This article was downloaded by: [University of Illinois at Urbana-Champaign]On: 01 August 2013, At: 05:52Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

International Journal of RemoteSensingPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tres20

Regional crop monitoring anddiscrimination based on simulatedENVISAT ASAR wide swath mode imagesX. Blaes a , F. Holecz b , H. J. C. van Leeuwen c & P. Defourny aa Department of Environmental Sciences – Geomatics,Université Catholique de Louvain, Croix du Sud 2/16, B‐1348Louvain‐la‐Neuve, Belgiumb Sarmap s.a., Cascine di Barico, CH ‐ 6989 Purasca, Switzerlandc Synoptics b.v., Costerweg 1k – 6700 Wageningen, TheNetherlandsPublished online: 31 Jan 2007.

To cite this article: X. Blaes , F. Holecz , H. J. C. van Leeuwen & P. Defourny (2007) Regionalcrop monitoring and discrimination based on simulated ENVISAT ASAR wide swath mode images,International Journal of Remote Sensing, 28:2, 371-393, DOI: 10.1080/01431160600735608

To link to this article: http://dx.doi.org/10.1080/01431160600735608

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

Regional crop monitoring and discrimination based on simulatedENVISAT ASAR wide swath mode images

X. BLAES*{, F. HOLECZ{, H. J. C. VAN LEEUWEN§ and P. DEFOURNY{

{Department of Environmental Sciences – Geomatics, Universite Catholique de

Louvain, Croix du Sud 2/16, B-1348 Louvain-la-Neuve, Belgium

{Sarmap s.a., Cascine di Barico, CH - 6989 Purasca, Switzerland

§Synoptics b.v., Costerweg 1k – 6700 Wageningen, The Netherlands

(Received 10 November 2004; in final form 3 April 2006 )

The current paper investigates the potential contribution of ENVISAT wide

swath (WS) images for discrimination and monitoring of crops at a regional

scale. The study was based on synthetic aperture radar (SAR) images acquired

throughout an entire growing season. Advanced synthetic aperture radar sensor

(ASAR) images in both narrow swath (NS) and WS modes were simulated based

on 15 European Remote Sensing (ERS) satellite images recorded over Belgium.

Unlike ‘real’ ASAR imagery, this exercise provided a consistent data set (i.e.

same incidence angle, same acquisition date, same acquisition hour) to study the

impact of spatial resolution on the SAR signal information content. A

quantitative approach using 787 parcels of medium field size and various data

combinations assessed monitoring and discrimination capabilities for six crop

types: wheat, barley, grasses, sugar beet, maize and potato. The spatial resolution

impact of the ASAR sensor was discussed with respect to the field size by

comparing the results obtained from NS (30 m) and WS (150 m) mode images.

WS temporal profiles were able to discriminate the various crops of interest and

were representative of the crop development observed in the region.

Furthermore, parcel-based unsupervised classifications successfully discrimi-

nated between grass, wheat, barley and other crops of large parcels (success rate

of 83%). Dedicated interpretation schemes were developed in order to

discriminate between cereal crops.

Keywords: Crop monitoring; Crop discrimination; ENVISAT; Wide swath-

ASAR; Coarse spatial resolution; Synthetic aperture radar

1. Introduction

The capabilities of synthetic aperture radar (SAR) sensors for agricultural

monitoring are well documented in the literature but the temporal resolution and

small scene extent of ERS, Japanese Earth Resources (JERS) satellite and Canadian

satellite aquiring SAR images (RADARSAT) hamper their use in operational

applications. Coarser SAR sensors such as the ScanSAR mode of RADARSAT and

the wide swath (WS) mode of the Environmental Satellite (ENVISAT) advanced

synthetic aperture radar sensor (ASAR), provide both better temporal resolution

and larger area coverage. Very few studies have explored the potential use of

*Corresponding author. Email: blaes@enge.ucl.ac.be

International Journal of Remote Sensing

Vol. 28, No. 2, 20 January 2007, 371–393

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2007 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160600735608

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

RADARSAT ScanSAR imagery for crop monitoring. Based on ScanSAR data a

rice field detection procedure was developed (Chakraborty and Panigrahy 2000). It

was proved to be cost-effective for rice monitoring over large areas. Classification

accuracy ranged from 91 to 95% for 11 states in India and the information was

delivered more than 30 days before the harvest. Rice fields were discriminated from

water bodies, forest areas, fallow fields and other crop types (Chakraborty et al.

2000). As the backscattering increased throughout the crop cycle owing to an

increase in volume scattering, rice sub-classes were separated based on growth stage

and associated crop rotation practices.

Other studies based on coarse spatial resolution images used ERS wind

scatterometer data. Despite angular effects, ERS scatterometer signal has been

used to discriminate regions predominantly covered by translucent vegetation

(grass, agricultural crops) from regions dominated by non-transparent vegetation

(forests, bushes and shrubs) (Wagner et al. 1999). In spite of its coarse spatial

resolution (50 km) Frison and Mougin (1996a) were able to observe the vegetation

seasonality from the signal over natural surfaces composed of semi-arid regions

(steppe and savannah) and boreal zones (tundra). These well-pronounced temporal

variations were related to the growing season over tree savannahs (Mougin et al.

1995) and annual differences of up to 4 dB were found in a Sahel test site

(Woodhouse and Hoekman 2000). The seasonal pattern of sigma nought was

attributed to an increase in the canopy water content from winter to summer for

most of the vegetation type (Abdel-Messeh and Quegan 2001). In a Siberian zone,

characterized by very large fields (mostly wheat), the temporal evolution of the

normalized radar cross -ection, observed for four different years, showed a

systematic increase in spring and a subsequent drop corresponding to the harvest

time (Schmullius 1997). In Asia, a strong seasonality was also observed for the

succession of rice and wheat crops (Frison and Mougin 1996a). The wheat cycle was

not clearly detected because of a slight variation of the sigma nought values.

However, the signal started to increase at the beginning of the rice cycle, indicating

the ability to detect the early stages of rice development. Acceptable agreement

between sigma nought and leaf area index (LAI) was also observed, even though

LAI was estimated annually by the Sheffield Dynamic Global Vegetation Model

(SDGVM) model, (Abdel-Messeh and Quegan 2001).

Current crop monitoring systems based on remote sensing often rely on coarse

spatial resolution optical remote sensing. Jakubauskas et al. (2001) showed different

crops (corn, soybean, alfalfa) exhibit distinctive seasonal Normalised Difference

Vegetation Index (NDVI) patterns having strong periodic characteristics. This

behaviour could potentially be used for crop-type identification. For three districts

in Zimbabwe, a good correlation was reported between corn yield at district level

and indices derived from advanced very high resolution radiometer (AVHRR)

imagery (Unganai and Kogan 1998). An AVHRR-based corn yield model was also

developed and was capable of providing information six weeks prior to harvest time.

However, coarse spatial resolution of these AVHRR and VEGETATION sensors is

not compatible with typical field sizes. Additional information is required as a priori

knowledge to select the most representative pixels in order to improve crop

monitoring results. In this way Stern et al. (2001) were able to estimate spring wheat

acreage at county and state levels using AVHRR imagery while LANDSAT TM

images predicted the percentages of spring wheat for each AVHRR class.

