A Dissertation on Crops Discrimination Researches using SAR Data

Erith .A. Munoz Ra

aHigh Spatial Studies Institute Mario Gulich, UNC/CONAE-ARGENTINA

Abstract

In this work a review of SAR imagery capability for crop discrimination is presented. It is very know that optical

images provided a suitable tool for crop discrimination since that results about classification overall reported are quite

satisfactory. However this kind of data set have shown disadvantages in some acquisition conditions, such as in cloudy

days where the penetration deep of optical instrument are seriously limited or in night condition where the absence of

sunlight make impossible the data acquisition. For this reason SAR imagery have relevant importance as a comple-

mented to optical data in order tu design a complete framework for crop discrimination. A brief dissertation about the

SAR properties and relevant results are also covered, and based on the research works cited some recommendations

for particular application are lodged.

Keywords: Remote Sensing, Synthetic Aperture Radar (SAR), Crop Discrimination

1. Introduction

1.1. Overview

Crop discrimination is usually an important step for

development and management of crop monitoring sys-

tems (Han et al., 2007). In the last two decades, has

been greatly increased the use of remote sensing tech-

niques for data assimilation to crop management and

crop yield forecasting models (Ding and Chen, 2012),

this is mainly due to, the use of remote sensing data

present many advantages over conventional methods, in

terms of economic investment and time, and spatial and

Email address: erith7@gmail.com (Erith .A. Munoz R)

temporal coverage of the analysis (Wardlow and Egbert,

2008).

Since remote sensing technology is used for crop dis-

crimination, both the theory and the technological tools

have been in constant development, this has led to a

remarkable increased in the range of applications and

scope of crop discrimination techniques. The most of

crop discrimination monitoring systems require as input

data associated with conditions of the plants and soil,

this data must not only be accurate and consistent, but

must also be available in appropriate spatial and tempo-

ral scales, which is quite feasible to reach from remote

platforms, such as via aircraft- and satellite-based sen-

sor systems (Pinter et al., 2003).

February 25, 2013

Crop discrimination types using remote sensing tech-

niques are based on the characterization and under-

standing of the electromagnetic behaviour of target.

This behaviour depends on the wavelength at which the

crop observation is performed, due to this, remote sen-

sors for Earth observation are designed to operate at

wavelengths in which the response of each target (crop

type) is characterized and known, facilitating on this

way the target identification in the scene.

It is important to note that the electromagnetic re-

sponse of the different parts of a crop cover not only

depend on the wavelength used for observation, it also

shows variations depending on the season, angle of inci-

dence of the sensor, crop’s features, illumination inten-

sity, weather phenomenon and topography among other

external factors. In this order of ideas, Pinter et al.

(2003) concluded that a significant challenge for agri-

cultural remote sensing applications is to be able to sep-

arate spectral signals originating with a plant response

to a specific stress from signals associated with normal

plant biomass or the background noise that is introduced

by exogenous non-plant factor.

1.2. Active and Pasive Microwave Sensors

Actually, there are various types of remote sensing

data available for identify crop types. In this context,

optical remote sensing data is between the most used,

some relevant application of this kind of data for sev-

eral sensors have been cited by Ding and Chen (2012),

and in the most of then have been reported remarkable

achievements due to the mature reached from the physi-

cal understanding about the response of crop for optical

bands. Nevertheless, under some whether conditions,

such as on cloudy days where multiple reflections gen-

erated by the presence of clouds prevent the sensor can

obtain relevant information about Earth’s surface, or for

night observation where the acquisition of useful opti-

cal data is seriously limited due to the sunlight absence

(Hsu et al., 2008), the ability of optical sensors for crop

monitoring is affected.

Other source of remote sensing data are the mi-

crowave sensors, whom have a great capability to pene-

trate clouds and to some extend rain, which mean a best

possibility of operate in a wider range of weather condi-

tions (Ulaby et al., 1981, Liu and Wu, 2001, Tian et al.,

2012), on the other hand microwave sensors are inde-

pendent of sun illumination, thereby having the capabil-

ity of operating both day and night. Another important

feature of microwave is that can penetrate deeper into

vegetation cover than optical waves, therefore it allows

a better description of the vegetation structure and in-

creases the references to discriminate crop types. this

features suggest that the information received by mi-

crowave sensors about the surface and structure of veg-

etation cover, varies from that obtained by optical, this

is the reason because some authors recommend the in-

tegrated use of both types of sensors to enhance the ca-

pabilities of crop types discrimination (Vincikova et al.,

2010).

There are two sources to get microwave remote sens-

ing data, active sensors and passive sensors. There is

some preference for the use of active sensors, perhaps

due to the fact that these have higher resolution than

passive sensors (Lu et al., 2008). The common active

sensor for microwave is the Synthetic Aperture Radar

(SAR), this sensor is called active because it uses its

own energy source to illuminate and measure the dif-

ference in power between the transmitted and received

electromagnetic radiation. The radar backscatter energy

depends on radar, target and external parameters. Fre-

2

quency or wavelength, polarization and angle of inci-

dence are primary parameters that define the features of

the radar backscattering, and everyone of these have an

important effect to be considered for evaluating the in-

teraction between the radar signal and the target.

When the effects of the target are considered, then the

effects associated with the radar take greater relevance.

For example, in the case of crops, the radar parameters

have variations on the influence on radar backscatter-

ing depending of the water content (Dielectric proper-

ties), orientation of leaves, or leaf shape and size (Geo-

metrical properties). Moreover, the external factors also

could alter the radar backscattering, as an example can

be mentioned the effect of seasons variability on crops

and also soil features under the crops cover (Herold,

2004).

1.3. Multichannel Characteristics of SAR data

Multichannel SAR data is required to provide mean-

ingful crop information. There are four general ways of

obtaining multichannel SAR data, a description about

the general features of these ways mentioned before

were given by Lopez-Sanchez and Ballester-Berman

(2009) in the next mode:

1. Multifrequency data: are acquired at different fre-

quency bands, generally combining low and high

microwave bands, thus becoming sensitive to dif-

ferent properties of the plants and different scales

of their components.

2. Multipolarization data: are acquired with differ-

ent combinations of transmit-receive polarizations.

