# Monitoring of Damage in Sunflower and Maize Parcels Using Radar and Optical Time Series Data

Authors: György Surek; Gizella Nádor
Journal: Journal of Sensors
 (2015)

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## Abstract

The objective of this paper is to monitor the temporal behaviour of geometrical structural change of cropland affected by four different types of damage: weed infection, Western Corn Rootworm (WCR), storm damage, and drought by time series of different type of optical and quad-pol RADARSAT2 data. Based on our results it is established that ragweed infection in sunflower can be well identified by evaluation of radar (mid-June) and optical (mid-August) satellite images. Effect of drought in sunflower is well recognizable by spectral indices derived from optical as well as “I”-component of Shannon entropy (SEI) from radar satellite images acquired during the first decade of July. Evaluation of radar and optical satellite images acquired between the last decade of July and mid-August proven to be the most efficient for detecting damages in maize fields caused by either by WCR or storm. Components of Shannon entropy are proven to have significant role in identification. Our project demonstrates the potential in integrated usage of polarimetric radar and optical satellite images for monitoring several types of agricultural damage.


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## Body

## 1. Introduction

Polarimetric characteristics of various crop types have been studied by a number of authors [1–3]. According to them these features depend on numerous properties of the reflecting surface element (e.g., surface reflectivity, water content, and roughness) particularly on the geometric structure of plants. Consequently, the polarimetric features can be applied for assessment of crop condition, identification of crop types, and phenological phases of crops [4–7]. This paper presents how damage may modify a plant’s response to polarimetric radar beam. The objective is to determine whether optical and SAR time series can detect damage-induced spatial and temporal changes in crop structure.Damage induces changes in spectral and geometric features of croplands. Therefore, the most efficient way of identification of this damage can be to coevaluate optical and polarimetric radar satellite images. Previous results of our survey on this subject have already been presented on several publications [8–10].The objective of our investigation (presented in this paper) was to monitor the temporal structural changes of cropland affected by weed infection, WCR, and storm damage as well as drought in some study areas in Hungary by the integrated use of time series of optical and radar data. The temporal behaviour of 4 different types of damage has been surveyed, carried out on 3 distinct sample areas (Csongrád, Békés, and Baranya).Effects of weed infection in sunflower had been examined over Baranya site. Weed infection generates discontinuities in regular geometry of row-sown crop cultivars. Figure1 presents the difference between geometric structure of weed-infected and healthy sunflower parcels.Figure 1
Demonstration of the difference between the geometric structure of ragweed infected and control (weed-free) sunflower fields.For Csongrád site drought-caused geometric changes in sunflower parcels had been examined and set against healthy sunflower fields. Figure2 illustrates the differences in geometric structure of these two states in sunflower parcels.Figure 2
Demonstration of the difference between geometric structure of control and drought stressed sunflower fields.As for Békés site, we examined two different types of damage occurring in corn fields, assessed in 2 different dates. Larvae of Western Corn Rootworm (WCR) attack the root of the plant, which engender individual corn stems tumble; hence the parcel shows a rather bruised scenery. Figure3 illustrates the scenic difference between the geometric structure of the damaged corn field and control one. Heavy storms may have similar effects on corn, although in that case stems lean according to wind direction. In both cases, geometric structure of corn field suffers appreciable changes, showing considerable differences compared to control parcels on which corn stems standing up orderly.Figure 3
Geometric structure of WCR/storm damaged corn fields compared to the healthy ones (control) corn fields.This paper presents the results of coevaluation of multitemporal RADARSAT-2 (standard quad-pol) and optical satellite images regarding to the abovementioned damage.


## 2. Materials and Methods

This paragraph presents the study areas selected, the data used (satellite and reference data), and methodology applied.