Geographical land cover data base (CORINE) land cover data were similarly used

372 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

to select the pixels in Satellite probatoire pour l’observation de la terre (SPOT)

vegetation images based on the percentage of land cover class (Genovese et al. 2001).

Implementing this method improved the estimations of crop yield at regional and

national scales for wheat and barley. New medium resolution optical sensors such as

MODIS and MERIS should further enhance these capabilities. However, the

presence of clouds and atmospheric perturbations will always limit the robustness of

crop monitoring systems based only on optical data. The 150 m spatial resolution of

the ASAR instrument combined with its ability to acquire imagery in all-weather

conditions are two major advantages for regional crop assessment.

The objective of this study was to investigate the potential contribution of the

ENVISAT WS ASAR images for crop monitoring and to provide an early crop

inventory at the regional scale. In particular, the research aimed to document the

potential contribution of the 150 m WS resolution data for crop growth monitoring

and crop discrimination in a landscape dominated by medium-sized field. The main

objective was to find out whether the WS ASAR time series could be applied to

various regions despite the small difference between its coarse spatial resolution and

the observed parcel size. The delivery time of crop information for agri-business

applications was also analysed.

2. The data set

The study area was located in an agriculturally intensive region of central Belgium,

which is dominated by loamy soils under various topographic and field size

conditions. Although ENVISAT was launched in March 2002, no ASAR temporal

series has been acquired throughout the growing season over Belgium until now.

The 2002 growing season corresponded to the ENVISAT commissioning phase

during which no data were delivered. After this period, the sensor calibration and

validation required systematic acquisitions of images over the Flevoland site in the

Netherlands which conflicted with Belgian acquisitions until the summer of 2003.

For this reason, ENVISAT ASAR time series were simulated using 15 ERS SLC

images acquired from early January to late October 1995 (figure 1). The ERS SAR

system operated in C-band at a frequency similar to the ENVISAT ASAR sensor

but only in a Vertical transmit–vertical (VV) polarization mode. The first four

images were acquired during the ERS F phase from four different orbits while the

remaining images were recorded during the G phase from only two adjacent tracks.

Additional information included field boundaries and crop type affectation. This

information was deduced from farmers’ declarations collected in the framework of

the Common Agricultural Policy (CAP) control. Field boundaries were digitized

from 1 m resolution ortho-photographs. The crop type was also extracted from the

farmers’ declarations and controlled by interactive photo-interpretation of 1/10 000

Figure 1. Temporal distribution of the 15 ERS images with the 1995 calendars of the sixmain crop types as reported for the study area.

Regional crop monitoring and discrimination by ASAR WS images 373

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

aerial photographs. The average field size in this region was 3 ha. This could be

considered as a medium size compared to the mean area per parcel in Europe, which

range from 1 ha in Greece to 8 ha in the south of Portugal. A subset of 787 parcels

with a surface area greater than 2 ha was selected. It corresponded to six major crop

types: winter wheat, winter barley, sugar beet, potato, maize and grass. In the study

area, these six crop types covered 87% of the agricultural surface. Figure 2 shows the

size distribution of the selected fields. Moreover, hourly rainfall distributions were

recorded from five meteorological stations spread over the study area.

3. Methodology

From the 15 ERS SLC images, a complete set of ASAR WS and narrow swath (NS)

simulations were produced. The respective geometric resolutions were 150 m and

30 m. The pixel spacing is dependent on the radar look angle. In the case of ENVISAT

ASAR, the incidence angle ranges from 14.4u to 45.3u from near to far range of a WS

image and from 15u to 45.2u depending on the NS position. Therefore an average of

100 m and 30 m was simulated respectively for the WS and NS pixel spacing. The WS

resolution is coarser than the current SAR sensors in orbit but a reduction in the

signal speckle is expected because of the higher equivalent number of looks

(ENL.11.5). The effect of spatial resolution on crop monitoring and discrimination

was investigated by comparing the results obtained from the NS and WS mode

simulated time series. The simulation technique is described below, whereas a

detailed description of the algorithms can be found in Pasquali et al. (1999). It should

be noted that the ASAR processor is working today based on the proposed approach.

The advantage of using simulated imagery was to dispose of the complete time

series with two different resolutions over the same test site. The simulated series

Figure 2. Size distribution of the selected fields (i.e. higher than 2 ha) in the study area. Theaverage size of this subset is 5.8 ha.

374 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

provided the opportunity to compare NS and WS images acquired with the same

configuration, i.e. the same acquisition date, incidence angle and polarization. Since

only the spatial resolution differed between NS and WS simulated images, the effect

of the spatial resolution with regard to the medium field size was assessed. As WS

images were simulated from ERS SAR temporal series, this paper only focus on the

spatial resolution effect to the crop monitoring and discrimination. Other important

aspect of real ENVISAT WS data could not be taken into account with simulated

data; e.g. the larger variation of the incidence angle cross the swath, the availability

of VV and horizontal transmit–horizontal (HH) polarizations and the much higher

temporal resolution.

3.1 ASAR narrow swath and wide swath data simulation

The 15 NS images were simulated based on the same ENL that is used to generate

ASAR alternating polarization (AP) products. Albeit the real AP products can

record signal in two polarizations, only the VV polarization could be simulated

based on the ERS time series. This section presents the simulation principle. A

detailed description and discussion of the algorithms are presented by Pasquali et al.

(1998, 1999).

The acquisition scheme of the raw AP data is shown in the figure 3(a). The system

acquires echoes in a first polarization for a time interval TB (burst time). It then

switches to a second polarization for a time interval of equal length (burst), before

switching back to the first polarization and so on. The unfocused response of three

point targets at different azimuth positions is drawn as a solid line: different parts of

Figure 3. ASAR AP data: (a) temporal distribution of the ASAR AP data in azimuth andrange and (b) spectral distribution of the ASAR AP data. The tinted bands refer to differentpolarizations.

Regional crop monitoring and discrimination by ASAR WS images 375

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

the Doppler history are acquired at different polarizations, corresponding to

different bursts.

It must be emphasized that the phase history of the different points depends only

on their range position and is independent of their azimuth location. Owing to the

azimuth–Doppler frequency of SAR data, a different spectral combination of

backscattered signal is acquired in different polarizations for each point on the

ground. This combination depends on the relative position of the target with respect

to the burst acquisition pattern. The Doppler bandwidth, Bwb, acquired within a

burst for a single ground scatterer is estimated as

Bwb~fMTB

where TB is the burst time and fM represents the Doppler rate (Monti Guarnieri and

Prati, 1996).

The complete Doppler spectrum of a single point consists therefore of a conti-

guous sub-bands sequence of width Bwb. The number NS of these sub-bands is

estimated as

NS~fPRF=Bwb

~fPRF=fMTB

~TA=TB

where TA is the aperture time and fPRF the pulse repetition frequency. It should be

noted that NS, generally not an integer, corresponds to twice the number of looks in

a normal ScanSAR system. The frequency location of the sub-bands corresponding

to the different bursts, i.e. different polarizations, varies according to the position of

the ground reflector relative to the acquisition pattern. The central frequency of

each sub-band varies linearly along the azimuth direction. The instantaneous

azimuth spectrum of the AP raw signal is described as a cyclostationary process,

with a period equal to 2*TB. The theoretical instantaneous azimuth spectrum of the

AP data is illustrated in figure 3(b) for the case where TB is exactly equal to half the

aperture time (TA).

