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Abstract Soil moisture is an important indicator for drought monitoring. Mapping of soil moisture in this study uses remote sensing, namely Landsat 9 OLI imagery, because it can be relied upon as a cheap source of information, and its good temporal resolution or revisit period. Two parameters indirectly related to soil moisture, namely vegetation were analyzed using the Normalized Difference Vegetation Index (NDVI) approach, and land surface temperature (LST). Drought analysis was verified using the Normalized Difference Drought Index (NDDI). The remote sensing imagery used in this study is Landsat 9 OLI imagery by selecting images with 30% cloud cover from 1 January 2022 to 31 December 2022 with the support of the cloud-based Google Earth Engine computing platform. The results of the analysis indicate high LST values in the southern part of the study area whose dominant land use is built-up areas, namely in the sub-districts of Depok, Gamping, Ngaglik, and Mlati. The effect of vegetation on soil moisture is indicated by the NDVI value, which has a relatively strong positive correlation with SMI (R= 0.46). The SMI value is in contrast to LST, where the spatial distribution of high SMI is spread in the northern part, namely Pakem, Cangkringan, and Turi districts. On the other hand, a low SMI is spread across the central and southern parts of the study area, which have a high drought index (extreme moderate). Overall, it is concluded that the SMI has the potential to map drought and is a reliable index for initial analysis of drought risk management.
Arif et al. (Fri,) studied this question.
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