What question did this study set out to answer?

The aim is to evaluate the accuracy of four satellite rainfall products against ground observations in South Sulawesi.

Clustering-based evaluation of satellite rain-fall products: A novel perspective

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Evaluation demonstrates satellite rainfall product accuracy in varying terrain regions, suggesting tailored assessments.

The aim is to evaluate the accuracy of four satellite rainfall products against ground observations in South Sulawesi.
Compared satellite rainfall products using seven statistical accuracy indicators.
Applied a K-Means clustering algorithm to categorize performance across regions.
Identified performance-based zones to facilitate localized recommendations.
No single product outperformed others across all clusters.
IMERG achieved the highest performance in Cluster Type 1 with a POD of 0.87.
CHIRPS had the lowest RMSE of 12.3 mm in Cluster Type 2.
GPCP demonstrated the highest accuracy in Cluster Type 3 with a PC of 0.92 and BIAS of 0.05.
Elevated areas showed greater errors, indicating terrain complexity affects performance.

Giarno et al. (2025) studied this question.

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