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HHYYDDRROOLLOOGGIICCAALL  MMOODDEELL    

Final Report 

 

By Rodrigo M. Edrosa 

 

Tutor: Prof. Nazzareno Pierdicca. Department of Electronic Engineering. Sapienza University   

Rome, Italy. 

 

July 2010 

E-mail: rodrigoedrosa@hotmail.com

INDEX

1.- Introduction------------------------------------------------------------------------------------------------------------------------- 3

2.- Objectives -------------------------------------------------------------------------------------------------------------------------- 4

3.- Papers reading -------------------------------------------------------------------------------------------------------------------- 5

3.1- “Inversion of Electromagnetic Models for Bare Soil Parameter Estimation from Multifrequency

Polarimetric SAR Data” ---------------------------------------------------------------------------------------------------------- 5

3.2 - “Synthetic Aperture Radar and Sar Scape” -------------------------------------------------------------------------- 6

3.2.1 - The System RADAR ------------------------------------------------------------------------------------------------- 6

3.2.2- Specific Parameters (Wavelength) -------------------------------------------------------------------------------- 7

3.2.3- Acquisition Modes ----------------------------------------------------------------------------------------------------- 7

3.2.4- Scattering Mechanism------------------------------------------------------------------------------------------------ 8

3.2.5- Speckle ------------------------------------------------------------------------------------------------------------------ 8

3.2.6- Data Statistics ---------------------------------------------------------------------------------------------------------- 8

3.2.7- Geometry ---------------------------------------------------------------------------------------------------------------- 9

3.3- Numerical Weather Prediction-------------------------------------------------------------------------------------------- 9

3.4 - Hydrometeorological and vegetation indices for the drought monitoring system in Tuscany Region,

Italy.--------------------------------------------------------------------------------------------------------------------------------- 11

3.5- Analytical description of the MOBIDIC MODEL-------------------------------------------------------------------- 14

3.6- The ASAR GUIDE. Special Features of ASAR--------------------------------------------------------------------- 16

4.- Pre-processing of SAR images---------------------------------------------------------------------------------------------- 17

4.1- Conformation, correction, resize data, re-projection and format change to ENVI of Digital Elevation

Models (DEM) -------------------------------------------------------------------------------------------------------------------- 17

4.2.- Format change of SAR images, multi-looking, geo-referenced and radiometric calibration------------ 18

5.- Pre-processing of Optical images ------------------------------------------------------------------------------------------ 18

5.1.- Reprojection and geometric correction ------------------------------------------------------------------------------ 18

6.- Usage of the Soil Moisture Module by Sapienza-DIE------------------------------------------------------------------ 19

6.1- Analysis and Results obtained by the humidity soil mapping for Rome province-------------------------- 19

7.- Usage of Soil Moisture Content (SMC) by software developed by Sapienza-DIE and MOBIDIC model for

Arno region --------------------------------------------------------------------------------------------------------------------------- 26

7.1- Analysis and Results obtained by the humidity soil mapping for Arno region ------------------------------ 26

7.1.1- Soil Moisture Content of the MOBIDIC model---------------------------------------------------------------- 26

7.1.2- Soil Moisture Content by Department of Electronic Engineering of Sapienza University (DIE) -- 31

7.1.3- Soil Moisture retrieved from radar data using the MOBIDIC model output as priori information

(1D – VAR algorithm developed by DIE) ------------------------------------------------------------------------------- 35

7.1.4- Difference between SMC of DIE and MOBIDIC model----------------------------------------------------- 40

8.- Conclusions ---------------------------------------------------------------------------------------------------------------------- 42

Bibliography -------------------------------------------------------------------------------------------------------------------------- 43

2

1.- Introduction

Surface soil moisture is a very important parameter in many hydrological processes. Due to

its high spatial and temporal variability it is difficult to measure and brings inaccuracy into the

modeling of the processes where it is involved. It is wide known the use of remote sensing in the

study of natural processes. Radar (SAR) has shown the potential for monitoring floods events due

to the high spatial and temporal resolution, and the independence of the sensors from sun

illumination and cloud cover. This technology is capable of extracting several parameters from soil.

This report is related to a work that has been conceived under an agreement between the

National Space Agency of Argentina (CONAE) and the Italian Space Agency. The academical

training was conducted by the Department of Electronic Engineering of Sapienza University (DIE).

In this report was performed a bibliographic revision in relation to the study of theories and

techniques for soil moisture estimation by using SAR technology and hydrological moldels.

Processing of optical and radar images, georeference information analysis, generating and

validating of maps of soil soil moisture were the results of the training.

3

2.- Objectives

To acquire experience in the manipulation of SAR data

To generate, interpret and validate soil moisture products obtained by information from

active and optical sensors

To acquire knowledge about how hydrological models work and relate this information with

SAR data.