The standard AP algorithm (Stevens et al. 1997) is designed to process the

different polarizations separately, by zeroing the raw data corresponding to the

polarization that is not considered. To avoid processing a large amount of null data,

a technique similar to the ScanSAR data processing method (Monti Guarnieri and

Prati 1996) is used. Since the Doppler history is not dependent on its polarization,

the full raw data set is processed at once. This produces an image where the

contributions coming from the two polarizations are focused together, i.e. where

the two polarizations are spatially aliased but are still separated in the Doppler

frequency domain. After azimuth compression, a further filtering step must be

applied to separate the two images corresponding to the different polarizations. This

step is carried out by applying a pair of complementary filters, each designed to

isolate a single polarization. These filters are azimuth-varying. Conventional

procedures are used for the doppler centroid and doppler ambiguity estimation

and for the autofocuss.

The polarization separation filter, shown in figure 4, can be easily constructed by

referring to figure 3(b). The basic filter is composed of a summation of NS/2 ideal

band pass filters of bandwidth Bwb, spaced in the frequency domain by a distance

2Bwb. Its frequency response is written as

376 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

H fð Þ~XNs=4

k~{Ns=4

rectf {2kBwb

Bwb

� �

The azimuth-varying filter can be carried out either by demodulating the images

differently for each azimuth position and then applying the basic filter, or by

modulating the filter differently along the same direction. In the first case, the

filtering may be performed as follows:

(i) deramping the data by multiplying them by the proper quadratic phase

function, to re-align their spectrum and make it azimuth-invariant;

(ii) filtering the different parts corresponding to the two polarizations,

generating the two separate images: this can be implemented in either the

time or the frequency domain;

(iii) multiplying the two images by the complex-conjugate phase function used to

deramp the combined data: this reconstructs the original spectra and fulfils

the requirements for a phase-preserving processor.

In both cases, the central frequency of the azimuth varying modulation must vary

by a value Bwb in an azimuth interval corresponding to the burst duration.

In a rigorous analysis, one must consider that the Doppler rate fM is a function of

the slant range position of the considered pixel. On the other hand, the variation of

the Doppler bandwidth of a single burst from near to far range is approximately

20 Hz, i.e. around 1% of the fPRF. Therefore, the filter variation along range

direction can be neglected without risking the introduction of significant cross-talkeffects in the final single look complex (SLC) products.

The WS mode simulation can be straightforward performed on the same prin-

ciple described to the AP mode. As in the AP case, WS data can be simulated from

SAR RAW or SLC data. However, in both cases, the burst must be modifiedaccording to the different acquisition modes, namely from 2 (AP case) to 5 (WS

case).

Figure 4. Polarization separation filter for the AP case. The different lines correspond to thetwo burst filters.

Regional crop monitoring and discrimination by ASAR WS images 377

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

3.2 Geometric and radiometric calibration

Based on the radar equation for distributed targets it is known that the received

power is modulated with the two-way antenna gain, the range spread loss and the

reciprocal value of the local incidence angle. For each pixel, these quantities are

therefore dependent on the radar look angle, the sensor position and altitude, the

position of the backscatter element, as well as on the processed pixel spacing in the

range and azimuth directions. Since SAR focusing does not include topographic

information, geometric and radiometric calibration must therefore be considered in

a post-processing step. A detailed discussion of the applied method is given in Meier

et al. (1993) and Holecz et al. (1994).

The calibration step was carried out in the following way. The slant range data

were geocoded (geometric calibration) considering the orbital data (derived from the

state vectors) and a digital elevation model (40 m resolution), applying a rigorous

range-Doppler approach (Meier et al. 1993). Some ground control point (GCP)

were selected on the 1 m ortho-photographs and used during the geocoding

procedure in order to correct orbital inaccuracies (around two to three pixels in the

azimuth direction and one to two pixels in the range direction for ERS data). During

the geocoding, all cartographic and geodetic transformations were taken into account,

and the SAR data were calibrated based on the radar equation, hence enabling to

determine the backscattering coefficient (sigma nought) (Holecz et al. 1994).

An inter-image normalization was applied, based on offset coefficients. Within

each track, the signal extracted over a forest area is very stable. The coefficients were

thus determined by matching the signal of this stable area, averaged for all the

images of a given track, to the mean signal observed for the same area on the images

of a reference track. The track number 151 produced in AP mode is here considered

as reference.

Averaging the intensity reduces the radiometric resolution errors, i.e. it reduces

the image speckle. From a statistical point of view, the radiometric confidence

intervals (IC) is given by the probability that the image intensity of a homogeneous

target lies within error bounds. This IC bound is a function of the ENL. The

intensity averaged over a homogeneous area has a gamma distribution. The

ENL_output parameter is approximated multiplying ENL_original by the number

N’ of independent pixels in the area of interest. The relationship between

ENL_original and ENL_output is ENL_output , ENL_original * N/R where

R5N/N’ is the number of pixels (N) per independent pixel (N’) in the simulated data

(Laur et al. 1998).

For the NS simulations, the ENL_output was estimated to be 1.8*N while the

ENL_output was 5.11*N for the WS simulation. Assuming the same radiometric

accuracy for the ENVISAT ASAR than the ERS SAR instrument, a radiometric

confidence interval bounds (IC bounds) equal to + /20.5 dB with a probability

higher than 90% were obtained based on 133 AP and 47 WS simulated pixels. For

example, with a parcel size equal to 6 ha, 90% IC bounds were + /21.25 dB for WS

and + /20.7 dB for NS simulations. The smallest field in the data set contained only

2 WS or 26 NS pixels. The corresponding 90% IC bound were + /22.25 dB and + /

21.1 dB respectively for WS and NS simulations.

Furthermore, the temporal profiles of each crop type were computed averaging

the signal for all of the fields in the region. Consequently the IC bounds of these

regional averages were much lower than bounds of individual parcels and were

mainly dependent on the number of averaged parcels. For example, five 6 ha parcels

378 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

corresponded to, on average, 30 WS pixels with an IC bound of + /20.625 dB

whereas for the NS images, it corresponded to about 330 pixels with an IC bound

lower than + /20.5 dB.

3.3 Crop monitoring and discrimination

The simulated images and field boundaries were combined to extract the mean

signal per field object. A subset of 787 parcels each greater than 2 ha, was used for

this operation. A buffer zone of 1 pixel was used around the field boundaries in the

NS simulated images in order to minimize the mixed pixels located at the edges of

field providing a minimum of 26 pixels within any field. No buffer zone was applied

to the simulated WS images because the field size compared to the 100 m pixel

spacing was small and therefore only a few pixels were contained in the smallest

fields.

In a first step, the regional temporal profiles were computed for each crop by

averaging the backscattered signal over all the pixels belonging to fields larger than

6 ha, which varied between 5 to 95 parcels, depending on the crop. The relative

comparison between temporal profiles permitted the identification of crop-specific

content within the WS ASAR signal.

The second step further investigated the crop-specific signal to discriminate crops

at both the parcel and the pixel level. Parcel-based classifications were completed

using a priori knowledge of field boundaries in order to put the emphasis on

analysing the WS signal content. Confusion matrices and overall accuracy were

calculated from ground reference data for all image combinations. The results were

compared with the accuracy of the pixel-based classification in order to document

the impact of the a priori knowledge of the field boundaries for the ASAR WS data

interpretation. The contribution of the higher temporal resolution of the ASAR WS

mode was discussed.

