您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE

文献类型： 外文期刊

作者： Yang, Guijun 1 ; Weng, Qihao 2 ; Pu, Ruiliang 4 ; Gao, Feng 5 ; Sun, Chenhong 1 ; Li, Hua 6 ; Zhao, Chunjiang 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.S China Normal Univ, Sch Geog, Guangzhou 510631, Guangdong, Peoples R China

3.Indiana State Univ, Dept Earth & Environm Syst, Ctr Urban & Environm Change, Terre Haute, IN 47809 USA

4.Univ S Florida, Sch Geosci, Tampa, FL 33620 USA

5.ARS, Hydrol & Remote Sensing Lab, USDA, Beltsville, MD 20705 USA

6.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100010, Peoples R China

关键词： ESTARFM;ASTER;MODIS;land surface temperature;evaluation

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2016 年 8 卷 1 期

页码：

收录情况： SCI

摘要： Land surface temperature (LST) is an important parameter that is highly responsive to surface energy fluxes and has become valuable to many disciplines. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between them. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of thermal infrared (TIR) data or LST, but rarely both. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. The aim of this study is to conduct a comprehensive evaluation of the ESTARFM algorithm for generating ASTER-like daily LST by three approaches: simulated data, ground measurements and remote sensing products, respectively. The datasets of LST ground measurements, MODIS, and ASTER images were collected in an arid region of Northwest China during the first thematic HiWATER-Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012 from May to September. Firstly, the results of the simulation test indicated that ESTARFM could accurately predict background with temperature variations, even coordinating with small ground objects and linear ground objects. Secondly, four temporal ASTER and MODIS data fusion LSTs (i.e., predicted ASTER-like LST products) were highly consistent with ASTER LST products. Here, the four correlation coefficients were greater than 0.92, root mean square error (RMSE) reached about 2 K and mean absolute error (MAE) ranged from 1.32 K to 1.73 K. Finally, the results of the ground measurement validation indicated that the overall accuracy was high (R-2 = 0.92, RMSE = 0.77 K), and the ESTARFM algorithm is a highly recommended method to assemble time series images at ASTER spatial resolution and MODIS temporal resolution due to LST estimation error less than 1 K. However, the ESTARFM method is also limited in predicting LST changes that have not been recorded in MODIS and/or ASTER pixels.

  • 相关文献

[1]基于边缘特征的多源影像空间配准精度研究. 石月婵,杨贵军,宋健,冯海宽,王仁礼. 2013

[2]PROSAIL冠层光谱模型遥感反演区域叶面积指数. 李淑敏,李红,孙丹峰,周连第. 2009

[3]Estimation of subpixel temperature over a heterogeneous area using an endmember index based technique. Yang Guijun,Huang Wenjiang,Wang Jihua,Zhao Chunjiang. 2011

[4]Estimation of Regional Leaf Area Index by Remote Sensing Inversion of PROSAIL Canopy Spectral Model. Li Shu-min,Li Hong,Zhou Lian-di,Sun Dan-feng. 2009

[5]Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area. Yang, Guijun,Yang, Guijun,Yang, Guijun,Zhao, Chunjiang,Huang, Wenjiang,Wang, Jihua,Pu, Ruiliang.

[6]基于ESTARFM模型的区域农田高时空分辨率影像产生与应用. 陈梦露,李存军,官云兰,周静平,王道芸,罗正乾. 2019

[7]Retrieving and Assessing Land Surface Temperature from ASTER Data. Yang, Guijun,Shi, Yuechan,Wang, Renli. 2012

[8]Spatio-temporal variation of alpine grassland spring phenological and its response to environment factors northeastern of Qinghai-Tibetan Plateau during 2000-2016. Li, Guangyong,Jiang, Guanghui,Li, Guangyong,Bai, Ju,Jiang, Cuihong. 2017

[9]Forecasting of Powdery Mildew disease with multi-sources of remote sensing information. Zhang, Jingcheng,Yuan, Lin,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yuan, Lin. 2014

[10]Vegetable quality and safety supervision technology Integration solutions and evaluation. Chen, Ma,Jin, Li,Chen, Ma,Jin, Li. 2015

[11]Application of Analytical Hierarchy Process on Evaluation of Teaching Quality in Farmer Distance Education Platform. Dai, Lina,Guo, Jianxin,Zhao, Jichun. 2013

[12]Development and evaluation of power consumption model for no-till planter based on working parameters. Gao Dongming,Li Lianhao,Qiao Xiaodong,Sarker, Khokan Kumer. 2017

[13]Environment Quality Evaluation of Heavy Metals in agricultural soils of Shilou town in Fangshan district of Beijing. Han, Ping,Feng, Xiaoyuan,Wang, Jihua. 2014

[14]ASSESSING AND MAPPING CROP VULNERABILITY DUE TO SUDDEN COOLING USING TIME SERIES SATELLITE DATA. Dong, Yansheng,Gu, Xiaohe,Wang, Jihua,Cui, Bei. 2012

[15]THE POTENTIAL OF MODIS FOR DROUGHT MONITORING IN NORTHERN CHINA. Long Huiling,Huang Wenjiang,Yang Xiaodong,Dong Yansheng. 2012

[16]中低分辨率小波融合的玉米种植面积遥感估算. 顾晓鹤,韩立建,王纪华,黄文江,何馨. 2012

[17]华北平原冬小麦总初级生产力的遥感监测. 赵晶晶,刘良云,徐自为,焦全军,彭代亮. 2011

[18]基于MODIS与TM时序插补的省域尺度玉米遥感估产. 顾晓鹤,何馨,郭伟,黄文江,燕荣江. 2010

[19]环境减灾卫星高光谱数据大气校正模型及验证. 杨贵军,黄文江,刘三超,邢著荣,王纪华. 2010

[20]MODIS土地利用/覆被多时相多光谱决策树分类. 刘建光,李红,孙丹峰,张微微,周连第. 2010

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台