terrain. IEEE Transactions on Geoscience and Remote Sensing, 43(9), 2148–2159. https://doi.org/10.1109/TGRS.2005.852480
Tanoori, G., Soltani, A., & Modiri, A. (2024). Machine learning for urban heat island (UHI) analysis: Predicting land surface temperature (LST) in urban environments. Urban Climate, 55, 101962. https://doi.org/10.1016/j.uclim.2024.101962
Thakur, G., Schymanski, S. J., Mallick, K., Trebs, I., & Sulis, M. (2022). Downwelling longwave radiation and sensible heat flux observations are critical for surface temperature and emissivity estimation from flux tower data. Scientific Reports, 12(1), 8592. https://doi.org/10.1038/s41598-022-12304-3
TSMS. (2025). 2025 yılı yaz mevsimi sıcaklıklarının değerlendirilmesi [Evaluation of summer 2025 temperatures]. Retrieved March 10, 2026, from https://www.mgm.gov.tr/veridegerlendirme/sicaklik-analizi.aspx?s=m
TSMS. (2026). 2026 yılı kış mevsimi sıcaklıklarının değerlendirilmesi [Evaluation of winter 2025–2026 temperatures]. Retrieved March 10, 2026, from https://www.mgm.gov.tr/veridegerlendirme/sicaklik-analizi.aspx?s=m
USGS. (2022). Landsat 9 data users handbook (LSDS-2082, Version 1.0). https://www.usgs.gov/media/files/landsat-9-data-users-handbook
Wadoux, A. M. J.-C., Heuvelink, G. B. M., de Bruin, S., & Brus, D. J. (2021). Spatial cross-validation is not the right way to evaluate map accuracy. Ecological Modelling, 457, 109692. https://doi.org/10.1016/j.ecolmodel.2021.109692
Wang, N., Shen, L., Fei, W., Liu, Y., Zhao, H., Liu, L., Wang, A., & He, B.-J. (2025). Seasonal effects of urban morphology on the thermal environment based on automated machine learning: A case study of Beijing. Remote Sensing, 17(18), 3150. https://doi.org/10.3390/rs17183150
Wen, J., He, Y., Yang, L., Wan, P., Gu, Z., & Wang, Y. (2025). A two-step downscaling model for MODIS land surface temperature based on random forests. Atmosphere, 16(4), 424. https://doi.org/10.3390/atmos16040424
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
Xu, H. (2008). A new index for delineating built‐up land features in satellite imagery. International Journal of Remote Sensing, 29(14), 4269–4276. https://doi.org/10.1080/01431160802039957
Zafar, Z., Zhang, S., Zha, Y., & Gilani, H. (2026). Evaluating land surface temperature trends and environmental interactions through machine learning and wavelet analysis. Science China Earth Sciences, 69(2), 528–551. https://doi.org/10.1007/s11430-025-1696-5
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987
Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94. https://doi.org/10.1016/j.rse.2011.10.028

Details

Primary Language

English

Subjects

Photogrammetry and Remote Sensing

Journal Section

Research Article

Authors

Halil İbrahim Gündüz ^*
0000-0002-0609-8032
Türkiye

Publication Date

October 5, 2026

Submission Date

August 10, 2026

Acceptance Date

September 22, 2026

Published in Issue

Year 2026 Volume: 8

DOI

https://doi.org/10.51489/tuzal.2014535

IZ

https://izlik.org/JA22LM37XJ

Cite

RIS / Bibtex

APA

Gündüz, H. İ. (2026). Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye. Turkish Journal of Remote Sensing, 8. https://doi.org/10.51489/tuzal.2014535

AMA

1.Gündüz Hİ. Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye. TJRS. 2026;8. doi:10.51489/tuzal.2014535

Chicago

Gündüz, Halil İbrahim. 2026. “Explainable Seasonal Land Surface Temperature Modeling With Tree-Based Machine Learning over Menteşe, Türkiye”. Turkish Journal of Remote Sensing 8 (October). https://doi.org/10.51489/tuzal.2014535.

EndNote

Gündüz Hİ (October 1, 2026) Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye. Turkish Journal of Remote Sensing 8

IEEE

[1]H. İ. Gündüz, “Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye”, TJRS, vol. 8, Oct. 2026, doi: 10.51489/tuzal.2014535.

ISNAD

Gündüz, Halil İbrahim. “Explainable Seasonal Land Surface Temperature Modeling With Tree-Based Machine Learning over Menteşe, Türkiye”. Turkish Journal of Remote Sensing 8 (October 1, 2026). https://doi.org/10.51489/tuzal.2014535.

JAMA

1.Gündüz Hİ. Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye. TJRS. 2026;8. doi:10.51489/tuzal.2014535.

