Remote Sensing–Driven Drought Assessment Using Temperature Vegetation Dryness Index (TVDI) Derived from Landsat 8 Imagery

Nur Azizah Affandy; Tika Ziadhatin Nisa; Salwa Nabilah; Entin Hidayah

doi:10.25299/jgeet.2026.11.3.27329

Authors

Nur Azizah Affandy Departement Civil Engineering, Faculty of Sains and Technology, Universitas Islam Lamongan, Lamongan 62211, Indonesia
Tika Ziadhatin Nisa Departement Civil Engineering, Faculty of Sains and Technology, Universitas Islam Lamongan, Lamongan 62211, Indonesia
Salwa Nabilah Departement Civil Engineering, Faculty of Sains and Technology, Universitas Islam Lamongan, Lamongan 62211, Indonesia
Entin Hidayah Department of Civil Engineering, Faculty of Engineering, University of Jember, Jember 68121, Indonesia

DOI:

https://doi.org/10.25299/jgeet.2026.11.3.27329

Keywords:

remote sensing, LST, NDVI, TVDI, Landsat 8

Abstract

Drought, a persistent state of water scarcity, is a critical challenge for agriculture and resource management. This study examines drought variability in Lamongan Regency from 2021 to 2023 using the Temperature Vegetation Dryness Index (TVDI) derived from Landsat 8 imagery, integrating Land Surface Temperature (LST) and NDVI to classify conditions from wet to dry. Results show the largest moderate drought in June 2021 (1,660.39 km²) and the most extensive severe drought in October 2023 (1,561.39 km²). In 2023, wet-normal conditions dominated early months, while dry areas expanded in the peak dry season. In this study, TVDI was derived using a simplified global normalization of LST_min and LST_max rather than the standard pixel-by-pixel NDVI-LST triangular feature space, which is acknowledged as a methodological limitation. An anomaly occurred in February, where high rainfall coincided with widespread dryness. Furthermore, validation with rainfall data showed a very weak correlation (R² =0.090). This low correlation indicates a distinct time lag between precipitation events and soil moisture response, as well as the strong influence of local evapotranspiration and soil water retention capacity rather than rainfall alone. This study demonstrates that while TVDI is useful for rapid drought mapping, integration with local hydrological parameters is essential for improved accuracy.

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