PASSIVE MICROWAVE TECHNIQUES FOR HYDROLOGICAL APPLICATIONS by : P. Ferrazzoli Tor Vergata University Roma, Italy ferrazzoli@disp. uniroma 2. it
CLASSIFICATION OF REMOTE SENSING INSTRUMENTS Based on physical processes
CLASSIFICATION OF REMOTE SENSING INSTRUMENTS: Summary Active Passive Optical/UV Lidar Radiometer Microwave Radar Radiometer
PASSIVE SYSTEMS: the emission process Background: every surface, at T>0 K, emits electromagnetic power
PASSIVE SYSTEMS: the emission process at microwaves Brightness: Black body brightness: Brightness temperature (definition): Microwave emissivity:
PASSIVE SYSTEMS: the emission process Reciprocity Pr Pi Pa Reflectivity: R(f, θ, φ)= Pr / Pi (reflectance) Absorbivity: A(f, θ, φ) = Pa / Pi = 1 - R(f, θ, φ) (absorbance) Kirchhoff (reciprocity) law: e(f, θ, φ) = A(f, θ, φ)
Horizontal polarization Vertical polarization depends on volumetric soil moisture (SM) is a roughness factor
SOILS Important for applications: εr is stongly influenced by moisture. Real and imaginary parts of soil permittivity as a function of volumetric soil moisture content (SMC) at 1. 4 GHz (L band) Measured values (by Ulaby, Moore, Fung, 82)
GENERAL PROPERTIES OF EMISSION FROM NATURAL MEDIA Emissivity of flat surfaces vs. angle (computations) (by Ulaby, Moore, Fung, 82) Increasing moisture, ε increases, Reflectivity increases, Emissivity decreases
BARE SOILS Emissivity vs. angle, L band (1. 4 GHz) Ground based measurements (by Ulaby, Moore, Fung, 82) moisture Constant roughness, Moisture variations
BARE SOILS Emissivity vs. angle, L band (1. 4 GHz) Ground based measurements (by Ulaby, Moore, Fung, 82) Constant moisture, Roughness variations roughness
e 3 VEGETATION COVERED SOILS e 1 e 2 e=e 1+e 2+e 3 e 1 = (1 - ) [1 - exp(- σev h sec )] e 2 = (1 - ) [1 - exp(- σev h sec )] exp(- σev h sec ) rs e 3 = es exp(- σev h sec ) : vegetation “albedo” τ = σev h (“optical depth”) es : soil emissivity r : soil reflectivity (r =1 – e )
RECENT MICROWAVE INSTRUMENTS Spaceborne radiometric systems launch bands (GHz) AMSR-E 2002 6. 9, 10. 6, 18, 21, 37, 89 SMOS 2009 1. 4
SMOS Launch: 2009 Spatial resolution: 35 -50 km (suitable to studies at global scale) Rivisit time: 3 days Goal in soil moisture retrieval accuracy: 4%
To improve spatial resolution: Interpherometric technique: 69 small antennas located on 3 long arms “The BIG Y” For each pixel: Simultaneous measurements at V and H polarization, 20°< θ <60°
Estimating soil moisture in the root zone is important: • short- and mediumterm meteorological modelling, • hydrological modelling, • monitoring of plant growth, • forecasting of hazardous events such as floods. By ESA SMOS site http: //www. esa. int/esa. LP/LPsmos. html
The soil-vegetation-atmosphere transfer (SVAT) schemes used in meteorology and hydrology are designed to describe the basic evaporation processes at the surface, the water partitioning between vegetation transpiration, drainage, surface runoff and soil moisture variations. At present, soil moisture maps are simulated and forecasts are generated by models Objective of SMOS: maps improvement, maps update By ESA SMOS site http: //www. esa. int/esa. LP/LPsmos. html
The retrieval process -Initial estimate of SM and Leaf Area Index LAI (ECMWF and ECOCLIMAP data bases) - Models for τ(LAI) and e. S (SM) -For each angle ( ) and polarization: TB = TS(1 - ) [1 - exp(- τ sec )] [1+exp(- τ sec ) (1 -es)] +TS es exp(- τ sec ) -Compare initial simulations with measurements -Start an iterative process -Adjust SM in order to obtain the minimum rms difference between simulations and measurements.
Tests: Multitemporal over single sites Global in selected dates
World map of retrieved Optical depth
World map of retrieved Soil moisture
AMSR-E Conical scanning. Local incidence angle: 55°
Application: flood monitoring Test area: Sundarbans delta Polarization Index:
Polarization Index: Increases with soil moisture, decreases with vegetation height Sensitive to flooding Best frequencies: C and X band
PI maps
Multitemporal PI trends in 2005, all bands PI vs. Day of Year Measured water level
Multitemporal PI trends in all years, X band PI vs. Day of Year Measured water level
Correlation PI vs. water level
References • F. T. Ulaby, R. K. Moore, A. K. Fung, “Microwave Remote Sensing. Active and Passive, Vol. II” Addison Wesley, Reading (USA), 1982 • F. T. Ulaby, R. K. Moore, A. K. Fung, “Microwave Remote Sensing. Active and Passive, Vol. III” Artech House, Dedham (USA), 1986 • ESA Living Planet Programme – SMOS http: //www. esa. int/esa. LP/LPsmos. html
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