MLA

Gündüz, Halil İbrahim. “Explainable Seasonal Land Surface Temperature Modeling With Tree-Based Machine Learning over Menteşe, Türkiye”. Turkish Journal of Remote Sensing, vol. 8, Oct. 2026, doi:10.51489/tuzal.2014535.

Vancouver

1.Halil İbrahim Gündüz. Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye. TJRS. 2026 Oct. 1;8. doi:10.51489/tuzal.2014535

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Required Submission Files;

1-Plagiarism Report:

While your article is uploaded to the system, your plagarism report will be created via the "intihal.net" system. After your plagarism report created your article upload will be copmleted. Click for detailed information about "intihal.net". If any manuscript is 25% plagiarized the article will be rejected and the author will be notified.

2-Copyright Agreement Form :

The copyright of the article remains with the author(s). By filling and signing the Copyright Agreement Form presented on the journal website, the author(s) grant the journal the right of first publication of the article. The signed Copyright Agreement Form must be downloaded from here, signed, scanned, and uploaded to the system as a PDF. Publications of author(s) who do not submit this form cannot be published.

3-Ethics Committee Permission (If required)

An Ethics Committee Permission document must be submitted for the publication application of articles that require ethics committee approval.

Please inform the Turkish Journal of Remote Sensing whether your study requires Ethics Committee Approval by filling out the Ethics Committee Declaration section within the Copyright Form.

The Turkish Journal of Remote Sensing has been checking the ethical committee approval status of articles submitted since 2020. In this context, if your study falls into a category that requires ethical committee approval, you must upload the permission document along with your article files. Studies submitted to the journal that require ethical committee approval but lack the necessary permission documents will not be considered for evaluation.

Information regarding ethics committee approval is shared on the “Ethics Committee Approval” page.

Manuscript Preparation Guidelines

The manuscript template file is provided above for author use. Detailed formatting and preparation instructions are outlined below. Authors are expected to prepare their manuscripts in accordance with these requirements.

Abstract should be a single paragraph of about 300 words maximum. Also, it should be Cambria 9 font size, written justified and single line spaced. Keywords should consist of at least 3 and a maximum of 5 words. Except for proper names and abbreviations, all words should be written in lower case.

“Introduction” part should be followed by “Material & Method”, “Results”, “Discussion" and "Conclusion” parts.

The text should be written in a single column, using Cambria 10-point font. The indentation should be 4.6 cm. In addition, the first line of the paragraph should be written with 0.75 cm indented. Line spacing value should be used as 14 pt.

The article should be saved in MS Office Word as either .doc or docx. Text should be written as A4 size with 2 cm spacing at top, 1.6 cm bottom, 1.3 cm left and right side.

The main sections of the manuscript are "introduction", "material & method", "results", "discussion" and "conclusion" they should be written in 10 font size, justify, bold.

The second level headings should be written with left aligned, 10 font size, first character capital, bold.

Figures and tables should be referenced in the main text as Figure X, Table X, and so on. They should be left and right aligned and numbered. Figure and table captions should be written 9 font size, cambria, first letter of first word capital. It should not be written as bold or italic.

>>Statements and Declarations

>>The following sections should be included in the manuscript where applicable and prepared in accordance with the journal guidelines.

Acknowledgments: If there is an acknowledgment, please indicate it.

Author Contributions: Please make sure that the contribution of each author is clarified based on the CRediT (Contributor Roles Taxonomy) standards. This classification allows for a transparent and accurate description of each author's individual contribution to the work.

Please select the appropriate roles for each author from the list below:
Conceptualization
Methodology
Software
Validation
Formal analysis
Investigation
Resources
Data Curation
Writing—Original Draft
Writing—Review & Editing
Visualization
Supervision
Project Administration
Funding Acquisition

Below are examples of how to format this section:

Single Author
For manuscripts with a single author, please use the following statement:
"The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation."

Two or More Authors
Please list authors' contributions using their names or initials as shown in the examples below: Author names must be written in the format of initial(s) followed by surname (e.g., O. Orhan, A. Yılmaz, M. Ö. Çelik).
Example Configuration :
O. Orhan: Conceptualization, Methodology,
A. Yılmaz: Writing-review and editing.
M. Ö. Çelik: Software, Data curation, Writing-original draft preparation.
A. T. Torun: Visualization, Writing-review and editing.

AI Contribution Statement: If generative AI or AI-assisted technologies were used during manuscript preparation, this should be clearly stated in this section. The purpose of use and the names of the tools utilized should be specified in detail. All AI-assisted outputs must be reviewed, controlled, and approved by the authors before submission.

Example: “Generative AI tools, including ChatGPT and DeepL Write, were used solely for grammar checking and academic language improvement. All scientific results and interpretations were reviewed, controlled, and approved by the authors.”

