夏晓圣,王军红,程先富

• 1:安徽师范大学地理与旅游学院
• 2:安徽自然灾害过程-防控研究省级实验室
• 3:霍山县水土保持试验站

摘要(Abstract)：

针对如何克服不同传感器地表温度的时空分辨率矛盾的问题,该文利用随机森林(RF)算法、BP神经网络(BP)算法和多元回归(MLR)算法,直接将原始1km分辨率的MODIS LST降尺度至250m分辨率,并评估地理要素对不同降尺度算法的多维响应。结果表明:①在不同海拔、坡度、坡向和土地利用类型中,RF算法的降尺度效果均为最佳;②在降尺度模型中考虑经纬度、地形因子等地理要素能显著提升降尺度效果;③降尺度效果随海拔升高先增后减,随坡度增加先增后降,从不同坡向看,降尺度效果分异明显,从不同土地利用类型看,林地、草地的降尺度精度最高,耕地次之,水域和建筑用地降尺度精度最低。

关键词(KeyWords)： 地表温度;降尺度;地理要素;多维响应;MODIS;随机森林

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目（41271516）;; 安徽师范大学研究生科研创新与实践项目（2018kycx051）

作者(Author): 夏晓圣,王军红,程先富

DOI: 10.16251/j.cnki.1009-2307.2019.12.020

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