Abstract
The VIIRS Land Surface Temperature (LST) Environmental Data Record (EDR) has reached validated (V1 stage) maturity in December 2014. This study compares VIIRS v1 LST with the ground in situ observations and with heritage LST product from MODIS Aqua and AATSR. Comparisons against U.S. SURFRAD ground observations indicate a similar accuracy among VIIRS, MODIS and AATSR LST, in which VIIRS LST presents an overall accuracy of −0.41 K and precision of 2.35 K. The result over arid regions in Africa suggests that VIIRS and MODIS underestimate the LST about 1.57 K and 2.97 K, respectively. The cross comparison indicates an overall close LST estimation between VIIRS and MODIS. In addition, a statistical method is used to quantify the VIIRS LST retrieval uncertainty taking into account the uncertainty from the surface type input. Some issues have been found as follows: (1) Cloud contamination, particularly the cloud detection error over a snow/ice surface, shows significant impacts on LST validation; (2) Performance of the VIIRS LST algorithm is strongly dependent on a correct classification of the surface type; (3) The VIIRS LST quality can be degraded when significant brightness temperature difference between the two split window channels is observed; (4) Surface type dependent algorithm exhibits deficiency in correcting the large emissivity variations within a surface type.
Highlights
Land surface temperature (LST) is a critical parameter in the weather and climate system controlling surface heat and water exchange with the atmosphere [1]
The number of Visible Infrared Imaging Radiometer Suite (VIIRS) matchups is twice that of Moderate Resolution Imaging Spectroradiometer (MODIS) matchups: On the one hand, this is due to a better coverage of VIIRS compared to MODIS; on the other hand, it is a result of different cloud flag definition
In the MODIS LST product, the cloud free pixels affected by nearby clouds are excluded, which is not the case for the VIIRS LST product
Summary
Land surface temperature (LST) is a critical parameter in the weather and climate system controlling surface heat and water exchange with the atmosphere [1]. It has been used in many applications, including weather forecasting [2,3], irrigation and water resource management agricultural drought forecasting [4,5], and urban heat island monitoring [6]. Many algorithms have been developed for LST retrieval including single and multi-channel algorithms, e.g., [8,9,10,11,12,13,14,15]. With modifications to treat the spatio-temporal and spectral variations of the Land
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