**Furthermore, authors must disclose any use of AI-based tools during manuscript preparation, in compliance with the journal’s AI Use Policy: https://dergipark.org.tr/en/pub/tuzal/page/19470

Funding: Please indicate whether the study is supported by an institution or not.
Example: “This study received no external funding” or “This study was funded by NAME OF FUNDER, grant number XXX”

Research and publication ethics statement: In the study, the author/s declare that there is no violation of research and publication ethics and that the study does not require ethics committee approval. (If exists will be notified)

Conflicts of Interest: Declare conflicts of interest or state “The authors declare no conflicts of interest.”. (If exists will be notified)

Data Availability: Authors should select the most appropriate Data Availability statement based on the nature of their study and data accessibility.
Example:” The data presented in this study are available on request from the corresponding author.”
“The data associated with this study are not publicly available due to privacy, institutional, or technical restrictions”

Abbreviations: (Optional)

References, tables, figures to be used must be prepared in accordance with APA 7. Articles that do not comply with the rules of writing and APA are eliminated in the first stage by the editor. You should review your references and check their compliance with APA 7. Some expamlpes of citations/references are given below;

References within the text;

(1) (Orhan, 2021). (Çoruhlu & Çelik, 2022). (Orhan et al., 2023; Zheng et al., 2025)
(2) Orhan (2021) stated…, As stated by Çelik et al. (2024)…

Articles
Surname, N., Surname, N., & Surname, N. (Year). Name of the article. Journal’s name, Vol(No), pp. XX-XX. (If there is doi)

Orhan, O. (2021). Land suitability determination for citrus cultivation using a GIS-based multi-criteria analysis in Mersin, Turkey. Computers and Electronics in Agriculture, 190, 106433. https://doi.org/10.1016/j.compag.2021.106433
Çoruhlu, Y. E., & Çelik, M. Ö. (2022). Protected area geographical management model from design to implementation for specially protected environment area. Land Use Policy, 122, 106357. https://doi.org/10.1016/j.landusepol.2022.106357

Books
Orhan, O., & Makineci, H. B. (2023). Agricultural land suitability analysis. Encyclopedia of Smart Agriculture Technologies. Springer.

e-Books
Brück, M. (2009). Women in early British and Irish astronomy: Stars and satellites. Springer Nature. https:/doi.org/10.1007/978-90-481-2473-2

Conference, symposium or paper presentation

Rutledge, L., LeMire, S., & Mowdood, A. (2015, March 25–28). Dare to perform: Using organizational competencies to manage job performance [Paper presentation]. Association of College & Research Libraries 2015 Annual Conference, Portland, OR, United States. http://www.ala.org/acrl/sites/ala.org.acrl/files/content/conferences/confsandpreconfs/2015/Rutledge_LeMire_Mowdood.pdf

Conference, symposium or paper proceedings published in a journal

Çoruhlu, Y. E., Çelik, M. Ö., Demir, O., & Yıldız, O. (2017). GIS applications in land management of protected areas. Proceedings Book of DOKAP Region International Tourism Symposium, Trabzon, Türkiye, 295-308.

Thesis

-Dissertation or Thesis from a Database

Orhan, O. (2018). Determining potential sinkhole areas using remote sensing and geographic information systems (Publication No. 565007) [Doctoral Thesis, Selçuk University]. YÖK National Thesis Center.

-Dissertation or Thesis Published Online (Not in a Database)

Lui, T. T. F. (2013). Experiences in the bubble: Assimilation and acculturative stress of Chinese heritage students in Silicon Valley [Master's thesis, Stanford University]. Graduate School of Education International Comparative Education Master's Monographs Digital Collection. https://searchworks.stanford.edu/view/10325276

Legislation

Official Gazette. (1983). Law 2942 Expropriation (Number: 18215).

Internet address

TUIK. (2024). Retrieved September 12, 2024, from https://www.tuik.gov.tr/

Click here for more reference citations.

Appendix (Optional)

Appendices should be placed after the References section and presented as separate sections labeled consecutively with capital letters (e.g., Appendix A, Appendix B), each with a descriptive title. Figures and tables included in the appendices should be numbered separately using the corresponding appendix letter (e.g., Figure A1, Table A1; Figure B1, Table B1). Appendices are considered an integral part of the manuscript and should be cited at appropriate points in the main text (e.g., “see Appendix A, Figure A1”).

