Mario A. Girdalo , 2008 , Uni.Georgia USA

Present by : MARI ; FOROOTAN.

We adressedTemporal & spatial

Variation

Of

SM

In a heterogenous

landscape

0-History !

1-OBJECTIVE&HYPOTHESIS

2-METHODOLOGY

3-RESULTS

4-CONCLUSION

SM= soil moistue

LRW=little river watershed=study area

LULC=LandCover-LandUse

ANOVA=analyze of variance

LS=landscape

H-P=hydra-probe

Var. =variation

P.C.C =pearson correlation coefficient

Western et al. ,1999

SM is not a random phen.

Sp. Var. according to

Size of Samp. Area & Env. Var.

Anderson et al. ,2003

Combination of Env. Factors

(Soil.Veg.c,Topo,Climate)

Create spatially distribution of SM

Over dif.scales of space & time

USDA-NASA 2003 (SMEX03)

In situ devices at LRW produce

Relieble inf. Of SM

Moran ,2004

Cashion ,2005

Under a complex LS.

Remote sensors

with coarse spatial resolution

produce inaccurate estimates

Investigate

Spacial var.

In ground-based

SM data collection

From small plots (30*30)

Similar to pixel size of

Small EFOV remote sensors

TM , ETM+& ASTER &ALI

In a heterogenous landscape

We want toAssessSuitabolity of

Using small plots

For field validation

Of

Satellites remote sensing

Instruments

With small EFOV( < 30m )

TM,ETM+

ASTER & ALI

All are expected

To better capture

Local field conditions

Under landscape environmental

Complexity

DispiteHeterogeneity of study area

It is possible to find Temporally & Spatiolly

Homogenous SM behavior

Within small areas of 30*30.

StudySM var.Within

5 dominate LULC

In LRW

Landscape.

1- increase field knowledge of

SM behavior in Hetero.LS

2-lead to better interpretation of

the

Satellite estimates

-Study Area

-Plot Data

collection

-Statistical Analysis

Northen eastern

Portion of

The 334 km2

Little river watershed

In South Atlantic coastal plain

Of US

Near Tifton ,Georgia

*Relatively flat

topography

*Broad flood plains

*Poorly defined

stream channels

*gently sloping %1-

%5

*sandyloam soil with

sandy surface

horizon

&heavier subsoil

Low W.holding

capacity

& fast surface

drainage

*Annual av,RF=120 mm

*unevently distribution

Short duration in Win.

High density in Sum.

Sum. are long &hot

&humid

Win. are short &mild

Landscape composed of a diversity of

LULC

Forest , Cropland , Pasture,

Residental area & Wetlands

An in situ Network

27 ground stations

As a source of ground-based

Point Data

To

1- validate remote sensing analysis of SM

and

Soil tempreture & Climate

2- long term Hydrological studies

In South eastern of US

H-P-s

Operated by USDA-ARS

And record

SM information &Ground tempreture

At 3 depths

5 , 20 , 30 m

Every 30 min

Typ. Installed along

Agriculture field

boundaries,

Fence rows

& pasture areas &

Typ. Surounded by

Native grass veg.cover.

8 of 27 sites

Selected

Surounding H-P

&

Associated with

the

In situ

SM

Monitoring

station

30 * 03 m area

Surrounded H.p

And

Subsequently sampled for SM

SM was measured using

Portable theta capacitance probe

(measure dielectric conductivity

similar to H.p)

To minimize Errors

We use

Same equipment

&

Same personnel

Data collected Randomly

Within 8(30*30m) plots

8

16

26

32

40

50

63

66

For each plot

10-20 reading

On

4 dif. Dates

March11

March28

April12

May24

2005

testing temporal stability of

SM reading over a fairly short time period

(here 48 h)

On 2 consecutive days

*Nov 30 & Dec 1 (2005)

*Jan 13 & Jan 14 (2006)

