基于随机森林偏差校正的农业干旱遥感监测模型研究

被引:13
作者
刘冀 [1 ,2 ]
张特 [1 ,2 ]
魏榕 [1 ,2 ]
张茜 [1 ,2 ]
刘艳丽 [3 ,4 ]
董晓华 [1 ,2 ]
机构
[1] 三峡大学水利与环境学院
[2] 三峡库区生态环境教育部工程研究中心
[3] 南京水利科学研究院水文水资源与水利工程科学国家重点实验室
[4] 水利部应对气候变化研究中心
基金
国家重点研发计划;
关键词
农业干旱监测; 遥感; 随机森林; 偏差校正;
D O I
暂无
中图分类号
S423 [旱灾]; S127 [遥感技术在农业上的应用];
学科分类号
082804 ;
摘要
以3个月尺度的标准化降水蒸散指数(SPEI3指数)为因变量,采用融合多源遥感数据的随机森林(RF)算法构建淮河流域2001—2014年作物生长季(4—10月)的农业干旱监测模型,采用简单线性回归、偏差估算法、旋转残差法和最优角度残差旋转法4种方法进行模型结果校正,以决定系数(R2)、均方根误差(RMSE)及干旱等级监测准确率对模型监测能力进行评估。选取最优校正方法,构建随机森林偏差校正干旱监测模型(Bias-correcting random forest drought condition,BRFDC),通过站点实测土壤相对湿度及干旱事件记录对模型干旱监测能力进行验证。结果表明:采用最优角度残差旋转法校正后,模型模拟精度指标R2和RMSE分别为0.897、0.874和0.335、0.362,优于其他校正方法;偏差估算法对各类干旱等级监测更为准确,尤其是对极端干旱的监测准确率最高,达到33.3%~50.0%,最终采用偏差估算法作为最优校正方法,构建BRFDC模型;相比SPEI3,BRFDC模型计算指数与大部分站点土壤相对湿度的相关性更加显著(P <0.01),适于农业干旱监测; BRFDC模型能够准确监测淮河流域2001年严重干旱事件的时空演变过程,并能有效识别极端旱情。该模型可为淮河流域农业抗旱工作的有效开展提供科学依据。
引用
收藏
页码:170 / 177
页数:8
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