### 2.1. Study Areas

Our survey was applied to three distinct study areas: Csongrád, Baranya, and Békés sites (Figure4).Figure 4
The location of the study areas and coverage of radar satellite images and location of reference parcels for Baranya site on SPOT5 (20/08/2012, R: NIR, G: SWIR, B: Red) satellite image composite.Csongrád site, located between the river Tisza and the Danube, can be characterized by small-size parcels (its average parcel size is 1–5 hectares), sandy soils with poor water holding capacity; therefore the area is extremely sensitive to drought.Baranya site, located on the southern part of Transdanubia, has medium-size parcels (20–30 hectares). The area, sunflower in particular, is extremely infected by ragweed and numerous other types of weeds.Békés site is located in the south-eastern part of the Hungarian Great Plain. Its average parcel-size takes 40–50 hectares. As being one of the most important corn-producing areas with maize monoculture, it opens the door to the spreading of WCR. The area was hit by serious storms several times in 2012. We analyzed the effects of storm which occurred on July 7, 2012.


### 2.2. Data Used

Coverage of satellite images and position of sample areas are presented on Figure4.Features of the applied optical and radar satellite images (acquisition data, date of ground reference data collection, and other characteristics) are shown in Tables1–3. Tables 4–9 contain the characterisation of the optical radar satellite images applied for the examinations. Each of the radar satellite images is coupled with optical satellite images acquired nearly at the same time. Although we tried our best, we did not always succeed in collecting ground reference data at the same time.Table 1
Data applied for Csongrád site.
Acquisition date RADARSAT2
Date of field survey
Optical satellite images and acquisition date
14/6/2012
14-15/06/2012
Landsat TM7 18/06/2012
8/7/2012
3–5/07/2012
Landsat TM7 04/07/2012,Geoeye: 30/06/2012, IRS-R2 AWiFS: 03/07/2012
1/8/2012
—
Landsat TM7 20/07/2012,SPOT5 29/07/2012, Landsat TM7 05/08/2012
25/8/2012
26/09/2012
SPOT5 19/08/2012,Landsat TM7 21/08/2012Table 2
Data applied for Baranya site.
Acquisition date of RADARSAT2
Date of field survey
Optical satellite images and acquisition date
17/6/2012
17/6/2012
IRS-R2 LISS III 1/5/2012Landsat TM7 31/5/2012Landsat TM7 16/6/2012
11/7/2012
3-4/7/2012
IRS-P6 LISS III: 30/6/2012,Landsat TM7 02/7/2012, Landsat TM7 11/7/2012
4/8/2012
—
SPOT4: 28/7/2012,Landsat TM7 3/8/2012, IRS-R2 LISS III 5/8/2012
28/8/2012
24–26/9/2012
Landsat TM7 19/08/2012,SPOT5 20/08/2012, Landsat TM7 28/08/2012, IRS-P6 LISS III 10/09/2012Table 3
Data applied for Békés site.
Acquisition date of RADARSAT2
Date of field survey
Optical satellite images and acquisition data
26/7/2012
6–8/8/2012
Landsat TM7 13/7/2012,Landsat TM7 20/7/2012, SPOT4 29/7/2012, IRS-P6 LISS III 8/8/2012
19/8/2012
—
Landsat TM7 14/8/2012,SPOT5 19/8/2012, Landsat TM7 30/8/2012Table 4
Characterisation of optical satellite images applied for Csongrád site.
Acquisition date
Satellite
Spatial resolution (m)
Spectral resolution
29/04/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIR
01/05/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
11/05/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIR
18/06/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
30/06/2012
GEOEYE
0.5
Blue, green, red, NIR
01/07/2012
IRS-P6 AWiFS
50
Green, red, NIR, SWIR
03/07/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIR
04/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
20/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
29/07/2012
SPOT5
10
Green, red, NIR, SWIR
05/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
05/08/2012
IRS-R2 AWiFS
50
Blue, green, red, NIR, SWIR1, SWIR2
19/08/2012
SPOT5
10
Green, red, NIR, SWIR
21/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
06/10/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIRTable 5
Characterisation of radar satellite images applied for Csongrád site.
Acquisition date
Satellite
Spatial resolution (m)
Polarisation
Wavelength (cm)
Incidence angle (°)
Beam
Pass
