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A vegetation index based technique for spatial sharpening of thermal imagery

机译:基于植被指数的热成像空间锐化技术

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High spatial resolution (~100 m) thermal infrared band imagery has utility in a variety of applications in environmental monitoring. However, currently such data have limited availability and only at low temporal resolution, while coarser resolution thermal data (~ 1000 m) are routinely available, but not as useful for identifying environmental features for many landscapes. An algorithm for sharpening thermal imagery (TsHARP) to higher resolutions typically associated with the shorter wavebands (visible and near-infrared) used to compute vegetation indices is examined over an extensive corn/soybean production area in central Iowa during a period of rapid crop growth. This algorithm is based on the assumption that a unique relationship between radiometric surface temperature (T{sub}R) relationship and vegetation index (VI) exists at multiple resolutions. Four different methods for defining a VI - T{sub}R basis function for sharpening were examined, and an optimal form involving a transformation to fractional vegetation cover was identified. The accuracy of the high-resolution temperature retrieval was evaluated using aircraft and Landsat thermal imagery, aggregated to simulate native and target resolutions associated with Landsat, MODIS, and GOES short- and longwave datasets. Applying TsHARP to simulated MODIS thermal maps at 1-km resolution and sharpening down to ~ 250 m (MODIS VI resolution) yielded root-mean-square errors (RMSE) of 0.67-1.35℃ compared to the 'observed' temperature fields, directly aggregated to 250 m. Sharpening simulated Landsat thermal maps (60 and 120 m) to Landsat VI resolution (30 m) yielded errors of 1.8-2.4℃, while sharpening simulated GOES thermal maps from 5 km to 1 km and 250 m yielded RMSEs of 0.98 and 1.97, respectively. These results demonstrate the potential for improving the spatial resolution of thermal-band satellite imagery over this type of rainfed agricultural region. By combining GOES thermal data with shortwave VI data from polar orbiters, thermal imagery with 250-m spatial resolution and 15-min temporal resolution can be generated with reasonable accuracy. Further research is required to examine the performance of TsHARP over regions with different climatic and land-use characteristics at local and regional scales.
机译:高空间分辨率(约100 m)的热红外波段图像可用于环境监测的各种应用中。但是,目前此类数据的可用性有限,并且仅在低时间分辨率下可用,而常规情况下可获得较高分辨率的热数据(约1000 m),但对于识别许多景观的环境特征却没有帮助。在作物生长迅速的时期内,在爱荷华州中部广泛的玉米/大豆生产地区研究了一种将热图像(TsHARP)锐化为高分辨率的算法,该算法通常与用于计算植被指数的较短波段(可见光和近红外光)相关联。该算法基于以下假设:在多种分辨率下,辐射表面温度(T {R} R)关系与植被指数(VI)之间存在唯一关系。研究了四种用于定义锐化的VI-T {sub} R基函数的不同方法,并确定了涉及到植被覆盖度转换的最佳形式。使用飞机和Landsat热成像仪评估了高分辨率温度取回的准确性,并汇总以模拟与Landsat,MODIS和GOES短波和长波数据集相关的原始分辨率和目标分辨率。将TsHARP以1 km的分辨率应用于模拟的MODIS热图,并锐化至约250 m(MODIS VI分辨率),与“观测”温度场相比,其均方根误差(RMSE)为0.67-1.35℃至250 m。将模拟的Landsat热图(60和120 m)锐化为Landsat VI分辨率(30 m)时产生的误差为1.8-2.4℃,而将模拟GOES热图从5 km锐化至1 km和250 m时的RMSE分别为0.98和1.97。 。这些结果证明了在这种类型的雨养农业地区提高热波段卫星图像空间分辨率的潜力。通过将GOES热数据与来自极地轨道器的短波VI数据相结合,可以以合理的精度生成具有250 m空间分辨率和15 min时间分辨率的热图像。需要进一步研究以检验TsHARP在地方和区域范围内具有不同气候和土地利用特征的区域的性能。

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