This technique, generally named SAR polarimetry

(PolSAR), exploits the sensitivity of the wave po-

larization to the orientation, shape and dielectric

properties of the elements in the scene.

3. Multitemporal data: the scene is observed for a pe-

riod of time, providing time series of images cor-

responding to the temporal evolution of SAR data.

4. Multiangles data: are acquired over the same area

but from different incident angles, thus becom-

ing sensitive to different crop and soil proper-

ties. There exists a particular case of special in-

terest, named SAR interferometry (InSAR), which

consists in combining two complex images with

slightly different incidence angles. InSAR pro-

vides observables related to the vertical distribu-

tion of scattering centers

The use of multichannel data increases the set of pos-

sible observables to be related with the crop param-

eters, so many studies have analysed correlations be-

tween different observables and different physical pa-

rameters. Indeed, the use of multipolarized and mul-

tifrequency SAR data has yielded successful results in

a wide variety of applications, such as crop classifi-

cation and crop-type mapping, crop condition assess-

ment, plant pathology detection, biomass estimation,

soil moisture retrieval, soil tillage and crop residue map-

ping. Later, relevant works will be commented for each

of the multichannel modes explained before.

2. Texture Features of SAR data

Several investigations have been carried out in order

to develop tools for processing satellite data to improve

levels of discrimination of crop types of these sensors.

A methodology used in crop discrimination processes

using SAR imagery stems from the ability of these sen-

sors to identify texture features. It has been shown that

the inclusion of texture features in crop discrimination

3

with SAR imagery, when only one band and one po-

larization was used could produce kappa values higher

than 0.85 (Soares et al., 1997), however this value tends

to improve as more bands and polarizations are added

(Vincikova et al., 2010).

First order statistical methods are the simplest mea-

sures for the textural description of images. As a re-

sult, they are generally very poor at discriminating be-

tween different textures. Common features include mo-

ments such as mean, variance, dispersion, mean square

value or average energy, entropy, skewness and kurto-

sis (Srinivasan and G., 2008, Irons and Petersen, 1981,

Hsu, 1978).

In this context, it is useful to mention that the features

obtained from First Order Statistic provide information

about the grey-level distribution of the image, however

they do not give any information about the relative po-

sitions of the various gray levels within the image. In

other words, they describe the histogram of a greyscale

image and contain no spatial information about the im-

age they describe (Forbes, 2007). Due to this, there are

works in which have been implemented Second Order

Statistic methods in discrimination process added to the

First Order Statistic.

Second-order statistics operate on probability func-

tion, that measures the probability of a pair of pixel val-

ues occurring some vector ~d apart in the image. This

probability function is also called co-occurrence matrix,

since it measures the probability of co-occurrence of

two pixel values. Methods based on second-order statis-

tics (i.e. statistics given by pairs of pixels) have been

shown to achieve higher discrimination rates than the

power spectrum (transform-based) and structural meth-

ods (Weszka et al., 1976). Accordingly, the textures in

grey-level images are discriminated spontaneously only

if they differ in second order moments. It is important

to highlight that spatial gray level co-occurrence esti-

mates image properties related to second-order statis-

tics which considers the relationship among pixels or

groups of pixels (usually two). (Haralick et al., 1976)

suggested the use of gray level co-occurrence matri-

ces (GLCM) which have become one of the most well-

known and widely used texture features. This method

is based on the joint probability distributions of pairs of

pixels, GLCM show how often each gray level occurs at

a pixel located at a fixed geometric position relative to

each other pixel, as a function of the gray level.

GLCM has been commonly used for making crop

types description from texture measures of SAR im-

agery, its popularity could be attributed to the fact that

its definition can explicitly represent the relative fre-

quency between neighboring pixels, it means that the

different features derived from GLCM provides abun-

dant information about texture within an image. In

GLCM each element P(i, j)∆x,∆y of the gray-level cooc-

currence matrix represents the relative frequency with

which two neighboring pixels separated by a distance

of ∆x columns and ∆y lines occur, one with gray tone i

and the other with gray tone j.

In this vein, it is worth mentioning that an impor-

tant quality of the GLCM is the fact that from it can be

obtained 14 features on the texture of the image, even

when some of it have a high level of correlation with

others, either directly or indirectly. As it was mentioned

before, these texture parameters can be use for enhance

the crop types discrimination, due to some of them are

autocorrelated there are some techniques to select be-

tween theses parameters in order to have relevant mea-

sures of texture (Soares et al., 1997).

Therefore, the Gray-Level Difference Vector (GLDV)

4

approach is based on the absolute difference between

pairs of gray levels i and j at a distance ∆x columns

and ∆x lines apart at angle φ with a fixed direction. on

the other hand, another very relevant method is the Sum

and Difference Histogram (SADH) approach, which re-

places the second-order statistics probability function

of a co-occurrence matrix with estimates of the first-

order probability functions along the principal axes of

the co-occurrence matrix (Unser, 1986, Welch et al.,

1990). Among the main advantages provided SADH

stand; Nine of the GLCM textural features can be com-

puted directly from the SADH approach and also the

five other second-order features can be approximated by

assuming mutual independence be tween PS (k)∆x,∆y and

PD(l)∆x,∆y (Welch et al., 1990).

In this sense, Chen and Wang (1990) developed a

methodology called the texture spectrum (TS) approach

based in the concept of texture unit. A relevant re-

sult of this proposed method is that a texture unit is

represented by eight elements, which allows simultane-

ously, the mutidireccional extraction of the characteris-

tics of the texture of an image, unlike the Gray-Level

Co-ocurrence Matrix (GLCM) which permits it only in

the direction given by the vector displacement. For this

reason, this methodology has been the basis for various

applications reported (Umarani et al., 2008, Chang and

Chen, 2004)

In a later study, Al-Janobi (1999) reported results for

The co-occurrence features extracted from the cross-

diagonal texture matrix provide complete texture infor-

mation about an image incorporating the properties of

both the GLCM and TS methods.According with the

author the classification error was 2,4 % with features

from the cross-diagonal texture matrix, whereas the er-

rors were 18.9 and 38.7% with features from the GLCM

and TS, respectively.