Tutor: Professor Nazzareno Pierdicca

Scholarship holder: Degree in Environmental Information. Rodrigo M. Edrosa

4

3.- Papers reading

3.1- “Inversion of Electromagnetic Models for Bare Soil Parameter Estimation from

Multifrequency Polarimetric SAR Data”

The paper called “Inversion of Electromagnetic Models for Bare Soil Parameter Estimation

from Multifrequency Polarimetric SAR Data” by (N. Pierdicca et. al.), is about the potential of the

SAR data to estimate geophysics parameters of bare soil (roughness and soil humidity). On it, two

models capable of simulating the multi-frequency measurements of polarimetrics radars were

implemented. A multiplicative noise has been considered in the multidimensional space of the

elements of the polarimetric Covariance Matrix, by adopting a complex Wishart distribution to

account for speckle effects. An additive error has been taken into account in the calibration and

model errors. The correlation between the roughness parameters was considered and the effects

were observed. The results were tested and compared with simulated data, using information

acquired from Mac EUROPE and SIR – C campaigns. In this paper, it is exposed that the presence

of vegetation difficult the measurements, as well as the dielectric component and the geometric

surface influence the radar signal.

The objective of this work was to investigate the potential estimation of bare soil parameters

mv (humidity), the roughness standard deviation s and length correlation l. Also, the most

appropriate configuration of the sensor was evaluated through neuronal networks.

To simulate the polarimetrics measurements, the IEM (Integral Equation Model) and SEM

(Semi empirical Model) models were used.

The most important conclusions state that neural networks provide the best results when

retrieval performances are measured by RMS error (normalized to parameter standard deviation).

The analysis has also provided quantitative answers to some questions regarding the most

profitable frequency band and incidence angle for accurate soil parameter retrieval. The L band

provides best results when compared with other frequency bands. Lower incidence angles are

better for s (roughness) and mv (soil moisture) estimation, but the accuracy is strongly reduced,

mostly at lower angles, when a model/calibration error is considered. An acceptable accuracy for

the parameter estimation is obtained only by multifrequency and multilook “per field” analysis.

The application of the retrieval methods to a small set of real data shows results which are

comparable to the predicted one for soil moisture and less satisfactory for roughness standard

deviation.

This paper is intended to contribute to an approach for the soil parameters estimation from

radar data. However, it intends to be an approach and not a definite solution.

5

3.2 - “Synthetic Aperture Radar and Sar Scape”

Summary: The paper is about a broad scope of topics. Among them it deals with:

3.2.1 - The System RADAR

When dealing with this topic, there are some core concepts that need to be considered. To

provide a first definition, imaging radar is an active illumination system. An antenna, mounted on a

platform, transmits a radar signal in a side-looking direction towards the Earth's surface. The

reflected signal, known as the echo, is backscattered from the surface and received a fraction of a

second later at the same antenna. A second concept is “Aperture”. It means the opening used to

collect the reflected energy that is used to form an image. In the case of radar imaging this is the

antenna. Finally, the spatial resolution of RAR is primarily determined by the size of the antenna

used: the larger the antenna, the better the spatial resolution. Other determining factors include

the pulse duration (t) and the antenna beam width.

RESrange = c t /2

Where c is the speed of light. Azimuth resolution is defined as

RESazimuth: λR/L

Where L is the antenna length, R the distance antenna-object, and λ the wavelength. For

systems where the antenna beam width is controlled by the physical length of the antenna, typical

resolutions are in the order of several kilometres.

SAR synthetically increases the antenna's size to increase the azimuth resolution though

the same pulse compression technique as adopted for range direction. Synthetic aperture

processing is a complicated data processing of received signals and phases from moving targets

with a small antenna, the effect of which should be theoretically convert to the effect of a large

antenna.

6

3.2.2- Specific Parameters (Wavelength)

Radio waves are the part of the electromagnetic spectrum that has wavelengths

considerably longer than visible light, i.e. in the centimetre domain. Penetration is the key factor for

the selection of the wavelength; the longer the wavelength (shorter the frequency) the stronger the

penetration into vegetation and soil.

The polarization of radar signals, irrespective of wavelength, can transmit horizontal (H) or

vertical (V) electric- field vectors, and receive either horizontal (H) or vertical (V) return signals, or

both. The basic physical processes responsible for the like-polarised (HH or VV) return are quasi-

specular surface reflection. For instance, calm water (i.e. without waves) appears black. The

cross- polarised (HV or VH) return is usually weaker, and often associated with different

reflections due to, for instance, surface roughness.

The incidence angle (Ө) is defined as the angle formed by the radar beam and a line

perpendicular to the surface. Microwave interactions with the surface are complex, and different

reflections may occur in different angular regions.

3.2.3- Acquisition Modes

While operating as a Stripmap SAR, the system is limited to a narrow swath. This

constraint can be overcome by utilising the ScanSAR principle, which achieves swath widening by

the use of an antenna beam which is electronically steerable in elevation.

Radar images can then be synthesised by scanning the incidence angle and sequentially

synthesising images for the different beam positions. The area imaged from each particular beam

is said to form a sub-swath. The principle of the ScanSAR is to share the radar operation time

between two or more separate sub-swaths in such a way as to obtain full image coverage of each.

During a Spotlight mode data collection, the sensor steers its antenna beam to

continuously illuminate the terrain patch being imaged.

Three attributes distinguish Spotlight and Stripmap mode: • Spotlight mode offers finer azimuth resolution than achievable in Stripmap mode using the same

physical antenna.

• Spotlight imagery provides the possibility of imaging a scene at multiple viewing angles during a

single pass.

• Spotlight mode allows efficient imaging of multiple smaller scenes whereas Stripmap mode

naturally images a long strip of terrain.

7

3.2.4- Scattering Mechanism

SAR images represent an estimate of the radar backscatter for that area on the ground.

