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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 #316936
Title: Using the Surface Temperature-Albedo Space to Separate Regional Soil and Vegetation Temperatures from ASTER Data

Author
item SONG, L. - Collaborator
item LIU, S. - Collaborator
item Kustas, William - Bill
item ZHOU, J. - Collaborator
item MA, Y. - Collaborator

Submitted to: Remote Sensing in Hydrology and Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/5/2015
Publication Date: 7/11/2015
Citation: Song, L., Liu, S., Kustas, W.P., Zhou, J., Ma, Y. 2015. Using the Surface Temperature-Albedo Space to Separate Regional Soil and Vegetation Temperatures from ASTER Data. Remote Sensing in Hydrology and Water Management. 7:5828-5848. DOI: 10.3390/rs70505828.

Interpretive Summary: Currently, satellite data offer the possibility to map land surface temperature over the entire globe effectively, with sufficiently high spatial and temporal resolution. The spatially distributed land surface temperature estimated from thermal infrared remote sensing data is widely used in studies of evapotranspiration (ET), climate change and the hydrological cycle, soil moisture estimation, forest fire detection, vegetation water stress and many other environmental monitoring applications. Efforts to derive component soil and vegetation temperatures from land surface temperature have shown major improvements in the accuracy of ET and vegetation stress estimated using two-source (soil + vegetation) energy balance models. As a result this has improved capabilities to monitor crop water stress and drought, estimate surface soil moisture and result in more accurate weather forecasts. The task of retrieving the component temperatures however is difficult, due to the variability in the distribution of land surface properties that affect land surface temperature. A practical method for separating the soil and vegetation component temperatures based on the land surface temperature-surface albedo space combined with a surface energy balance model is described. Satellite data from the Advanced Scanning Thermal Emission and Reflection radiometer (ASTER) are used to test the method . The method is validated with ground component temperature measurements. These results support the use of this practical approach to derive soil surface temperatures. It was also found that the estimated vegetation temperatures were extremely close to the near surface air temperature observations when the landscape was well watered under full vegetation cover. More robust soil and vegetation temperature estimates will improve estimates of soil evaporation and vegetation transpiration, leading to more reliable monitoring of crop water stress, drought and yield useful to USDA NASS, FAS, USGS and NOAA.

Technical Abstract: Soil and vegetation component temperatures in non-isothermal pixels encapsulate more physical meaning and are more applicable than composite temperatures. The component temperatures however are difficult to be obtained from thermal infrared (TIR) remote sensing data provided by single view angle observations. Here, we present a land surface temperature and albedo (T-alpha) space approach combined with the mono-surface energy balance (SEB-1S) model to derive soil and vegetation component temperatures. The T-alpha space can be established from visible and near infrared (VNIR) and TIR data provided by single view angle observations. This approach separates the soil and vegetation component temperatures from the remotely sensed composite temperatures by incorporating soil wetness iso-lines for defining equivalent soil temperatures; this allows vegetation temperatures to be extracted from the T-alpha space. This temperature separation methodology was applied to ASTER VNIR and high spatial resolution TIR image data in an artificial oasis area during the entire growing season. Comparisons with ground measurements showed that the T-alpha space approach produced reliable soil and vegetation component temperatures in the study area. Low root mean square error (RMSE) values of 0.83 K for soil temperatures and 1.64 K for vegetation temperatures, respectively, were obtained, compared to component temperatures measurements from a ground-based thermal camera. These results support the use of soil wetness iso-lines to derive soil surface temperatures. It was also found that the estimated vegetation temperatures were extremely close to the near surface air temperature observations when the landscape is well watered under full vegetation cover. More robust soil and vegetation temperature estimates will improve estimates of soil evaporation and vegetation transpiration, leading to more reliable the monitoring of crop water stress and drought.