Transcript Document

DMSP Special Sensor
Microwave/Imager (SSM/I)
Soil Moisture Algorithm Results Oklahoma
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VolumetricSoilMoisture(%)
• Shown here are 1.4 GHz results
obtained using an aircraft
sensor and 19 GHz satellite data
• The difference between the
sensitivity of the two
instruments is quite apparent
• Standard error of estimates:1.4
GHz = 2.6% and 19 GHz =
5.3%.
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E mi s s i
SSM/I 19 GHz
ESTAR 1.4 GHz
Microwave emissions and soil moisture
Soil moisture
Soil moisture
Heavy Vegetation
AMSR-E 10.4 GHz
Light Vegetation
AMSR-E 10.4 GHz
Sensitivity of
microwave brightness
temperature to soil
moisture (SGP’97)
Soil moisture
Good dynamic range
over lightly vegetated
surfaces
Soil moisture
Princeton University
AMSR-E OSSE for soil moisture
Soil Moisture (1 km)
AMSR estimated soil moisture
(25 km)
TOA
TOAbrightness
brightnesstemperature
temperature
240 250 260 [oK]
AMSR brightness temperature
(60 km)
Princeton University
OSSE for AMSR-E Soil Moisture Retrievals
Princeton University
Aqua AMSR Three Day Composite Brightness Temperature
July 4-6
Aqua AMSR Three Day Composite Brightness Temperature
July 4-6
LAND COVER CLASSIFICATION AND NDVI ANALYSIS
FOR THE SGP 1999 EXPERIMENT AREA
INTRODUCTION
One of the goals of the Southern Great Plains 1999
(SGP99) Hydrology Experiment is to obtain information of soil
moisture pattern on the regional scale. The soil moisture
algorithm based on remote sensing measurements requires land
cover information as one of the inputs into the model.
Vegetation cover strongly affects the microwave emission from
the soil and it is important in soil moisture investigation and
analysis LAND COVER CLASSIFICATION AND NDVI
METHOD
A land cover study was conducted between July 8 – July 21 1999. For
this period, and prior to it, Landsat TM scenes were collected. A ground
truth survey was designed based on aerial photographs, which indicated
16 main land cover categories. During the experiment there was some
harvesting of wheat, alfalfa and corn. Land cover categories were
selected to follow the changes in vegetation cover by distinguishing such
classes as bare soil, bare soil with wheat stubble and harvested fields
with growing weeds (bare soil with green vegetation). Also, special
attention was paid to different types of pasture. Two types of pasture
were distinguished – grass areas that are grazed and used as regular
pastures and grass areas left idle. The list of 15 land cover categories
reflects all the main types of vegetation of the experimental period.
Land cover classification was performed based on those images
that had the least cloud coverage. From the available Landsat 5 and
Landsat 7 TM images, 4 scenes were used – March 9, May 12, July 15
and July 23 1999. Unfortunately, no useful Landsat images for the
months of April or June were available
RESULTS
The classification results were analyzed using confusion and
separability matrices. Battacharrya Distance was used as a
separability measure between the categories. Most classes had good
separability (above 1.9 on a scale from 0.0 to 2.0).
The Normalized Difference Vegetation Index was calculated
in order to follow the changes in the biomass during the experimental
period. Special attention was paid to those test sites within which
sampling of soil moisture was done during the time of experiment.
For these locations, analysis of NDVI and vegetation water content
will be performed in order assess the optical depth of vegetation layer
which have significant influence on microwave emission for the soil.
LANDSAT TM
IMAGES
•March 09
1999
•May 12 1999
•July 15 1999
•July 23 1999
GROUND SURVEY
July 07-July 20
1999
• based on airphotographs
• 1320 individual
training sites
• originally 44
categories
• regrouped to 15
basic land cover
classes
Alfalfa
Bare soil
Bare soil with
wheat stubble
Bare soil with
green
vegetation
Corn
Legume
Outcrops
Pasture grazed
Pasture
ungrazed
Quarries and
sand bars
Shrubs
Trees
Urban
Water
Wheat stubble
El Reno test
site area
with training
sites from
the ground
truth survey
Maximum
Likelihood
Classification
• bands
3,4,5,and 7
for each date
• PCIWorks
software
V.6.3.0
0 Unclassified
1 Alfalfa
2 Bare soil
3 Bare soil with 30
wheat stubb.
4 Bare soil with 20
green veg.
5 Corn
10
6 Legume
7 Outcrops
0
8 Pasture
grazed
9 Pasture
ungrazed
10 Quarries
and sand bars
11 Shrubs
12 Trees
13 Urban
14 Water
15 Wheat
stubble
Land cover categories
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
ER18
ER19
ER20
ER01
ER17
ER05
Results of land cover
classification for El Reno
test site area
0.7
0.6
NDVI
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0.3
0.2
0.1
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-0.1
9-M ar
1999
12-M ay
1999
7 - J ul
1999
1 5 - J ul
1999
2 3 - J ul
1999
ER 01
ER 05
ER 17
ER 19
ER 20N
ER 20S
1-Sep
1999
27-Oct
1999
ER 18
NDVI temporal analysis allows for detecting
seasonal changes in biomass and helps in
assigning the vegetation parameters derived from
land cover categories, that are needed for the soil
moisture algorithm calculation
1
NDVI of July 15 for El Reno test site
area
0
Land cover and NDVI data can
be found on the web site of
GSFC Earth Sciences
Distributed Active Archive
Center:
http://daac.gsfc.nasa.gov/CA
MPAIGN_DOCS/
SGP99/veg_cov.html
NDVI image calculated from
Landsat 5 TM July 15 1999
USING GIS IN SOIL MOISTURE RETRIEVAL BASED ON
PASSIVE MICROWAVE REMOTE SENSING
-102W
-100W
-98W
-96W
ARM CART Region
38N
Central Facility
36N
El Reno
34N
100 km
10 km
LITTLE WASHITA
WATERSHED
X
W
B
ARM CART
Mesonet
Mesonet with Soil Moisture
Micronet with Soil Moisture
ARM Wind Profiler
ARM Boundary Facility
INTRODUCTION
Soil moisture is very important in hydrology, agriculture and land
management but is difficult to measure using conventional methods. Remote
sensing, in particular passive microwave, has a great potential for providing areal
estimates of soil moisture. An example of how passive microwave soil moisture
mapping could be implemented with aircraft based sensors is described here.