Darker areas in the image represent low backscatter, while brighter areas represent high

backscatter. Bright features mean that a large fraction of the radar energy was reflected back to

the radar, while dark features imply that very little energy was reflected.

Backscatter for a target area at a particular wavelength will vary for a variety of conditions,

such as the physical size of the scatterers in the target area, the target's electrical properties and

the moisture content, with wetter objects appearing bright, and drier targets appearing dark. (The

exception to this is a smooth body of water, which will act as a flat surface and reflect incoming

pulses away from the sensor. These bodies will appear dark). The wavelength and polarisation of

the SAR pulses, and the observation angles will also affect backscatter.

3.2.5- Speckle

Speckle refers to a noise-like characteristic produced by coherent systems such as SAR

and Laser systems (note: Sun’s radiation is not coherent). It is evident as a random structure of

picture elements (pixels) caused by the interference of electromagnetic waves scattered from

surfaces or objects. When illuminated by the SAR, each target contributes backscatter energy

which, along with phase and power changes, is then coherently summed for all scatterers, so

called random-walk. This summation can be either high or low, depending on constructive or

destructive interference. This statistical fluctuation (variance), or uncertainty, is associated with the

brightness of each pixel in SAR imagery.

When transforming SAR signal data into actual imagery - after the focusing process - multi-

look processing is usually applied (so called non-coherent averaging). The speckle still inherent in

the actual SAR image data can be reduced further through adaptive image restoration techniques

(speckle filtering).

3.2.6- Data Statistics

SAR data are composed by a real and imaginary part (complex data), so-called in-phase

and quadrature channels. These data are usually multi-looked by averaging over range and/or

azimuth resolution cells - the so-called incoherent averaging. Fortunately, even multi- looked

Intensity data have a well-known analytic Probability Density Function: In fact, an L-look- image (L

is the number of looks) is essentially the convolution of L-look exponential distributions.

8

3.2.7- Geometry

Due to the completely different geometric properties of SAR data in range and azimuth

direction, it is worth considering them separately to understand the SAR imaging geometry.

According to its definition, distortions in range direction are large. They are mainly caused by

topographic variations. The distortions in azimuth are much smaller but more complex.

3.3- Numerical Weather Prediction

This paper is about the operative models numerical prediction of weather. At first it is

explained that the better and the more reliable the entrance data of a determined model, the better

the estimated predictions will be.

In this work, the use of operative centers of weather prediction which use the atmospheric

observation and information obtained in situ, are mentioned. It points out OI (optimal Interpolation)

as an interpolation system of determined weigh by the minimization of error on each point. Another

system of interpolation analyzed is 3D – VAR, in it, an approach defined as a cost (function)

proportional to the square distance between the background and the observations is used. This

function is reduced to the minimum to obtain the analysis. In OI, the weigh is obtained for each

point of the grid (matrix), in 3D-VAR, the minimization has major flexibility due to the fact that it can

use global and simultaneously the data.

As regards to the background, it is made evident that for the operative models it is not

enough to do interpolation of the real observations (taken in situ) because in some of them there

are not data to define the initial state. One of the alternatives for this might be the use of satellite

information, but these do not measure the variables used in the models and their spatial

distribution is very uniform. A short term forecast is used as a first approximation, it is called

analysis cycle. If the forecast is not available (for example the cycle is broken), it might be

necessary to use the initial state data. To provide with an example, it can be observed a global

analysis cycle of 6 hours (see fugure1).

9

Figure 1. Global 6-h analysis cycle (00, 06, 12, and 18 UTC).

The forecast models play a very important role. In the richer in data regions, the analysis is

dominated by the information contained in the observation. On the other hand, in poorer in data

regions, the forecast is benefited from the information upstream.

10

3.4 - Hydrometeorological and vegetation indices for the drought monitoring system

in Tuscany Region, Italy.

This paper discusses a first attempt for developing an integrated system to monitor water

resource in Tuscany Region in Central Italy. In this work, a Standard Precipitation Index (SPI), a

vegetation Index from remote sensing (from MODIS and SEVIRIMSG), and outputs from the

distributed hydrological model MOBIDIC in the Tuscany Region were used. Also clarifies among

other things, the importance of information obtained from the model of MOBIDIC for estimating

meteorological variables.

Monitoring drought means to simultaneously evaluate all possible causes and impacts on

soil-vegetation system. The spatial factor is essential, because contrary to other hazards, drought

impacts are spread over large geographical areas and depend on many environmental local

factors. In this paper it is considered positive to monitor in a regional scale the availability of

telemetric network and advent of new measuring techniques from remote sensing. This paper

presents the first experiments for the development of a monitoring system at Centro Funzionale

Regione Toscana (CF Toscana), the hydrometeorological monitoring agency of Tuscany Region in

Italy.

The observation data, such as, hydrometeorological system (rain gauges, thermometers,

anemometers and hydrometers) of Tuscany can benefit of estimates and prediction of hydrological

variables from MOBIDIC model (MOdello di Bilancio Idrologico DIstribuito e Continuo). This model

is fed with data from the hydrometeorological network with a 15 minutes time period and

quantitative precipitation forecast (QPF) from four different meteorological limited area models.

The outputs of MOBIDIC are estimated and predictions of soil water content (in large and

small pores), hydrological and energy balance components (evotranspiration, soil temperature)

and discharge in each branch of the river network, including minor branches.