Contact:

Assoc. Prof. Dr. Osman ORHAN

Telephone: +90-5059875275 (Editor)

Address: Mersin University, Faculty of Engineering, Department of Geomatics Engineering,

Çiftlikköy Campus, 33343, Yenişehir/Mersin, Turkey

e-mail: osmanorhan@mersin.edu.tr / tr.remotesensing@gmail.com / osmanorhan44@gmail.com

Ethical Principles and Publication Policy

The Turkish Journal of Remote Sensing (TJRS) is committed to maintaining the highest standards of academic integrity, transparency, and ethical responsibility in the publication of scientific research. The journal’s ethical principles and publication policies are developed in accordance with internationally recognized guidelines, including those of the Committee on Publication Ethics (COPE), the World Association of Medical Editors (WAME), and the Declaration of Helsinki.

TJRS adopts a transparent, fair, and accountable publication process in which all submissions are evaluated based on their scientific merit, originality, and relevance to the scope of the journal. The journal implements a double-blind peer review system and follows established procedures to ensure objectivity, confidentiality, and impartiality throughout the evaluation process.

All stakeholders involved in the publication process, including authors, reviewers, and editors, are expected to comply with these ethical principles. The journal takes all necessary measures to prevent scientific misconduct such as plagiarism, data fabrication, falsification, and unethical authorship practices, and follows internationally accepted procedures in handling such cases.

1. Editorial Responsibilities

Editors of the Turkish Journal of Remote Sensing (TJRS) are responsible for managing the editorial and peer review processes in a fair, transparent, and accountable manner, in accordance with international publication ethics standards.
Editors evaluate all submitted manuscripts solely based on their scientific merit, originality, methodological rigor, and relevance to the journal’s scope. Editorial decisions are made independently and are not influenced by the authors’ nationality, institutional affiliation, gender, political views, or other personal characteristics.
Editors are committed to maintaining the confidentiality of all submitted materials. Manuscripts, reviewer reports, and all related communication are treated as confidential documents and are shared only with individuals directly involved in the editorial and peer review process.
Editors must ensure the confidentiality of all submitted manuscripts and must not upload manuscript content to AI tools or platforms. All editorial decisions must be based solely on human judgment.
The editorial team ensures that the peer review process is conducted objectively and without bias. In cases where conflicts of interest arise, appropriate measures are taken, including reassignment of editorial responsibility.
Editors are also responsible for overseeing ethical compliance throughout the publication process. In cases of suspected misconduct such as plagiarism, data fabrication, falsification, duplicate publication, or inappropriate authorship editors follow the guidelines and flowcharts provided by COPE. Depending on the nature and severity of the issue, the editorial office may request clarification from authors, suspend the review process, reject the manuscript, or initiate post-publication actions such as corrections or retractions.
The Editor-in-Chief holds the final authority in all editorial decisions and ensures that the journal’s policies are implemented consistently and transparently.

2. Responsibilities of Authors

Authors submitting manuscripts to the Turkish Journal of Remote Sensing (TJRS) are expected to adhere to the highest standards of academic integrity and ethical research practices.
Authors must ensure that their submissions are original and have not been published previously or submitted to another journal simultaneously. All sources, including data, figures, and previously published work, must be properly cited. Any form of plagiarism, including self-plagiarism, is strictly prohibited.
The research presented in the manuscript must be reported accurately, clearly, and transparently. Authors are responsible for ensuring that the methods, data, analyses, and results are presented in a way that allows verification and reproducibility. Fabrication, falsification, or selective reporting of data is considered a serious ethical violation.
Authors must disclose any financial, institutional, or personal relationships that could be perceived as influencing the results or interpretation of their work. If no conflict of interest exists, this must be explicitly stated.
For studies involving human participants or animals, authors are required to obtain approval from relevant ethics committees and to comply with international ethical standards, including the Declaration of Helsinki. Information regarding ethical approval (committee name, approval number, and date) must be clearly stated in the manuscript.
Authors are expected to follow best practices in data management. When requested, authors should provide access to raw data, supplementary materials, or methodological details necessary for verification of the study.
During the peer review process, authors must respond to reviewer and editor comments in a timely, respectful, and scientifically justified manner. All revisions should be clearly explained and documented.
If authors identify a significant error or ethical issue in their published work, they are obligated to inform the editorial office promptly and cooperate in the correction or retraction process.

3. Authorship Criteria and Changes Policy

Authorship in the Turkish Journal of Remote Sensing (TJRS) is limited to individuals who have made a substantial intellectual contribution to the research and the preparation of the manuscript.
To qualify as an author, each contributor should have participated in at least one or more of the following stages: the conception or design of the study, data collection, data analysis and interpretation, drafting the manuscript, or critically revising it for important intellectual content. All authors must approve the final version of the manuscript and agree to its submission and publication.
Individuals who have contributed to the study but do not meet the criteria for authorship such as technical support, data collection assistance, or general supervision should be acknowledged appropriately in the acknowledgements section.
Unethical authorship practices, including guest (gift) authorship, ghost authorship, or inappropriate changes in the order of authors, are strictly prohibited. All authors share collective responsibility for the accuracy, integrity, and originality of the work.
In cases of authorship disputes, the editorial office may request detailed explanations and supporting documentation. If necessary, the matter may be referred to the relevant institutions in accordance with international ethical guidelines.

