Evaluation of the S-NPP VIIRS land surface temperature product using ground data acquired by an autonomous system at a rice paddy
Evaluation of the S-NPP VIIRS land surface temperature product using ground data acquired by an autonomous system at a rice paddy
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DOI：
10.1016/j.isprsjprs.2017.10.017
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发表时间：
2018
影响因子：
12.7
通讯作者：
R. Niclós;Lluís Pérez-Planells;C. Coll;J. A. Valiente;E. Valor
中科院分区：
文献类型：
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作者：
R. Niclós;Lluís Pérez-Planells;C. Coll;J. A. Valiente;E. Valor
S-NPP VIIRS地表温度（LST）产品于2014年底达到验证成熟度V1阶段（临时验证）。本文将当前的VIIRS V1 LST产品与2014年12月至2016年8月在稻田站点获取的同步地面数据进行了比较。实验场地全年有三种不同的季节性和均匀的土地覆盖，这使得该场地对验证活动很有趣。使用自主多角度系统记录现场的连续地面数据。在类似于VIIRS视角的天顶角获取的数据用于验证，以避免由于lst的角度依赖性而导致卫星和地面视图之间可能存在的差异。在地面数据的同时，该系统还测量了不同入射角下的天空辐射，用于改进验证数据集的云筛选，因为在之前的验证研究中发现云泄漏是一个需要进一步改进的重要问题。验证结果表明，在天顶角≤40°时，VIIRS V1 LST产品具有良好的性能，系统不确定度在±0.5 K以内，精度在1.2 K左右。这些值在为VIIRS LST产品建立的阈值要求范围内，并且它们优于之前发布的产品测试版或使用2014年4月实施的校准算法系数重新处理的VIIRS数据的验证结果。由于VIIRS LST算法具有依赖于土地覆盖类型的回归系数，因此还评估了土地覆盖错误分类对VIIRS LST数据精度的影响。预计将VIIRS产品分配的表面类型更改为站点像素上更合适的类型应该会改善验证结果。然而，改进是有限的，可能是由于在VIIRS LST算法系数的回归过程中，考虑不同土地覆盖类型的发射率的变率范围减小。结果表明，使用地表类型相关系数的地表温度算法检索地表温度存在困难和不确定性。
The S-NPP VIIRS Land Surface Temperature (LST) product attained the stage V1 of validation maturity (provisional validated) at the end of 2014. This paper evaluates the current VIIRS V1 LST product versus concurrent ground data acquired at a rice paddy site from December 2014 to August 2016. The experimental site has three different seasonal and homogeneous land covers through the year, which makes the site interesting for validation activities. An autonomous and multiangular system was used to record continuous ground data at the site. The data acquired at zenith angles similar to the VIIRS viewing angles were used for the validation to avoid possible differences between satellite and ground views due to angular dependences of the LSTs. Concurrently to surface data, downwelling sky radiances were measured at different incidence angles by the system, which were used to improve the cloud screening of the validation dataset, since cloud leakage was identified in previous validation studies as an important issue for further improvement. The validation results show good performance for the VIIRS V1 LST product at zenith angles ≤40°, with systematic uncertainties within ±0.5 K and accuracies around 1.2 K. These values are within the threshold requirements established for the VIIRS LST product, and they are better than the validation results published previously for the beta version of the product or using VIIRS data reprocessed with the calibrated algorithm coefficients implemented from April 2014. As the VIIRS LST algorithm has regression coefficients dependent on land cover type, the impact of land cover misclassifications on VIIRS LST data accuracy was also evaluated. It was expected that changing the surface type assigned by the VIIRS product to more appropriate types at the site pixels should improve the validation results. However, the improvement was limited, likely due to the reduced range of variability of the emissivities considered for the different land cover types in the regression process of the VIIRS LST algorithm coefficients. The results reveal the difficulties and uncertainties involved in the LST retrieval when using a LST algorithm with surface type dependent coefficients.