The authors in this work used the Standardized Precipitation Index (SPI). This index is

robust and it is calculated a time series of accumulated rainfall with a gamma distribution. The

positive SPI values indicate greater than median precipitation, while negative values indicate less

than median precipitation. Because the SPI was normalized, wetter and drier climates can be

represented in the same way, and wet periods can also be monitored using the SPI. The

classification proposed (Table 1) is adopted to tag wet/dry condition. This index is usually

calculated on different time scale, considering for each calculation step the cumulated precipitation

in a given period (e.g. 1, 6, 12, 24 months). Each SPI index contains particular information about

phenomena with different time scale (e.g. 1 month time scale can be irrelevant for impacts on

stream flow and groundwater while it might affect types of crops that need a constant provision of

water, while 12 months can be a relevant scale for groundwater resources but not yet for perennial

vegetation, and so on). Figure 2 shows the SPI indices over Tuscany in 2007, these were

11

implemented only in stations where it was possible to compare with a historical time series at least

30-year long. As regards to recent rain gauge stations that do not have such a long historical

record, a corresponding “historical” station can be used, but only if it was placed in the same

location and within the same microclimate. The authors clarify that the results should be

considered as preliminary and experimental.

Figure 2. Standardized Precipitation Index (SPI) for the first eight months of 2007: top row) –month

time scale; central row) 6-months; lower row) 12-months.

12

Table 1. Classification of dry and wet condition according to SPI index

This paper explains that the impact of a prolonged precipitation deficit depends on the type

of plants and phenological cycle, and it is also affected by other climatic factors such as air and soil

temperature, humidity and solar radiation. Therefore, the analysis of meteorological indices such

as SPI alone is not sufficient to monitor and analyse the conditions of crop and vegetation. The

spectral indices from remote sensing are very good tools for monitoring vegetation conditions. For

example, the Normalised Difference Vegetation Index (NDVI) estimates pixel by pixel the energy

reflected in near infrared (NIR) and red bands. To capture this effect, and to compare drought

conditions in areas with different land cover, NDVI has to be scaled with regard to some

measurement of potential vegetation growth for the given crop type and climatic condition. In this

work the Vegetation Condition Index (VCI) for a given period i was used, VCI is calculated as:

Where NDVI is the Normalized Vegetation Index for the analyzed period (i), the NDVImax

and NDVImin are the absolute extreme values of NDVI calculated from a set of historical NDVI

images. VCI was developed in order to assess changes in the NDVI signal over time due to

weather conditions only, minimizing the effects of local conditions and topography. VCI can be

calculated only in “reliable” images such as MODIS or METEOSAT Second Generation. Figure 4

shows the VCI index for Tuscany in January - August 2007 (from MODIS NDVI), values close to

zero indicate alleged water stress. It is import to clarify the shortness of time series of MODIS

images (only 7 years were used for the evaluation of minimum and maximum NDVI values) may

seriously influence the estimation.

13

The work concludes that in the analyzed drought indices over Tuscany in the first eight

months of 2007, neither the SPI nor the vegetation indices alone can fully describe the severity of

drought conditions. It concludes that the interactions are complex and different time scales play a

fundamental role, although, low precipitation leads to low soil moisture that leads to low water

availability for plants, etc. The work states that the drought prevision can be investigated for

different approach, using auto-regressive models, such as, ARIMA/SARIMA, and/or through the

integration of seasonal weather predictions into an adapted version of the hydrological model

MOBIDIC. The latter approach implies feeding the MOBIDIC model with long-term precipitation

forecast from seasonal models and computing the expected moisture conditions in soil, vegetation

and groundwater layers. Once operational and improved, the drought monitoring system will serve

as a very valuable decision support tool for water resources management and evaluation in this

case the Tuscany region, and can be used for many agricultural and environmental applications.

3.5- Analytical description of the MOBIDIC MODEL

The soil moisture estimated by SAR is the so called volumetric soil moisture or soil moisture

content (SMC) and it is defined as:

SMC = Vw / (Vw + Vair + Vland)

= Vw / (Vland + Vvoid) = Vw / Vt

where: Vw = volume of water

Vair = volume of air

Vland = volume of solid component, i.e. of sand, clay, etc.

Vvoid = Vair + Vw , is the volume of void or space with can be partially filled by water

Vt = the total volume

The degree of saturation or soil moisture index is defined as:

SAT = Vw / (Vw + Vair) = Vw / Vvoid

When all for void or pore space is that’s the soil is saturated filled by water (SAT = 1)

It results also SAT = Vw / Vvoid = Vw / Vvoid = Vt / Vt = SMC / φ

As for MOBIDIC model the following quantities are introduced:

Vw = Vcap + Vgrav

14

Vcap = water retained in the micropores of the soil (capillary)

Vgrav = water runoff in the macropores of the soil whose retention is too weak for opposing to, and

gravity (and thus it is not retained and lost in the drainage)

In order to match the two formulations the volume of the air in soil in the micropores and

macropores as:

Vair_m = volume of air in the micropores

Vair_M = volume of air in the macropores

It Is observed that

SAT = Vw / Vw + Vair = (Vcap + Vgrav) / (Vcap + Vgrav) + (Vair_m + Vair_M)

