*Article* **Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation**

**Jong-Suk Kim <sup>1</sup> , Seo-Yeon Park 2,\*, Joo-Heon Lee <sup>2</sup> , Jie Chen <sup>1</sup> , Si Chen <sup>3</sup> and Tae-Woong Kim <sup>4</sup>**


**Abstract:** To proactively respond to changes in droughts, technologies are needed to properly diagnose and predict the magnitude of droughts. Drought monitoring using satellite data is essential when local hydrogeological information is not available. The characteristics of meteorological, agricultural, and hydrological droughts can be monitored with an accurate spatial resolution. In this study, a remote sensing-based integrated drought index was extracted from 849 sub-basins in Korea's five major river basins using multi-sensor collaborative approaches and multivariate dimensional reduction models that were calculated using monthly satellite data from 2001 to 2019. Droughts that occurred in 2001 and 2014, which are representative years of severe drought since the 2000s, were evaluated using the integrated drought index. The Bayesian principal component analysis (BPCA)-based integrated drought index proposed in this study was analyzed to reflect the timing, severity, and evolutionary pattern of meteorological, agricultural, and hydrological droughts, thereby enabling a comprehensive delivery of drought information.

**Citation:** Kim, J.-S.; Park, S.-Y.; Lee, J.-H.; Chen, J.; Chen, S.; Kim, T.-W. Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation. *Remote Sens.* **2021**, *13*, 272. https://doi.org/10.3390/rs13020272

Received: 20 November 2020 Accepted: 11 January 2021 Published: 14 January 2021

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**Keywords:** remote sensing; integrated drought monitoring; meteorological drought; hydrological drought; agricultural drought; Bayesian principal component analysis (BPCA); statistical simulation