Changes in Authorship

Authors are strongly encouraged to carefully determine the authorship and the order of authors prior to submission and to provide the final and complete author list at the time of initial submission.
Any request for the addition, removal, or rearrangement of author names should be made before the manuscript is accepted and will only be considered with the approval of the Editorial Office.
To request a change in authorship, the corresponding author must submit:
(a) a clear justification explaining the reason for the requested change, and
(b) written confirmation (e.g., e-mail or signed letter) from all listed authors indicating their agreement with the proposed modification.
In cases involving the addition or removal of an author, confirmation must also be obtained from the author being added or removed.
The journal reserves the right to decline authorship changes that do not comply with these requirements.

4. Responsibilities of Reviewers

Reviewers play a fundamental role in ensuring the scientific quality, integrity, and credibility of the Turkish Journal of Remote Sensing (TJRS). They are expected to contribute to the peer review process in an objective, constructive, and timely manner.
Reviewers should accept review invitations only if the manuscript falls within their area of expertise and if they can complete the evaluation within the specified time frame. If a reviewer identifies a potential conflict of interest such as personal, professional, or financial relationships with the authors they must decline the review and inform the editorial office.
All manuscripts under review must be treated as strictly confidential documents. Reviewers must not share, distribute, or use any part of the manuscript for personal advantage or research purposes prior to publication.
Reviewers must treat all manuscripts as confidential and must not upload manuscript content to AI tools or platforms. Peer review reports must be based solely on the reviewer’s own expertise and independent judgment. (see the journal's AI use and authorship policy).
Reviewer reports should be based on scientific reasoning and should provide clear, constructive, and evidence-based feedback aimed at improving the quality of the manuscript. Personal criticism or inappropriate language directed at the authors is not acceptable.
Reviewers are also expected to identify relevant published work that has not been cited by the authors and to alert the editorial office to any substantial similarity, potential plagiarism, data inconsistency, or ethical concerns they detect during the evaluation process.
The review process should be conducted independently and ethically, without any attempt to influence the editorial decision inappropriately.

5. Peer Review Process

The Turkish Journal of Remote Sensing (TJRS) implements a double-blind peer review system to ensure the objectivity, fairness, and scientific quality of all published manuscripts.
In this system, the identities of both authors and reviewers are concealed throughout the evaluation process in order to eliminate potential bias and ensure impartial assessment. Manuscripts that successfully pass the editorial preliminary evaluation are assigned to at least two independent reviewers who are experts in the relevant field.
Reviewers evaluate manuscripts based on criteria such as originality, scientific contribution, methodological rigor, clarity of presentation, and relevance to the scope of the journal. Based on the reviewers’ reports, manuscripts may be accepted, rejected, or returned to the authors for minor or major revisions.
In cases where reviewer opinions differ significantly, the editor may, when deemed necessary, assign an additional reviewer to ensure a balanced and fair evaluation. The final decision is made by the Editor-in-Chief, taking into account the reviewers' reports and the manuscript's overall scientific merit.
All stages of the peer review process are conducted in accordance with international ethical standards, ensuring confidentiality, transparency, and accountability.
For a detailed description of all stages of the evaluation workflow, please refer to the journal’s “Review Process” page.

6. Evaluation Process (Editorial Screening)

All manuscripts submitted to TJRS undergo an initial editorial screening prior to the peer review process. This preliminary evaluation is conducted to ensure that submissions meet the journal’s basic requirements and are suitable for further review.
During this stage, manuscripts are assessed in terms of their compliance with the journal’s aims and scope, scientific relevance, originality, and adherence to submission and formatting guidelines. In addition, key elements such as anonymization, ethical declarations, conflict of interest statements, and required documentation (e.g., ethics committee approval, copyright forms) are checked.
The editorial office may also conduct an initial similarity screening using plagiarism detection tools at this stage. Manuscripts that do not meet the journal’s standards or fail to comply with ethical and technical requirements may be returned to the authors for correction or rejected without being sent for peer review.
Only manuscripts that successfully pass this preliminary evaluation are forwarded to the double-blind peer review process.

