Title: ERS186: Environmental Remote Sensing
1ERS186Environmental Remote Sensing
2Overview
- Applications
- Soil Science
- Physical Principles
- Reflectance (specular and diffuse scattering)
- Absorption bands
- Dielectric constants
- Sensors
- RADAR
- Thermal
- Hyperspectral
3Definitions
- Soil the weathered material between the surface
of the Earth and the bedrock. - Soils are composed of different composition and
sizes of particles of inorganic mineral and
organic matter - Particles are about 50 of the soil volume, pores
occupy the rest of the space. Pores can contain
air or water (or ice!) - Soils have vertical zonation (soil horizons)
created by biological, chemical and physical
processes
4Soil Horizons
- O horizon gt 20 partially decayed organic matter
(humus) - A horizon zone of eluviation/leaching water
leaches many minerals often pale and sandy - E horizon mineral layer with loss of some
combination of silicate clay, iron, aluminum - B horizon zone of illuviation materials leached
from other zones end up here often lots of clay
and iron oxides - C horizon weathered parent material mostly
mineral - W horizon water layer Wf if permenantly frozen
- R horizon bedrock
5Soil Grain Size
6Soil Grain Size
- Different size particles play different roles in
soil - Sand (0.05 to 2.0 mm) large air spaces, rapid
drainage of water - Silt (0.002 to 0.05 mm) enhance movement and
retention of soil capillary water - Clay (lt 0.002 mm) enhance movement and retention
of soil capillary water carry electrical charges
which hold ions of dissolved minerals (e.g.
potassium and calcium)
7Soil Texture
- Proportion of sand, silt and clay in a soil (or
horizon), usually calculated as weight for each
type of particle. - These s can be broken up into different
soil-texture classes.
8Soil Taxonomy
- Similar to biological taxonomy -- dichotomous
keys based on soil profiles, soil color,
soil-texture class, moisture content, bulk
density, porosity, and chemistry are used to ID
different types of soils.
9The Question
- What are the important properties of a soil in an
RS image? - Soil texture
- Soil moisture content
- Organic matter content
- Mineral contents, including iron-oxide and
carbonates - Surface roughness
10Exposed Soil Radiance
- Lt Lp Ls Lv
- Lt at-sensor radiance of a pixel of exposed
soil - Lp atmospheric path radiance, usually needs to
be removed through atmospheric correction - Ls radiance reflected off the air-soil
interface (boundary layer) - Soil organic matter and soil moisture content
significantly impact Ls typically characterize
the O horizon, the A horizon (if no O), or lower
levels if A and O are nonexistant. - Lv volume scattering, EMR which penetrates a
few mm to cm. - penetrates approximate 1/2 the wavelength
- Function of the wavelength (so RADAR may
penetrate farther), type and amount of
organic/inorganic constituents, shape and density
of minerals, degree of mineral compaction, and
the amount of soil moisture present.
11Exposed Soil Radiance
12Exposed Soil Radiance
13Basic Dry Soil Spectrum
Key characteristic of soil spectrum increasing
reflectance with increasing wavelength through
the visible, near and mid infrared portions of
the spectrum
14Soil Moisture
- Water is a strong absorber, so soils with more
moisture will be darker over most of the VNIR and
SWIR portions of the spectrum than drier soils. - The depths of the water absorption bands at 1.4,
1.9 and 2.7 ?m can be used to determine soil
moisture.
15Soil Moisture and Texture
- Since clayey soil holds water more tightly than
sandy soil, the water absorption features will be
more prominent in clayey soils given the same
amount of time since the last precipitation or
watering. - AVIRIS can be useful for quantifying these
absorption features.
16Soil Moisture from RADAR
- Different materials conduct electricity better
than others (different complex dielectric
constant). - Higher dielectric constants (more moisture)
yields higher RADAR backscatter.
Melfort, Saskatchewan, Canada, ERS-1 Rainfall
was incident on the lower half of the image but
not on the upper half.
17Soil Moisture from Thermal Sensors
- Water has a higher thermal capacity than soil and
rock. - Moist soils will change in temperature more
slowly than dry soils.
18Soil Moisture from Thermal Sensors
- Daedalus thermal image (night time). If we had a
daytime image to compare it to, we could see the
amount of change in temperature and make
inferences on the soil moisture content (less
change more moisture).
19Identifying Clayey Soils
Soils with a large amount of clay exhibit
hydroxyl absorption bands at 1.4 and 2.2 ?m. 2.2
?m is more useful since it doesnt overlap the
water absorption feature.
20Soil Organic Matter
- Organic matter is a strong absorber of EMR, so
more organic matter leads to darker soils (lower
reflectance curves).
21Soil Organic Matter
- Organic matter content in the Santa Monica
mountains mapped using AVIRIS (Palacios-Orueta et
al. 1999).
22Iron Oxide
- Recall that iron oxide causes a charge transfer
absorption in the UV, blue and green wavelengths,
and a crystal field absorption in the NIR (850 to
900 nm). Also, scattering in the red is higher
than soils without iron oxide, leading to a red
color.
23Iron Oxide
- Iron content in the Santa Monica mountains mapped
using AVIRIS (Palacios-Orueta et al. 1999).
24Surface Roughness
- If a surface is smooth (particle size is small
relative to wavelength), we expect a lot of
specular reflection. - Only sensors positioned at the correct angle will
see the bright reflectance. All other angles
will see a dark surface (including all RADAR
imagery). - Smooth surfaces are clayey or silty and often
contain strong absorbers such as moisture,
organic content, and iron oxide. - A rough surface generates a lot of diffuse
reflection. - Conversely, well drained sands are often very
bright, regardless of angle.
25Surface Roughness
- C/X-SAR (C-band) image of Oxford County, Ontario,
Canada Conservation tillage (the retention of
crop residue on the soil surface) can diminish
soil erosion. Conventional tillage produces a
much rougher surface, and therefore brighter
backscatter. The goal of this study was to
determine if tillage practices could be
identified using SAR imagery.