摘要
降水的季节性时空分布研究对东北地区的生态保护和农业生产有重要意义。基于植被指数、地形因子与降水的相关性,采用深度学习模型,对2009—2018年10 a间平均1,4,7,10月TRMM_3B43产品降尺度至0.01°(约1 km),使用站点实测数据进行精度校正,并填补TRMM未覆盖的50°N以上地区。结果表明,该模型效果优于随机森林,可有效获得各季节较高空间分辨率与精度的研究区域降水分布,校正后全局决定系数R2介于0.881~0.952之间,均方根误差介于1.222~13.11 mm之间,平均相对误差介于7.425%~28.41%之间,其中4月和10月份拟合度较好,1月和7月份相对稍差。
The research on the seasonal spatial and temporal distribution of precipitation is of great significance to the ecological protection and agricultural production in northeast China.Based on the correlation between vegetation index,topographical factors and precipitation,this paper utilizes deep learning models to downscale TRMM_3B43 products to 0.01°(about 1 km)in January,April,July,and October during 2009—2018,and uses site measured data to make accuracy correction and fill areas above 50°N which are not covered by TRMM.The results show that the model is better than random forest and can effectively obtain the precipitation distribution in the study area with higher spatial resolution and accuracy in each season.The corrected global determination coefficient R2 is between 0.881 and 0.952,the root mean square error(RMSE)is between 1.222 mm and 13.11 mm,and the mean relative error(MRE)is between 7.425%and 28.41%,among which the fitting degree is good in April and October,and relatively poor in January and July.
作者
杜方洲
石玉立
盛夏
DU Fangzhou;SHI Yuli;SHENG Xia(School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)
出处
《国土资源遥感》
CSCD
北大核心
2020年第4期145-153,共9页
Remote Sensing for Land & Resources
基金
国家自然科学基金项目“异速增长和资源限制模型结合多源遥感数据估算森林地上生物量研究”(编号:41471312)资助。