3. Multichannel Capabilities of SAR data

3.1. Multi-Polarization and Multi-Frequency Features

Single channel SAR imagery have shown high perfor-

mance for crop discrimination, when is used with statis-

tical spectral or textural classifiers methods. However,

in several times have been suggested the use of multi-

channel SAR data in order to improve the overall classi-

fication accuracy (Lee et al., 2000). Because of the wide

range of information associated to the polarimetry and

frequency characteristics of SAR sensors, these param-

eters are between the most important to be considered in

the design of a radar mission. Although, it is desirable

to enable a multifrequency fully polarimetric SAR sys-

tem, there are some important factors that prevent this

possibility becomes reality.

Among the main factors that prevent the provision

of multifrequency fully polarimetric SAR space-borne

systems are: platform payload, data rate, mission bud-

get, require resolution, and area of coverage, others. In

this context, for a particular application is important to

know the optimum features required in order of design a

platform system that combines the needed basis for the

extraction of a complete information set. Some research

have reported relevant results in order to provide refer-

ences to select polarimetry and frequency features for

crop discrimination applications based in SAR imagery.

In order to investigate the benefit of polarimetric data

for crop type discrimination, Groot et al. (1993) deter-

mined useful polarimetric features to this purpose, and

also they inspected the co-polarized signatures for sev-

eral crop types.The investigation was based in the ex-

traction of the next polarimetric features:

5

1. The average backscattering coefficients: They

used the field average backscattering coeffiecients

γHH and the difference γVV − γHH and γHV − γHH

2. The average co-polarized phase difference: The

co-pol phase difference is the average difference

between the phase angles of the corresponding pix-

els 1 in the HH and the VV channels ¯φHH − ¯φVV

(Boerner et al., 1987), it was obtained together

with its standard deviation ∆( ¯φHH − ¯φVV ) from the

distribution of one look complex scattering matri-

ces withing the polygons .

3. The degree of polarization: Is given by the ratio

of the power in the polarized part of an electro-

magnetic wave to the total power in the electro-

magnetic wave. It was calculated from the Stokes

matrix data with vertically polarized incident radi-

ation (Born and Wolf, 1980).

4. The co-polarized signature: It was only shown

cross-cut along the χ = 0o axis for some cases.

For more information about this feature you can

see Van Zyl et al. (1987)

5. The orientation of the minimum linear polariza-

tion: They studied the distribution of orientation

angles of linearly polarized incidents waves for

which the backscattered power is a minimum. it

was denoted the mode of distribution as ψmin, this

is a useful feature since it let discriminate between

isotropic crop (as sugar beet) and anisotropic crop

types (as maize).

It was reported that is quite possible to discriminate

reliable bare soil fields from vegetation fields for the po-

larimetric data used. However, they were not able to

deduce statistically reliable and general results for the

1or group of pixels

crop discrimination between the differents crop types.

An exception was Lucerne which showed clear differ-

ences from the other crop types. Others remarkables

results reported by Groot et al. (1993) are that the scat-

tering by the underlying soil is dominant at P-band, also

flat signatures are specially found for potato and sugar

beet which have more isotropic backscattering proper-

ties. In the case of rough surface it was found that

σoHH < σo

VV which apparently indicates that vertically

polarized waves are better absorbed or scattered in other

directions than horizontally polarized waves.

Lee et al. (2000) adopted the supervised maximum

likelihood classification algorithm (MLC) based on the-

oretical speckle distributions of multi-polarization and

polarimetric SAR data to evaluate the classification ca-

pability of polarimetric P-, L- and C-band data from

JPL/AIRSAR. They shown that the phase difference be-

tween HH and VV co-polarization is an important factor

for crop classification (overall classification accuracy of

80,91%), due to the high correlation compared with the

cross-polarization case (see figure 1). In contrast, when

the phase difference is not included in the classification

the rate drops to 56,35%, this behaviour was attributed

tu the fact that the penetration deep of HH and VV po-

larizations are different for the crops under considera-

tion.

In this sense, Radionova (2009) highlighted the im-

portance of the interchannel phase information 2 in SAR

polarimetric data. The co-polar phase difference in an

important factor for crop classification specially in dual

polarization SAR data. He also found in histograms

of L-band phase difference for several crops class, that

2Compound by co-polar, cross-pol and the phase difference be-

tween HV and VH channel

6

Figure 1: Classification Results obtained by Lee et al. (2000)

classes have their phase difference highly concentrated

near peaks, and the peaks do not coincide. The total cor-

rect classification rate of complex HH and VV increases

almost at 25% when the co-polar phase difference is in-

clude in the classification3. Other important conclusion

is the fact that the classification with VV and HV phase

difference is only slightly better than for the intensity.

Lee et al. (2000) summarized that for crop discrimi-

nation, the combination of HH and VV polarization is

preferred, if fully polarimetric data is not available. Ad-

ditionaly he planted that the classification results using

P-band and C- band data are similar, but inferior to those

using L-band. Lee et al. (2000) also analysed combi-

nations to improve tree age classification, between the

more remarkable inference are mention that for this pur-

pose the combination of HH and HV perfomrs better

than the HH and VV polarization, also he lodged that

phases differences are less influential on the classifica-

tion because scattering mechanism in the areas are very

random, and in respect to frequency it was reported for

the L-band the classification results is similar but some-

3This influence is not so relevant for fully parametric SAR data

what inferior than C- and P-band.

Many researchers have proved that multi-frequency

fully polarimetric radar data can be used to discriminate

between crops types, evaluate crop biomass, soil mois-

ture, and wetland vegetation classification. For instance

Shao et al. (2005) compared retrieved data from scenes

of HH/HV and VV/HH polarization with field observa-

tion, in order to build the relation between the backscat-

tering coefficient and the Leaf Area Index (LAI) for rice

and banana, to analyse the ASAR data capability for

crop grow monitoring 4. To do this, they derived the

backscattering coefficients of main ground object from

images and used the crop microwave scattering model to

describe the relationship getting satisfactory results (see

figure 2). The results showed that the different ground

objects in test site have distintive and different charac-

teristics of backscattering coefficient in ASAR, so the

LAI obtained from ASAR data could be used in crop

growing monitoring. This work also reported that the

backscattering coefficient is sensitive to crop structure

4Growing is very interesting crop parameter that could be used for

crop type discrimination

7

and moisture content, in the other hand, the accuracy

of crop microwave scattering model also can be im-

proved if more ASAR data with multi-incidence angle

and multi-polarization is available for study.

