Estimating air surface temperature in Portugal using MODIS LST data
Estimating air surface temperature in Portugal using MODIS LST data
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DOI：
10.1016/j.rse.2012.04.024
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发表时间：
2012-09-01
影响因子：
13.5
通讯作者：
Santos, A.
中科院分区：
文献类型：
--
作者：
Benali, A.;Carvalho, A. C.;Santos, A.
空气表面温度（T-air）是一个重要的参数，广泛应用于病媒传播疾病生物学、水文学和气候变化研究。气温数据通常是从气象站的测量中获得的，只提供关于大面积空间模式的有限信息。遥感数据的使用可有助于克服这一问题，特别是在台站密度低的地区，有可能改进区域和全球范围的T-air估计。一些研究试图使用不同的方法得出最高（T-max）、最低（T-min）和平均气温（T-avg），估计精度各不相同;误差一般在2-3摄氏度范围内，而一般认为准确的精度水平为1-2摄氏度。本研究的主要目的是准确地估计T-max，T-min和T-avg为10年的时间段的基础上遥感地面温度（LST）数据从MODIS和辅助数据，使用统计方法。采用混合自举法和折刀法进行优化。统计模型估计Tavg的MEF（模型效率指数）为0.941，RMSE为133 ℃。关于T-max和T-min，实现的最佳MEF分别为0.919和0.871，RMSE分别为1.83和1.74 ℃。开发的数据集提供每周1公里的估计，并准确地描述了T-air的年度内和年度间的时间和空间模式。还分析和确定了不确定性和误差的潜在来源。提出了最有希望的发展，目的是在未来更大规模地开发准确的T-air估计。(c)2012 Elsevier Inc. All rights reserved.
Air surface temperature (T-air) is an important parameter for a wide range of applications such as vector-borne disease bionomics, hydrology and climate change studies. Air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of T-air at both regional and global scales. Some studies have tried to derive maximum (T-max), minimum (T-min) and average air temperature (T-avg) using different methods, with variable estimation accuracy; errors generally fall in the 2-3 degrees C range while the level of precision generally considered as accurate is 1-2 degrees C. The main objective of this study was to accurately estimate T-max, T-min and T-avg for a 10 year period based on remote sensing-Land Surface Temperature (LST) data obtained from MODIS-and auxiliary data using a statistical approach. An optimization procedure with a mixed bootstrap and jackknife resampling was employed. The statistical models estimated Tavg with a MEF (Model Efficiency Index) of 0.941 and a RMSE of 133 degrees C. Regarding T-max and T-min, the best MEF achieved was 0.919 and 0.871, respectively, with a 1.83 and 1.74 degrees C RMSE. The developed datasets provided weekly 1 km estimations and accurately described both the intra and inter annual temporal and spatial patterns of T-air. Potential sources of uncertainty and error were also analyzed and identified. The most promising developments were proposed with the aim of developing accurate T-air estimations at a larger scale in the future. (c) 2012 Elsevier Inc. All rights reserved.