Chapter 1
Introduction
1.0 Background
The ability to predict the state of the ground is essential to vehicle mobility, both manned
and unmanned, and personnel movement, as well as determining sensor performance.
Trafficability, or ease of travel, is dictated by both soil strength and surface friction.
Surface friction decreases in the presence of an ice or snow layer or when the top of the
ground becomes too wet. Soil strength depends on the soil type and the distribution of
moisture and ice with depth. For instance, the presence of a thawed layer (wet, low
bearing capacity) overlying a competent layer of frozen ground has a negative impact on
mobility as motion resistance increases and traction decreases (Sullivan et. al. 1997).
Infrared and radar sensor performance is determined, in part, by the state of the ground.
Weather-impacted state-of-the-ground conditions resulting in a high degree of clutter can
degrade sensor performance.
1.1 Overview
The fundamental operations of FASST (Fast All-seasons Soil STrength) are the
calculation of an energy and water budget that quantifies both the flow of heat and
moisture within the soil and also the exchange of heat and moisture at all interfaces
(ground/air or ground/snow; snow/air). To do this, FASST uses up to nine modules
(Section 1.2). If all the necessary weather parameters (Table 1.1.1) are known from real-
time data entry, a climatic database, or a mesoscale forecast model, then the only
modules used are those that determine the state of the ground. These modules predict the
soil temperature and moisture profiles and ultimately the thickness of any frozen and/or
calculated using the two radiation modules (Module 4) and the snow depth is calculated
with the Snow AccretionDepletion Module (Module 7). In both cases, FASST requires
information on latitude, longitude, slope, aspect, elevation, and ground cover. The
required information can be obtained from a TEM (Terrestrial Ecosystem Mapping)
geographic information system. FASST is a one-dimensional model run in a distributed
mode. That is, FASST is then run for each region object in the Area Of Interest (AOI).
Region objects represent areas with similar attributes (slope, aspect, soil, and land cover).
Table 1.1.1 Meteorological information necessary for predicting the state of the ground
Incident and reflected shortwave radiation
(0.33 ); Downwelling and upwelling
longwave radiation (350)
Radiation (W/m2)
Air temperature (C)
Relative humidity (%)
Wind speed (m/s)
Snow depth (cm)
Precipitation (mm water/hr)
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