Transcript Remote Sensing of Soil Moisture

Remote Sensing of Soil Moisture
Lecture 7
What is soil moisture?
•
Soil moisture is the water that is held in the spaces between soil particles. Surface
soil moisture is the water that is in the upper 10 cm of soil, whereas root zone
soil moisture is the water that is available to plants, which is generally considered
to be in the upper 200 cm of soil.
•
Is defined as the ratio of liquid water content to the soil in percentage of volume or
weight, is an in heritage and memory of previous precipitations.
– Gravimetric water content on mass (weight) basis: ratio of the mass of liquid phase to
solid soil mass
– Volumetric water content on volume basis: ratio of the liquid phase in soil to total
volume of the soil.
– Degree of saturation ratio of water volume over total soil pore volume
•
Commonly this is used as a measure of the amount of water in the vadose zone
(above the water table).
•
Soil moisture is a key variable used to describe water and energy exchanges at
the land surface/atmosphere interface
How to get soil moisture
(in situ)
• Directly in the laboratory, it is measured gravimetrically;
by weighing the moist volume of soil, drying it, and then
weighing it again.
• Indirectly: Time Domain Reflectometry (TDR), neutron
probe, capacitance probe, etc. these methods must be
calibrated against gravimetric measurements.
• Global soil moisture data bank
– http://climate.envsci.rutgers.edu/soil_moisture/
• USA SCAN
– http://www.wcc.nrcs.usda.gov/scan/
Profile of Soil Moisture
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-10
Soil Depth (cm)
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Soil Moisture
Soil moisture: Ratio of liquid water content to soil in
volume or weight
http://www.habitat.adfg.state.ak.us/geninfo/kbrr/coolkbayinfo/kbec_cd/html/ecosys/physical/soils.htm
0.30
An NRCS soil scientist placing a piezometer
in the ground to measure soil moisture
http://www.habitat.adfg.state.ak.us/geninfo/kbrr/coolkbayinfo/kbec_cd/html/ecosys/physical/soils.htm
Measuring soil moisture content
using cosmic-ray neutrons
• Inferred from measurements of lowenergy cosmic-ray neutrons that are
generated within soil, moderated
mainly by hydrogen atoms, and
diffused back to the atmosphere.
• Non-invasive, spatial scale of ~ 700
m horizontal, depth of decimeters
• (Zreda 2008)
Remote sensing soil moisture
• Thermal infrared techniques
– Through assimilation/modeling to get root-zone soil
moisture
• Microwave
– SAR
– Passive
– Top 2-5 cm, shallower than 10cm, could be
modeling to root-zone soil moisture
• Optical (visible/near infrared)
– Using solar radiation as a direct energy source, is a
passive remote sensing method covering visible
and near infrared
– Indirectly to root-zone soil moisture
Passive microwave remote sensing
• Passive microwave remotely sensed data providing estimates of
soil moisture with good temporal resolution on a daily basis and
on a regional scale (~10 km)
• Vegetation cover, soil temperature, snow cover, topography and
surface roughness play a significant role in the microwave
emission from the surface. Other parameters: soil texture, bulk
soil density, and atmospheric effects have a smaller influence.
• Many approaches to retrieve soil moisture from microwave
radiometric measurements
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Statistical approaches
Forward model inversion
Neural networks
Data assimilation
Advantages of Microwave RS
• Transparent atmosphere, all weather coverage in
decimeter range of EMR
• Vegetation semitransparent
• Microwave measurement strongly dependent on
dielectric properties of soil water
• Not dependent on solar illumination
Basis for Microwave Remote Sensing of
Soil Moisture
• Basis for microwave remote sensing of soil moisture is
contrast in dielectric constant of water (80) and dry soil
(<5), causing emissivity contrast of 0.4 for water and
0.95 for dry land (Schmugge 2002)
• Research concludes surface layer sm can be
determined to about ¼ wavelength, i.e. 0-5 cm layer
using microwave λ = 21 cm
• Longer λ better for increased depth, less noise
Jensen, 2007
Soil moisture 
• Soil moisture in pasture
• λ = 21 cm responded
λ = 21 cm 
Schmugge 2002
Emissivity and Soil Moisture
• Brightness temperature related to emissivity for 0 to 5 cm
surface layer
TB = εMTM + (1-εM)Tsky
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εM is soil surface emissivity, TM is soil surface temperature
(1-εM)Tsky is ~ 2K, therefore εM ~ TB/TM
If TM estimated independently, εM can be determined
Typical range for εM is 0.9 for dry soil to 0.6 for smooth
wet soil
Schmugge 2002
Factors affecting accuracy
• Vegetation cover
– Most important, dense vegetation (corn, forest) can obscure soil
surface
– Greater effect at shorter λ
• Soil properties
– Density and texture
• Surface roughness
– Commonly 10 to 20% reduction in response range
• Density and roughness relatively constant
Radar Remote Sensing— Soil
Moisture
Southern Great Plains Hydrology Experiment (SGP97)
Surface Soil Moisture Derived From Remotely Sensed Microwave Data
37.0
Radar
Pol: VV, HH & HV
Radiometer
Lamont
Soil Moisture (%)
5050
36.0
ElReno
35.5
ElReno
OklahomaCity
Chickasha
OklahomaCity
4040
Chickasha
35.0
3030
July 2
July 3
Lamont
Lamont
36.5
SGP’97
2020
36.0
35.5
Pol: H, V
July 1
Lamont
Latitude (Degrees)
Res – 3 and 10 km
June 30
36.5
ElReno
OklahomaCity
ElReno
1010
OklahomaCity
Chickasha
Chickasha
35.0
-98.5
00
-98.0
-97.5
-98.0
-97.5
-97.0
Longitude (Degrees)
Res =40 km,
dT= 0.64º K
NASA Land Surface Hydrology Program
Courtesy: Tom Jackson, USDA
• HYDROS (http://www.skyrocket.de/space/doc_sdat/hydros.htm)
– Back-up ESSP mission for global soil moisture.
