The Operational and Climate Land Surface Temperature Products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B

被引:1
作者
Ghent, Darren [1 ]
Anand, Jasdeep Singh [1 ]
Veal, Karen [1 ]
Remedios, John [1 ]
机构
[1] Univ Leicester, Natl Ctr Earth Observat NCEO, Sch Phys & Astron, Space Pk, Leicester LE4 5SP, England
基金
英国自然环境研究理事会;
关键词
land surface temperature; LST; SLSTR; ESA-CCI; validation; TRACK SCANNING RADIOMETER; HIGH-RESOLUTION RADIOMETER; EMISSIVITY PRODUCTS; GEOV1; LAI; LONG-TERM; IN-SITU; VALIDATION; MODIS; RETRIEVAL; ALGORITHM;
D O I
10.3390/rs16183403
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth's surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an operational LST product has been available from the Sentinel-3A mission, with the corresponding product being available from Sentinel-3B since 17 November 2018. Here, we present the first paper describing formal products, including algorithms, for the Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard Sentinel-3A and 3B (SLSTR-A and SLSTR-B, respectively). We evaluate the quality of both the Land Surface Temperature Climate Change Initiative (LST_cci) product and the Copernicus operational LST product (SL_2_LST) for the years 2018 to 2021. The evaluation takes the form of a validation against ground-based observations of LST across eleven well-established in situ stations. For the validation, the mean absolute daytime and night-time difference against the in situ measurements for the LST_cci product is 0.77 K and 0.50 K, respectively, for SLSTR-A, and 0.91 K and 0.54 K, respectively, for SLSTR-B. These are an improvement on the corresponding statistics for the SL_2_LST product, which are 1.45 K (daytime) and 0.76 (night-time) for SLSTR-A, and 1.29 K (daytime) and 0.77 (night-time) for SLSTR-B. The key influencing factors in this improvement include an upgraded database of reference states for the generation of retrieval coefficients, higher stratification of the auxiliary data for the biome and fractional vegetation, and enhanced cloud masking.
引用
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页数:26
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