Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data
Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data
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DOI：
10.1016/j.rse.2013.09.002
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发表时间：
2014
影响因子：
13.5
通讯作者：
Qihao Weng;Peng Fu
中科院分区：
文献类型：
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作者：
Qihao Weng;Peng Fu
陆面温度（LST）是了解全球环境变化、城市气候和陆气能量交换的重要基础。长期遥感LST数据的分析仍然是研究人员面临的一个挑战。以往的研究大多使用有限数量的晴空图像或通过去除云污染像素来探索城市热模式的空间和时间。图像场景数量的限制阻止了导出特定区域的长期LST气候学。此外，简单地消除云像素不可避免地模糊了LST的空间和时间模式。本研究利用年温度循环模式（ATC），对洛杉矶2000 - 2010年的年、季温度变化特征进行了研究。所有115个图像场景（路径41，第36行）的云覆盖率不到30%，可从陆地卫星档案用于分析。三个ATC参数，即，利用Levenberg-Marquardt最小化方案对年平均地表温度（MAST）、年地表温度振幅（YAST）和相位变化进行了优化，以了解LST的年和季节特征。对于所有115张图像的优化，总体RMSE为7.36 K;而对于中位数月复合，RMSE达到2.85 K。月平均合成图部分地消除了特定日期的异常和云量的影响，并在很大程度上反映了晴空条件下的LST，从而改善了模拟结果。从月中位数综合数据的建模结果得出的MAST和YAST进一步分析与三个归一化指数的关系，即，标准化差异植被指数（NDVI）、标准化差异水指数（NDWI）和标准化差异建成指数（NDBI）。结果表明，洛杉矶城区的平均气温与荒地区一样高，接近310 K，但波动较小。季节性分析表明，在冬季，城市地区观察到的温度高于贫瘠的土地/沙漠，这意味着城市材料持有更大的热量比贫瘠的土地更长的时间。将多云场景分成云百分比组，使得评估云覆盖对ATC建模过程的影响成为可能。敏感性分析表明，包括多云的图像带来了减少MAST范围从0.18到2.0 K，这取决于云覆盖的百分比和用于建模的多云场景的数量。这种下降可能是由于在分析中包括了云的温度，而不是阴影或其他陆地表面的温度。当所有的云图像包括在建模中，减少了2 K的MAST和增加了0.15的YAST。回归分析表明，NDBI和NDVI是影响MAST和YAST空间变异的主要因子，R2值分别为0.63和0.49。此外，YAST的空间变异被发现比MAST的更复杂，因为这三个指数只能解释高达53%的方差。
Land surface temperature (LST) is of primary importance in understanding global environment change, urban climatology, and land–atmosphere energy exchange. Analysis of long-term remotely sensed LST data remains a challenge for researchers. Most previous studies explored urban thermal pattern over space and time using a limited number of clear-sky images or by removing cloud-contaminated pixels. The limitation in the number of image scenes prevents from deriving long-term LST climatology for a particular region. Moreover, simply eliminating cloudy pixels inevitably obscures the spatial and temporal patterns of LST. This research attempts to characterize the annual and seasonal temperature behaviors during the period of year 2000 to year 2010 in Los Angeles by employing an annual temperature cycle (ATC) model. All 115 image scenes (path 41, row 36) of less than 30% of cloud cover available from the Landsat archive were utilized for the analysis. Three ATC parameters, i.e., mean annual surface temperature (MAST), yearly amplitude surface temperature (YAST), and the phase shift, were optimized with the Levenberg–Marquardt minimization scheme to understand the annual and seasonal characteristics of LST. The overall RMSE of 7.36 K was achieved for the optimization for all 115 images; while for the median monthly composite, the RMSE reached 2.85 K. The monthly median composite partly removed day-specific anomalies and the impact of cloud cover and reflected LSTs largely under clear sky conditions, leading to the improvement in the modeling result. The MAST and YAST derived from the modeling result of the monthly median composite data were further analyzed to relate to three normalized indices, i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built-up index (NDBI). The results showed that the mean temperature of urban areas in LA was as high as in the barren land area, reaching almost 310 K, but the urban areas possessed a less fluctuation. Seasonal analysis suggested that in winter, the urban areas observed a higher temperature than the barren land/desert, implying that urban materials held a larger amount of heat for a longer time than the barren land. The separation of cloudy scenes into cloud percentage groups made it possible for the evaluation of the effect of cloud cover on the ATC modeling process. The sensitivity analysis indicated that the inclusion of cloudy images brought about a decrease in MAST ranging from 0.18 to 2.0 K, depending on the percentage of cloud cover and the number of cloudy scenes used for the modeling. The decrease is due likely to the inclusion of cloud temperatures in analysis rather than shaded or other land surface temperatures. When all cloudy images were included in the modeling, a decrease of 2 K in MAST and an increase of 0.15 in YAST were observed. The regression analysis demonstrated that NDBI and NDVI were the main factors influencing the spatial variations of MAST and YAST with theR2value of 0.63 and 0.49, respectively. In addition, the spatial variation of YAST was found more complex than that of MAST, since the three indices can only explain up to 53% of its variance.