何志远,钟九生,代仁丽

• 1:贵州师范大学地理与环境科学学院

摘要(Abstract)：

干旱是由降水不足引起的，受温度、蒸散发等各种环境因素的影响，导致缺水和作物歉收。传统的干旱监测方法主要侧重于气象、水文等单一因子，而对多因子综合干旱监测的研究相对有限。本文利用2001—2015年的温度状态指数(TCI)、植被状态指数(VCI)、植被供水状况指数(VSWI)、降水状态指数(PCI)、土壤湿度状态指数(SMCI)、高程(DEM)及田间持水量(AWC)等7个干旱因子为自变量，以综合气象干旱指数(CI)为因变量，利用随机森林(RF)、增强回归树(BRT)和人工神经网络(ANN)构建干旱监测模型，并以西南五省为研究区进行了评价和分析。结果表明，基于人工神经网络的干旱监测指标(ANN-CI)预测效果最好，在测试集中预测值和观测值间的可决系数(R~2)为0.94,均方根误差(RMSE)为0.23。三种基于机器学习的综合干旱指标均在草地区表现最好，林区精度最差。在2001—2015年间，ANN-CI和植被生长状况具有显著的时空相关性(R~2=0.70,p<0.01)。最后选用ANN-CI对西南地区2009/2010年干旱事件的发展过程进行监测，并且与帕默尔干旱指数(PDSI)的监测结果进行对比，结果表明ANN-CI能够较好地应用于区域的旱情监测。

关键词(KeyWords)： 干旱监测;机器学习;卫星遥感;西南干旱

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41661081);; 贵州省科技计划项目(黔科合平台人才[2017]5726-56)

作者(Author): 何志远,钟九生,代仁丽

DOI: 10.13928/j.cnki.wrahe.2022.02.004

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