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