4. Results

The 15 ASAR WS and 15 ASAR NS images were simulated successfully in the VV

polarization mode. Visual comparison of both data sets is presented in figure 5. In

spite of the coarser spatial resolution of the WS simulated images, the same general

pattern was observed in both types of images.

4.1 Crop monitoring based on WS time series

The WS signal content was first assessed looking at the regional profiles computed

averaging the signal over the largest fields (i.e. larger than 6 ha) located within the

study area [figure 6(a)]. The differences between the temporal profiles of the main

crops clearly demonstrated a differentiation of the ASAR WS temporal profiles

according to the crop type. These profiles agreed with temporal evolutions described

in the literature for C-band imagery where sugar beet, potato and winter wheat

exhibited similar patterns to those described by Nieuwenhuis and Kramer (1996).

Moreover, Saich and Borgeaud (2000) described the behaviour of an ERS signal for

five crops (all of which are included in this study except for maize) over several

years. Similar temporal trends were observed in the WS profiles. As expected,

grasses were the darkest vegetation type during autumn and winter and exhibited the

most stable signal throughout the entire season. Three dates, June 13, July 18 and

September 7, did not exhibit the same behaviour because intense precipitations were

Regional crop monitoring and discrimination by ASAR WS images 379

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

recorded prior to the image acquisition. Potato and sugar beet profiles were almost

always brighter and showed similar profiles throughout the growing season. The

maize profile exhibited a similar pattern to these two crops because it was sown in

the same time period and shares a similar crop calendar (figure 1). Small differences

were observed between late June and the end of September which corresponds to the

harvest period for maize. The profiles of the winter cereals decreased early in the

Figure 5. Colour composites of simulated ENVISAT ASAR WS (left) and NS images(right). The input ERS SLC data are 7 September (R), 29 June (G) and 20 April 20.

380 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

season and then increased until harvest time which is mid July for barley and firsthalf of August for wheat. In early spring, the barley crop growth profile decreases

more rapidly than that of wheat. In summer, the signal increase for barley occurred

earlier than for wheat. The main differences in crop growth profiles between winter

wheat and barley and between winter cereals and spring crops occurred in a period

that ranged from April to July.

The same crop growth profiles were compared with the ASAR NS mode time

series for the same set of fields [figure 6(b)]. Relative comparisons made for the WS

crop profiles were also observed for NS mode. However, for any given date, thesignal differences between the various crop types were almost twice as large for the

NS mode than for the WS mode. The profiles for the different crops were better

separated using the NS mode than the WS mode. The coarse spatial resolution of

the WS images caused a smoothing effect. The difference between WS and NS time

series clearly illustrated the significant contribution of the mixels (i.e. mixed pixels)

associated with the field boundaries. Despite the small number of pixels included in

each field, the WS regional profiles were similar to the NS profiles indicating the real

potential of ENVISAT WS mode imagery for crop monitoring at regional scale.This potential should be confirmed by real WS data that present larger incidence

angle range.

Figure 6. Regional average profiles over the largest fields (bigger than 6 hectares) for (a) theASAR WS mode and (b) ASAR NS mode. The curves are computed using 95 winter wheat,27 winter barley, 70 grasses, 20 potato, 5 maize and 88 sugar beet fields.

Regional crop monitoring and discrimination by ASAR WS images 381

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

4.2 WS images for crop discrimination

In figure 6, the standard deviation and IC bound was not plotted. However,

statistical comparisons between each couple of crop types were computed for each

date. Table 1 reports the tests comparing the means 2-by-2 for the NS simulated

images with a significance level of 95% (a50.05). Only fields size higher than 6 ha

were taken into account (same subset than figure 6). The same comparisons were

made for the WS signal averaged per field (table 2). The temporal profiles and the

comparisons between crop averages permitted documenting the potential ability to

discriminate different crops based on individual image acquisitions throughout the

season. In general, the number of significantly different means was greater for the

Table 2. Significant differences between values plotted in the WS profiles at figure 6(a). Thesame legend than the table 1 is applied here.

Date Winter wheat Winter barley Grass Potato Maize Sugar beet

10/01/95 gr,po po,ma,sb ww,po,ma,sb ww,wb,gr wb,gr wb,gr27/01/95 gr,po gr,po ww,wb,po,ma,sb ww,wb,gr Gr gr16/02/95 gr,po,ma,sb po,ma,sb ww,po,ma,sb ww,wb,gr ww,wb,gr ww,wb,gr20/04/95 gr,po,sb po,sb ww,po,sb ww,wb,gr ww,wb,gr09/05/95 wb,sb ww,gr,po,ma,sb wb,sb wb wb ww,wb,gr25/05/95 gr,sb gr,ma,sb ww,wb wb ww,wb13/06/95 gr,po,ma,sb po,ma,sb ww,po,ma,sb ww,wb,gr ww,wb,gr ww,wb,gr29/06/95 wb,gr,po,sb ww ww,po,sb ww,gr ww,gr18/07/95 po,ma,sb po,ma,sb po,ma,sb ww,wb,gr ww,wb,gr ww,wb,gr03/08/95 po,sb po,sb po,sb ww,wb,gr ww,wb,gr07/09/95 Gr gr ww,wb,po,sb gr gr26/09/95 wb,gr ww,gr,po,sb ww,wb,po,ma,sb wb,gr Gr wb,gr12/10/95 Gr sb ww,po,sb gr wb,gr31/10/95 Gr sb ww,po,ma,sb gr Gr wb,gr

Table 1. Significant differences between values plotted in the NS profiles at figure 6(b). Foreach crop type (as titled in each column) at each acquisition date (each line), the signalsignificantly different (for a50.05) than the signal of the other crops are reported. The croptypes are abbreviated as follows: winter wheat (ww), winter barley (wb), grass (gr), potato

(po), maize (ma) and sugar beet (sb).

Date Winter wheat Winter barley Grass Potato Maize Sugar beet

10/01/95 wb,gr,po,sb ww,po,sb ww,po,ma,sb ww,wb,gr gr ww,wb,gr27/01/95* wb,gr,po,sb ww,po,sb ww,po,ma,sb ww,wb,gr gr ww,wb,gr16/02/95 gr,po,sb po,sb ww,po,sb ww,wb,gr ww,wb,gr20/04/95 All ww,po,ma,sb ww,po,ma,sb All ww,wb,gr,po ww,wb,gr,po09/05/95 wb,po,ma,sb All wb,po,ma,sb ww,wb,gr,sb ww,wb,gr ww,wb,gr,po25/05/95 gr,po,ma,sb gr,po,ma,sb ww,wb,sb ww,wb,sb ww,wb ww,wb,gr,po13/06/95 All ww,po,ma,sb ww,po,ma,sb ww,wb,gr ww,wb,gr ww,wb,gr29/06/95 All ww,gr,ma ww,wb,po,sb ww,gr,ma ww,wb,po,sb ww,gr,ma18/07/95 wb,po,ma,sb ww,gr,po,sb wb,po,ma,sb ww,wb,gr,sb ww,gr ww,wb,gr,po03/08/95 wb,po,ma,sb ww,po,sb po,ma,sb ww,wb,gr ww,gr ww,wb,gr07/09/95 wb,gr ww,gr,po,sb All wb,gr gr,sb wb,gr,ma26/09/95 wb,gr ww,gr,po,sb All wb,gr gr wb,gr12/10/95 gr,sb gr,sb ww,wb,po,sb gr,sb sb All31/10/95 wb,gr,sb ww,gr,sb All gr,sb gr ww,wb,gr,po

*The images of the 27 and 30 of January were merged together because they do not cover thewhole test site.

