Estimating Soil Moisture Using Satellite Observations in Puerto Rico

By

Harold Cruzado

Advisor: Dr. Ramón Vásquez

University of Puerto Rico - Mayagüez Campus

1. Introduction

2. Study area characteristics

3. Ground weather stations4. Instrumentation 5. Algorithm to estimate volumetric soil

moisture6. Preliminary results

Contents

Soil moisture is a key component in the land surface schemes in regional climate models in the tropics. An application of an algorithm for a selected area of Puerto Rico is presented. NOAA satellite observations produce the remote sensing data, which supply the input parameters for the algorithm. Satellite images with one (1) km resolution were used to implement the algorithm using Matlab software.

Introduction

Characteristics of Selected Region and Vegetation Types

Detailed vegetation types information

Topographic Map

Combining vegetation, soil types and topographic maps using ERDAS software

Soil Types and Profiles

The polygon arrays of the soil maps were digitalized, resulting in a complex soil surface. Each of these polygons represents a soil profile, some with more than one soil textural class and others with a single one. The depth of a complete profile is more than 2 meters for all the polygons.

Detailed and Generalized Soil Type Information

South-West map of Puerto Rico and its weather stations, visualized by Arcmap software

An aerial photo showing locations of ground weather stations

Ground weather stations

Theta prove ML2x

This device is a sensor to estimate volumetric soil moisture

with ±1% accuracy

Instrumentation

Data logger HH2

This device is used to store information from the theta probe

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

Algorithm to estimate volumetric soil moisture

Brightness Temperature

The radiating (or brightness) temperature is the apparent temperature of a blackbody. It can be measured by a remote sensing device such as a radiometer.

The possible data sources used are Band 3, 4 or 5 from NOAA satellite or L-band of SAR.

Brightness Temperature

Brightness temperature from channel 3, NOAA satellite, using Matlab software

Surface Temperature

) 5 4 ( *3. 3 4ch ch ch Ts This parameter can be approximated from air temperature near the soil surface and may also be obtained from satellite images from NOAA, using channels 4 and 5

Surface Temperature

Surface temperature image from channel 3,

NOAA satellite, using Matlab software.

The blue color indicates cloud presence.

7.2827

30.8720

Classified Soil Surface Temperature

Classified images (unsupervised, ERDAS software) of a thermal band

of a NOAA satellite showing 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 in the study area.

• Because of insufficient data from the stations other methods need to be considered.

Soil Temperature• Method 1:

– Assuming some degrees less than surface temperature

– In presence of dense vegetation the surface and deep temperature are 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 preferred 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 estimated by method 1 as mentioned before); actual colors do not represent the real value.

17.5357

28.8586

Vegetation Correction

)secexp(* VWCb

This process is required to determine the initial radiation emitted by the soil surface which depends on transmisivity. There are more than two ways to determine the transmisivity. The simplest and practical way is mentioned here.

• The first way to determine the transmisivity 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. Actual colors do not represent the real value.

-0.5426

0.6230

0

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 losses 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:

Preliminary Results

• The algorithm was performed in Matlab software.• Soil moisture readings from satellites need to be

validated with more experimental work.• Point measurements using the soil probe are lower

than the satellite readings, which is not unexpected.

• The term “soil moisture” may need to be refined. The term “surface moisture” seems to describe the conditions better from a remote sensing point of view.

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

Table below shows the quantitative characteristics of different 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 values of soil moisture for different locations, given by the station and algorithm are as follows: