<![CDATA[Bali has been one of the most popular tourist destinations in Indonesia. However, on the other hand, Bali has a high risk of natural disaster vulnerability. The number of landslides in Bali took the first position compared to other natural disasters. Currently, remote sensing platforms can present Grid Satellite Rainfall Products (GSRPs), which provide rainfall information that can identify rainfall conditions for landslide events. This study aims to analyze the potential GSRPs application of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), and Global Satellite Mapping of Precipitation (GSMaP) in determining the mean rainfall intensities and duration (I-D); accumulated rainfall and duration (E-D) thresholds for landslide occurrences over Bali Island. The method used to develop I-D and E-D thresholds is the power-law equation and frequentist sampling method in various probability levels (5%, 10%, 20%, 30%, 40%, and 50%). The result shows that I-D and E-D thresholds established by GSRPs are generally lower than the threshold defined by rain gauge observations. Among the three GSRPs, IMERG is performing the best in establishing the I-D and E-D thresholds for landslide phenomena. The level of potential that IMERG can use in developing the I-D and E-D thresholds is 59.16% and 84.06%, respectively. The E-D threshold derived from the IMERG product can be used to establish the landslide early alert system over Bali Island because it has a high spatial-temporal resolution, word-wide coverage, and near-real-time observation.]]>
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