Cross Comparison Between Landsat New Land Surface Temperature
Product and the Corresponding MODIS Product
LI Chun-qiang1, 2, GAO Yong-gang1, 2, XU Han-qiu1, 2*
1. College of Environment and Safety Engineering, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China
2. Fujian Provincial Key Laboratory of Remote Sensing Monitoring and Assessment of Soil Erosion and Disaster Prevention, Fuzhou 350116, China
Abstract:Landsat Collection 2 Level-2 Surface Temperature (LC2L2ST) was formally released in December 2020 by the U.S. Geological Survey (USGS). However, there are few reports on this new land surface temperature (LST) product. As this product will be the only LST data provided by the USGS starting in 2022, it is necessary to evaluate the product timely. Among various satellite LST products, the quality of the MODIS LST product is well recognized, and widely used. Therefore, this paper, for the first time, performed a cross-comparison between the new Landsat LST product and the MODIS LST product to examine the quality of the new product. Different regions in China (Fuzhou, Taihu, Yinchuan and Dunhuang) were selected as the test areas, and 20 pairs of LC2L2ST and MODIS LST synchronous images were used for the comparison. The images cover different land types, such as vegetation, water, town and deserts across different seasons. A total of 560 homogeneous regions of interest (ROI) were selected from the images of the test areas. The regression analysis was carried out to examine the fit of the ROIs and the quantitative relationship between the two LST products. The conversion model between them was also developed. The results showed that the new LC2L2ST product is highly correlated with the MODIS LST product. Each of the four test areas can achieve a coefficient of determination (R2) greater than 0.98. Integrating the 560 samples from the four test areas also obtain an R2 close to 0.98. Nevertheless, differences between the two products have also been founded. The LC2L2ST is 0.90℃ averagely higher than the MODIS LST (RMSE = 2.29 ℃). However, LC2L2ST can be slightly lower than MODIS LST in late fall and winter seasons but significantly higher than extremely hot summer seasons with a bias close to 7 ℃. The analysis revealed that the differences were related to spatial resolution, sensor viewing angles, land cover types and seasons. In general, the new LC2L2ST product strongly correlates with the MODIS LST, but significant differences were also observed in the summer months. Therefore, the new Landsat LST product must be further tested with in-situ measured LST data. Due to the differences in this paper, the two LST data products need to be converted when they must be collaboratively used. This study developed the conversion equation between the two LSTs based on the 560 ROIs. The verification found that the differences between the two data after conversion were greatly reduced. It is conducive to the cooperative use of the two LST data and providing continuous remote sensing data for long-term LST monitoring.
李春强,高永刚,徐涵秋. Landsat新型热红外地表温度产品与MODIS地表温度产品的交互对比[J]. 光谱学与光谱分析, 2023, 43(03): 940-948.
LI Chun-qiang, GAO Yong-gang, XU Han-qiu. Cross Comparison Between Landsat New Land Surface Temperature
Product and the Corresponding MODIS Product. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 940-948.
[1] United States Geological Survey (USGS). Landsat 8-9 OLI/TIRS Collection 2 Level 2 Data Format Control Book (DFCB), 2020.
[2] Hazaymeh K, Hassan Q K. Journal of Applied Remote Sensing, 2015, 9(1): 96095.
[3] ZOU De-fu, ZHAO Lin, WU Tong-hua, et al(邹德富, 赵 林, 吴通华, 等). Journal of Glaciology and Geocryology(冰川冻土), 2015, 37(2): 308.
[4] Laraby K G, Schott J R. Remote Sensing of Environment, 2018, 216: 472.
[5] ZHANG Ai-yin, ZHANG Xiao-li(张爱因, 张晓丽). Journal of Beijing Forestry University(北京林业大学学报), 2019, 41(3): 1.
[6] XING Meng-ling, WANG Di-feng, HE Xian-qiang, et al(邢梦玲, 王迪峰, 何贤强, 等). Journal of Marine Sciences(海洋学研究), 2020, 38(4): 72.
[7] Burnett M, Chen D. Land, 2021, 10(7): 672.
[8] Jacob F, Petitcolin F, Schmugge T, et al. Remote Sensing of Environment, 2004, 90(2): 137.
[9] Guillevic P C, Biard J C, Hulley G C, et al. Remote Sensing of Environment, 2014, 154: 19.
[10] Reiners P, Asam S, Frey C, et al. Remote Sensing, 2021, 13(17): 3473.
[11] Coll C, Caselles V, Galve J M, et al. Remote Sensing of Environment, 2005, 97(3): 288.
[12] Zhao E, Han Q, Gao C. IEEE Access, 2021, 9: 9403.
[13] XU Guang-zhi, XU Han-qiu(徐光志, 徐涵秋). Remote Sensing Technology and Application(遥感技术与应用), 2021, 36(1): 165.
[14] United States Geological Survey (USGS). How Do I Use a Scale Factor With Landsat Level-2 Science products, 2020.
[15] NASA. MODIS Collection 6. 1 (C61) Product User Guide, 2019.
[16] XU Han-qiu, LIU Zhi-cai, GUO Yan-bin(徐涵秋, 刘智才, 郭燕滨). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(8): 148.
[17] WU Xiao-ping, XU Han-qiu(吴晓萍, 徐涵秋). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(1): 310.
[18] Flood N. Remote Sensing, 2014, 6(9): 7952.
[19] JIANG Qiao-ling, XU Han-qiu(蒋乔灵, 徐涵秋). Remote Sensing Technology and Application(遥感技术与应用), 2018, 33(6): 1084.
[20] WU Xiao-ping, XU Han-qiu, JIANG Qiao-ling(吴晓萍, 徐涵秋, 蒋乔灵). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2020, 45(1): 150.
[21] DUAN Si-bo, RU Chen, LI Zhao-liang, et al(段四波, 茹 晨, 李召良, 等). Journal of Remote Sensing(遥感学报), 2021, 25(8): 1591.
[22] ZHU Jin-shun, REN Hua-zhong, YE Xin, et al(朱金顺, 任华忠, 叶 昕, 等). Journal of Remote Sensing(遥感学报), 2021, 25(8): 1538.
[23] Cao B, Liu Q, Du Y, et al. Remote Sensing of Environment, 2019, 232: 111304.
[24] Guillevic P C, Privette J L, Coudert B, et al. Remote Sensing of Environment, 2012, 124: 282.
[25] Malakar N K, Hulley G C, Hook S J, et al. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 5717.