Comparison of integrating LAS/MODIS data into a land surface model for improved estimation of surface variables through data assimilation

Author(s): Wang, K (Wang, Kun)[ 1,2,3 ] ; Tang, RL (Tang, Ronglin)[ 3,4 ] ; Li, ZL (Li, Zhao-Liang)[ 3,5 ]

Source: INTERNATIONAL JOURNAL OF REMOTE SENSING  Volume: 34   Issue: 9-10   Special Issue: SI   Pages: 3193-3207   DOI: 10.1080/01431161.2012.716914   Published: MAY 1 2013

Times Cited: 1 (from Web of Science)

Cited References: 30      [ view related records ]     Citation Map    

Abstract: In this article, land surface temperature (LST) and sensible heat flux (H) data assimilation schemes were developed separately using the ensemble Kalman filter (EnKF) and the common land model (CoLM). Surface measurements of ground temperature, H, and latent heat flux (LE) collected at the Yucheng (longitude: 116 degrees 36 E; latitude: 36 degrees 57 N) and Arou (longitude: 100 degrees 27 E; latitude: 38 degrees 02 N) experimental stations were compared with the predictions by assimilating different observation sources into the CoLM. The results showed that both LST and H data assimilation schemes could improve the estimation of ground temperature and H. The root mean square error (RMSE) compared between the predictions and in situ measurements decreased more significantly with the assimilation of values of H measured by a large aperture scintillometer (LAS). Assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) LST only slightly improved the predictions of H and ground temperature. Daytime to night-time comparison results using both assimilation schemes also indicated that accurately quantifying model, prediction, and observation error would improve the efficiency of the assimilation systems. The newly developed land data assimilation schemes have proved to be a feasible and practical method to improve the predictions of heat fluxes and ground temperature from CoLM. Moreover, integrating multisource data (LAS and MODIS LST) simultaneously into the land surface model is believed to result in an efficient and robust way to improve the accuracy of model predictions from a theoretical point of view.

Accession Number: WOS:000319173900004

Document Type: Article

Language: English

KeyWords Plus: LARGE-APERTURE SCINTILLOMETER; REMOTELY-SENSED DATA; KALMAN FILTER; SENSING DATA; HEAT-FLUX; TEMPERATURE; SYSTEM; LATENT

Reprint Address: Li, ZL (reprint author)

         Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.

Addresses:

         [ 1 ] Chinese Acad Sci, Inst Remote Sensing Applicat, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

         [ 2 ] Beijing Normal Univ, Beijing 100101, Peoples R China

         [ 3 ] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China

         [ 4 ] Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China

         [ 5 ] CNRS, UdS, LSIIT, F-67412 Illkirch Graffenstaden, France

E-mail Addresses: lizl@igsnrr.ac.cn