1• Microwave Remote Sensing Group

P. Pampaloni

Microwave Remote Sensing Group (MRSG)

Institute of Applied Physics -CNR, Florence, Italy

Microwave remote sensing of soil moisture and snow cover

• crowave Remote Sensing Group

EC. Project EnviSnow- Snow Parameter Retrieval Algorithms

EC. Project Floodman - flood forecasting, warning and management system based on satellite radar images,

ASI- PC Project : Nowcasting

2• Microwave Remote Sensing Group

Introduction

  Soil moisture and snow cover area (and physical conditions) play a fundamental role in the energy and hydrologcal budget at global and local scale as well as in dramatic events such as landslides, avalnches and floods.

Soil moisture is a key variable, which influences re-distribution of radiant energy, the runoff, and percolation

Snow represents an important natural reservoir of fresh water and a primary resource for the production of electric power (at least in many countries). Moreover, rapid changes in snow accumulation and physical conditions can cause dramatic events

The estimate of these quantities by using RS methods is very important and a challenge for the research

3• Microwave Remote Sensing Group

The need of Remote Sensing data

Science Issues: Knowledge of the Earth system

Climate changes

Hydrological and carbon cycles

Ocean circulation

Applications

Management of renewable and non renewable resources

Disaster monitoring and forecasting

4• Microwave Remote Sensing Group

The ESA ENVISAT/ASARAdvanced Synthetic Aperture Radar

800 Km

Ground resolution 15-30 m

5• Microwave Remote Sensing Group

Why microwaves ?

Operation independent of solar light Low sensitivity to clouds and precipitations Strong sensitivity to water in land surfaces Penetration in vegetation, snow (and soil)

6• Microwave Remote Sensing Group

The test areas

SCRIVIA

CORDEVOLE

7• Microwave Remote Sensing Group

The scrivia watershed Po

Scrivia

Tanaro

Bormida

Orba

Alessandria

8• Microwave Remote Sensing Group

ASAR composite image: R = HH, G = HV, B = k

water

dense vegetationbare/rough soil

urbanbare/smooth soil

November 2003

9• Microwave Remote Sensing Group

Soil moisture maps

07/11/2003

29/04/2004

04/06/2004

30 km

10• Microwave Remote Sensing Group

Cordevole watershedCordevole watershed

Thick Alpine Grass

Soil + Roots

Dead Grass

SoilMid-May

End-June

- Microwave Remote Sensing Group

11• Microwave Remote Sensing Group

Soil moisture maps

June

July

August

September

15 km

7 km

November

12• Microwave Remote Sensing Group

Temporal variation (Cherz)

Resolution ~ 150 m

o

smc

- Microwave Remote Sensing Group

13• Microwave Remote Sensing Group

Result of soil moisture retrieval

ANN1: Training with a subset of exp data

ANN2: Archive data + correction for vegetation

- Microwave Remote Sensing Group

14• Microwave Remote Sensing Group

Soil moisture: summary

3- 5 levels of SMC can e detected between 10% and 40%

Iteration (Nelder) is the most accurate but slow

Bayes is the most stable but very slow

Regression is the fastest but less accurate

ANN gives the best compromise

15• Microwave Remote Sensing Group

Mount Cherz

Cordevole: snow maps

16• Microwave Remote Sensing Group

Temporal Variation of backscattering coefficient

- Microwave Remote Sensing Group

o

Backscattering

-16

-14

-12

-10

-8

-6

-4

-2

0

30-ott 29-nov 29-dic 28-gen 27-feb 28-mar 27-apr 27-mag 26-giu

Date

(dB

)

Backscattering

bare soil

2003-2004

wet

dry

o

moist bare soil dry snow

wet snow

dry bare soil .Bare soil

17• Microwave Remote Sensing Group

Snow cover maps

- Microwave Remote Sensing Group

Light blue: dry-snow Blue: wet snowGreen: forestsBrown: bare soilRed: layover and shadow areas (rocks)

21 March 2005

25 April 2005

5 April 2004

10 May 2004

Threshold:

- 3 dB

18• Microwave Remote Sensing Group

Snow liquid water content

9 km

15 km

Cherz

Check points Meas. (classes)

ANN (%)

stdv

Cherz 3- 8% 7 1

Campolongo 1 8-15 % 9 2

Campolongo 2 8-15 % 10 1

19• Microwave Remote Sensing Group

Thanks for your attention

• crowave Remote Sensing Group

20• Microwave Remote Sensing Group

Multi-temporal SAR Images

- Microwave Remote Sensing Group

Red: November 2003, Green: December 2003, Blue: January 2004)

Red:April 2004, Green: May 2004, Blue: June 2004

21• Microwave Remote Sensing Group

Sensitivity to SMC: Experimental results

Bare + vegetated soils

Spatial variations C-band = 25o

Temporal variations R

ad

ar

sig

nal

R2 = 0.98

smc

time

smc

radar

22• Microwave Remote Sensing Group

The soil moisture retrival algorithms

0

5

10

15

20

25

30

35

40

< 10 10 - 17 17 - 24 24 - 30 >30

SMC classes

retr

ieve

d S

MC

%

NN

Nelder

Linear

bayes

0

5

10

15

20

25

30

35

40

45

< 10 10 - 17 17 - 24 24 - 30 >30

SMC classes%

erro

r %

NN

Nelder

bayes

Linear

0.0 5.0 10.0 15.0

NN

Nelder

Linear

bayes

error%

1. Linear regression

2. Nelder Mead Iteration

3. Bayes theorem

4. Neural Networks

Classification:5 classes

smc

error

Average error