During the Southern Great Plains’97 Hydrology Experiment, a passive
microwave imaging instrument operating at  =21 cm was used for measuring
soil brightness temperature. The equipment - Electronically Scanned Thinned
Radiometer (ESTAR) - was deployed on a NASA P-3 aircraft. The images of
brightness temperature can be converted to volumetric soil moisture maps using
additional environmental information and a Geographical Information Systems
(GIS).
The experimental region, marked as a black rectangular on the picture, is
more then 10,000-sq. km in size. In order to illustrate the changes in soil moisture
pattern on the local scale, the area of the Little Washita watershed was chosen for
this presentation.
Altitude ~ 7.6 km
DATA INPUT
Brightness
Temperature
Data
DATA INPUT
Soil
Temperature
Data
Irregular grid
of brightness
temperature
measurements
NASA P-3 aircraft with
passive microwave
radiometer operating at
1.415 GHz (L band)
Data from point
measurements Oklahoma
Mesonet stations
Soil temperature on June 30th>24.9°C
Data regridded to
200-m
resolution
Result - uniform
data layers with
soil temperature
- for the each
day of the
experiment
Vegetation
parameter
Relationship between
vegetation water content
and NDVI for grass areas
VWC = f(NDVI)
Surface
roughness
Vegetation
water content
Data of each data
layer averaged for
6 x 6 pixel blocks
Resampling
original grid
to 200m
Sand
Sandy Loam
Loam
Silt Loam
Silty Clay
Loam
Clay Loam
Silty Clay
Clay
Loamy Sand
Attribute data
assignment
to texture
categories in
200 m grid
% clay
content
% sand
content
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Bulk density
NDVI image
DATA INPUT
Soil Texture
Categories
<23,7° C
Attribute data
assignment to land
cover categories in
30 x 30 m grid
DATA
INPUT
Thematic
Mapper
Images
Soil texture classes of
the top layer (0-5 cm)
Result - uniform data
layers with
brightness
temperature - for the
each day of the
experiment
Data regridded to
200-m
resolution
Land Cover Categories
from Landsat TM Classification
DATA INPUT
Ground
Measurements of
Vegetation Water
Content (VWC)
Brightness temperature image on
June 30th
6 output
data layers constant
values for
the whole
experimenta
l period
B
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June 29
M
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June 30
A
L
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SOIL MOISTURE ALGORITHM CALCULATIONS
USING GIS
The soil moisture algorithm requires the remotely sensed input
data layer (the soil brightness temperature), and also the physical
temperature of the soil and additional environmental data layers, as
shown on the adjacent flowchart. The Geographical Information
System facilitated the processing and computation. All data layers
were combined together in order to generate the final product – soil
moisture maps over the investigated area.
July 01
July 02
July 03
Volumetric Soil Moisture
<5%
>50%
RESULTS
Images presented here for the Little Washita watershed cover an area of 848.2 sq. km and
show the spatial variation of soil moisture on 5 consecutive days. Distinct and consistent
spatial patterns are observed in the image sequence. This information would be difficult to
obtain using conventional methods.
Comparison of soil moisture maps with soil texture classes distribution reveals a close
relation between the soil moisture pattern and soils texture. Sandy soils (generally in the
central part of the images) are drier at the beginning of the drying down cycle and loose soil
moisture faster compared to the adjacent silt loam areas. As the drying period proceeds, all
soil types become drier, yet the pattern of soil moisture following soil texture classes can still
be easily detected.
Land use patterns follow the soil moisture distribution. Comparing the land cover image
with soil texture map we can observe that winter wheat occurs on soils that can hold enough
water for plant growth. Otherwise, land cover is generally grass and shrubs.
Passive microwave remote sensing measurements offer an efficient way of obtaining
information on soil moisture distribution over space and time. An aircraft based system can
provide information on the soil moisture distribution at local and regional scales. In the near
future spaceborne instruments will have the ability to measure soil moisture dynamics on the
global scale.
The potential for a space-borne
Global Water Cycle observation system
Clouds
Soil
moisture
Radiation (CERES)
Snow (AMSR)
Vegetation (MODIS)
Soil moisture (AMSR)
Precipitation (liquid)
Current missions have a capacity to
monitor water cycle.
Missing global observations:
River/lake monitoring, Precipitation,
Soil Moisture, Snow
Princeton University
Global Precipitation Mission (GPM)
Reference Concept
Princeton University
GPM Systematic Measurement Coverage
(Core + 6 constellation members)
GPM Core + DMSP(F18) + DMSP(F19) + GCOM-B1 +
NASA-GPM I + Euro-GPM I + Euro-GPM II
3-hour sensor ground trace
Princeton University
Soil Moisture and Freeze-Thaw States:
HYDROS: Hydrosphere State Mission
MEASUREMENTS:
- L-band active and passive
Hydrosphere
- Soil moisture: 10-40km
(2-3 day revisit)
- Freeze-thaw : 3 km
(1-2 day revisit)
Mission
Princeton University
Observing rivers and water bodies from space:
Hydrologic Altimetry Mission
Princeton University