7. Plagiarism and Similarity Policy

All manuscripts submitted to the Turkish Journal of Remote Sensing (TJRS) are screened using recognized plagiarism detection software such as Turnitin, intihal.net and/or iThenticate prior to peer review.
The journal adopts a maximum similarity threshold of 25%, excluding references, methodological standard expressions, and properly cited quotations. However, the editorial office evaluates not only the similarity percentage but also the context, source, and nature of the overlap.
Manuscripts found to contain plagiarism, excessive similarity, improper citation, or text recycling may be returned to the authors for revision or rejected outright. The final decision regarding similarity evaluation rests with the editorial office.

8. Research Misconduct and Ethical Violations

TJRS strictly prohibits all forms of scientific misconduct and unethical practices. These include, but are not limited to:

Plagiarism
Data fabrication (making up data)
Data falsification (manipulating research data or results)
Duplicate or redundant publication
Salami slicing (inappropriate division of research findings)
Unethical authorship practices
Peer review manipulation

In cases of suspected misconduct, the editorial office follows internationally accepted procedures and the guidelines provided by COPE. Depending on the severity of the issue, the journal may request clarification, reject the manuscript, notify relevant institutions, or initiate correction or retraction procedures after publication.

9. Conflict of Interest Policy

All authors, reviewers, and editors are required to disclose any financial, institutional, or personal relationships that could influence the research or its evaluation process.
Conflicts of interest may include funding sources, consultancy roles, employment relationships, personal connections, or any situation that may compromise objectivity.
If no conflict of interest exists, this must be explicitly stated in the manuscript.
Failure to disclose a conflict of interest may result in rejection of the manuscript or post-publication actions such as correction or retraction.

10. Data Transparency and Verification

TJRS supports scientific transparency and encourages authors to make their research data, code, and supporting materials publicly available whenever possible to foster open science.
While public data sharing is encouraged, authors must be prepared to provide access to the raw data and methodological details if requested by the editorial office for verification purposes during the peer-review process.
If data sharing is not possible due to ethical, legal, or confidentiality constraints, authors must clearly state these reasons in the "Data Availability" section of their manuscript.
Authors should select the most appropriate statement based on their data accessibility. Examples include:

>> “The data presented in this study are available on request from the corresponding author.”

>> “The data associated with this study are not publicly available due to privacy, institutional, or technical restrictions.”

>> “The data presented in this study are available in [Name of Repository] at [URL/DOI].”

11. Ethical Compliance, Legal Requirements and Human/Animal Research Ethics

The Turkish Journal of Remote Sensing (TJRS) requires that all submitted studies comply with applicable ethical standards, legal regulations, and internationally accepted research ethics principles.
For studies involving human participants or animals, authors must obtain approval from a relevant ethics committee prior to data collection. The name of the ethics committee, approval date, and approval number must be clearly stated in the manuscript. In studies involving human participants, informed consent must be obtained, and participants’ rights, privacy, and dignity must be protected. For animal studies, internationally accepted standards of humane treatment must be followed.
Manuscripts that require ethics committee approval but fail to provide the necessary documentation will not be considered for evaluation. In cases where ethical approval is not required, authors must provide a clear justification. Detailed requirements are provided on the journal’s Ethics Committee Approval page.
TJRS places great importance on the protection of personal data and confidentiality. Authors must ensure that personal data used in research are processed in accordance with applicable data protection regulations and ethical standards. Identifiable personal information must not be disclosed without explicit consent, and confidentiality must be maintained throughout the research and publication process.
The journal also respects intellectual property rights and expects authors to comply with copyright regulations. Authors are responsible for obtaining all necessary permissions for the use of third-party materials, including figures, tables, images, datasets, and scales. Any legal or ethical responsibility arising from copyright violations belongs solely to the authors.

12. Policy on the Use of Artificial Intelligence

TJRS regulates the use of artificial intelligence tools in accordance with international ethical standards. Artificial intelligence tools cannot be listed as authors and may only be used for limited and transparent purposes such as language editing or technical assistance. Authors are required to disclose the use of AI tools and remain fully responsible for all content generated or modified with their assistance. Detailed guidelines are provided in the journal’s Artificial Intelligence Policy.

13. Correction, Retraction, and Post-Publication Policy

The Turkish Journal of Remote Sensing (TJRS) is committed to preserving the accuracy, integrity, and reliability of the scientific record. In cases where errors, ethical concerns, or potential misconduct are identified after publication, the journal takes appropriate actions in accordance with international publication ethics standards and the guidelines of the Committee on Publication Ethics (COPE).
If minor errors that do not affect the overall conclusions of the study are identified, a correction (erratum or corrigendum) may be issued and linked to the original article.
In cases involving serious ethical violations or significant errors that invalidate the findings, the journal may initiate a retraction process. Retracted articles remain accessible in the journal archive but are clearly marked to ensure transparency and maintain the integrity of the scholarly record.
When necessary, the journal may also publish an expression of concern while an investigation is ongoing.
Authors are expected to inform the editorial office promptly if they identify errors or ethical issues in their published work. Requests for correction or retraction may also be submitted by authors, reviewers, or readers.
Detailed procedures regarding article retraction and withdrawal are available on the journal’s dedicated Correction, Retraction and Withdrawal Policy page.

