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首页> 外文期刊>International journal of remote sensing >Estimating daily maximum air temperature from MODIS in British Columbia, Canada
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Estimating daily maximum air temperature from MODIS in British Columbia, Canada

机译：根据加拿大不列颠哥伦比亚省的MODIS估算每日最高气温

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摘要

Air temperature (T-a) is an important climatological variable for forest research and management. Due to the low density and uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of T-a, especially in mountainous areas with complex terrain and high local variability. In this paper, the daily maximum T-a in British Columbia, Canada was estimated by satellite remote sensing. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the summer period (June to August) from 2003 to 2012 were collected to estimate T-a. Nine environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), latitude, longitude, distance to ocean, altitude, albedo, and solar radiation) were selected as predictors. Analysis of the relationship between observed T-a and spatially averaged remotely sensed LST indicated that 7 x 7 pixel size was the optimal window size for statistical models estimating T-a from MODIS data. Two statistical methods (linear regression and random forest) were used to estimate maximum T-a, and their performances were validated with station-by-station cross-validation. Results indicated that the random forest model achieved better accuracy (mean absolute error, MAE = 2.02 degrees C, R-2 = 0.74) than the linear regression model (MAE = 2.41 degrees C, R-2 = 0.64). Based on the random forest model at 7 x 7 pixel size, daily maximum T-a at a resolution of 1 km in British Columbia in the summer of 2003-2012 was derived, and the spatial distribution of summer T-a in this area was discussed. The satisfactory results suggest that this modelling approach is appropriate for estimating air temperature in mountainous regions with complex terrain.
机译：气温（T-a）是森林研究和管理的重要气候变量。由于气象站的低密度和不均匀分布，传统的地面观测无法准确地捕获T-a的空间分布，尤其是在地形复杂且局部变化较大的山区。本文通过卫星遥感估算了加拿大不列颠哥伦比亚省的每日最大T-a。收集了2003年至2012年夏季（6月至8月）的Aqua MODIS（中等分辨率成像光谱仪）数据和气象数据，以估算T-a。选择了九个环境变量（土地表面温度（LST），归一化植被指数（NDVI），修正归一化差异水指数（MNDWI），纬度，经度，到海洋的距离，高度，反照率和太阳辐射）作为预测因子。对观测到的T-a与空间平均遥感LST之间关系的分析表明，对于从MODIS数据估算T-a的统计模型而言，7 x 7像素大小是最佳窗口大小。使用两种统计方法（线性回归和随机森林）来估计最大T-a，并通过逐站交叉验证来验证其性能。结果表明，与线性回归模型（MAE = 2.41摄氏度，R-2 = 0.64）相比，随机森林模型具有更高的准确性（平均绝对误差，MAE = 2.02摄氏度，R-2 = 0.74）。基于7 x 7像素大小的随机森林模型，推导出2003-2012年夏季不列颠哥伦比亚省1 km分辨率下的每日最大T-a，并讨论了该地区夏季T-a的空间分布。令人满意的结果表明，这种建模方法适用于估算地形复杂的山区的气温。

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  • 来源
    《International journal of remote sensing》 |2014年第24期|8108-8121|共14页
  • 作者

    Xu Yongming; Knudby Anders; Ho Hung Chak;

  • 作者单位

    Nanjing Univ Informat Sci & Technol, Sch Remote Sensing, Nanjing 210044, Jiangsu, Peoples R China|China Meteorol Adm, Beijing Inst Urban Meteorol, Beijing 100089, Peoples R China;

    Simon Fraser Univ, Dept Geog, Burnaby, BC V5A 1S6, Canada;

    Simon Fraser Univ, Dept Geog, Burnaby, BC V5A 1S6, Canada;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
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