数字农科院2.0

Intercomparison Of Amsr2-And Modis-Derived Land S.urface Temperature Under Clear-Sky C onditions

文献类型： 外文期刊

作者： Huang, C; Duan, SB; Jiang, XG; Han, XJ; Wu, H; Gao, MF; Leng, P; Li, ZL

作者机构：

关键词： Advanced Microwave Scanning Radiometer 2 (AMSR2); intercomparison; land surface temperature (LST); Moderate Resolution Imaging Spectroradiometer (MODIS)

期刊名称： IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

ISSN： 1939-1404

年卷期： 2019 年 12 卷 9 期

页码：

收录情况： JCR（2021版） ; EI（2021版）

摘要： Land surface temperature (LST) is an important parameter in various fields, including hydrological, meteorological, and agricultural studies. Passive microwave techniques provide a practicable method to retrieve LST under both clear and cloudy conditions. In this study, LST derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature data during nighttime in the period 2015-2016 using a physically-based algorithm was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) LST product MYD11A1 over 16 study sites that represent four different land cover types, i.e., barren/sparsely vegetated, grasslands, croplands, and evergreen broadleaf forest. Compared to MODIS-derived LST, the root-mean-square error (RMSE) of AMSR2-derived LST is 6.0 K and the bias is 4.4 K over all study sites. For barren/sparsely vegetated sites, LST was overestimated by 6.7 K. To eliminate the systematic bias induced by the penetration depth effect of microwave radiation over barren/sparsely vegetated sites, a linear regression between AMSR2- and MODIS-derived LST was applied and the RMSE decreases from approximately 7.8 to 3.5 K. For the other three land cover types, the bias ranges from approximately 1.4 to 4.2 K and the RMSE ranges from approximately 2.1 to 5.9 K. The bias between AMSR2- and MODIS-derived LST is related to vegetation coverage. The value of bias increases with the decrease of normalized difference vegetation index. Furthermore, the RMSE has a strong dependency on precipitable water vapor (PWV). It presents a descending pattern of RMSE with the increase of PWV.

分类号：

  • 相关文献

[1]Estimation Of Land Surface Temperature F.rom Modis Data For T he Atmosphere With Air Temperature Inversion Profile. Tang, BH, Zhan, C, Li, ZL, Wu, H, Tang, RL. 2017

[2]Net Surface Shortwave Radiation Retrieval Using Random Forest Method With Modis/Aqua Data. Li, Zhao-Liang,Wu, Hua,Wu, Hua,Wu, Hua,Ying, Wangmin,Ying, Wangmin,Li, Zhao-Liang. 2019

[3]Assessment Of The Impacts From T.he World'S Largest Floating M acroalgae Blooms On The Water Clarity At The West Yellow Sea Using Modis Data (2002-2016). Li, L, Xing, QG, Li, XR, Yu, DF, Zhang, J, Zou, JQ. 2018

[4]Interannual Spatiotemporal Variations of Land Surface Temperature in China From 2003 to 2018. Yanru Yu,Si-Bo Duan,Zhao-Liang Li,Sheng Chang,Zefeng Xing,Pei Leng,Maofang Gao. 2021

[5]Evaluation Of Machine Learning Algorithms In Spatial Downscaling Of Modis Land Surface Temperature. Wu, Hua,Duan, Si-Bo,Li, Zhao-Liang,Wu, Hua,Li, Zhao-Liang,Li, Wan,Li, Wan,Ni, Li,Wu, Hua. 2019

[6]A Method for Downscaling Satellite Soil Moisture Based on Land Surface Temperature and Net Surface Shortwave Radiation. Yawei Wang,Pei Leng,Jianwei Ma,Jian Peng. 2021

[7]An Improved Method For Separating Soil And Vegetation Component Temperatures Based On Diurnal Temperature Cycle Model And Spatial Correlation. Liu, XY, Tang, BH, Li, ZL, Zhou, CH, Wu, WB, Rasmussen, MO. 2020

[8]Estimation Of Land Surface Temperature U.sing Fengyun-2E (Fy-2E) Data: A Case Study Of The Source Area Of The Yellow River. Song, XN, Wang, YW, Tang, BH, Leng, P, Chuan, S, Peng, J, Loew, A. 2017

[9]A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data. Rui Yao,Lunche Wang,Xin Huang,Liang Sun,Ruiqing Chen,Xiaojun Wu,Wei Zhang,Zigeng Niu. 2021

[10]Influence Of Temperature Inertia On Thermal Radiation Directionality Modeling Based On Geometric Optical Model. Lu, XY, Tang, BH, Li, ZL, Rasmussen, MO. 2020

[11]Evaluation Of Machine Learning Algorithms I.n Spatial Downscaling Of M odis Land Surface Temperature. Li, W, Ni, L, Li, ZL, Duan, SB, Wu, H. 2019

[12]Spatiotemporal Reconstruction Of Land Surface T.emperature Derived From Fengyun G eostationary Satellite Data. Liu, ZH, Wu, PH, Duan, SB, Zhan, WF, Ma, XS, Wu, YL. 2017

[13]Surface Soil Moisture Retrieval Using O.ptical/Thermal Infrared Remote Sensing D ata. Wang, YW, Peng, J, Song, XN, Leng, P, Ludwig, R, Loew, A. 2018

[14]A Practical Two-Stage Algorithm For R.etrieving Land Surface Temperature F rom Amsr-E Data-A Case Study Over China. Zhou, FC, Li, ZL, Wu, H, Duan, SB, Song, XN, Yan, GJ. 2018

[15]Impact of Atmospheric Correction on Spatial Heterogeneity Relations Between Land Surface Temperature and Biophysical Compositions. Xin-Ming Zhu,Xiao-Ning Song,Pei Leng,Da Guo,Shuo-Hao Cai. 2021

[16]Study On The Coupling Relationship B.etween Land Surface Composition A nd Land Surface Temperature Based On 3S Technology. Liu, HH, Shi, Y, Fu, L. 2018

[17]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>>