In Honor of National Surveyors Week Duncan Parnell provided brand new Trimble R12i’s, TSC7’s and a S7 Robotic kit to Florida Atlantic Geomatics Engineering program for their field labs so students get some real hands-on experience with some of the latest technology out in the surveying market.
Paper published by Remote Sensing
A article titled “Using Simplified Thermal Inertia to Determine the Theoretical Dry Line in Feature Space for Evapotranspiration Retrieval” was published by Remote Sensing on August 24, 2015.
This paper reports the development of a new model, named DDTI (Determination of Dry line by Thermal Inertia), which determines the theoretical dry line based on the relationship between the thermal inertia and the soil moisture. The Simplified Thermal Inertia value estimated in the North China Plain is consistent with the value measured in the laboratory. Three evaluation methods, which are based on the comparison of the locations of the theoretical dry line determined by two models (DDTI model and the heat energy balance model), the comparison of ET results, and the comparison of the evaporative fraction between the estimates from the two models and the in situ measurements, were used to assess the performance of the new model DDTI. The location of the theoretical dry line determined by DDTI is more reasonable than that determined by the heat energy balance model.
The flowchart of the DDTI approach is give below and the full article can be accessed from here:
Paper published by Remote Sensing of Environment
In July 2015, a research paper titled “An enhanced two-source evapotranspiration model for land (ETEML): Algorithm and evaluation” was published by Remote Sensing of Environment, the top journal in remote sensing. Dr. Hongbo Su is the second author of this research paper. In this paper, a two-source ET model is proposed, a physical pixel-based surface temperature decomposition method is presented, and the ETEML promotes the application of the trapezoid-based ET modeling approaches based on satellite remote sensing.