Using the surface temperature-albedo space to separate regional soil and vegetation temperatures from ASTER data

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-α) space approach combined with the mono-surface energy balance (SEB-1S) model to derive soil and vegetation component temperatures. The T-α 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-α space. This temperature separation methodology was applied to advanced scanning thermal emission and reflection radiometer (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-α 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.