Generation of 1 km high resolution Standardized precipitation evapotranspiration Index for drought monitoring over China using Google Earth Engine

作者：Yile He, Youping Xie, Junchen Liu, Junchen Liu, Zengyun Hu, Jun Liu, Jun Liu, Yuhua Cheng, Lei Zhang, Zhihui Wang, Man Li · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2024 · DOI：10.1016/j.jag.2024.104296 · 被引用次数：10 · 研究领域：Hydrology and Drought Analysis、Climate variability and models、Precipitation Measurement and Analysis

• A novel random forest approach is proposed to enhances SPEIbase resolution from 0.5° to 1 km. • Data gridding and sample balancing in preprocessing step enhance the accuracy. • The proposed method yields stable, high-precision 1 km SPEI with strong spatiotemporal alignment to SPEIbase. • Utilizing GEE's public data significantly lowers demands for meteorological data quantity and quality. Under the background of climate change and global warming, extreme drought events in China are becoming increasingly frequent. Drought is one of the primary natural causes of damage to China’s agriculture, economy, and environment, making timely, accurate, and high-resolution drought monitoring particularly crucial. The global standardized precipitation − evapotranspiration index database (SPEIbase) is a widely accepted and used global-scale drought monitoring product. However, limited by its spatial resolution of 0.5 degrees, it is difficult to describe the local spatio-temporal structure of drought. How to improve its spatial resolution while maintaining spatio-temporal consistency is one of the current research hotspots. Based on the response of vegetation growth status to drought, this paper proposes a simple and feasible SPEI prediction method, which improves the resolution of SPEIbase from 0.5 degrees to 1 km. Sixteen remote sensing inversion indices, reflectance and elevation data related to drought were selected from Google Earth Engine (GEE) as features. After preprocessing such a...