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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Comparison of the MuSyQ and MODIS Collection 6 Land Surface Temperature Products Over Barren Surfaces in the Heihe River Basin, China
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Comparison of the MuSyQ and MODIS Collection 6 Land Surface Temperature Products Over Barren Surfaces in the Heihe River Basin, China

机译：黑河流域贫瘠地区MuSyQ和MODIS Collection 6地表温度产物的比较。

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摘要

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.78 and -2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of -0.26 and -1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.60 and -1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products.
机译：在这项研究中，为了提高在贫瘠土地上的地表温度（LST）产品的准确性，我们提出了一种运算算法，使用物理检索的发射率产品从中分辨率成像光谱仪（MODIS）热红外数据中检索LST。 LST算法涉及两个步骤。首先，使用植被覆盖方法估算了两个MODIS分割窗口（SW）通道中的发射率，而裸土成分的发射率则来自ASTER全球发射率数据集。然后，使用改进的广义SW算法检索LST。该算法是在多源数据协同定量（MuSyQ）遥感产品系统中实现的。在2012年6月至2015年12月的黑河流域联合遥测实验研究（HiWATER）实验中，使用从中国西北四个贫瘠地面采集的地面测量结果对MuSyQ MODIS LST产品和Collection 6 MODIS LST产品（MxD11_L2）进行了比较和验证。总共使用了2268和2715个晴空样本进行Terra和Aqua的验证。评估结果表明，在所有四个站点的白天和晚上，MuSyQ LST产品都比C6 MxD11产品提供更好的精度。对于白天的结果，所有四个位置的C6 MxD11产品都低估了LST，Terra和Ts的平均偏差为-1.78和-2.86 K，均方根误差（RMSE）为3.16和3.94K。分别为Aqua，而MuSyQ LST产品的平均偏差在1 K以内，Terra和Aqua的平均偏差分别为-0.26和-1.03 K，平均RMSE为2.45和2.71K。对于夜间结果，所有四个地点的C6 MxD11产品也低估了LST，Terra和Aqua的平均偏差分别为-1.60和-1.26 K，均方根均方误差（RMSE）分别为1.93和1.60 K，而均值MuSyQ LST产品的偏差分别为0.16和0.58 K，Terra和Aqua的平均RMSE分别为1.12和1.25K。结果表明，在所有四个位置上对C6 MxD11 LST产物的低估主要是由于高估了MODIS 31和32波段的发射率。这项研究表明，物理回收的发射率产物是在贫瘠的地面和地面上进行LST回收的有用来源。可用于提高全球LST产品的准确性。

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