382 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

NS mode images than for the WS mode images. For every crop pair, we can observe

one or several acquisition dates at which the NS signals of both crops were

significantly different (table 1). The more critical crop is the maize that could only be

statistically discriminated from the other crops on a couple of dates during the

growing season. The two other spring crops, potato and sugar beet, exhibited mean

signals that were significantly different at the beginning (April to May) and at the

end of the growing season because the potatoes were harvested before the sugar beet

(figure 1). Grasses were easily discriminated from the other crops. Winter cereals

(wheat and barley) were also easily differentiated from the spring crops.

Smaller differences between crops were observed within the WS images (table 2).

Spring crops (sugar beet, potato and maize) were always confused with each others.

However, significant differences were observed at some dates when comparing

spring crops with grasses, winter wheat and winter barley. Moreover, the winter wheat,

winter barley and grasses exhibited greater differences in their WS signal means.

Based on the above analysis, the spring crops could not be accurately dis-

criminated with a single WS channel which differs from the results based on the NS

images. However, for winter wheat, winter barley, grasses and spring crops, there is

always at least one WS image for each crop providing distinct signal enabling their

discrimination from the other crops.

4.3 The impact of field size on the distribution of crop signals

The available data were subset into classes based on similar sized fields and each

class was made up of the same number of fields. This subset was used to investigate

the variability in WS temporal signal profiles for the winter wheat and sugar

beet crops because they were the only crop types with an acceptable field size

distribution. A maximum change of 0.5 dB was observed for the different field sizes

for the winter wheat crop [figure 7(a)]. Greatest differences in the mean signal were

exhibited on drier days (29 June and 3 August). Figure 7(b) shows the influence of

the field size subsets on the field to field variability. A higher standard deviation

always corresponded to the smaller field sizes and the greatest variations over time

were observed within the smaller fields. Whereas the impact of field size on the

standard deviation was high, the greatest difference between the mean signal was

only 0.5 dB [figure 7(a)].

Similar results were obtained in the case of the sugar beet as shown in figure 7(c)

and (d). Consequently, a similar effect could be expected in the average estimation

of the backscattering coefficient at a given date for the other crops, i.e. 0.5 dB

between larger and smaller field averages.

4.4 The impact of spatial resolution on crop discrimination

In order to analyse the impact of the spatial resolution on the ability to discriminate

between crop types, the fields were clustered into four subsets defined by a range of

field sizes. Therefore, the number of fields varied within any size class (table 3).

As shown in figure 8 (b)–(e), the differences between the WS profiles of the six

crops of interest decreased with the size of the fields that were included in the

regional average. Furthermore, the ability to discriminate different types of crops

using the WS mode imagery was lost for small fields owing to their higher

proportion of mixels. Although the smaller fields introduced some noise into the

profiles including all the fields, crop discrimination was still possible [figure 8 (a)].

Regional crop monitoring and discrimination by ASAR WS images 383

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

This was made evident in figure 8 (g)–(j) in the case of discriminating between wheat

and barley crops during the ripening. This phase occurred about 15 days earlier for

the winter barley (late June) than for the wheat based on the specific crop calendar

of the region (figure 1). In the NS mode image case there was no field size effect

observed because the buffer zone removed most of the mixels at the edge of the field

boundary and even the smallest fields still included 26 NS pixels.

4.5 The impact of the images temporal resolution

The loss of information attributed to the coarse pixel size was expected to be

balanced by the very high temporal resolution capability of the ASAR WS mode.

The available data set with a 16 to 19 day temporal resolution was unable to

properly simulate the 3 to 5 day capability of the ASAR instrument. However, the

Table 3. Number of fields used to compute the mean profiles of the figure 8 for each crop typeand each fields size cluster.

Crop types 2 to 3 ha 4 to 5 ha 6 to 9 ha .10 ha All size

Winter wheat 67 90 63 32 252Winter barley 12 27 21 6 66Grass 41 85 64 6 196Potato 4 12 14 6 36Maize 10 21 5 0 36Sugar beet 35 78 59 29 201

Figure 7. Field size impact on WS winter wheat [(a) and (b) ]and sugar beet [(c) and (d)]regional profiles computed from the same number of fields (n563 for each subset of winterwheat, n550 for each subset of sugar beet fields). Graphs (a) and (c) present the evolution ofthe mean (i.e. average of the field-average) and the graphs (b) and (d) show the standarddeviation of the mean (i.e. field to field variation). The mean size of field is expressed inhectares.

384 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

simulated images clearly demonstrated the strong interaction between rainfall and

crop types, and its specific variability during the growing season. The regional

profiles obtained for the different crops shown in figure 6 were strongly affected by

the distribution of rainfall and the two peaks observed in June and in July were

directly related to this. The cumulated rainfall that fell 24 h before the acquisitions

reached 14 mm on 13 June, 2 mm on 18 July and 2 mm on 7 September (figure 9). No

rainfall was recorded for the 24 h period prior to the other acquisitions.

The SAR signal sensitivity to the soil moisture content depends on the crop

development stage. This sensitivity varied in time according to the crop type and

could be used to discriminate between crops. For instance, the sugar beet signal was

more strongly affected by the high mid-June soil moisture content than the winter

wheat. The difference in the signal between 25 May and 13 June was higher for sugar

beet and was directly related to differences in crop development. This coincided with

Figure 8. Evolution of the mean WS backscattering coefficient computed using all field sizesfor all crop types (a) and for winter cereals (f). The other graphs correspond to the subsetswith decreasing fields size: equal or bigger than 10 ha [(b), (g)], from 6 to 9 ha [(c), (h)], from 4or 5 ha [(d), (i)] and from 2 or 3 ha [(e), (j)]. The numbers of fields used to compute thesecurves are reported in the table 3.

Regional crop monitoring and discrimination by ASAR WS images 385

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

the end of the growing period for winter wheat (figure 1) that consisted of fully

developed green plants. Comparatively, the sugar beet vegetation did not cover the

soil during these periods because they were sown only a few weeks earlier. Typically

at this stage of growth the canopy cover is lower than 50% for sugar beet and

reaches 90–100% for winter wheat (Blaes and Defourny 2003). The soil contribution

and consequently the influence of soil moisture to the backscattered signal were

higher in the sugar beet fields than in the winter wheat fields.

In mid-July, the soil moisture content influenced more the winter wheat signal

than the sugar beet signal. The signal difference between images simulated on 18

July and 3 August was higher for winter wheat. Indeed, at this time winter wheat

was either already harvested or quite transparent to the SAR signal owing to the low

moisture content of the plant (after the drying stage in the development cycle).

Therefore, the soil in the wheat fields had a greater influence on the backscattered

signal than soil in the sugar beet fields, which was completely masked by the

vegetation cover. However, the increase in soil moisture was not the only effect that

the rain produced because it has been shown that droplets suspended within the crop

canopy could also significantly impact the signal return (Saich and Borgeaud 2000).

