Spatiotemporal Analysis of Agricultural Drought in Tambakboyo District, Tuban Regency (2020–2025) Using the Normalized Difference Drought Index

Widya Devi Febianti R.S; Amaludin Arifia; Marita Ika Joesidawati; Fajar Rahmawan

doi:10.55681/sentri.v5i1.5517

Authors

Widya Devi Febianti R.S Universitas PGRI Ronggolawe
Amaludin Arifia Universitas PGRI Ronggolawe
Marita Ika Joesidawati Universitas PGRI Ronggolawe
Fajar Rahmawan NRM Peta Alam Indonesia

DOI:

https://doi.org/10.55681/sentri.v5i1.5517

Keywords:

Normalized Difference Drought Index; remote sensing; Sentinel-2; spatiotemporal analysis

Abstract

Drought is a recurring hydrometeorological disaster that poses a serious threat to agricultural productivity and food security, particularly in rain-fed agricultural regions of Indonesia’s northern coastal areas. Tambakboyo District, Tuban Regency, is characterized by high dependence on seasonal rainfall, limited irrigation infrastructure, and fluctuating climatic conditions, making it highly vulnerable to agricultural drought. This study aims to analyze the spatiotemporal patterns of agricultural drought in Tambakboyo District during the period 2020–2025 using the Normalized Difference Drought Index (NDDI) derived from Sentinel-2 satellite imagery. Sentinel-2 Level-2A surface reflectance data from September to December for each study year were processed to calculate the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), which were subsequently combined to generate NDDI values. Drought severity was classified into five categories ranging from normal to very severe drought. The results indicate a consistent seasonal drought pattern, with drought intensity beginning to increase in September, peaking in October–November, and declining in December with the onset of the rainy season. The most severe drought conditions occurred in 2022, when 68.5% of the district area experienced severe to very severe drought. Spatial analysis revealed that Kenanti, Gadon, and Plajan villages were persistently identified as drought-prone areas throughout the study period. These findings demonstrate the effectiveness of NDDI for monitoring agricultural drought in rain-fed farming systems and highlight its potential application for drought mitigation planning, early warning systems, and sustainable water resource management at the local scale.

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