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首页> 外文期刊>Advances in space research >Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province, Iran
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Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province, Iran

机译：卫星3卫星图像验证陆地温度估计的非线性分裂窗口算法：案例研究; 德黑兰省，伊朗

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摘要

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (-1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018-2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.
机译：近年来，土地表面温度（LST）在环境研究和地球科学方面变得至关重要。遥感技术在大尺度上实现了对此参数的时空监测。该参数可以由具有至少一个热带的卫星图像估计。 Sentinel-3 SLST数据提供LST产品，空间分辨率为1公里。在本研究中，采用直接和间接验证程序在2018年至2019年的不同季节中的研究区域评估Sentinel-3 SLSTR LST产品。验证方法基于本产品的绝对（直接）评估现场数据使用非线性拆分窗口（NSW）算法与MODIS LST产品和估计LST的比较（间接）评估。此外，两个发射率估计方法，（1）基于分类的分类的发射率法（CBEM），用于使用根据Sentinel的两个热带的NSW方法来估计LST的基于分类的基于分配方法（CBEM）。 3图像。然后，使用现场数据和植被的时间变化来评估这些方法在估计LST中的准确性，NDVI-THM方法产生更好的结果。对于Sentinel-3 LST产品，MODIS LST产品和使用NSW的LST之间的间接评估，使用基于空间和时间分离的四个滤波器与像素对和像素质量分开，以确保比较对的准确性和一致性像素。通常，Modis和Sentinel-3的LST产品的准确性结果和使用NSW估计的LST估计显示了在季节期间LST变化的类似趋势。关于Sentinel-3 LST产品的两个绝对和比较验证，夏季具有最高偏差值（-1.24 k），标准差（STDV = 2.66 k）和RMSE（2.43 k）和冬季最低的（偏差为0.14 k，STDV为1.13 K，RMSE为1.12 k），分别为2018 - 2019年期间的季节提供了最糟糕的和最佳成果。根据绝对和比较评估结果，Sentinel-3 SLSTR LST产品为我们研究的区域的大型时间和空间量表提供了可靠的所有季节。

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