Figure 2: Relation between LAI measured and estimated using C-HH

combination(Shao et al., 2005)

A very interesting research in which was evaluated

capabilities of several agricultural crops classification

methods based on single full polarization SAR data us-

ing L-band was reported by Chen et al. (2007). the clas-

sification methods evaluated are listed at next.

1. WML-(T):Wishart-Maximum Classifier applied to

coherency matrix

2. NML-(6I):Normal distribution Likelihood classi-

fier applied to the six intensity images derived from

T

3. NML-(6I+3P):NML based an all six intensity and

the three phase matrix

4. ECHO-(6I+3P):Extraction and Classification of

Homogeneous Objects classifier applied to all six

intensity and three phase

5. NML-(H+alpha+A):NML to entropy (H), alpha

and anisotropy (A) images, which were generated

from H − alpha polarimetric decomposition

6. ECHO-(H+alpha+A):ECHO to entropy (H),

alpha and anisotropy (A) images

7. ECHO-(6I+H+alpha+A): ECHO to 6 intensity,

entropy (H), alpha and anisotropy (A) images

Results shown that the classification accuracy of

WML − (T ) is 75,2% what represents 10% more than

NML − (6I) and 8,6% higher than NML − (6I + 3P),

(see figure 3). Since was proved that the polarimetric

covariance matrix have a complex multivariate Wishert

distribution, there were a evident difference between the

classification accuracy of WML and NML, considering

that was assumed a false normal distribution in the NML

classifier. Other important assumption of NML classifier

is that the variables are independent of each other, but

the six intensity and phase variable are not independent

according to the Wishart distribution assumption.

By other side, if a spatial-spectral classification

method such as ECHO is used, the classification accu-

racy can be improved. In this context, the ECHO clas-

sification achieved a total accuracy of 81,3%, it is 6,1%

higher than the WML results. Chen et al. (2007) sug-

gested that in the case of selecting between traditional

classification methods such as NML and spatial-spectral

classifier, should be better to choose spatial-spectral-

based classifier ECHO with all the information that can

be derived from complex coherency matrix.

When maximum likelihooh classifier, such as WML

and NML can be utilized, it is better to choose the

WML method and to apply the coherency or covariance

matrix. But if these images are supplied to a spatial-

spectral-based classifier such as ECHO, higher classifi-

cation accuracy can be obtained. Additionally, H and

8

Figure 3: Classification accuracy obtained from the evaluation of classification methods for 10 different clases(Chen et al., 2007)

alpha decomposition images have proved to be useful

for H-alpha segmentation and Wishart-H-alpha unsu-

pervised terrain type classification. Finally the results

reported shown that very low crop type discrimination

accuracy can be achieved only when entropy H, alpha,

and anisotropy A are supplied to NML and ECHO clas-

sifiers

At this point, different approaches used to extract

crop information from polarimetric SAR data have

been lodged, For example statistical methods based

on the Wishart distributions, or covariance matrix ele-

ments transformed into backscatter coefficients. Here

is presented some very interesting results for a suitable

knowledge-based approach. Skriver (2007) used the

Jeffries-Matusita distance to evaluate signatures of 43

polarimetric parameters in order to identify their poten-

tial for crop discrimination as a function of frequency

and acquisition time of the SAR data.

Certainly the research reported by Skriver (2007) is

based on Air-borne platform, however it represents a

very interesting conclusions that can be evaluated for

future space-borne missions considerations. Included

in the list of parameters evaluated are the linear polar-

ization backscatter coefficients and their ratios, circu-

lar polarization backscatter coefficients and their ratios,

45 degree linear co- and cross-polarization backscatter

coefficients and a ratio, as well as backscatter coeffi-

cients with other combinations of transmit and receive

polarizations, which are used in the Hoekman and Vis-

sers (2003) classification method. Also included are

the phase difference and correlation coefficients for HH

and VV polarization, the Cloude and Pottier (1997) de-

composition parameters and the Freeman and Durden

(1998) decomposition parameters.

The first approach was orientated to discrimination

between spring/winter crop classes. At C-band the pa-

rameters with a large distance are: σoHV/σ

oVV , entropy,

ρHHVV , and σoLL/σ

oRL.These are parameters that pro-

vide a large difference between surface scattering and

depolarization in the vegetation (see figure 4). Also

σoHV/σ

oVV parameters as a function of incidence angle

can be used as a simple classification rule. For the L-

band, it is seen in see figure 5 that the acquisition in

May has the largest potential in discriminating between

spring and winter crops. In the case the parameters with

largest potential are: γoLL, γo

+−45 and γo+45R + γ

o−45L, but

no clear separation between winter and spring crops are

found for these parameters. It is important to consider

that due to the higher wavelength of L-band, the vegeta-

tion must be larger than for C-band to provide a differ-

9

ence between bare and vegetation field.

Figure 4: Jeffries-Matusita Distance evaluating separability for 43

SAR polarimetric parameters at C-band in the beginning of the crop

growing cycle (Skriver, 2007)

Figure 5: Jeffries-Matusita Distance evaluating separability for 43

SAR polarimetric parameters at L-band in the beginning of the crop

growing cycle (Skriver, 2007)

The second approach is the classification based on

Broad leaves/small stem crop classes. The separation

between broad leaves crop (as beets or potatoes) and

small stem crops (such as cereals peas) is called sepa-

ration based on structural differences. At C-band, the

acquisition that provides the largest discrimination po-

tential was taken in May, for this acquisition the param-

eters with the largest differences are γoHV , γo

HV/γoVV , en-

tropy, ρHHVV , ρvolume, γoRR, γo

LL and γoLL/γ

oRL, and it was

found that these parameters clearly show potential for

separation.