= (Vcap + Vgrav) / [(Vcap + Vair_m) + (Vgrav + Vair_M)]

Here we can introduce the maximum capacity of the capillary and gravity soil defined as:

Vcap_MAX = Vcap + Vair_m ; Vgrav_MAX = Vgrav + Vair_M

If there is not air in the pores, but only water, Vcap = Vcap_MAX, i.e., the capillary water volume

is equal to the maximum capacity (equal for the macrospores)

The soil total volume that we considered in the soil humidity is Vw + Vair + Vland, If we refer

to a unit of area of the soil surface and we consider the quantities refer to a depth of h, we can

write: Vw + Vair + Vland = 1 x 1 x h = h

Since it was taken Vw + Vair = Vcap_MAX + Vgrav_ MAX, we have:

Vcap_MAX + Vgrav_MAX + Vland = 1x1xh

Than it comes out

(Vcap_MAX + Vgrav_ MAX)[1+ Vland / (Vcap_MAX + Vgrav_MAX)] = h

Vcap_MAX + Vgrav_ MAX / h = 1 / [1+ Vland / (Vcap_MAX + Vgrav_MAX)] = Vmax/( Vmax + Vland ) = n

(where Vmax is the maximum capacity, Vmax = Vcap_MAX + Vgrav_ MAX) is defined as the SMC when

there is only water inside void and pore that is the soil is saturated. We consider that n between 0,4

– 0,5 is the maximum amount of SMC when the soil is saturated and the degree of saturate can be

also written as (Vcap + Vgrav) / (Vcap_MAX + Vgrav_ MAX) = Vw / VMAX

The porosity Ф = (Vw + Vair) / VT = VMAX / VT, can be is defined as the ratio between the

volume of void or pore space and the volume Vt

SMC = Vw / (Vw + Vair + Vland) = Vw / (Vmax + Vland) = (Vcap + Vgrav) / (Vcap_MAX + Vgrav_MAX + Vland) =

(Vcap + Vgrav) / h = (Vcap + Vgrav) / h = n (Vcap + Vgrav) / (Vcap_MAX + Vgrav_MAX)

15

3.6- The ASAR GUIDE. Special Features of ASAR

Dual Polarization:

The radars can transmit and obtain information in two electric-field polarization states,

Horizontal (H) or vertical (V). The return for the cross - polarization is usually weaker and often

associated with surface roughness or multiple volumes scattering due to scattering mechanism

from different surfaces. Also, the return signal depends on the incidence angle of the impinging

radar wave.

The VV polarization is a configuration preferred for many applications as it is important for

the study to small-scale roughness, for example the waves on the water surface. In these cases,

VV is better than HH or cross-polarized.

For the studies of soil moisture the vertical oriented crops make the penetration of HH be

higher allowing the backscatter to better represent the soil moisture.

The SAR backscatter is reduced in cross polarization. This technique (HV or VH) is

appropriate for detecting specified targets above on the water surface. Examples to mention would

be ship superstructures and ice deformation monitoring well as the separation of applications

broadleaf from grain crops among other topics.

For studies of bare soil, where attention focuses on the retrieval of soil moisture and soil

roughness, the use of different polarisations will improve the inversion into soil parameters. Cross

polarisation provides an important improvement for soil moisture retrieval since the radar

backscatter is less sensitive to surface roughness, row direction, etc.

For many studies of vegetation, the use of different polarization, in particular the cross

polarization, improve the vegetation discrimination. In the case of forestry, the cross polarization

will improve the discrimination between the forest and non forested areas and the retrieval of low

biomass values (forest regeneration, regrowth, plantation).

16

4.- Pre-processing of SAR images

The SAR images were processed using ENVI 4.5 software with SarScape license. The

used images correspond to an area centered in Roma city and it surroundings. They cover

about 785.069 km 2. The images dates are reported in table 2.

Table 2. Dates of the acquired data from remote sensing

3

.

1

The images ENVISAT-ASAR utilized have a polarization VV and, in this case, have a

microwave of band C (3,8 – 7.5 cm / 8 – 4 GHz), a spatial resolution and temporal resolution of 30

m and 35 days respectively.

4.1- Conformation, correction, resize data, re-projection and format change to ENVI

of Digital Elevation Models (DEM)

radar

system that flew onboard the Space Shuttle Endeavour during an 11-day mission in

- It was used a mosaic conformed by 6 altimetric data files (DEM) derived from the

spatial mission called “Shuttle Radar Topography Mission” (SRTM). It obtained

elevation data on a near-global scale to generate the most complete high-resolution

digital topographic database of Earth. SRTM consisted of a specially modified

February of 2000. This information has a spatial resolution of approximately 90m.

- To correct the relative altimetric difference between the geoid (SAR) and the ellipsoid

(SRTM), we use software designed for the calculation of a geoid undulation at a point

whose latitude and longitude is specified. The program is designed to use the potential

coefficient model EGM96 and a set of spherical harmonic coefficients of a correction

term from the internet site (http://sps.unavco.org/geoid/), it was just necessary to

introduce the geographical coordinates of the central pixel of each SRTM file. This

17

software calculates the recently mention altimetric difference, which once obtained were

averaged between themselves. Then, the mosaic was generated and through the ENVI

corrected mosaic was resampled to 20 metres, the same as the

r) to (.hdr_bil) with SARScape in order to be used in the

SAR image ortorectification.

change of SAR images, multi-looking, geo-referenced and radiometric

calibration

was done using

ach of the SAR single look complex by diverse values (based on

SAR images were geo-referenced (using DEM) and radio-metrically

calibrated

.- Pre-processing of Optical images

The used optical images were 3 ENVISAT – MERIS y 1 MODIS

.1.- Reprojection and geometric correction

t correspond to

pecific pixels in the image and are reprojected to UTM 33 N with datum WGS 84.

tool this average was added.