14. Open Access Policy

The Turkish Journal of Remote Sensing (TJRS) adopts a fully open access publishing model, ensuring that all published content is freely available to readers without any subscription fees or access barriers.
The journal supports the principles of the Budapest Open Access Initiative (BOAI) and promotes the unrestricted dissemination of scientific knowledge. All articles published in TJRS can be accessed, read, downloaded, and shared without limitation, provided that appropriate attribution is given.
Detailed information regarding the journal’s open access practices is available on the Open Access Policy page.

15. Copyright and Licensing Policy

The Turkish Journal of Remote Sensing (TJRS) respects the intellectual property rights of authors while supporting the wide dissemination of scientific knowledge.
Authors retain the copyright of their work and grant the journal the right of first publication. All published content is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license, which permits sharing, distribution, adaptation, and reuse of the material for any purpose, including commercial use, provided that appropriate credit is given and derivative works are distributed under the same license.
Authors are responsible for the originality of their work and for obtaining all necessary permissions for the use of third-party materials.
Further details regarding open access, copyright, licensing, and author agreements are provided on the Licenses and Copyright Policy pages.

16. Publication Fee Policy

The Turkish Journal of Remote Sensing (TJRS) does not charge any fees for manuscript submission, evaluation, or publication processes.
There are no article processing charges (APCs), submission fees, or publication fees at any stage of the editorial and publication process.
All accepted articles are published free of charge as part of the journal’s commitment to supporting open and accessible scientific communication.

17. Publication Period of Journal

Turkish Journal of Remote Sensing is a peer-reviewed journal published under a continuous publication model. Articles are published online individually upon acceptance and completion of the production process, without waiting for a scheduled issue, and are assigned to an annual volume.

18. Revenue, Advertising, and Direct Marketing Policy

The Turkish Journal of Remote Sensing (TJRS) is an independent, open-access, peer-reviewed journal. The journal does not charge any fees for submission, evaluation, or publication and does not generate revenue from authors or readers.
TJRS does not accept advertisements, and all editorial decisions are made independently based solely on academic merit.
The journal does not engage in misleading or aggressive direct marketing practices and ensures that all communications are conducted ethically and transparently.

19. Complaints and Appeals Policy

The Turkish Journal of Remote Sensing (TJRS) provides a transparent and fair mechanism for handling complaints and appeals related to editorial decisions, peer review processes, and ethical concerns.
Authors, reviewers, and readers may submit complaints or appeals regarding issues such as editorial decisions, delays in the review process, ethical concerns, or conflicts of interest.
All complaints and appeals must be submitted in writing to the Editorial Office via the journal’s official email address (tr.remotesensing@gmail.com) or directed to the Editor-in-Chief. Submissions must be supported by a clear justification and relevant evidence.
All complaints and appeals are evaluated objectively by the editorial office in accordance with international publication ethics standards and COPE guidelines. When necessary, the editorial board may seek additional opinions from reviewers, editorial board members, or relevant institutions.
The journal is committed to ensuring that all complaints and appeals are handled in a fair, transparent, and timely manner.

Editor-in-Chief

Osman ORHAN

Climate Change-Impact and Adaptation, Geospatial Information Systems and Geospatial Data Modelling, Photogrammetry and Remote Sensing, Geographical Information Systems (GIS) in Planning, Geological Sciences and Engineering, Geographic Information Systems, Remote Sensing

Co-Editors

Ahmet Tarık TORUN

Engineering, Geospatial Information Systems and Geospatial Data Modelling, Photogrametry, Photogrammetry and Remote Sensing, Geographical Information Systems (GIS) in Planning, Geological Sciences and Engineering, Remote Sensing

Resul ÇÖMERT

Geospatial Information Systems and Geospatial Data Modelling, Photogrametry, Photogrammetry and Remote Sensing, Geographical Information Systems (GIS) in Planning, Natural Hazards, Remote Sensing

Mustafa ÜSTÜNER

Pattern Recognition, Photogrammetry and Remote Sensing, Remote Sensing

Editorial Board

Kaan KALKAN

Geomatic Engineering, Geospatial Information Systems and Geospatial Data Modelling, Photogrametry, Photogrammetry and Remote Sensing, Geographic Information Systems, Remote Sensing