At L-band, the distances are smaller than at C-band,

and the largest distance are seen in the acquisitions of

May and July. The potential classification rule between

the two types was very much dependent on the incident

angle for the parameters in May. The results showed

more difficulty to stablish a clear classification using L-

band. Skriver (2007) summarised that for a crop clas-

sification scheme where the first step is to separate be-

tween spring and winter crops in an early acquisition,

it result clearly feasible at C-band, where the results

showed that several parameters can be used to perform

this separation. For L-band, a clear separation was,

however, not reached. For the separation between broad

leaves and small stem crops, the best parameters are

found at C-band for acquisition taken in May, while the

separability is relatively small at L-band.

Although is very known that the classification accu-

racy using SAR data is optimally improved with full po-

larimetric data, in the most of the cases is not available

this kind of data. In this sense, Karjalainen et al. (2008)

studied crop species discrimination using dual polariza-

tion (VV/VH) ENVISAT SAR images with a high tem-

poral resolution in association with ancillary data mon-

itor crop growth and to classify crop species. Crop in-

formation retrieval from SAR images has proved to be a

complicated inverse problem, for example in crop yield

estimation , one would like to estimate the biomass of

the vegetation, but its inversion from the recorded SAR

backscattering is very difficult since others parameters

are usually unknown. In this sense, Brown et al. (2003)

proposed that the HH and VV amplitud difference in

C-band could be a good measurement for estimate the

10

biomass of crop.

Karjalainen et al. (2008) used altogether 12 EN-

VISAT alternating polarization SAR images5 colected

roughly at one-week intervals in the growing season

2003. The results of the VV polarization backscatter-

ing signatures of ENVISAT were similar to the results

obtained with EARS-1 satellite (Saich and Borgeaud,

2000). VH backscattering signatures showed an in-

crease of 3dB from bare soil to full crop cover, how-

ever, the problem of using VH polarization was that the

backscattering in the beginning of the growing season

was very close to the noise equivalent σo of ENVISAT

alternating polarization images. It is important to high-

light that the VH backscattering started to increase in

the middle of July, when crop height exceeded about

50cm on the average in our test parcels. In summary

Karjalainen et al. (2008) reported an overall classifica-

tion accuracy of 74,7%, when crop species classes of

grassland, potato, turnip rape, autumn rye, spring wheat,

barley, and oats were used. Furthermore lodged that the

30-meters spatial resolution of the ENVISAT SAR was

too low for detecting crop height variations within a par-

cel.

It have been shown the important influence that have

polarimetric and frecuency characteristics on crop dis-

crimination capabilities of SAR imagery. In this con-

text, an important application in order to evaluate the

monitoring of crop growth and rice-planted area was

reported by Suga and Konishi (2008) in which they

used multitemporal SAR data of TerraSAR-X(X-band),

ENVISAT-1/ASAR(C-band) and ALOS/PALSAR(L-

band) to investigate temporal change of SAR backscat-

tering coefficient during the rice growing cycle. Ad-

5VV and HH polarization

ditionally, ground truth data were measured simultane-

ously with satellite observation to analyse the correla-

tion between SAR backscattering and these parameters.

Such measurements included magnitudes as height of

plant, vegetation cover and LAI corresponding to SAR

observation.

Suga and Konishi (2008) found that the backscat-

tering coefficient of TerraSAR-X strip map mode data

changed from -40 to -10 dB in HH polarization and from

-45 to -32 dB in HV polarization and concluded that

TerraSAR-X was the best of the tree kinds of temporal

SAR data sets for rice crop monitoring using backscat-

tering coefficients of temporal change until 90 days af-

ter transplanting. By other side, ENVISAT-1/ASAR

showed that the backscattering coefficients with HH po-

larization indicate the highest value at -2 dB is these

SAR data. Furthermore, the backscattering coefficient

for HH polarization increase linearly until 90 days after

transplanting, which is very useful to monitor the rice

growing cycle. Respect to ALOS/PALSAR, the results

reported showed that the range of the backscattering co-

efficients in rice field is the smallest compared with C-

and C-band SAR for HH polarization.

The work reported by Suga and Konishi (2008) is a

clear example of the great importance that represents to

know about of the electromagnetic characteristics of the

target which is going to be analysed in order to choose

correct data or combination of data that provide more

capability of reaching the stated objective. As a refer-

ence table 1 shows, ordered by launch date, the main

operative space-borne platform at the present, there are

observed for each platform the center frequency, polar-

ization modes, and the maximum spatial resolution.

Continuing with this brief compilation of research

works orientated to crop discrimination techniques us-

11

Platform Freq./Pol.Spatial

Resol.(m)

TerraSAR-X X/dual-pol 1

RADARSAT-2 C/quad-pol 3

COSMO-SkyMed X/dual-pol 1

TecSAR X/quad-pol 1

RISAT C,X/dual-pol 3

TanDEM-X X/dual-pol 1

TerraSAR-L L/quad-pol 5

Table 1: Spaceborne SAR sensors

ing polarimetric data, it is cited the work of McNairn

et al. (2009), which tested the capability of PALSAR

multipolarization and polarimetric data for crop dis-

crimination. They used all L-band linear polarizations,

corn, soybeans, cereals, and hay-pasture were claasified

reporting an overall accuracy of 70%. After this, they

used a more temporally rich C-band data set achieving

an accuracy of 80%. By other side, they also used a mul-

tifrecuency data set, and an overall accuracy of 88,7%

was reached, and many individual crops where classi-

fied to accuracies better than 90%.

Although McNairn et al. (2009) found satisfactory re-

sults for linear polarization, they reported that L-band

parameters derived from decomposition approach6 pro-

duced superior crop classification accuracies relative to

those achieved using the linear polarization. As a com-

plementation, McNairn et al. (2009) compared classifi-

cation results obtained using L-HH and C-HH combina-

tions. They found that L-band provided slightly higher

overall classification accuracies, however, although the

6For example Cloude and Pottier (1997) and Freeman and Durden

(1998)

longer wavelenght L-band data were better at classify-

ing large biomass crops such as corn, while C-band pro-

vided better classifications for lower biomass crops such

as hay-pasture.