- The generated and

used SAR images.

- The projection system used was UTM 33 North with datum WGS 84. It was necessary

to convert the format file (.hd

4.2.- Format

- As a first approach, the format transformation of the SAR images

SARScape. It was necessary to select the sensor and the data type.

- To generate the multi-looking, it was necessary to know the azimuth and range of each

image. With this process, not only the speckle is reduced, but also it is obtained an

approximately squared pixel which in this case was of 20 x 20 m. For this, it was

necessary to multiply e

the range or azimuth).

- Finally, the

5

5

The images were geometrically corrected from the data with the geolocation information

included in the ENVISAT file. ENVISAT imagery contains geolocation tie points tha

s

18

6.- Usage of the Soil Moisture Module by Sapienza-DIE

- The mentioned module has a main menu with five operations to select. One of them is

algorithm where the statistic criteria of minimun variation soil moisture estimation or the

MAP (maximum posterior probability estimator) can be selected. As a default

configuration, the media filter with a mobile window of 5x5 pixels option is active.

Another option in the main window of the module is polarization. In it, the type of

polarization used by the sensor can be selected. For example, 1 pol makes reference to

the simple polarization (in the presence of the file .sms product of SARScape, where

the module obtains information such as the incidence angle and polarization, HH o VV,

when the datum was obtained from simple polarization but the file .sml is not existent,

HHHV o HHVV o VVHV, when the SAR datum has double polarization. Then, there is

the option “system data” where the type of sensor used is automatically read (when

entering the SAR file) as well as its incidence angle and look number. After

that, the

ent

all information files have been selected, there is an option in the main

me

types of soil

cov ask of the areas that

are or not to be taken into account for the estimation of the soil moisture.

Finally, the results are obtained in percentage of soil moisture

.1- Analysis and Results obtained by the humidity soil mapping for Rome province

e dates, it was observed that, for the method of maximum

posterior probability posterior, the range are higher with respect to minimum variance method; this

difference is observed in figure 3 and 4.

rance file has to be selected (complementary): optic image pre and post to the SAR

image and a classification file (vectorial information about the coverage types).

Once that

nu where the a priori probability of the roughness or minimum variation moisture can

be selected.

The thematic class files needs to be associated to the different

erage. This operation is done by the module to generate a m

6

In order to acquire experience in the generation of soil moisture estimates from radar, in

this case for Rome region, the ASAR data were acquired error subsequently processed by the

previously explained module developed by Electronic Engineering Department (DIE) of the

Sapienza University. This module works statistically with two algorithms, MAP (maximum posterior

probability) and minimum variance. The retrieved moisture map were subsequently analysed and

namely. Scatter plot of the obtained soil moisture images were performed. In all the information

analyzed corresponding to their respectiv

19

Figure 3. Soil humidity. Minimum variance with MAP for the image of 29 / 09 /2006

Figure 4. Soil humidity. Minimum variance with MAP for the image of 29 / 03 /2008

20

To validate the generated information about soil moisture, it was utilized a precipitation data

series obtained by stations located in the region and corresponding to the dates of acquisition of

the SAR data. The data were acquired through a NOAA web sites (ftp.ncdc.noaa.gov) The series

was averaged by daily rainfall data compared with one week after the data acquisition, but in some

cases, the absence of daily data for any specified date made the series to be modified and

narrowed (see figure 5). The NOAA data set is formed by daily accumulated rain rate. We have

computed the average daily main rate in the week preceded the day of the SAR acquisition. In

case the data set was not containing main rate they were same daily that day considered in the

average.

Figure 5. Rainfall of information utilized to validate the soil humidity maps

The soil moisture information maps and the rainfall, data (m.m.) were combined to produce

the end – user map reported in the following figures. I it is shown an acceptable correlation

between the meteorological information (rainfall) and soil moisture expressed as a percentage (%)

(Figures 6, 7, 8 and 9)

21

Figure 6. Soil humidity map for Rome province generated by Minimum Variance algorithm for 29/09/2006

22

Figure 7. Soil humidity map for Rome province generated by Minimum Variance algorithm for 29/03/2008

23

Figure 8. Soil humidity map for Rome province generated by Minimum Variance algorithm for 20/09/2008

24

Figure 9. Soil humidity map for Rome province generated by Minimum Variance algorithm for 03/10/2008

25

7.- Usage of Soil Moisture Content (SMC) by software developed by Sapienza-DIE

and MOBIDIC model for Arno region

It has been processed an image of the region of the Arno river catchment basin in the same

way as for the work performed in Rome. In this case it was used an image acquired on September

23, 2003 by the Active Microwave Instrument (AMI) aboard of the satellite ERS – 2. This sensor

has a polarization VV, at C – band, a spatial resolution of 30 meters with a revisits period of 35

days and a 100 km swath.