Mehmet Ali DERELI

Geographical Information Systems (GIS) in Planning, Remote Sensing

Saygın ABDIKAN

Photogrammetry and Remote Sensing

BJ

Ben JARIHANI

Remote Sensing , Earth System Sciences

FC

Fabiana CALO

Photogrammetry and Remote Sensing, Natural Hazards

Aliihsan ŞEKERTEKIN

Geomatic Engineering, Photogrammetry and Remote Sensing

Muammer TÜN

Geographic Information Systems, Natural Hazards, Applied Geophysics

Alper ÇABUK

Ecology, Sustainability and Energy, Information Technologies in Architecture and Design, Design Instruments and Technology, Protection, Restoration and Repair in Buildings, Environment, Habitation and Products

NM

Nebiye MUSAOĞLU

Image Processing, Engineering, Photogrammetry and Remote Sensing

DA

Djamil AL-HALBOUNI

Photogrammetry and Remote Sensing, Numerical Modelling and Mechanical Characterisation, Remote Sensing , Earth System Sciences, Groundwater Hydrology, Electrical and Electromagnetic Methods in Geophysics, Applied Geophysics, Computational Modelling and Simulation in Earth Sciences

MH

Mahmud HAGHSHENAS HAGHIGHI

Remote Sensing , Earth and Space Science Informatics

GP

Giuseppe PULIGHE

Remote Sensing , Sustainable Agricultural Development, Agricultural Spatial Analysis and Modelling

YM

Yasser MAGHSOUDI

Remote Sensing , Earth and Space Science Informatics

Taşkın KAVZOĞLU

Neural Networks, Semi- and Unsupervised Learning, Machine Learning Algorithms, Extreme Learning Machines, Classification Algorithms, Remote Sensing

Sadra KARIMZADEH

Remote Sensing

Siti Aekbal SALLEH

Geospatial Information Systems and Geospatial Data Modelling, Geographical Information Systems (GIS) in Planning

DS

Dursun Zafer SEKER

Image Processing, Artificial Intelligence, Photogrametry, Geographical Information Systems (GIS) in Planning, Remote Sensing , Geoscience Data Visualisation, Geoinformatics (Other)

Aşır Yüksel KAYA

City in Human Geography, Environmental Geography, Environmental Impact Assessment, Turkish Human Geography, Urbanization Policies, Urban Geography in Regional Planning, Urban Morphology, Urban History, Geographical Information Systems (GIS) in Planning

OG

Omid GHORBANZADEH

Artificial Intelligence (Other), Geographic Information Systems, Remote Sensing

JC

João Pedro Leal Abalada De Matos CARVALHO

Electrical Engineering (Other), Photogrammetry and Remote Sensing

Müge ÜNAL

Climate Change Impacts and Adaptation (Other), Land Use and Environmental Planning, Landscape Planning

Senem TEKIN

Earthquake Engineering, Geological Sciences and Engineering, Applied Geology

Artur JANOWSKI

Computer Vision, Remote Sensing

Khalil VALIZADEH KAMRAN

Information Security Management, Geographical Information Systems (GIS) in Planning, Geographic Information Systems

MU

Murat UYSAL

Virtual and Mixed Reality, Geomatic Engineering, Photogrametry, Remote Sensing

Uğur AVDAN

Geomatic Engineering, Photogrametry, Geographical Information Systems (GIS) in Planning, Remote Sensing

EH

Emre HAVAZLI

Photogrammetry and Remote Sensing, Geodesy

Ahmet MERT

Wildlife and Habitat Management, Cartography and Digital Mapping, Forestry Sciences, Forest Biodiversity

Advisory Board

Caner ÖZDEMIR

Electrical Engineering, Engineering Electromagnetics, Signal Processing

YL

Yaohui LIU

Ecology (Other), Photogrammetry and Remote Sensing, Natural Hazards, Remote Sensing

SE

Semih EKERCIN

Photogrammetry and Remote Sensing, Geographical Information Systems (GIS) in Planning

FY

Ferruh YILDIZ

Land Management, Photogrammetry and Remote Sensing, Geographical Information Systems (GIS) in Planning

Fevzi KARSLI

Photogrametry, Photogrammetry and Remote Sensing, Geographical Information Systems (GIS) in Planning

Cüneyt GÜLER

Photogrammetry and Remote Sensing, Hydrogeology, Geographic Information Systems, Watershed Management, Contaminant Hydrology, Surface Water Hydrology

Language Editor

ÖÖ

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Groundwater Quality Processes and Contaminated Land Assessment, Geomatic Engineering, Land Management, Geospatial Information Systems and Geospatial Data Modelling, Geographical Information Systems (GIS) in Planning, Groundwater Hydrology, Climate Change Processes, Sustainable Agricultural Development

Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye

Explainable seasonal land surface temperature modeling with tree-based machine learning over Menteşe, Türkiye

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