Since McNairn et al. (2009) work, it is feasible to

integrate multi-temporal SAR data from different plat-

form in order to improve the crop classification accu-

racy. It is important highlight that they suggested that

SAR images acquired early in the growing season pro-

duced the poorest accuracy, while late season SAR im-

ages were key to a successful classification. Based

on these results, a very interesting framework for crop

monitoring based on SAR imagery could be built inte-

grating methodologies of Skriver (2007) and McNairn

et al. (2009).

In a recent work, Silva et al. (2012) evaluated air-

borne data from MAPSAR sensor for make discrimi-

nation among crops through graphical and cluster anal-

ysis of mean backscatter values, considering single,

dual and triple polarizations. they reported that com-

bination of two polarizations could differentiate vari-

ous fields of crops, highlighting the combination VV-

HV that reached 78% overall accuracy. They also an-

nounced that the use of three polarization resulted in

85,4% overall accuracy, indicating that the classes pas-

ture and parallel coffee were fully discriminated from

the other classes (The results can be observed in fig-

ure 6).

Silva et al. (2012) showed that the use of only one po-

larization provide overall poor ability in crop discrimi-

nation, while using three single polarizations 7 signif-

icant variations and overlap could be observed among

classes. They analysed several classes in their work, for

7HH, VV, HV

12

example as coffee8. For VV polarization the coffee A

class presented greater values than the coffee B class,

while for HH polarization the coffee B had a higher

value than coffee A, this could be attributed to the differ-

ent number of plants that are exposed to the microwave

radiation depends on the row direction. It could be seen

in the way that when planting rows are perpendicular to

the sensor look direction, a higher number of plants and

less exposure of the soil are viewed, in comparison with

parallel rows. For HV polarization, pasture presented

the highest mean value.

Moreover, the use of two polarization provided par-

tial differentiation of the crops, for instance the VV-HV

polarization combination enabled the differentiation of

the greatest number of fields of the crops, with a kappa

coefficient of 0.701. In the HH-HV polarization combi-

nation, all pasture fields were separated from the other

classes, however the rest of the crop fields were highly

confused. For HH-VV polarization combination differ-

entiated coffee B well, and just one field of this crop was

confused with the other classes. Respect to the simulta-

neous use of the three polarization (VV-HH-HV) they

reported that improved discrimination among classes

raising the kappa coefficient to 0.804.

3.2. Multitemporal Approach

In the last section, were discussed some crop discrim-

ination approach based on multi-polarization and multi-

frequency SAR data. Also it was shown relevant results

and conclusions about how to combine these SAR data

features for some particulars applications. In this part, it

is lodged some research works in which have been inte-

grated different techniques for the extraction of crop dis-

crimination parameters and multi-temporal SAR data.

8Two different coffee species labelled as coffee A and coffee B

Between the range of applications of multitemporal

SAR data, crop growing monitoring has been studied by

many researchers. For instance, Lemoine et al. (1996)

used multitemporal ERS-1 data to analyse the backscat-

tering signature for early season crop discrimination.

This study, supported by numerous ancillary data sets,

showed that well-chosen early season imagery can sig-

nificantly improve crop class separation with ERS-1 im-

agery. Some results reported in this work show that

based in summer signatures only, it is not possible to

separate summer cereals from spring cereals, also they

found that the total backscattering variation associated

to bare soil is in the order of 6 dB which suggest and

sensitivity of ERS-1 instrument to soil moisture of 0.25

dB/vol%. It was also highlighted that the use of mete-

orological data allows to predict signal variation quite

accurately, except in summer season, due to soil wet-

ness induced signal variations, however, this is still ob-

vious in season summer even for fully developed crops

according to Lemoine et al. (1996).

The results reported by Lemoine et al. (1996) showed

classification accuracies up to 85% for the most of

the crop types, however this results sometimes were

hardly affected by misregistration, especially for small-

est fields and when ascending/descending combinations

were used. In other sense, it was also shown that spe-

cially signatures from the period February-May enhance

the possibility of separate classes due to the effect of

crop specific tillage on the backscattering coefficient.

In this context, Nieuwenhuis and Kramer (1996) found

that in the beginning of the crop growing season can be

observed large fluctuation on backscattering due to the

difference in soil preparation and change in soil mois-

ture conditions.

Multitemporal data have a lot of benefits, for exam-

13

Figure 6: Classification accuracy obtained from the evaluation of different polarimetric combinations for some classes(Silva et al., 2012)

ple it can be evaluated using statistical analysis texture

features to improve the classification accuracy. In this

context, Tso and Mather (1999) evaluated a variety of

dataset derived from multitemporal ERS-1 for crop dis-

crimination. They compared pixel-based classifications

with per-field classification, reaching the second a better

performance giving accuracies higher than 75%, respect

to 60% obtained by pixel-based method, if a suitable

classifier is used. It was also reported that this accuracy

can be enhance if the SAR datasets are combined with

optical imagery. They proved several classifiers to per-

form this comparative analysis finding that for both the

per-pixel and the per-field experiments the neural based

Kohonen’s self-organized feature map (SOM) classifier

produced a better result than any of the statistical clas-

sifiers as measured by overall classification accuracy.

Multitemporal data have been also useful to eval-

uate the potential of SAR in substituting optical im-

ages for early crop discrimination, for example Yakam-

Simen et al. (1999) analized the operational feasibil-

ity of very early RADARSAT based acreage estima-

tion of non-cultivated terrain and economically impor-

tant crops during spring and early early summer report-

ing a complete successful in achieving this goal based

on the statistical analysis of the data. Other relevant ad-

vantages from using multitenporal data is the possibility

of retrieving bio-physical parameters through different

extraction techniques. In this context, Verhoest et al.

(2000) based on the sensitivity of microwave backscat-

tering from bare soil surfaces to the moisture content of

the upper soil layer used a principal component analy-

sis on a winter time series of eight speckle filtered ERS

images to separate topography, soil moisture and vege-

tation effects within the signal implementing Wavelet-

Based filtering techniques.

The used of advanced processing techniques has im-

proved considerably the range of information extracted

from multitemporal SAR datasets. Waske and Schiefer

(2006) reported classification accuracy of 78,2% that

shows that multitemporal SAR daa from an area dom-

inated by agriculture can be successfully classified us-

ing Support Vector Machine (SVM). A recent and very

interesting research was carried out by Lohmann et al.

