{
  "abstract": "Relevance. High-resolution mapping of urban land surface temperature is essential for locating intra-urban heat hotspots, assessing heat-exposure inequities, and supporting heat-mitigation planning in rapidly growing cities such as Da Nang (Vietnam). However, satellite thermal infrared observations are typically much coarser than optical imagery (e.g., 30 m for Landsat LST), which limits neighborhood-scale analysis and introduces mixed-pixel smoothing in heterogeneous urban surfaces. Aim. To develop and evaluate a machine-learning downscaling workflow that produces 10 m urban land surface temperature maps by integrating Landsat 8/9 thermal information with Sentinel-2 multispectral predictors in Google Earth Engine for Da Nang city, Vietnam. Methods. Reference land surface temperature was retrieved from Landsat 8/9 (30 m) thermal bands using standard physical procedures. Sentinel-2 (10 m) spectral indices describing moisture, water and built-up surfaces (the normalized difference moisture index, the modified normalized difference water index using the second shortwave-infrared band, and the normalized difference built-up index) and Shuttle Radar Topography Mission topography were used as predictors. Feature screening and correlation analysis were applied to reduce redundancy. A random forest regression model was trained (70%) and tested (30%), and performance was assessed using the coefficient of determination (R2), mean absolute error, and root mean square error. Spatial patterns of 30 and 10 m land surface temperature were compared across representative surface types to examine mixed-pixel effects in heterogeneous urban areas. Results. Moisture-, water- and built-up related indices (NDMI, MNDWI2, NDBI) were the strongest predictors of land surface temperature, whereas the normalized difference vegetation index alone was insufficient in a heterogeneous urban setting. The 10 m land surface temperature product achieved good agreement with the Landsat reference (R²=0.84, MAE=1.14°C, RMSE=1.53°C) and revealed finer spatial detail over reservoirs, sports complexes and mixed land-cover zones. Independent validation with in-situ surface/air-temperature measurements is still needed to further constrain uncertainty and strengthen practical deployment.\n\nFor citation: Nguyen Tien Thanh, Lam Van Hao. Downscaling land surface temperature to high resolution using Sentinel-2 and Landsat in Google Earth Engine: a case study in Da Nang City. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2026, vol. 337, no. 10, pp. 90–102. http://doi.org/10.18799/24131830/2026/10/5254",
  "abstract_shown": true,
  "affiliations": [
    "University of Science, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tien Thanh Nguyen",
      "orcid": "0009-0001-8340-8444"
    },
    {
      "affs": [
        0
      ],
      "name": "Lam Van Hao",
      "orcid": "0000-0002-0928-3145"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8q64f7kz4m1dt.bib",
  "cited_by": 0,
  "doi": "10.18799/24131830/2026/10/5254",
  "fulltext_url": "https://izvestiya.tpu.ru/archive/article/view/5254",
  "id": "8q64f7kz4m1dt",
  "issns": [
    "2500-1019",
    "2413-1830"
  ],
  "landing_url": "https://doi.org/10.18799/24131830/2026/10/5254",
  "language": "en",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QE1-996.5"
    ]
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  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-31",
  "publisher": "Tomsk Polytechnic University",
  "ris": "https://research.draconitic.com/paper/8q64f7kz4m1dt.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T14:21:00Z",
      "native_id": "0e1cfd4534ac4d9884addcd9c4ba28f5",
      "source": "doaj",
      "url": "https://doi.org/10.18799/24131830/2026/10/5254"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "lcc:QE1-996.5",
      "subfield": "geology-geochemistry"
    }
  ],
  "title": "Downscaling land surface temperature to high resolution using Sentinel-2 and Landsat in Google Earth Engine: a case study in Da Nang City",
  "title_text": "Downscaling land surface temperature to high resolution using Sentinel-2 and Landsat in Google Earth Engine: a case study in Da Nang City",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T14:20:26Z",
  "url": "https://research.draconitic.com/paper/8q64f7kz4m1dt",
  "venue": "Известия Томского политехнического университета: Инжиниринг георесурсов",
  "versions": []
}