Systematic sampling

Each reading was taken in

3 intervals in

4 directions

From H.P station

Total 20 sample per Plot

Hdra-probe

Station

Sampling

location

4 perpendicular

direction

30 M

30 M

At some locations

Fewer than

4 directions

Were evaluated

Due to the presence of

obstacles

Such as

Road and Channels

collected from

5 field adjacent to plot areas

Under the LULC;

Grass ,Orchard,Bare land&

Agriculture Cotton & Peanuts

Associated with H.P sits:

50 , 32 , 66 & 40

Each LULC 8 – 10 SM readings

At 3 intervals along a

25 – 30 m transect

In 4 dif.times

Nov – Dec 2005

&

Jan 2006

Precipitation from each

site

Raingages at H.P stations

For 12-day period at intervals 5

min

For each Sampling Dates

1- ANOVA

2- time stability anaysis

3- Tukey & tamhane hoc

analysis

4-Pearson Correlation

Coefficient

5- t- test

No. Of R.F

For 12 day

period previos

to

Sampling date

Ranged

between

3 - 6

Min average was less

than 2.2

In 24 May

Max average was

10 cm

In 28 March

March 11

March 28

April 12

May 24

Nov & Dec

January

2006

During R.F events

all sites received simultanous precipitation

with small var. among them.

SO

Water supply

was homogeneous

for all sites prior to Sample Date

SO

SM dif. Can be considered

As a result of

Intrinsic

& mostly independet of water supply

Mean volumetric

SM & infield

Variation

Recorded in

8 locations

Presented

8 highest valus of Vol.SM

40 lowest valus of Vol.SM

When we compared it with precipitation

record:

8 greatest cumulative prec. only for 3

Sam.Date.

While 63 , 16 , 50 greatest prec. inputs

for 5 other days

Aand

For lowest 40 , 32

Prec. Record shows they have above

average

These observations

support

the Hypothesis:

SM caused by intrinsic

Enviromental var.

Beyond prec.acting

At the local scale

This analys use 2 sets of variation to

perform

Composition between plots

Variation among groups

Variation within groups

It shows:

• High statistical dif. In SM for

all Samp.D.

• Low SM var. within a given

plot which suggest

homogenous

A multiple comparesion among the

plots

It one by one comparsion between

sites

0 = means weak

relationship

&

More than 0 means have a

relationship

1 , 2 , 3 ,…

Bigger score

Stronger relationship

SO

Weakest relationship

Were find between

8-26 /8-40 /8-66 / 40-

50

SO

Strongest

Relationship are

Between

26-32 / 32 – 66 / 50 -63

SO

Site 8

Is most unique

SM

Behaviuor

Site 66

High level of

Similarity

With other sites

Similarity of 26 & 32 *Precipitation record:

similar and

Less than 2.5 cm Dif.for all

Samp.D.

*Soil type:

Both are belong to

Tifton soil

Series

*Veg. cover:

32 short grass

26edge of agr.field in wich hay is cut for cattle consumption

site Ppt S.t V.c

26 + + +

32 + + +

Similarity of 16 & 50 *Precipitation:

Dif. Cumulative rain

10 & even 18 cm

*Soil type:

16 Tifton series

50Sunsweet series

*Veg. cover:

Similar homogenous grass

&exposed to transit agri. Equipment

Influenc

soil physical characteristics

site Ppt S.t V.c

16 - - +

50 - - +

Most dissimilar sites were

8-26 / 8-40 /8-66 /40-50/50-32

Low assosiation was found between 16

&32

In total 6 Sam.D.

8 – 26 / 8-40 / 8-66

Dif. Combination of Soil type

SO

Var. in water infiltration process

SO

Dif. SM content

40-50 / 50-32

Dif. Cumulative rainfall

In LRW

LULC

Is a greater factor

affecting

SM conditions

Than

Cumulative Rainfall

Influnce of LULC

In SM

Indicated by our results in LRW. LS.

That make:

Dif.

Evatranspiration

&

Soil water usage

Spatial resolution of R.sensing Sensors

That are More appropriate to

Capture SM conditions under this LS.