(2009), they applied a standard pixel-based Maximum

Likelihood classification techniques, to obtain multi-

temporal classification, of TerraSAR-X image pairs

(HH and VV) amended by the use of regional crop cal-

endar to account for seasonal variations of specific culti-

14

vations with respect to permanent crops. They reported

a considerable classification accuracy of more than 75%

and suggested that the improvement of the datasets pre-

processing can enhance the results.

Other results presented by Lohmann et al. (2009),

showed that based on three different strategies for super-

vised classification (pixel-based Maximum Likelihood)

listed as:

1. Based on entired set of dual-pol images (all)

2. Using images according to crop calendar (Cal)

3. Using images as indicated by factor analysis

(SPSS)

And taking an area of 176 hectares for the analysis.

They found for all strategy a classification overall accu-

racy of approximately 100% was achieved, for Cal 98%

while for SPSS 87%, when asparagus, pasture, winter

grains, sugar beets, pea beans, potatoes, and maize cops

are analysed. Furthermore, they suggested some com-

binations using VV and HH polarization to improve the

classification accuracy.

The work cited here are a brief sample of the mul-

titemporal SAR datasets capabilities for crop type dis-

crimination, this coupled with the multifrequency and

multipolarimetric features of SAR data and the use of

optical data are a powerful tool for improve crop classi-

fication methodologies.

4. Conclusion

In order to highlight the SAR data capability for crop

discrimination, in this work have been presented sev-

eral research work in which have been reported tech-

niques and results that justified the feasibility of SAR

imagery for crop discrimination. Furthermore, based on

such works was presented a dissertation about SAR pa-

rameters that can be used for particulars applications.

The high results observed, allows to conclude that

the use of SAR imagery can be operatively imple-

mented into crop monitoring. Although when the SAR

imagery demands more pre-processing and processing

techniques, this characteristics is due to intrinsic pro-

perties that complemented with optical images conform

a suitable satellite framework for agricultural manage-

ment. Properties such as multipolarization or multifre-

cuency escenes acquisition enhance the discriminative

potential of SAR imagery to crop discrimination. All

these factors, coupled with the constants technological

and theoretical progress in SAR imagery systems, make

of SAR instrument a promising way to find solutions for

a many difficulties that represents a challenge for agri-

cultural development systems in the near future.

References

Al-Janobi, A., 1999. Performance evaluation of cross-diagonal texture

matrix method of texture analysis. Elsevier Science Ltd. 2

Boerner, W. M., Foo, B.-Y., Eom, H., 1987. Interpretation of po-

larimetric copolarization phase term in the radar images obtained

by the jpl airborne l-band sar system. IEEE Transactions on Geo-

science and Remote Sensing. 2

Born, M., Wolf, E., 1980. Principles of optics. Pergamon Press, New

York. 3

Brown, S., Quegan, S., Morrison, K., Bennett, J., Cookmartin,

G., 2003. High-resolution measurements of scattering in wheat

canopies-implications for crop parameter retrieval. IEEE Transac-

tions on Geoscience and Remote Sensing. 3.1

Chang, C., Chen, Y., 2004. Gradient texture unit coding for texture

analysis. Opt. Eng. 2

Chen, C. H., Wang, L., 1990. Texture unit, texture spectrum, and tex-

ture analysis. IEEE Trans. Geosci. Remote Sens. 2

Chen, E., Li, Z., Pang, Y., Tian, X., 2007. Quantitative evalua-

tion of polarimetric classification for agricultural crop mapping.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENS-

ING. 3.1, 3.1, 3

15

Cloude, S., Pottier, E., 1997. An entropy based classification scheme

for land applications of polarimetric sar. IEEE Trans. Geosci. Rem.

Sens. 3.1, 6

Ding, Y., Chen, Z., 2012. A review of crop identification and

area monitoring based on sar image. National high technol-

ogy research and development program of China 863Program

(2012AA12A307). 1.1, 1.2

Forbes, G., 2007. Texture and bubble size measurements for mod-

elling concentrate grade in flotation froth systems. Ph.D. thesis,

UNIVERSITY OF CAPE TOWN. 2

Freeman, A., Durden, S., 1998. A three-component scattering model

for polarimetric sar data. IEEE Trans. Geosci. Rem. Sens. 3.1, 6

Groot, J., Van den Broek, A., Freeman, A., 1993. An investigation

of 1989 polarimetric sar data for the flevoland testsite. EARSeL

Advances in Remote Sensing. 3.1, 3.1

Han, L. J., Pan, Y. Z., Zhu, X. L., Wang, S., Xu, L., 2007. Acquisi-

tion of paddy rice coverage based on multi-temporal irs-p6 satellite

awifs rs-data. Transactions of the CASE. 1.1

Haralick, B. M., Shamnugan, K., Dinstein, I., 1976. Texture features

for image classification. IEEE Trans. Syst. Man Cybernet. 2

Herold, N. D., 2004. An evaluation of radar texture for land use/cover

extraction in varied landscapes. International Journal of Applied

Earth Observation and Geoinformation. 1.2

Hoekman, D., Vissers, M., 2003. A new polarimetric classification ap-

proach evaluated for agricultural crops. IEEE Trans. Geosci. Rem.

Sens. 3.1

Hsu, M. K., Liu, A., Zhao, Y., Hotta, K., 2008. Satellite remote sens-

ing of spratly islands using sar. Int. J. Remote Sens. 1.2

Hsu, S. Y., 1978. Texture-tone analysis for automated land-use map-

ping. Eng. Remote Sens. 2

Irons, J. I., Petersen, G. W., 1981. Texture transforms of remote sens-

ing data. Remote Sens. Environ. 2

Karjalainen, M., Kaartinen, H., Hyyppa, J., 2008. Agricultural mon-

itoring using envisat alternating polarization sar images. Pho-

togrammetric Engineering & Remote Sensing. 3.1

Lee, J., Grunes, M., Ainsworth, T., Pottier, E., Krogager, E., Boerner,

W., 2000. Quantitative comparison of classification capability:

Fully-polarimetric versus partially polarimetric sar. IEEE. 3.1, 3.1,

1, 3.1

Lemoine, G., de Groof, H., van Leeuwen, H. J. C., 1996. Monitoring

agricultural land preparation with ers-1 to complement early sea-

son crop acreage estimates in the framework of the mars project.

