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Estimating Soil Moisture Using Satellite Observations. By RamonVasquez. Contents. Introduction Some characteristics of the selected region Ground weather stations The algorithm to estimate volumetric soil moisture Partial results instrumentation. Introduction. - PowerPoint PPT Presentation
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Estimating Soil Moisture Using Satellite Observations
By
RamonVasquez
Contents
1. Introduction
2. Some characteristics of the selected region
3. Ground weather stations
4. The algorithm to estimate volumetric soil moisture
5. Partial results
6. instrumentation
Introduction
The soil moisture is an important parameter in climate modeling, its high variability occur on the firsts centimeters of top layer of soil surface.
About the selected region
South-West map of Puerto Rico and its weather stations, visualized by Arcmap software
An aerial photo showing points of ground weather stations
Vegetation types
Detailed vegetation types information
Combining vegetation, soil types, and elevation maps by use ERDAS software
Soil types
• For this work will be useful obtain the map with the same sand and clay contents mainly.
• This work part was done initially digitalizing in ArcView software.
• Was performed for this proposal by joining equal classes
Very detailed soil type informationLess detailed soil type information
Resampling type soils
The algorithm to estimate volumetric soil moisture
2
2
2 cos1
1sin
R
R
Soil texture
Soil Temperature
Surface temperature
Apparent emissivity
Roughness correction
Effective Temperature
Inversion of
Fresnel Equation
Vegetation correction
eReff
B
TT 1)( dsfdeff TTCTT
22 2
4
)cosexp()()(
h
hRR rs
Brightness temperature
Brightness temperature
Vegetation Type (ndvi)
Surface roughtness
Compute
Soil moisture
Brightness Temperature
The possible data sources to use are Band 3, 4 or 5 from NOAA satellite or L-band of SAR
This temperature is obtained by considering that the radiance perceived by the sensor is coming from blackbody.
Brightness Temperature
Brightness temperature from channel 3, NOAA satellite, this was achieved by use the Matlab software
Surface Temperature
) 5 4 ( *3. 3 4ch ch ch Ts This parameter can be approximated from air temperature near to the soil surface, may also be obtained from satellite images as follow, from NOAA, using 4 and 5 channels
Surface Temperature
surface temperature image, from channel 3 NOAA satellite, this was achieved by use the Matlab software
Classified soil surface temperature
This figure shows an image classified (unsupervised, ERDASsoftware) of an image of a thermal band of NOAA satellite. It shows levels of land surface temperature
Soil Temperature
• The algorithm requires soil temperature for 10 to 15 cm of depth. This is Provided by experimental stations such as Maricao, Adjuntas, Guanica, and Cabo Rojo.
• A difficult with this parameter is the little amount of data. For that it will be estimate by some empirical methods 1 and 2, this work consider the first method.
Soil Temperature• Method 1:
– Assuming some degrees less than surface temperature
– In presence of dense vegetation the surface and deep temperature almost the same
• Method 2:– By training an artificial neural network, whose inputs are the
following variables:
• Vegetation type
• Soil type
• Elevation levels
• Satellite observations on thermal frequency range
The second method is considered for research
Apparent Emissitivity
eR
eeff
B
TT
1
e : apparent emissitivity
R: apparent reflectivity
Due to signal attenuation, the emissivity isn’t real before making the correction
Effective soil temperature
)( dsfdeff TTCTT
2.8 0.802±0.006
6.0 0.667±0.008
11.0 0.480±0.010
21.0 0.246±0.009
Wavelength (cm) C
49.0 0.084±0.005
• For remote sensing applications there are a simple form to obtain this effective soil temperature, mean look up table for C constant for the wavelength being used
• The net intensity (called the effective temperature) at the soil surface is a superposition of intensities emitted at various
depths within the soil.
Effective soil temperature
This image (effective soil surface temperature) is generated in Matlab software using surface temperature and depth soil temperature (depth temperature is estimate by method 1 mentioned before), actually colors do not represent the real value.
Vegetation Correction
)secexp(* VWCb
This process is required to determine the initial radiation emitted by the soil surface which depending of transmisivity, there are more than two ways to determine the transmisivity, the simplest and practical way is mentioned here,
• As first way to determine the transmisitivity is:
Vegetation Correction
• Another way, used for this work, more directly to obtain transsmisivity through vegetation is by considering NDVI too:
)(6141.07049.0 NDVI
5429.1)(2857.4:5.0
)(3215.0)(9134.1:5.0 2
NDVIVWCNDVIif
NDVINDVIVWCNDVIif
To get an estimation of VWC, there was considered a function piecewise defined depending of vegetation index (NDVI):
Vegetation Correction
Then, when the transmissivity is already estimate, the reflectivity is corrected by
2/RRv
Vegetation Correction
This image (NDVI) is generated in Matlab software using channels 1 and 2 of NOAA satellite, actually colors do not represent the real value (.
Apparent Emissitivity
eReeff
B
TT 1
where e is the apparent emissitivity, and R is apparent reflectivity
Due to signal attenuation, the emissivity isn’t real before making the correction, the following estimations for emissitivity and reflectivity are apparent, because its not considering the loses through signal trajectory:
Roughness Correction
)cosexp()()(2
42
2 hRRh rs
Where respectively Rs and Rr are reflectance of smooth and rough surface
For this preliminary work, this parameter is estimate y considering the class of soil only, in each region with same soil characteristics.
Computing soil moisture
ClaySandwp 0047.000064.006774.0
• The relationship between volumetric soil moisture and dielectric constant was comprised in two distinct parts separated at a transition soil moisture value wt,
where the wp is an empirical approximation of the wilting point moisture given by:
wpwt 49.0165.0
Compute the soil moisture
wtwpa
acbbwp
wp
and
PPcbwt
a
a
acbbwp
riw
effriiw
for ,2
4
0.57-0.481 and
porosity, soil theis P ly,respective
rock and ice, for water, constants dielectric theare,where
))1((,1,)(
2
41
2
2
For soil moisture less than wt:
Compute the soil moisture
)(
where
2for ,1
)1()(2
nit iwi
w
rwiniteff wtwpPPwt
wp
For soil moisture greater than wt:
Partial results
• The algorithm was performed in Matlab software.
•
loacation town depth Sand clay Bulk density
Monte del Estado maricao 8-25 31.4 42 1.5
Monte Guillarte adjuntas 0-10 10.3 57.7 1.09
Bosque Seco Guanica 0-10 25 55 1.5
combate Cabo rojo 0-12 81.8 11.9 1.59
The table bellow shows the quantitative characteristics of diferent places where the stations provide the data
station % moisture(from station)
%moisture (from algorithm)
Monte del Estado
Monte Guillarte
Bosque Seco 2.4 0.540
Combate 2.3 0.2537
The following is the values of soil moisture for different locations, given by the station and algorithm
instrumentation
Theta prove ML2x
This devise is a sensor to estimate volumetric soil moisture with ±1%
accuracy
Data logger HH2
This devise is used to store information of sampling red by theta probe