Similarly to the work performed for the Rome was used the software module developed by

Department of Electronic Engineering of Sapienza University (DIE). It was utilized a Landsat ETM

image of September 14, 2002, in order to generate the NDVI used to identify types of coverage

and to mask the vegetation. The result was a map of soil moisture retrieved from remote sensing

data.

It has been also used the Soil Moisture Content (SMC) predicted by the MOBIDIC

hydrological model at the same of the ERS – 2 acquisition.

Additionally, a 1 D – VAR algorithm developed at DIE merge estimates from radar with of

the hydrological model used as prior information has been run. The three soil moisture (SAR only,

MOBIDIC and 1D – VAR assimilation) have been compared. The comparison has been performed

limiting answer to the image pixels where remote sensing retrievals was feasible, i.e., those pixels

not masked based on the NDVI.

7.1- Analysis and Results obtained by the humidity soil mapping for Arno region

7.1.1- Soil Moisture Content of the MOBIDIC model

The map shown in figure 1 represents the output of the MOBIDIC model and shows in

different shades the SMC calculated by the model. The values are expressed in % of soil moisture.

The highest values in order 20 % were present in a small area North of Carvanella and North of

Calcione (see figure 10)

The statistical analysis of the SMC map produced by MOBIDIC shows that the max value

was 20.02 %, with a standard deviation of 2.81 % and a mean values of 7.07 % (see table 3). The

most frequent soil moisture (background) in this case was 6.17 % as it is observed in the

histogram. It must be notice that most of the SMC values are below 15 % (see figure 11)

26

It has been performed a comparison between the SMC from MOBIDIC model and the

Digital Elevation Model by the SRTM mission. The comparison do not shows a linear relationship

between the soil moisture and surface height (see figure 12).

27

Figure 10. Soil Moisture Content map from the MOBIDIC model represented in cartographic UTM

projection

28

Table 3. Statistical analysis of SMC values predicted by the MOBIDIC model

Npt

s

(%) Soil Moisture Content

Figure 11. Histogram of SMC predicted by the MOBIDIC model over the Arno region

29

(%) Soil Moisture Content

SR

TM

(m

)

Figure 12. Relation between SMC by MOBIDIC model and SRTM

30

7.1.2- Soil Moisture Content by Department of Electronic Engineering of Sapienza University (DIE)

The map shown in figure 4 represents the output of the SMC retrieved from the radar image

only using the software developed at DIE and shows in different shades the SMC calculated by the

model. The values are expressed in % of soil moisture. The highest values in order 35 % were only

present in an area west of Northeast (see figure 13)

For this product, i.e. soil moisture retrieved from remotely sensed data, the statistical

analysis showed the following values: the mode was 18.78 %, minimum of 0 %, maximum of 34.75

%, mean of 20.42 % and a standard deviation of 4.83 % (see table 4). In the histogram we

observed the range of variation of soil moisture in the product and the background (mode) (see

figure 14).

The statistical algorithm used to retrieve soil moisture from SAR model was the MAP

(maximum posterior probability).

For this case, the height of the terrain (SRTM) and the soil moisture content do not show a

linear relationship (see figure 15)

31

Figure 13. Soil moisture content map retrieved by radar image using the software developed by the Department of Electronic Engineering of Sapienza University (DIE)

32

Table 4. Statistical analysis SMC of the DIE

Npt

s

(%) Soil Moisture Content

Figure 14. Histogram of SMC retrieved from radar data using the software developed by DIE for the Arno region

33

(%) Soil Moisture Content

SR

TM

(m

)

Figure 15. Relation between SMC by DIE and SRTM

34

7.1.3- Soil Moisture retrieved from radar data using the MOBIDIC model output as priori information (1D – VAR algorithm developed by DIE)

The product was generate with the SMC derived from radar by using MOBIDIC model as

prior information (i.e., the 1D – VAR algorithm) and shows in different shades the SMC in % of soil

moisture. The statistical algorithm used to generate the product was the MAP. The highest values

in order 20 % were only present in a small area North of Calvanella and Northeast of Calcione (see

figure 16)

For this product the statistical analysis showed the following values: the mode was 11.28,

minimum of 0 , maximum of 34.88, mean of 12,01 and a standard deviation of 3.46 (see table. 5).

From the histogram it can observed that range of variation of soil moisture of the 1D – VAR

product and its background (mode) are lower than the previous case by using the radar data only

(see figure 17).

As in the previous case of SMC derived from radar only, the height of the terrain (SRTM)

and the soil moisture content do not show a linear relationship (see figure 18).

Finally it has been compared the soil moisture values of the MOBIDIC model with soil

moisture values obtained by using radar data only. It was observed that the values provided by the

MOBIDIC model are generally lower than those estimated by the software DIE (see figure 19).This

is not true for same points the MOBIDIC provides values longer than 35 %. The two products do

not show any correction.

