Submitted to: Remote Sensing Reviews
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 2, 1997
Publication Date: N/A
Interpretive Summary: Arid and semiarid rangelands comprise a significant portion of the earth's land surface, yet little is known about their effect on global climate change. The study reported here was an attempt to address this issue for semiarid rangelands. Images from two satellite systems were acquired at intervals throughout the dry, wet and drying seasons from April to November rin a semiarid rangeland southeast of Tucson, Arizona. Results showed that the data from one system were useful for assessing vegetation characteristics and data from the other were sensitive to variations in soil moisture. By combining the information from both satellites with some on-site meteorological measurements, it was possible to estimate the water loss due to evaporation. Such knowledge of evaporation rates at regional scale is crucial to understanding the regional and global climate changes and is useful to both scientists and regional/national decision-makers.
This study reports the first results of the Walnut Gulch' 92 experiment concerning the combined use of radar backscattereing (ERS-1) and thermal infrared (Landsat TM) data to estimate surface sensible heat flux. The purpose is to use the radar/thermal synergy to retrieve both vegetation and soil temperatures required by a two-layer type model. The first step investigates the potential use of ERS-1 SAR images for surface soil moisture monitoring of the watershed using five calibrated images acquired during the year 1992 (dry to wet conditions). Results show that despite the typical low level of biomass of semi-arid rangeland, an attenuation of the soil backscatter (up to 2dB) can occur during the rainy season mainly due to the vegetation characteristics. A statistical relationship is then used to retrieve the volumetric surface soil moisture from ERS-1 backscattering (sensitivity of 0.23 dB/% moisture) with a resulting root mean square error (RMSE) of 1.3% of soil moisture. In a second step a semi-empirical approach based on energy balance relates soil temperature Ts to this estimated surface soil moisture with an accuracy of 1.3 degrees C. Vegetation temperature is then deduced from both Ts and Landsat TM composite temperature Tr in order to estimate sensible heat flux according to the two-layer model. To extend the validation data set, additional Ts and Tr values are also obtained from ground soil moisture measurements and thermal aircraft flights respectively. The overall low RMSE of 35 W/m2 obtained between ground and remote sensible heat flux confirms the potentiality of radar/thermal synergy over semi-arid sparse vegetation for energy fluxes estimate.