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RESEARCH PAPER
Enhancing agricultural drought monitoring in semi-arid regions using spatiotemporal image fusion and a comprehensive agricultural drought index
1
Department of Geography, Yazd University, Yazd 8915818411, Iran
2
Institute for Atmospheric Sciences-Weather and Climate and Department of Physics, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland
3
Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Final revision date: 2026-01-20
Acceptance date: 2026-02-10
Publication date: 2026-04-20
Corresponding author
Iman Rousta
Institute for Atmospheric Sciences-Weather and Climate and Department of Physics, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland
Institute for Atmospheric Sciences-Weather and Climate and Department of Physics, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland
Jaromir Krzyszczak
Department of Metrology and Modelling of Agrophysical Processes, Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290, Lublin, Poland
Department of Metrology and Modelling of Agrophysical Processes, Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290, Lublin, Poland
Int. Agrophys. 2026, 40(3): 255-276
HIGHLIGHTS
- Spatiotemporal data fusion was applied for agricultural drought monitoring
- MODIS and Landsat 8 were fused via STI-FM for high spatiotemporal resolution
- Synthetic Landsat 8 images showed strong agreement with observations
- A novel Agricultural Drought Index was developed using NDWI, VSDI, NDVI, and LST
- ADI indicated persistent drought in Iranshahr County during 2014–2018
KEYWORDS
TOPICS
ABSTRACT
Effective agricultural drought monitoring requires data with both high spatial and temporal resolution to capture rapid vegetation dynamics at the field scale, while current satellite systems rarely provide both simultaneously. This study addresses this limitation by applying a spatiotemporal data fusion and developing a robust agricultural drought index (ADI). The spatial-temporal image fusion model (STI-FM) was used to fuse moderate resolution imaging spectroradiometer (MODIS) and Landsat-8 data over Iranshahr County, southeastern Iran, producing high-temporal-resolution synthetic Landsat-8 imagery. A novel ADI was then developed by integrating uncorrelated drought-related indicators (normalized difference water index (NDWI), visible and shortwave infrared drought index (VSDI), temperature vegetation dryness index (TVDI), and land surface temperature (LST)), with TVDI included to enhance sensitivity to soil moisture and thermal stress in semi-arid fields. The fused images showed strong agreement with observed Landsat data, confirming reliable reconstruction of spectral and thermal patterns in agricultural areas. ADI-based drought assessment revealed persistent drought conditions during the 2014-2018 growing seasons (April-June). Comparisons with meteorological and hydrological drought indices showed consistent drought patterns, confirming the robustness and applicability of the proposed framework.
FUNDING
This work was supported by Vedurfelagid, Rannis and Rannsoknastofa i vedurfraedi.
CONFLICT OF INTEREST
The Authors do not declare any conflict of interest.
ADDITIONAL INFORMATION
Data Availability Statement: The data presented in this study are available on request from the first author.
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