摘要
干旱作为影响农业生产与生态安全的主要气象灾害之一,其长期动态监测对区域水资源管理具有重要意义。针对渭河流域干旱监测的实际需求,基于Google Earth Engine(GEE)平台,构建了2000–2024年长时间序列的改进型垂直干旱指数(Modified Perpendicular Drought Index, MPDI)数据集。本数据集以MOD09A1地表反射率产品为基础,经过云掩膜处理、投影转换等标准预处理流程,结合归一化植被指数(NDVI)和植被覆盖度(f_(v))参数计算,在传统垂直干旱指数(PDI)模型中引入植被覆盖度因子,从而生成空间分辨率为500 m的年度干旱监测产品。为确保数据的准确性与一致性,本研究在数据集构建过程中实施了严格的质量控制措施,包括云量筛选填充、土壤线方程拟合(R^(2)≥0.85)以及与实测土壤湿度监测数据相关性分析等。本数据集为评估渭河流域干旱发生的频率、强度及空间扩展趋势提供了可靠依据,可为区域干旱动态监测、水资源精细化管理和灾害风险评估等相关研究与实践提供有力支撑。
Drought,as one of the primary meteorological disasters affecting agricultural production and ecological security,holds significant importance for regional water resource management through long-term dynamic monitoring.To address the practical needs of drought monitoring in the Wei River Basin,an improved vertical drought index(Modified Perpendicular Drought Index,MPDI)dataset,based on the traditional Perpendicular Drought Index(PDI),was constructed spanning a long-term time series from 2000 to 2024,using the Google Earth Engine(GEE)platform.This dataset is based on the MOD09A1 surface reflectance product and has undergone standard preprocessing procedures,including cloud masking and projection transformation.It combines the calculation of the normalized difference vegetation index(NDVI)and vegetation coverage(fv)parameters,and incorporates a vegetation coverage factor into the traditional perpendicular drought index(PDI)model to generate annual drought monitoring products with a spatial resolution of 500 m.To ensure data accuracy and consistency,this study implemented strict quality control measures during the dataset construction process,including cloud cover screening and filling,soil line equation fitting(R²≥0.85),and correlation analysis with measured soil moisture observations.This dataset provides a reliable basis for assessing the frequency,intensity,and spatial expansion trends of drought events in the Wei River Basin,and can provide strong support for regional drought dynamic monitoring,refined water resource management,disaster risk assessment and related research.
作者
田琛琛
康亚琴
冯克庭
TIAN Chenchen;KANG Yaqin;FENG Keting(State Key Laboratory of Loess Science,Institute of Earth Environment,Chinese Academy of Sciences,Xi’an 710061,P.R.China;Loess Science Data Center,Institute of Earth Environment,Chinese Academy of Sciences,Xi’an 710061,P.R.China;College of Geography and Environment Sciences,Northwest Normal University,Lanzhou 730070,P.R.China)
基金
黄土科学全国重点实验室自主部署项目(E452840100)。
