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Remote sensing has entered a new era in the twenty-first century with a
series of operating satellites from the NASA Earth Observing System (EOS)
program, other international programs, and commercial programs. Since
November 2000 the first civilian spaceborne hyperspectral sensor, Hyperion,
has observed land surfaces on the NASA Earth Observer-1 (EO-1) platform.
The first hyperspatial commercial satellite, IKONOS, produces global imagery
at the spatial resolutions of 1 m (panchromatic band) and 4 m
(multispectral bands) operationally. An increasing number of people are
processing and analyzing vast amounts of optical remotely sensed observations
for monitoring land surface processes and other applications on both
local and global scales.
One basic characteristic of optical remote sensing in the twenty-first
century is the extensive use of quantitative algorithms for estimating Earth
surface variables. New sensors have much higher measurement precision with
new technology and are far better calibrated. Empirical statistical models are
being replaced by physically based models developed mostly since the early
1980s. In the NASA Earth Vision by 2020, environmental prediction is the
primary goal. The reliable prediction relies on the physical dynamic models
of land surface processes whose variables must be quantitatively estimated
from Earth observing data.
This book emphasizes both the basic principles of optical remote sensing
(0.4-14 pm) and practical algorithms for estimating land surface variables
quantitatively from remotely 5ensed observations. It presents the current
physical understanding of remote sensing as a system with a focus on
radiative transfer modeling of the atmosphere, canopy, soil, and snow, and
also the state-of-the-art quantitative algorithms for sensor calibration, atmospheric
and topographic correction, estimation of a variety of biophysical
variables (e.g., leaf area index, fraction of photosynthetically active radiation
absorbed by vegetation) and geophysical variables (e.g., broadband albedo,
emissivity, and skin temperature), and four-dimensional data assimilation. |
