1. Microwave Remote Sensing Group 1 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 2 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 3 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 4 The ESA ENVISAT/ASAR Advanced Synthetic Aperture Radar 800 Km Ground resolution 15-30 m

5. Microwave Remote Sensing Group 5 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 6 The test areas SCRIVIA CORDEVOLE

7. Microwave Remote Sensing Group 7 The scrivia watershed Po Scrivia Tanaro Bormida Orba Alessandria

8. Microwave Remote Sensing Group 8 ASAR composite image: R = HH, G = HV, B = k water dense vegetation bare/rough soil urban bare/smooth soil November 2003

9. Microwave Remote Sensing Group 9 Soil moisture maps 07/11/2003 29/04/2004 04/06/2004 30 km

10. Microwave Remote Sensing Group 10 Cordevole watershed Thick Alpine Grass Soil + Roots Dead Grass Soil Mid-May End-June - Microwave Remote Sensing Group

11. Microwave Remote Sensing Group 11 Soil moisture maps June JulyAugust September 15 km 7 km November

12. Microwave Remote Sensing Group 12 Temporal variation (Cherz) Resolution ~ 150 m oo smc - Microwave Remote Sensing Group

13. Microwave Remote Sensing Group 13 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 14 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 15 Mount Cherz Cordevole: snow maps

16. Microwave Remote Sensing Group 16 Temporal Variation of backscattering coefficient - Microwave Remote Sensing Group oo bare soil 2003-2004 wet dry oo moist bare soil dry snow wet snow dry bare soil. Bare soil

17. Microwave Remote Sensing Group 17 Snow cover maps - Microwave Remote Sensing Group Light blue: dry-snow Blue: wet snow Green: forests Brown: bare soil Red: 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 18 Snow liquid water content 9 km 15 km Cherz Check pointsMeas. (classes) ANN (%) stdv Cherz3- 8%71 Campolongo 18-15 %92 Campolongo 28-15 %101

19. Microwave Remote Sensing Group 19 Thanks for your attention crowave Remote Sensing Group

20. Microwave Remote Sensing Group 20 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 21 Sensitivity to SMC: Experimental results Bare + vegetated soils Spatial variations C-band  = 25 o Temporal variations Radar signal R 2 = 0.98 smc time smc radar

22. Microwave Remote Sensing Group 22 The soil moisture retrival algorithms 1.Linear regression 2.Nelder Mead Iteration 3.Bayes theorem 4.Neural Networks Classification:5 classes smc error Average error