35

Figure 16. Map of soil moisture content retrieved by radar image using the software developed by the

Department of Electronic Engineering of Sapienza University (DIE) with prior information of MOBIDIC model

36

Table 5. Statistical analysis SMC of the DIE with base information of the MOBIDIC model

Npt

s

(%) Soil Moisture Content

Figure 17. Histogram of SMC by DIE software with prior information from the MOBIDIC model for the Arno region

37

(%) Soil Moisture Content

SR

TM

(m

)

Figure 18. Relation between SMC retrieved by radar DIE with prior information from MOBIDIC model and SRTM

38

% S

oil m

oist

ure

valu

es b

y D

IE

% Soil moisture values by MOBIDIC model

Figure 19. Relation between SMC by software DIE and MOBIDIC model

39

7.1.4- Difference between SMC of DIE and MOBIDIC model

The comparison between SMC derived from SAR only and those predicted by MOBIDIC did

not provide good results. SAR produces higher values of SMC with respect to MOBIDIC, which

seems however to be more reliable, considering that SAR products have been compared with

ground truth data in the frame of projects run by DIE with satisfactory results. Conversely, SAR

based SMC maps do not show relevant spatial patterns of the SMC field as opposed to the

MOBIDIC model. The 1D-VAR assimilation could be a way to profitable merge the two products.

However, one of the conclusions of this work addresses for a different way to merge those

products which must take into account as much as possible of the respective capability of the two

approaches. One could assume that the model is capable to reproduce the spatial pattern of SMC

thanks to the knowledge of the precipitation data, soil properties and so on. The radar data could

contribute to provide the actual average value of the SMC within the studied area, being a direct

observation of such quantity. Although the development of such a novel procedure is beyond the

scope of this 6 month stage, we have tried just to make a very preliminary test of the concept. We

have therefore used the MOBIDIC output pattern of SMC and scaled its magnitude in order to

match the mean value produced by SAR. Specifically, we have computed the mean values of SMC

from radar in the pixels where such information was available (SMC_SAR=20,02), the mean value

of the MOBIDIC output in the same pixels (SMC_model=7,07) and than added to the MOBIDIC

output the difference SMC_SAR-SMC_model. This product is shown in figure 20. Even if this is a

very rough approach, it provides a picture of what it could be expected by a more advanced

method able to merge the two sources of information, keeping the best from either model and

direct radar observations.

40

Figure 20. Difference Map between SMC retrieved by radar image using the software developed by the Department of Electronic Engineering of Sapienza University (DIE) with SMC by MOBIDIC model

41

8.- Conclusions

The work performed at the Sapienza University allowed me to acquire knowledge related to

remote sensing applied a hydrological problems which is important for the Master in Space

Applications Early Warning and Response to Emergencies organized by the National Space

Agency of Argentina (CONAE) and Gulich institute.

All the papers that were read, related to the topic, improved of my knowledge about

different theories and techniques related to in remote sensing and hydrologic modeling. Predictive

numeric model, hydrologic and vegetation indices for the drought monitoring, analysis of hydrologic

model, studies on basic principles and processing of SAR information were, among others, the

subject’s studies.

Tasks accomplished during these six months were related to image processing of images

acquired by sensors (namely ENVISAT, ERS and Cosmo SkyMed radars active sensors,

generation and manipulation of geospatial information as well as assessment of product Soil

Moisture Content (SMC) estimated by MOBIDIC model and comparison with derived by SAR

sensors.

It is important to say that due to the lack of ground truth information, such as hydrologic

information from meteorological stations, it has been performed only a descriptive analysis and

comparison between the products developed by MOBIDIC model, the retrieval from radar only and

the combination of both. In addition, this information has been confronted with data from a Digital

Elevation Model (SRTM) in order to observe as part of is the variability of soil moisture is related to

the altimetry of the areas.

The results were obtained a laboratory study. The collection of ground truth would have

allowed a better assessment and quantitative comparison of the different products.

Finally, I want to thanks to the team of researchers from the Antenna Laboratory of

Sapienza University for their help and kindness since my arrival in Italy.

42

43

Bibliography

Electronic documentation:

“Hydrometeorological and vegetation indices for the drought monitoring system in Tuscany

Region, Italy”. Retrieved from http://www.adv-geosci.net/17/105/2009/adgeo-17-105-

2009.pdf the 25/04/2010.

Nazzareno Pierdicca, Paolo Castracane and Luca Pulvirenti.”Inversion of Electromagnetic

Models for Bare Soil Parameter Estimation from Multifrequency Polarimetric SAR Data”.

Sensors 2008, 8, 8181-8200; DOI: 10.3390/s8128181. Retrived from

http://www.mdpi.com/1424-8220/8/12/8181/ the 15/02/2010

NOAA Meteorological Station. Retrieved from ftp.ncdc.noaa.gov the 28/04/2010.

Prof Peter Lynch. Second Semester, 2005–2006. “Numerical Weather Prediction.

Meteorology & Climate Centre School of Mathematical Sciences University College Dublín”.

Retrived from http://www.atmos.umd.edu/~ekalnay/AOSC614/CH5-

1IntrotoDataAssimilation.pdf the 05/03/2010.

The ASAR GUIDE. Special Feature of ASAR. Retrived from

http://envisat.esa.int/handbooks/asar/CNTR1-1-.htm#eph.asar.ug.choos.specfeat.dupol the

the 04/05/2010.

The SARScape-Guidebook. Synthetic Aperture Radar and SARScape. Retrived from

http://www.creaso.com/english/12_swvis/13_envi/SARscape/sarscape.htm the 22/02/2010

UNAVCO. Science Product Support – Geoide Height Calculator. Retrived from

http://sps.unavco.org/geoid/ the 24/02/2010.