This was especially true in this case because at least 14 mm of rain fell prior to image

acquisition and a large portion of that water would have been suspended in the

wheat canopy which contributed to an increased backscattering coefficient. This

effect was not as marked for the sugar beet canopy because the plant was much less

developed on 13 June. Nevertheless, the different soil moisture impacts on the

backscattered signal (related to the differential crop development) were clearly

visible in both the WS and the NS signal profiles (figure 6).

Various indices were tested to discriminate between crop types using the

contrasting behaviours attributed to soil moisture. Finally, a ratio (r) of two

temporal differences was defined from the WS signal as

s025=05{s0

13=05

� �.s0

18=07{s003=08

� �

where indices correspond to acquisition dates.

When grasses were not taken into account, spring crops and cereals were classified

with an accuracy of 70% applying a threshold on this ratio. This promising result

Figure 9. The 24 h cumulated rainfalls for the year 1995 with regard to the ASAR WS modeacquisition capabilities (small triangles).

386 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

will undoubtedly be improved by a finer temporal resolution of the images. Indeed,

the probability of acquiring the appropriate data set to take advantage of the

differential interaction between soil moisture content and the backscattering signal,

that is crop type and growth stage specific, is quite high for a 3 to 5 day temporal

resolution sensor (figure 9).

In a more classical perspective the high temporal resolution will also serve to

discard all the acquisitions affected by recent rainfalls. This constitutes a prior

condition for crop monitoring and would also be a critical step forward for a more

automated SAR data classification scheme. For instance, the temporal average of

multiple images acquired within a short period (not affected by rainfall) should

increase the radiometric resolution by reducing the speckle noise. Such average

should improve the crop monitoring and crop discrimination possibilities even for

small sized fields.

4.6 Parcel-based classification

The above results led to an assessment of the parcel-based crop discrimination

possibilities. Because the larger parcels contributed to most of the crop production

and showed more consistent time profiles, only the largest 305 parcels (.6 ha) were

considered during the classification. This set of fields corresponds to only 59% of the

agricultural land covered by the 787 parcels.

Various classification schemes were tested including different channel combina-

tions (table 4). The WS images were selected using the crop calendar (figure 1) and

the temporal profiles (figure 6). All the classifications were based on the

unsupervised ISODATA algorithm (Tou and Gonzalez 1974). The best result was

obtained based on four images acquired in April, May and June which provided an

overall accuracy of 67%. This result agrees with the work of Saich and Borgeaud

(2000) which demonstrated that mid-season (May and June) acquisition dates

produced the best classes’ separability. The main advantage of applying this

combination was that the results could be delivered before the end of June. A slight

improvement was achieved based on two time series indices in addition to the above

mentioned combination (68% accuracy). The range index corresponded to the

difference between the maximum and the minimum backscattering values over the

time series while the second index was the ratio (r) defined in section 4.5 which took

advantage of the differential rainfall effect depending on the crop type.

Analysis of the results illustrated the best image combinations for the

discrimination of specific classes contributed simultaneously to the confusion for

other classes. Therefore a three-step crop specific strategy was devised and first

applied to the identification of cereal crops (table 5). The ISODATA algorithm was

Table 4. Performances of parcel-based unsupervised classifications (ISODATA algorithm)obtained using different data combinations. The dates refer to images acquisitions.

Test Input data combination Accuracy Delivery time

1. 20/04/95, 25/05/95, 13/06/95 and 29/06/95 67% End of June2. Range and ratio indices (r) 59% September3. 20/04/95, 25/05/95, 13/06/95, 29/06/95, range and

ratio indices (r)68% September

4. 09/05/95, 25/05/95, 29/06/95 and 03/08/95 (dry days) 63% August5. 13/06/95, 18/07/95 and 07/09/95 (wet days) 63% September

Regional crop monitoring and discrimination by ASAR WS images 387

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

applied for each step. The first step was based on January and September images

and separated the grasses from the other crops with an accuracy of 85%. The idea

was to differentiate grass fields from spring crops based on images acquired in

January (when these crops were not yet sown) and from winter crops based on a

September image acquisition (after the harvest of the winter cereals). The set of

fields considered as ‘crops’ in this first step was then classified into spring crops or

winter cereals. This second step also reached a classification accuracy of 85% based

on the ratio index and images acquired in June and July. As mentioned in the

previous section, the ratio index was able to separate most of the cereals from the

spring crops with an accuracy of 70% based on a simple threshold. Combining the

June and July images with this index improved the classification accuracy to 85% for

cereals versus spring crops. Finally, a classification accuracy of 82% was achieved in

the third step discriminating the winter cereals between wheat and barley based on

April, May and June images. The June date alone contributed 79% of the

discrimination power which agreed with the temporal profiles of figures 8 (g) and (h)

where the highest difference between both crops was reported. Overall, this three-

step strategy was able to discriminate 83% of the fields between winter wheat, winter

barley and the other crops.

Since the unsupervised classification and labelling process were applied to the

entire set of available fields and included those used for validation, the accuracy

assessment could not be considered as being strictly independent. However, the

analysis showed that unsupervised clustering was able to separate crop classes and

thus illustrated the information content of the WS signal for crop discrimination.

4.7 Pixel-based classification

Unlike parcel-based classifications, per-pixel approaches do not require information

on the field boundaries. Unsupervised classification was used to cluster pixels within

the agricultural zone into 69 classes based on the following dates: 20 April, 25 May,

13 and 19 June. Only pixels found within parcels larger than 6 ha were used in order

to compare the result with those of the previous section. The overall classification

accuracy only reached 49.7%, which is quite low compared with the 67%

classification accuracy achieved by the parcel-based classification scheme using

the same combination of dates.

5. Discussion

The simulated WS mode images based on ERS acquired imagery provided only part

of the possible ASAR WS mode time series. However these simulations allowed

Table 5. Cereals identification scheme based on a 3-steps’ method (ISODATA algorithm).The dates refer to images acquisitions.

Classification step Input data combination Accuracy Delivery time

1) Grass discrimination 10/01/95, 28/01/95 and 07/09/95 85%2) Cereals discrimination 13/06/95, 18/07/95 and ratio

index (r)85%

3) Wheat/barleydiscrimination

20/04/95, 25/05/95 & 29/06/95 82%

Overall 83% September

388 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

testing the ability of 150 m spatial resolution images for crop growth monitoring and

crop type identification. Reduced image speckle owing to the high ENL com-

pensated for the coarse spatial resolution effect of the WS mode quite significantly.

The classification results obtained were lower than those obtained in studies that

used higher spatial resolution SAR imagery but were still comparable. Tso and

Mather (1999) found that the field-based classification of seven different crops based

on ERS SAR imagery was superior to their per-pixel approach using a neural

network algorithm. The accuracy of both methods was respectively 78% and 60%

based on 7 images acquired from April to August. Using a minimum Euclidean

distance classifier, field-based classification accuracy dropped to less than 60%. In

this experiment, training and evaluation sets were not strictly independent; half of

the available ground data were used for training purposes and the total ground data

were used for evaluation. In consequence these overall accuracies were most likely

overestimated. However, the field-based classification accuracy was superior to the

pixel-based one (Tso and Mather 1999). No more than 55% accuracy was obtained

by Michelson et al. (2000) when multi-temporal SAR images were used for field-

based classifications of nine crop types. A similar level of accuracy was also

obtained by Le Hegarat-Mascle et al. (2000) using SAR satellite data. Xie and Quiel

(2001) obtained even lower accuracies ranging between 40% and 50% for 11

different agricultural classes when applying the per-pixel based classifications. They

encountered difficulty in discriminating between wheat, barley and oat crop types.

