数字农科院2.0

Validation of Landsat land surface temperature product in the conterminous United States using in situ measurements from SURFRAD, ARM, and NDBC sites

文献类型： 外文期刊

作者： Duan Si-Bo;Li Zhao-Liang;Zhao Wei;Wu Penghai;Huang Cheng;Han Xiao-Jing;Gao Maofang;Leng Pei;Shang Guofei

作者机构：

关键词： land surface emissivity; Land surface temperature; Landsat; validation

期刊名称： International Journal of Digital Earth

ISSN： 1753-8947

年卷期： 2021 年 14 卷 5 期

页码：

收录情况： JCR（2021版）

摘要： Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD (Surface Radiation Budget Network) sites, 6 ARM (Atmospheric Radiation Measurement) sites, and 9 NDBC (National Data Buoy Center) sites. The results indicate that a relatively consistent performance among Landsat 5, 7, and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity (LSE) correction methods for Landsat 5, 7, and 8 sensors. Large bias and root mean square error (RMSE) of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index (NDVI). Except for the sites with incorrect LSE estimation, a mean bias (RMSE) of the differences between Landsat LST and in situ LST is 1.0 K (2.1 K) over snow-free land surfaces, −1.1 K (1.6 K) over snow surfaces, and −0.3 K (1.1 K) over water surfaces. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

分类号：

  • 相关文献

[1]Land Surface Temperature Retrieval From Passive Microwave Satellite Observations: State-Of-The-Art And Future Directions. Duan, SB, Han, XJ, Huang, C, Li, ZL, Wu, H, Qian, YG, Gao, MF, Leng, P. 2020

[2]Estimation of evapotranspiration using all-weather land surface temperature and variational trends with warming temperatures for the River Source Region in Southwest China. Ma Y., Zhou J., Liu S., Zhang W., Zhang Y., Xu Z., Song L., Zhao H.. 2022

[3]AN ALGORITHM FOR RETRIEVING INSTANTANEOUS MICROWAVE LAND SURFACE EMISSIVITY FROM PASSIVE MICROWAVE BRIGHTNESS TEMPERATURE AND PRECIPITABLE WATER VAPOR DATA. Zhou, Fang-Cheng,Wu, Hua,Tang, Bo-Hui,Tang, Rong-Lin,Li, Zhao-Liang,Song, Xiaoning,Yan, Guangjian. 2016

[4]Retrieving Land Surface Temperature from Hyperspectral Thermal Infrared Data Using a Multi-Channel Method. Zhong, Xinke,Labed, Jelila,Huo, Xing,Ren, Chao,Li, Zhao-Liang. 2016

[5]LAND SURFACE EMISSIVITY ESTIMATION FROM SATELLITE DATA WITH MACHINE LEARNING. Xiu Juan Li,Hua Wu,Zhao Liang Li,Yong Gang Qian,Si Bo Duan. 2021

[6]Research on optimizing the inversion of thermal infrared remote sensing surface temperature and emissivity based on mixture of experts. Liu Ma,Kebiao Mao,Zhonghua Guo,Zijin Yuan. 2025

[7]Annual Cropland Mapping Using Reference L.andsat Time Series-A Case S tudy In Central Asia. Loew, Fabian,Biradar, Chandrashekhar,Hao, Pengyu,Hao, Pengyu. 2018

[8]An Index Of Non-Sampling Error I.n Area Frame Sampling B ased On Remote Sensing Data. Wu, MQ, Peng, DL, Qin, Y, Niu, Z, Yang, CH, Li, W, Hao, PY, Zhang, CY. 2018

[9]Understanding the dynamics of integrated rice–crawfish farming in Qianjiang county, China using Landsat time series images. Wei Yanbing,Lu Miao,Yu Qiangyi,Xie Ankun,Hu Qiong,Wu Wenbin. 2021

[10]Spatial Accessibility Of Urban Forests I.n The Pearl River D elta (Prd), China. Zhang, R, Chen, JQ, Park, H, Zhou, XH, Yang, XC, Fan, PL, Shao, CL, Ouyang, ZT. 2019

[11]An Efficient Approach To Remove T.hick Cloud In Vnir B ands Of Multi-Temporal Remote Sensing Images. Du, WH, Qin, ZH, Fan, JL, Gao, MF, Wang, F, Abbasi, B. 2019

[12]Early-season crop type mapping using 30-m reference time series. HAO P.-Y., TANG H.-J., CHEN Z.-X., MENG Q.-Y., KANG Y.-P.. 2020

[13]Method Development, Matrix Effect, And R.isk Assessment Of 49 M ulticlass Pesticides In Kiwifruit Using Liquid Chromatography Coupled To Tandem Mass Spectrometry. Kim, YA, Abd El-Aty, AM, Rahman, MM, Jeong, JH, Shin, HC, Wang, J, Shin, S, Shim, JH. 2018

[14]Comparison Of Six Methylation Methods F.or Fatty Acid Determination I n Yak Bone Using Gas Chromatography. Jia, W, Liu, WY, Mi, S, Zhang, CH, Li, X, Wu, T, Yu, QL. 2017

[15]Using A Process-Oriented Methodology To P.recisely Evaluate Temperature Suitability F or Potato Growth In China Using Gis. He, YB, Zhou, YF, Cai, WM, Wang, ZZ, Duan, DD, Luo, SJ, Chen, JZ. 2017

[16]Development And Validation Of A C.ompetitive Elisa Based On B acterium-Original Virus-Like Particles Of Serotype O Foot-And-Mouth Disease Virus For Detecting Serum Antibodies. Ran, XH, Yang, ZY, Bai, MY, Zhang, Y, Wen, XB, Guo, HC, Sun, SQ. 2019

[17]An Optimal Sampling Design For Land Surface Temperature Validation With Spatial And Diurnal Variations. Li, J, Ni, L, Li, ZL, Qian, YG, Wu, H. 2019

[18]Evaluation Of Seven Atmospheric Profiles From Reanalysis And Satellite-Derived Products: Implication For Single-Channel Land Surface Temperature Retrieval. Yang, JJ, Duan, SB, Zhang, XY, Wu, PH, Huang, C, Leng, P, Gao, MF. 2020

[19]An Uncertainty-Based Outlier Detection Method for Satellite-Derived Land Surface Temperature Validation Using In Situ Measurements. Si-Bo Duan, Zhao-Liang Li, Xiaoxiao Min, Penghai Wu, Ran Wei, Xiangyang Liu , Caixia Gao. 2025

[20]Improving Land Surface Temperature Retrieval From MODIS Data: Explicit Correction for Aerosol Optical Depth Variability. 段四波,,凌凯,,闵肖肖,,魏冉,,管永娟. 2025

作者其他论文 更多>>