Vegetation Mapping with

Using Landsat TM data for soil moisture mapping

Group # 4 Members: Antonio Pandolfo, Paulo Antunes, Xing Gong Advisor: Artur Gil

Research Problem

• Can Landsat TM imagery be used to study and assess soil properties? • Which band(s) or which index(es) is/are more indicated for assessing soil conditions?

Motivations • Environmental research requires new approaches for large scale studies while preserving high/medium spatial resolution. • Climate change is provoking significant effects on the hydrological cycle (IPCC, 1996 and 2007). – More evaporation and more precipitation unequally distributed around the globe. – Environment, economy and society are dependent upon water resources. • Remote sensing technology offers the possibility to study large areas while reducing time and logistics costs associated to fieldwork, being more cost-effective.

Motivations • Changes in the hydrological system will have several impacts in the normal regime of many hydrological, biological and biogeochemical processes with severe impacts on environmental quality, economic development and social wellbeing. • Soil moisture is a key variable controling the exchange of water and heat energy between land surface and the atmosphere with several impacts in weather and Land-cover/Land-use (LULC) patterns

Motivations • Changes in hydrological regime

outcome / effects.

Motivations • The project is related to the European Union Directives at the following levels: - Implementation of the “Soil Thematic Strategy” (awareness raising, research, integration and legislation); - “Water Framework Directive” (establishing a common strategy for implementation of the water framework directive).

Motivations Industry Public services

Agriculture

Services or Commercial Businesses Policies

Environment Research

Soil Assessment and Satellite Data Technological advances in satellite remote sensing have offered a variety of techniques for measuring different soil characteristics (moisture, organic matter content, clay matter, sand matter, etc.) across a wide area continuously over time. We focused on the analysis of soil moisture

Summary of remote sensing techniques for near-surface soil moisture estimation (after Engman, 1991; Moran et al., 2004)

LANDSAT LANDSAT 7

Launch Date: April 15, 1999 Altitude: 705 km Inclination: 98.2° Orbit: polar, sun-synchronous Equatorial Crossing Time: nominally 10 AM (± 15 min.) local time (descending node) Period of Revolution : 99 minutes; ~14.5 orbits/day Repeat Coverage : 16 days

SENSOR ETM+ (Enhanced Thematic Mapper Plus) Band Number Sensor type: opto-mechanical Spatial Resolution: 30 m (60 m thermal, 15-m pan) Spectral Range: 0.45 - 12.5 µm Number of Bands: 8 Temporal Resolution: 16 days Image Size: 183 km X 170 km Swath: 183 km (NASA)

µm

Resolution

1

0.45-0.515

30 m

2

0.525-0.605

30 m

3

0.63-0.69

30 m

4

0.75-0.90

30 m

5

1.55-1.75

30 m

6

10.4-12.5

60 m

7

2.09-2.35

30 m

8

0.52-0.9

15 m

Case-study Area: Sicily Island (Italy) • Island located in the central Mediterranean. • Area ≈ 26 km2; Population ≈ 5 M. • Tallest active volcano in Europe (3320 m). • Semi arid region with a Mediterranean climate. • Deforestation drying rivers. • Rich fauna diversity.

decline of rainfall and

DATA Landsat 7 ETM+ images of Sicilia - August 2000 (dry season) - October 2000 (rainy season)

Methodological Approaches Two examples of methodological approaches that use Landsat data for Soil Moisture assessment are: Moisture Index (MI) Soil Moisture Index (SMI) REFERENCES:

REFERENCES:

“Satellite remote sensing applications for surface soil moisture monitoring” – Lingli WANG et al. 2009

“A Moisture lndex for Surface characterization over a Semiarid area” – Dupigny-Giroux & Lewis,

“Assessment of soil moisture using Landsat ETM+ temperature/vegetation index in semiarid environment” – Yongnian Zeng et al. 2004

1999 “Remote Sensing/GIS techniques for risk assessment of Borrelia burgdorferi infection” – Altobelli Alfredo et al. “Analysis of vegetation using spectral information - the use of indices derived from Landsat satellite images” – Altobelli Alfredo et al.

Soil Moisture Index (SMI) Normalized Difference Vegetation Index (NDVI) + Temperature (Ts) Soil Moisture Index (SMI)

Where: Ts max , Ts min are the maximum and minimum surface temperature for a given NDVI

The scatterplot in Ts- NDVI space and the definition of SMI

Ts is the remotely-sensed data-derived surface temperature at a given pixel for a given NDVI a1, a2, and b1, b2 are empirical parameters

Moisture Index (MI) Blue Band and Near Infrared Band

TIR Temperature to analyze

Moisture Index (MI)

Index ranges between 0 and 1 Theoretical diagram of the Moisture lndex

RESULTS: NDVI-Temperature distribution

Aug,2000,summer

Oct, 2000, winter

Results RGB Image of Sicilia

Soil Moisture Index

Results: Method 1 (SMI) Oct,2000, rainy season

DRY

(SMI) Agu,2000, dry season

WET

Results: Method 2 (MI) Oct,2000 rainy season

DRY

(MI) Aug. 2000 dry season

WET

Comparison of different methods Aug,2000, MI

DRY

Aug,2000 SMI

WET

Comparison of different methods Oct,2000, MI

DRY

Oct,2000 SMI

WET

CONCLUSIONS: Pro – Cons of using SMI and MI indexes for Soil Moisture assessment PRO: • Low cost methodologies • Usable in large areas • Systematic and easy-to-implement methods • Able to be improved and modelled with ground/field data. • Able to be applied with new Sentinel-2 sensor CONS: • Highly dependent on the existence of Landsat imagery • Highly dependent on the land cover and terrain morphology • Difficult or even impossible to be used if there are clouds and shadows • Impossible to assess and verify the results without the ground/field data or further ancillary data (meteorological stations)

THANK YOU