TY - JOUR
T1 - Merging the MODIS and Landsat terrestrial latent heat flux products using the multiresolution tree method
AU - Xu, Jia
AU - Yao, Yunjun
AU - Liang, Shunlin
AU - Liu, Shaomin
AU - Fisher, Joshua B.
AU - Jia, Kun
AU - Zhang, Xiaotong
AU - Lin, Yi
AU - Zhang, Lilin
AU - Chen, Xiaowei
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 41671331, in part by the National Key Research and Development Program of China under Grant 2016YFA0600102, and in part by the California Institute of Technology. The work of J. B. Fisher was supported by NASA: SUSMAP, IDS, INCA, and ECOSTRESS.
Funding Information:
Manuscript received April 27, 2018; revised August 2, 2018; accepted October 4, 2018. Date of publication November 9, 2018; date of current version April 22, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 41671331, in part by the National Key Research and Development Program of China under Grant 2016YFA0600102, and in part by the California Institute of Technology. The work of J. B. Fisher was supported by NASA: SUSMAP, IDS, INCA, and ECOSTRESS. (Corresponding author: Yunjun Yao.) J. Xu, Y. Yao, K. Jia, X. Zhang, L. Zhang, and X. Chen are with the State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 1980-2012 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth's surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods.
AB - The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth's surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods.
KW - Eddy covariance
KW - Landsat data
KW - MODIS
KW - multiresolution tree (MRT)
KW - terrestrial latent heat flux (LE)
KW - n/a OA procedure
KW - ITC-CV
U2 - 10.1109/TGRS.2018.2877807
DO - 10.1109/TGRS.2018.2877807
M3 - Article
AN - SCOPUS:85056327982
SN - 0196-2892
VL - 57
SP - 2811
EP - 2823
JO - IEEE transactions on geoscience and remote sensing
JF - IEEE transactions on geoscience and remote sensing
IS - 5
M1 - 8529259
ER -