However, reducing speckle within the imagery often improved the quality of pixel-

based classifications. A study done by Capstick and Harris (2001) increased the

overall accuracy of the per-pixel classifications from 38% for the original ERS data

to 60% for the filtered data based on ten classes. An even greater accuracy (80%) was

obtained based on ERS imagery and field-based classification of 980 parcels for 12

crop types using the maximum likelihood algorithm (Schotten et al. 1995). However,

this result was obtained over the Flevoland site that is characterized by very large

(11.6 ha on average) and flat fields. An alternative to the speckle reduction technique

when using coarse spatial resolution sensors was to use a priori information on field

boundaries which can be delineated from high-resolution data or in Europe, are

made available through the information system developed for CAP

Ban and Howarth (1999) used a sequential masking approach based on three ERS

images that was able to differentiate between five groups of crops with an overall

accuracy of 85 %. Even though this study used very large fields (covered by at least

500 pixels), similar accuracy was found for small fields (with respect to the sensors

spatial resolution) in our three-step strategy which correctly classified 83 % of fields

discriminating between winter wheat, winter barley and the other crops.

The results obtained from this study of the simulated ASAR signal are expected to

be further enhanced by the very high acquisition rate of the WS mode. However,

such a high temporal resolution will also produce a variety of incidence angles

between image dates that will introduce variability into the signal. Ban and Howarth

(1998) found an average difference of nearly 3 dB in the backscattering coefficient

over maize fields using ERS-1 data from contiguous orbits that varied by 4u of

incidence angle. In our study, the effect of the incidence angle was not taken into

account. Only a partial analysis would have been possible as the incidence angles of

ERS images only varied between 19.4 to 26.6u while the ASAR WS mode can have

incidence angles ranging from 14.4 to 45.3u. This effect has been addressed in several

studies for different land cover types based on wind scatterometer data, which can

Regional crop monitoring and discrimination by ASAR WS images 389

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

have incidence angles ranging from 18 to 57u. On bare soil, the backscattering signal

decreased when the incidence angle increased and this effect was less pronounced

over vegetated regions (Wagner et al. 1999). This general angular behaviour of

sigma nought was linear within the 25–55u incidence angle range (Frison and

Mougin 1996b). Since radar measurements are highly dependent on the incidence

angle, five correction models were discussed to normalize to a single incidence angle.

The angular signature presented by Mougin et al. (1995) showed that the variations

were more pronounced for sparse vegetation (0.13 dB per degree) and bare surfaces

(0.21 dB per degree) for which surface roughness strongly influenced the back-

scattered signal. These authors proposed a procedure to generate a global map

based on any incidence angle using ERS wind scatterometer data which permitted to

eliminate the angular effect of the signal. Schmullius (1997) improved land cover

discrimination by taking into account the backscatter intensity at favourable

incidence angles (large incidence angles) and combining it with information about

the backscatter mechanism. Water was properly identified (classification accuracy of

95%) owing to the small temporal variation in the backscatter signal that has been

attributed to the large incidence angle and the HH polarization of RADARSAT

data (Panigrahy et al. 1999). This combination of polarization and incidence angle

should be further assessed for crop discrimination when a complete ENVISAT

ASAR time series will become available.

6. Conclusions

ENVISAT ASAR WS mode simulated images have allowed to investigate the

potential contribution of the ASAR WS data for agricultural applications. This

simulation exercise provided a very consistent data set with equal incidence angles,

corresponding acquisition dates and times for couples of WS and NS mode images.

The impact of the spatial resolution on the signal content was studied using these

time series.

The study was carried out in a region made up of medium field sizes compared

with the typical European agricultural field. Within this landscape, both a priori

knowledge of field boundaries and a selection of larger fields (.6 ha) were required

for crop identification and monitoring. Even though the field sizes and the spatial

resolution were mismatched, the WS mode time series was relevant for crop growth

monitoring at the regional scale. Averaging all of the pixels belonging to a specific

crop type significantly reduced the ASAR signal noise and allow to monitor the

different stages of crop development. Even though spring crops could not be

adequately discriminated, the per-parcel classification approach produced accurate

classification results for large fields (.6 ha). These results also illustrated the critical

role of knowing the field boundaries in order to monitor and identify crops.

The high classification accuracy was found distinguishing between winter wheat

and winter barley (.80%) based on large fields. This discrimination could be

delivered by the end of June because most of the discrimination was due to the

temporal shift of the ripening phase between both crops. Grasses moreover, were

easily detected with an accuracy of 85%.

These results were obtained based on a time series of 15 images taken throughout

a single growing season. The ASAR in WS mode can provide 60 images over the

same period. This finer temporal resolution should have a positive impact on crop

monitoring and crop identification methodologies when detailed rainfall distribu-

tions are also available in a timely manner. The contribution of vegetation and soil

390 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

moisture in the backscattered signal varied in time depending on crop type and stage

of development. This dependence was highlighted in the present study with respectto crop identification. However, such fine temporal resolution image series should

be carefully used because of the effect that different incidence angles have on the

signal when images are acquired from different tracks.

The above conclusions were based on the assumption that the simulations

achieved a similar data quality of the real ASAR WS images. These results should be

confirmed once a comprehensive and constant ENVISAT ASAR WS time series will

be available.

Acknowledgments

This research was carried out in the framework of the project ‘Dedicated Remote

Sensing Product Generation for the Agro-Industry: Cereal Case’ lead by Synoptics

b.v. (NL) (van der Werf 2000) and sponsored by the Data User Programme (ESA-

ESRIN). The authors are also grateful to the Belgian Ministry of Agriculture – CTS

for the field boundary data and the AGRIFISH unit of the Joint Research Centre

for the European field size distribution statistics. The SAR data were provided by

ESA in the framework of the ERS-1/2 Pilot Project A02-B102.

ReferencesABDEL-MESSEH, M. and QUEGAN, S., 2001, Relating ERS scatterometer data to global

vegetation models. ERS-ENVISAT Symposium ‘Looking down to Earth in the New

Millennium’, 16–20 October 2000, Noordwijk, The Netherlands.

BAN, Y. and HOWARTH, P.J., 1998, Orbital effects on ERS-1 SAR temporal backscatter

profiles of agricultural crops. International Journal of Remote Sensing, 19, pp.

3465–3470.

BAN, Y. and HOWARTH, P.J., 1999, Multitemporal ERS-1 SAR data for crop classification: a

sequential-masking approach. Canadian Journal of Remote Sensing, 25, pp. 438–447.

BLAES, X. and DEFOURNY, P., 2003, Retrieving Crop Parameters based on tandem ERS 1/2

interferometric coherence images. Remote Sensing of Environment, 88, pp. 374–385.

CAPSTICK, D. and HARRIS, R., 2001, The effects of speckle reduction on classification of ERS

SAR data. International Journal of Remote Sensing, 22, pp. 3627–3641.

CHAKRABORTY, M. and PANIGRAHY, S., 2000, A processing and software system for rice crop

inventory using multi-date radarsat scansar data. ISPRS Journal of Photogrammetry

and Remote Sensing, 55, pp. 119–128.

