Multi-temporal gridded panel dataset of NDVI, NDBI, MNDWI, and Land Surface Temperature for the Dhaka Metropolitan Region (1990–2025)

This dataset provides a multi-temporal gridded panel of Landsat-derived spectral indices and Land Surface Temperature (LST) for the Dhaka Metropolitan Region, Bangladesh, covering five decadal epochs (1990, 2000, 2010, 2020, 2025). It supports analysis of urban densification and its thermal consequences in one of the world's most rapidly urbanising megacities.
Spatial coverage: Detailed Area Plan (DAP) jurisdiction administered by Rajdhani Unnayan Kartripakkha (RAJUK), approximately 39 km × 60 km.
Resolution: 6,427 grid cells of 500 m × 500 m (~250,000 m² each), with mean values computed via zonal statistics.
CRS: WGS 84 / UTM Zone 46N (EPSG: 32646).
Variables per cell per epoch:

NDVI — Normalised Difference Vegetation Index
NDBI — Normalised Difference Built-up Index
MNDWI — Modified Normalised Difference Water Index
LST — Land Surface Temperature (°C)
LST_relative — year-demeaned LST (controls for interannual climatic variability)
Centroid coordinates (Easting, Northing)

Source imagery: Landsat 5 TM (1990), Landsat 7 ETM+ (2000, 2010), Landsat 8 OLI/TIRS (2020, 2025), Collection 2 Level-2 Surface Reflectance with LaSRC atmospheric correction. Annual median composites were generated in Google Earth Engine with a 20% scene-level cloud filter; reprojection and zonal statistics were performed in QGIS.
Intended use: Spatial econometric and machine learning analyses of vegetation–built-up–temperature relationships, urban heat island characterisation, cooling threshold estimation, and heat-mitigation feasibility assessment. The data are structured as a balanced panel (6,427 cells × 5 epochs = 32,135 observations).
Notable patterns: Mean LST peaked at 31.1 °C in 2025; a 2020 anomaly (25.5 °C) reflects reduced anthropogenic heat emissions during COVID-19 restrictions. Mean NDBI increased from −0.166 (1990) to −0.134 (2025). Mean NDVI declined from 0.392 (1990) to 0.358 (2010) before partial recovery to 0.436 (2025), reflecting urban-core vegetation loss alongside peri-urban greening.
Companion publication: Accompanies the manuscript "Density-Dependent Limits of Vegetation Cooling under Urban Densification: A Spatial Econometric and Machine Learning Analysis of Dhaka" (under review, Sustainable Cities and Society).
Quality control: Cells with excessive cloud contamination or insufficient valid pixels in any epoch were removed to ensure a balanced panel.
Limitations: Annual median composites represent yearly structural averages rather than seasonal extremes; LST represents radiative surface temperature, not near-surface air temperature.