• L-band radiometer.
• L-band radar.
– Died mission
SAR for surface soil moisture
• Can map soil moisture at high resolution over
large areas
• Affected by surface roughness, vegetation
cover, and incidence angle
Linear relation between soil moisture
and radar signal (backscatter)
Zribi et al. 2005
Radar signal has an inverse relation with incidence angle,
so soil moisture has a relation between both radar signal
and incidence angle.
Zribi et al. 2005
http://smap.jpl.nasa.gov/
• An algorithm for merging SMAP radiometer and
radar data for high resolution soil moisture retrieval
- Das, N., Entekhabi, D., Njoku, E., IEEETransactions on Geoscience and Remote Sensing,
In press. Download File
• http://smap.jpl.nasa.gov/files/smap2/0250_Das.pdf
Optical Remote Sensing
Soil Moisture
Total Upwelling Radiance (Lt) Recorded by a Remote
Sensing System over Exposed Soil is a Function of
Electromagnetic Energy from Several Sources
Jensen, 2007
Soil Properties
• Spectral reflectance function of
– Soil texture, moisture, salinity, surface roughness,
organic matter, iron oxide
Jensen, 2007
Reflectance from Dry versus Wet Soils
Radiant energy may be reflected from the
surface of the dry soil, or it penetrates
into the soil particles, where it may be
absorbed or scattered. Total reflectance
from the dry soil is a function of specular
reflectance and the internal volume
reflectance.
As soil moisture increases, each soil
particle may be encapsulated with a thin
membrane of capillary water. The
interstitial spaces may also fill with
water. The greater the amount of water
in the soil, the greater the absorption of
incident energy and the lower the soil
reflectance.
Jensen, 2007
Reflectance from Moist
Sand and Clay Soils
Higher moisture content in
(a) sandy soil, and (b)
clayey soil results in
decreased reflectance
throughout the visible and
near-infrared region,
especially in the waterabsorption bands at 1.4, 1.9,
and 2.7 mm.
Jensen, 2007
Estimating SM with vegetation
• Surface soil moisture correlation with sub-surface
sm decreases with depth
5 cm
Pearson Correlation
1
Sig. (2-tailed)
N
10 cm
20 cm
50 cm
100 cm
Pearson Correlation
1390
.926(**)
Sig. (2-tailed)
.000
N
1390
Pearson Correlation
.882(**)
Sig. (2-tailed)
.000
N
1390
Pearson Correlation
.576(**)
Sig. (2-tailed)
.000
N
1390
Pearson Correlation
Sig. (2-tailed)
N
.284(**)
.000
536
** Correlation is significant at the 0.01 level (2-tailed).
Soil Climate Analysis Network (SCAN) Sites
Soil Climate Analysis Network
• http://www.wcc.nrcs.usda.gov/scan/
• SCAN soil moisture (hourly) at
– 5cm, 10cm, 20cm, 50cm, and 100cm
– 3 sites, humid grassland, semi-arid grassland,
semi-arid shrubland
• Terra-MODIS 250 m 8-day NDVI
• Analysis based on both raw time series and
deseasonalized time series
Results
(1) The deseasonalized time series results in consistent and
significant correlation (0.46–0.55) between NDVI and rootzone soil moisture at the three sites;
(2) Vegetation (NDVI) at the humid site needs longer time (10
days) to respond to soil moisture change than that at the
semi-arid sites (5 days or less);
(3) The time-series of root-zone soil moisture estimated by a
linear regression model based on deseasonalized time series
accounts for 42–71% of the observed soil moisture variations
for the three sites; and
(4) In the semi-arid region, root-zone soil moisture of shrubvegetated area can be better estimated using NDVI than that
of grass-vegetated area.
• Correlation
coefficient
between soil
moisture and
NDVI versus
time lag of
NDVI during
growing
season.
Humid grassland
• Estimated versus observed soil moisture at the 10 cm, 20
cm, and 50 cm depths during May to October of 2000–
2003 at the Prairie View site (TX) using delta method (left
panel) and raw method (right panel).
Semi-arid grassland
• Estimated versus observed soil moisture at 20 cm depths
during May to September of 2000–2003 at the Adams
Ranch site (NM) using delta method (left panel) and raw
method (right panel).
Semi-arid shrub
• Estimated versus observed soil moisture at the 20 cm
depths during May to September of 2000–2003 at the
Walnut Gulch site (AZ) using delta method (left panel) and
raw method (right panel).
Mapping Time Series Root Zone Soil Moisture in a
Semi-Arid Region Using Satellite Images
Land Cover