Will be defined by

*LS. Fragmentation

*LULC composition

*Sizes of LULC fragments

High levels of fragmetation at LRW

With fragments of

Small size

So

Moderate to

Fine spatial resolutionsensors

(~ 3 0 m)

Are expected better suit the

countinous sutdy

Of SM in LRW

Parasmeter mean relative diffrence:

1-Measure how particular sample

compare with

Av. SM of plot

2-Indicator of

Infield

Variation of surface

SM

26 has highest

range on

mean Dif.

(36%-39%)

16 has

Lowest

Var.

(-4%-+4%)

High level of

Homogeneity

Within each

Sample plot

Highest Av. Of mean

relative Dif. Was

2.6% in 66

Followed by

8 , (2.1%)

16 (1.2 %)

26 (1.0%)

Otherwise mean relative Dif. Was

0.1 % below the plot Dif. Approach

Zero

Results indicateSamp. Point within the plots are very

close to each

Plot mean

SO

Can accurately

Estimate

Surface SM behavior

Of

Entire 30*30 m plot

Our results shows

High level of in field

Homogeniety

That’s in contrast with

The results of similar

research performed

In large plots

in our research

By selecting Small LS.

fragments.

The in field heterogenity

casued by

Topography Diffrence

Was

Minimize

Stability in plots 40 & 50

Can be explained by their

Relatively Homogenous LC

50 part of a larger pasture and

entire plot covered by same grass

type

40 part of a mechanized

AGR.Field & change

Veg-cover throughout growing

season

So

Hemogeneity Veg.Cover

&

Homogeniety

precipitation,Slope,Soil physical

characters

High stability of

SM

Recorded at these 2 sites

Was compare to evaluate if all

in field locations experience similar

SM VAR.s

within a

short period(one day in this case)

Correlations were significant in both

sets

But Only for 26 , 32 , 63

P.C.C

Is an indicator of

Spatial stability

Of SM

Point data

Low pcc with low

significant level

Process is unstable in

space and for time lag

In which Data collected

Computed to evaluate

The Variation in

Av.SM values

From one day to next

Results suggest that

Surface SM

Will not necesserily

Reflect profile conditions

SO

Remote sensing retrieving

algorithms

Based only in direct or indirect

Quantification of

Surface conditions

As

Temp. , SM

May produce Errors

When used in this LS.

These ERRORS

Can be

Minimized

by incorporatig in to Rem.Sen.

Analysis data of

Precipitation events prior to Samp.D

&

Infiltration analysis for soil type

dominated

of study area

Discriptive statistics for

SM

On 4 dif. Date collection

On 25-30 m long transects

Related to Dif. LU

Adajent to 5 plots

Results shows

Orchard & grass

Have the wettest

conditions

While

Cotton & peanut fields

Were the driest

Tilled AG.fiels

lowest spatial variability with Av.

Values around %1

Grass AG.field

highest standard deviation with

values btween

%1.8 - %2.6

Bare land

Intermediate behavior

ANOVA

Between SM valuse for

five LULC

Showed

Significant statistical Dif.

For all 4 days

Peanut &

Cotton

Most similar

with

No stat.Dif

Among them

for any 4 days

Greatest Dif. With

orchard, grass &

bareland

Showing Stat.Dif on all

Samp.Dates

AOVA high Diffrences in SM

among the plots

& high Hemogeneity within them

Precipitation analysis similar

rainfall conditions

So SM.Var. explained by in situ local

conditions

Temporal stability analysis SM has

high stability within the small plots

and single point can use to monitor

SM Var. of all plot

T-statistical analysisSM statesin

upper soil layer changes within

24H

And finally we found

stat.Dif. In SM between

Dif. LULC as there

adjacent plots

A remote sensing

approach that consider

Homogeneous LULC ,LS fragments

can be used to identify

LS units of similar SM behavior

under Heterogeneous landscapes

In addition

The insitu USDA-ARS network

will serve better in remote

sensing studies

in wich sensors

with fine spatial resolution

are evaluated