Agricultural Land Dryness Distribution Using the Normalized Difference Drought Index (NDDI) Algorithm on Landsat 8 Imagery in Eromoko, Indonesia

Abstract

The study area, Eromoko, has agricultural land covering 79.76% of the area, which experiences drought every year, causing a decrease in crop yields. Information on agricultural land dryness is needed to reduce the impact of dryness conditions on the agricultural sector. The effect of drought can be minimized using the transformation of the Normalized Difference Drought Index (NDDI) algorithm on Landsat 8 Imagery because it is considered capable of being used for land drought analysis that is accurate and efficient in time and cost. This study created a model for estimating soil moisture with actual soil moisture as the dependent variable and NDDI as the independent variable in several agricultural land uses in Eromoko. The results showed that the estimation model could estimate soil moisture with accuracy in plantations at 85.31%, irrigated paddy fields at 75.99%, rainfed paddy fields at 76.62%, and moors at 88.48%. The dryness category in the study area is 3,314.82 ha (35% of the total area). The variability of land use greatly affects the drying conditions. Dryness conditions can be reduced by controlling the dryness factors. Mitigation efforts to maintain soil moisture include irrigation planning based on the estimation model, applying bio-mulch and organic mulch, organic fertilization, and meeting water requirements in the harvesting period.

Keywords

• estimation model
• land drought category
• soil evaporation
• soil moisture