CHAKRABORTY, M., PARIHAR, J.S. and PANIGRAHY, S., 2000, Operational use of SAR Images

for crop surveillance: issues regarding identification and congruency accuracy. CEOS

SAR Workshop, 26–29 October 1999, Toulouse, France, pp. 31–36.

FRISON, P.L. and MOUGIN, E., 1996a, Monitoring global vegetation dynamics with ERS-1

wind scatterometer data. International Journal of Remote Sensing, 17, pp. 3201–3218.

FRISON, P.L. and MOUGIN, E., 1996b, Use of ERS-1 Wind scatterometer data over land

surfaces. IEEE Transactions on Geoscience and Remote Sensing, 34, pp. 550–560.

GENOVESE, G., VIGNOLLES, C., NEGRE, T. and PASSERA, G., 2001, A methodology for a

combined use of normalized difference vegetation index and corine land cover data

for crop yield monitoring and forecasting. A case study on Spain. Agronomie, 21, pp.

91–111.

HOLECZ, F., MEIER, E., PIESBERGEN, J., NUESCH, D. and MOREIRA, J., 1994, Calibration of

airborne and spaceborne SAR data. Proceedings of the SAR Calibration Workshop,

CEOS CAL/VAL Group, Ann Arbor, USA.

JAKUBAUSKAS, M.E., LEGATES, D.R. and KASTENS, J.H., 2001, Harmonic analysis of time-

series AVHRR NDVI data. Photogrammetric Engineering and Remote Sensing, 67,

pp. 461–470.

Regional crop monitoring and discrimination by ASAR WS images 391

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

LAUR, H., BALLY, P., MEADOWS, P., SANCHEZ, J., SCHAETTLER, B., LOPINTO, E. and

ESTEBAN, D., 1998, Derivation of backscattering coefficient sigma in ESA ERS SAR

PRI products. Tech. Rep. ES-TN-RS-PM-HL09, Issue 2, Rev. 5b, Frascati, Italy.

LE HEGARAT-MASCLE, S., QUESNEY, A., VIDAL-MADJAR, D., TACONET, O., NORMAND, M.

and LOUMAGNE, C., 2000, Land cover discrimination from multitemporal ERS

images and multispectral landsat images: a study case in an agricultural area in

France. International Journal of Remote Sensing, 21, pp. 435–456.

MEIER, E., FREI, U. and NUESCH, D.R., 1993, Precise terrain corrected geocoded images.

SAR Geocoding: Data and Systems, G. Schreier (Ed.) (Heidelberg: Wichmann Verlag).

MICHELSON, D.B., LILJEBERG, B.M. and PILESJO, P., 2000, Comparison of algorithms for

classifying Swedish landcover using Landsat TM and ERS-1 SAR data. Remote

Sensing of Environment, 71, pp. 1–15.

MONTI GUARNIERI, A. and PRATI, C., 1996, ScanSAR focusing and interferometry. IEEE

Transactions on Geoscience and Remote Sensing, 34, pp. 1029–1038.

MOUGIN, E., LOPES, A., FRISON, P.L. and PROISY, C., 1995, Preliminary analysis of ERS-1

wind scatterometer data over land surfaces. International Journal of Remote Sensing,

16, pp. 391–398.

NIEUWENHUIS, G.J.A. and KRAMER, H., 1996, Monitoring of agricultural crops with ERS-1

and JERS-1 multi-temporal SAR data and crop growth modelling. Second ERS

Applications Workshop, London, UK, pp. 25–29.

PANIGRAHY, S., MANJUNATH, K.R., CHAKRABORTY, M., KUNDU, N. and PARIHAR, J.S.,

1999, Evaluation of RADARSAT Standard Beam data for identification of potato

and rice crops in India. ISPRS Journal of Photogrammetry and Remote Sensing, 54,

pp. 254–262.

PASQUALI, P., SMALL, D., HOLECZ, F., SCHUBERT, A., MEIER, E. and NUESCH, D., 1998,

Study of the ENVISAT ASAR alternating polarization mode: preliminary results.

Proceedings of the European Conference on Synthetic Aperture Radar, Friedrichshafen,

Germany.

PASQUALI, P., SMALL, D., HOLECZ, F., SCHUBERT, A., MEIER, E. and NUESCH, D., 1999,

ENVISAT ASAR ESL alternating polarisation mode product study and simulation,

Final report, University of Zurich, Switzerland.

SAICH, P. and BORGEAUD, M., 2000, Interpreting ERS SAR signatures of agricultural crops in

Flevoland, 1993–1996. IEEE Transactions on Geoscience and Remote Sensing, 38, pp.

651–657.

SCHMULLIUS, C.C., 1997, Monitoring Siberian forests and agriculture with the ERS-1 wind

scatterometer. IEEE Transactions on Geoscience and Remote Sensing, 35, pp.

1363–1366.

SCHOTTEN, C.G.J., VAN ROOY, W.W.L. and JANSSEN, L.L.F., 1995, Assessment of the

capabilities of multi-temporal ERS-1 SAR data to discriminate between agricultural

crops. International Journal of Remote Sensing, 16, pp. 2619–2637.

STERN, A.J., DORALSWAMY, P.C. and COOK, P.W., 2001, Spring wheat classification in an

AVHRR image by signature extension from a Landsat TM classified image.

Photogrammetric Engineering and Remote Sensing, 67, pp. 207–211.

STEVENS, D., WONG, F., LIM, P. and DESNOS, Y.L., 1997, A processing algorithm for the

Envisat Alternating Polarization Model SLC product. Proceedings of IGARSS’97

Symposium, Singapore.

TOU, J.T. and GONZALEZ, R.C., 1974, Pattern Recognition Principles (New York: Addison

Wesley).

TSO, B. and MATHER, P.M., 1999, Crop discrimination using multi-temporal SAR imagery.

International Journal of Remote Sensing, 20, pp. 2443–2460.

UNGANAI, L.S. and KOGAN, F.N., 1998, Drought monitoring and corn yield estimation in

Southern Africa from AVHRR data. Remote Sensing of Environment, 63, pp.

219–232.

392 X. Blaes et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013

VAN DER WERF, N., 2000, DUP dedicated remote sensing generation for the agro-industry:

cereal case. Final report, Synoptics s.a, The Netherlands.

WAGNER, W., LEMOINE, G., BORGEAUD, M. and ROTT, H., 1999, A study of vegetation cover

effects on ERS scatterometer data. IEEE Transactions on Geoscience and Remote

Sensing, 37, pp. 938–948.

WOODHOUSE, I.H. and HOEKMAN, D.H., 2000, Determining land-surface parameters from the

ERS Wind Scatterometer. IEEE Transactions on Geoscience and Remote Sensing, 38,

pp. 126–140.

XIE, X. and QUIEL, F., 2001, Crop classification in Southern Sweden with Multitemporal

ERS-2 SAR Data. ERS-ENVISAT Symposium ‘Looking down to Earth in the New

Millennium’, 16–20 October 2000, Noordwijk, The Netherlands.

Regional crop monitoring and discrimination by ASAR WS images 393

Dow

nloa

ded

by [

Uni

vers

ity o

f Il

linoi

s at

Urb

ana-

Cha

mpa

ign]

at 0

5:52

01

Aug

ust 2

013