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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #303207
Title: Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data

Author
item WENG, QIHAO - Indiana State University
item FU, PENG - Indiana State University
item Gao, Feng

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/4/2015
Publication Date: 4/5/2014
Publication URL: http://handle.nal.usda.gov/10113/59904
Citation: Weng, Q., Fu, P., Gao, F.N. 2014. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment. 145:55-67.

Interpretive Summary: Thermal infrared band imagery provides key information for mapping land surface energy fluxes and evapotranspiration, and monitoring drought. Thermal infrared (TIR) imagery at high temporal and spatial resolution is required for field scale applications. However, such information is not available from any single satellite sensor. An improved data fusion approach has been developed to fuse TIR images from two sensors (Landsat and MODIS). The original Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was improved and modified for predicting thermal radiance and temperature data by considering annual temperature cycle and urban thermal landscape heterogeneity. A case study was conducted in Los Angeles County, California from July to October 2005. 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 presents a new 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 a crucial parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. These studies require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensor can deliver TIR data at both high temporal and spatial resolution. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of TIR data, but rare of those can enhance both spatial and temporal details. This paper presents a new data fusion algorithm for producing Landsat-like LST data by blending daily MODIS and periodic Landsat TM datasets. The original Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was improved and modified for predicting thermal radiance and LST data by considering annual temperature cycle (ATC) and urban thermal landscape heterogeneity. The technique of linear spectral mixture analysis was employed to relate the Landsat radiance with the MODIS one, so that the temporal changes in radiance can be incorporated in the fusion model. This paper details the theoretical basis and the implementation procedures of the proposed data fusion algorithm, Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT). A case study was conducted that predicted LSTs of five dates in 2005 from July to October in Los Angeles County, California. The results indicate that the prediction accuracy for the whole study area ranged from 1.3 K to 2 K. Like existing spatio-temporal data fusion models, the SADFAT method has a limitation in predicting LST changes that were not recorded in the MODIS and/or Landsat pixels due to the model assumption.