ERS Applications, Proceedings of the Second International Work-

shop held 6-8 December. 3.2

Liu, A., Wu, S., 2001. Satellite remote sensing: Sar. Encyclopedia of

Ocean Sciences. 1.2

Lohmann, P., Soergel, U., S., T., Farghaly, D., 2009. Multi-temporal

classification for crop discrimination using terrasar x spotlight

images. ISPRS Hannover Workshop 2009 High-Resolution Earth

Imaging for Geospatial Information. 3.2

Lopez-Sanchez, J., Ballester-Berman, J., 2009. Potentials of polari-

metric sar interferometry for agriculture monitoring. RADIO SCI-

ENCE. 1.3

Lu, L. L., Guo, H. D., Han, C. M., 2008. Research advance on appli-

cation of wavelet remote sensing in agriculture. Journal of Anhui

Agriculture.Sciences. 1.2

McNairn, H., Shang, J. Jiao, X., Champagne, C., 2009. The contri-

bution of alos palsar multi-polarization and polarimetric data to

crop classification. ISPRS Journal of Photogrammetry and Remote

Sensing. 3.1

Nieuwenhuis, G. J. A., Kramer, H., 1996. Monitoring of agricultural

crops with ers-1 and jers-1 multitemporal sar data and crop growth

modelling. ERS Applications, Proceedings of the Second Interna-

tional Workshop held 6-8 December. 3.2

Pinter, J. P., Hatfield, L. J., Schepers, J., Barnes, E. M., Moran, S.,

Craig, S., Upchurch, R. D., 2003. Remote sensing for crop man-

agement. Photogrammetric Engineering and Remote Sensing. 1.1

Radionova, N., 2009. Phase difference application in fully polsar im-

ages. Proc. of 4th Int. Workshop on Science and Applications

of SAR Polarimetry and Polarimetric Interferometry PolInSAR

2009. 3.1

Saich, P., Borgeaud, M., 2000. Interpreting ers sar signatures of agri-

cultural crops in flevoland, 19931996. IEEE Transactions on Geo-

science and Remote Sensing. 3.1

Shao, Y., Chen, J., Guo, H., Lin, H., 2005. The potential of envisat-

asar data in monitoring crop growth. Proc. of the 2004 Envisat &

ERS Symposium, Salzburg, Austria. 3.1, 2

Silva, W., Rudorff, B., Formaggio, A., Paradella, W., Mura, J., 2012.

Simulated multipolarized mapsar images to distinguish agricul-

tural crops. Sci. Agric. 3.1, 6

Skriver, H., 2007. Signatures of polarimetric parameters and their im-

plications on land cover classification. IEEE. 3.1, 4, 5, 3.1, 3.1

Soares, J., Daleles, C., Formaggio, A., Da Costa, C., Frery, A., 1997.

An investigation of the selection of texture features for crop dis-

crimination using sar imagery. REMOTE SENS. ENVIRON. 2

Srinivasan, G. N., G., S., 2008. Statistical texture analysis. ENGI-

16

NEERING AND TECHNOLOGY. 2

Suga, Y., Konishi, T., 2008. Rice crop monitoring using x, c and l

band sar data. Remote Sensing for Agriculture, Ecosystems, and

Hydrology. 3.1

Tian, X., Chen, E., Li, Z., Ling, F., Bai, L., Wang, F., 2012. Rice/dry-

land crop discrimination using multi-polarization satellite sar dataa

case study in haian county of jiangsu province. Remote Sensing

Technology and Application. 1.2

Tso, B., Mather, P. M., 1999. Crop discrimination using multi-

temporal sar imagery. Int. J. Remote Sens. 3.2

Ulaby, F., Moore, R., Fung, A., 1981. Microwave remote sensing.

Active and Passive. 1.2

Umarani, C., Ganesan, L., Radhakrishnan, S., 2008. Combined sta-

tistical and structural approach for unsupervised texture classifi-

cation. INTERNATIONAL JOURNAL OF IMAGING SCIENCE

AND ENGINEERING (IJISE). 2

Unser, M., 1986. Sum and difference histograms for texture classifi-

cation. IEEE Trans. Patter Anal. Math. Intell. 2

Van Zyl, J., Zebker, H., Elachi, C., 1987. Imaging radar polarization

signatures: Theory and observation. Radio Science. 4

Verhoest, N., Pizurica, A., Philips, W., De Troch, F., 2000. The ap-

plication of wavelet-based filtering techniques for retrieving bio-

physical parameters from multi-temporal ers-images. ESA ERS-

ENVISAT symposium: Looking down to earth in the new mille-

nium. Presented at the ESA ERS-ENVISAT Symposium: Looking

down to earth in the new millenium, Noordwijk, The Netherlands:

ESA Publications Division. 3.2

Vincikova, H., Hais, M., Brom, J., Prochazka, J. Pecharova, E.,

2010. Use of remote sensing methods in studying agricultural

landscapes-a review. Journal of Landscape Studies 3. 1.2, 2

Wardlow, B. D., Egbert, S. L., 2008. Large-area crop mapping using

time-series modis 250m ndvi data: An assessment for the u.s. cen-

tral great plains. Remote Sensing of Environment. 1.1

Waske, B., Schiefer, S., 2006. Classifying segmented multitemporal

sar data from agricultural areas using support vector machines.

Proceedings of the 2nd Workshop of the EARSeL SIG on Land

Use and Land Cover. 3.2

Welch, R. M., Kuo, K., Sengupta, S. K., 1990. Cloud and surface tex-

tural features in polar regions. IEEE Trans. Geose. Remote Sens.

2

Weszka, J., C., D., Rosenfeld, A., 1976. A comparative study of tex-

ture measures for terrain classification. IEEE Trans. System, Man

and Cybernetics. 2

Yakam-Simen, F., Nezry, E., Zagolski, F., 1999. Early estimation of

crop surfaces, and agriculture monitoring using radarsat data. Geo-

science and Remote Sensing Symposium, 1999. IGARSS ’99 Pro-

ceedings. IEEE 1999 International. 3.2

17