Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #324623

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Evaluation of ASTER-like daily land surface temperature by fusing ASTER and MODIS data during the HiWATER-MUSOEXE

item YANG, GUIJUN - Beijing Academy Of Agricultural Sciences
item WENG, QIHAO - Indiana State University
item PU, RUILIANG - University Of South Florida
item Gao, Feng
item SUN, CHENHONG - Beijing Academy Of Agricultural Sciences
item LI, HUA - Chinese Academy Of Sciences
item ZHAO, CHUNJIANG - Beijing Academy Of Agricultural Sciences

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/14/2016
Publication Date: 1/21/2016
Publication URL:
Citation: Yang, G., Weng, Q., Pu, R., Gao, F.N., Sun, C., Li, H., Zhao, C. 2016. Evaluation of ESTARFM based algorithm for generating land surface temperature products by fusing ASTER and MODIS data during the HiWATER-MUSOEXE. Remote Sensing. 8, 75; doi:10.3390/rs8010075.

Interpretive Summary: Land surface temperature is a key parameter for mapping land surface energy fluxes and evapotranspiration. Land surface temperature can be retrieved from thermal infrared (TIR) band imagery. For field scale applications, TIR imagery at high temporal and spatial resolution is required. However, such information is not available from any single satellite sensors. This paper aims to generate high temporal and spatial TIR images through fusing the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, 16 days repeat cycle at 90m spatial resolution) and the Moderate Resolution Imaging Spectrometer (MODIS, daily visit at 1km spatial resolution) TIR images using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). Data fusion results were evaluated using simulated data, ground measurements and remote sensing products over an arid region in Northwest China. In general, spatial and temporal variations of the surface temperature can be identified with a high level of detail from the fused data. This study examines and assesses data fusion approach for producing thermal imagery at fine spatial and temporal resolution and has potential uses in crop water use and drought monitoring that will greatly benefit the USDA National Agricultural Statistics Service (NASS) and Foreign Agricultural Service (FAS) for more accurate yield assessments and predictions.

Technical Abstract: 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 product, 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 (R2 = 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.