14/06/2012
RADARSAT2
20
Quad-pol
5.6
37.4–38.9
SQ18
Ascending
08/07/2012
RADARSAT2
20
Quad-pol
5.6
37.4–38.9
SQ18
Ascending
01/08/2012
RADARSAT2
20
Quad-pol
5.6
37.4–38.9
SQ18
Ascending
25/08/2012
RADARSAT2
20
Quad-pol
5.6
37.4–38.9
SQ18
AscendingTable 6
Characterisation of optical satellite images applied for Baranya site.
Acquisition date
Satellite
Spatial resolution (m)
Spectral resolution
01/05/2012
IRS-R2 LISS III
20
Green, red, NIR, SWIR
11/05/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIR
31/05/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
16/06/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
30/06/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIR
02/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
03/07/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIR
11/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
28/07/2012
SPOT4
20
Green, red, NIR, SWIR
03/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
05/08/2012
IRS-R2 LISS III
20
Green, red, NIR, SWIR
19/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
20/08/2012
SPOT5
10
Green, red, NIR, SWIR
28/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
10/09/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIRTable 7
Characterisation of radar satellite images applied for Baranya site.
Acquisition date
Satellite
Spatial resolution (m)
Polarisation
Wavelength (cm)
Incidence angle (°)
Beam
Pass
17/06/2012
RADARSAT2
20
Quad-pol
5.6
41.0–42.4
SQ22
Ascending
11/07/2012
RADARSAT2
20
Quad-pol
5.6
41.0–42.4
SQ22
Ascending
04/08/2012
RADARSAT2
20
Quad-pol
5.6
41.0–42.4
SQ22
Ascending
28/08/2012
RADARSAT2
20
Quad-pol
5.6
41.0–42.4
SQ22
AscendingTable 8
Characterisation of optical satellite images applied for Békés site.
Acquisition date
Satellite
Spatial resolution (m)
Spectral resolution
03/07/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIR
13/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
29/07/2012
SPOT4
20
Green, red, NIR, SWIR
29/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
08/08/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIR
14/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
19/08/2012
SPOT5
10
Green, red, NIR, SWIRTable 9
Characterisation of radar satellite images applied for Békés site.
Acquisition date
Satellite
Spatial resolution (m)
Polarisation
Wavelength (cm)
Incidence angle (°)
Beam
Pass
26/07/2012
RADARSAT2
20
quad-pol
5.6
35.4–37.0
SQ16
Descending
19/08/2012
RADARSAT2
20
quad-pol
5.6
35.4–37.0
SQ16
DescendingReference data was collected from weed-infected sunflower parcels, WCR and storm damaged maize parcels, and sunflower parcels hit by drought. Control data was collected from correspondent healthy parcels as well. Results of reference data collecting are given in Tables10–12.Table 10
Characterization of reference sunflower fields, Csongrád site.
Time period
Control
Hit by drought
Total
#field
Area (ha)
#field
Area (ha)
#field
Area (ha)
14-15/6/2012
5
79
5
79
3–5/7/2012
34
103
8
31.4
42
134
26/9/2012
5
11.8
5
12
Total
44
194
8
31
52
225Table 11
Characterization of reference sunflower fields, Baranya site.
Time period
Weed-free
Weed-infected
Total
#field
Area (ha)
#field
Area (ha)
#field
Area (ha)
17/6/2012
2
18
2
18
3–7/7/2012
17
204
10
80
27
284
24–26/9/2012
11
67
13
51
24
118
Total
30
289
23
131
53
420Table 12
Characterization of reference maize fields, Békés site.
Time period
Control
WCR damaged
Storm damaged
Total
#field
Area (ha)
#field
Area (ha)
#field
Area (ha)
#field
Area (ha)
6–8/8/2012
35
370
14
96
11
146
60
612
Total
35
370
14
96
11
146
60
612


### 2.3. Presentation of the Damages Examined

This paragraph describes the main effects of the various damage impacts. On Figures5–7 we used special band combinations for colour composite to emphasize the difference between damaged and control areas.Figure 5
Sunflower hit by drought (marked by brown polygon) and control (marked by green polygon) sunflower fields on Geoeye (30/6/2012, R: NIR, G: red, B: blue) (a) and RADARSAT2 (8/7/2012, R: SE, G:SEi, B: l3) satellite image composites (b) and photos taken on ground survey (3–5/07/2012) (c, d): sunflower hit by drought (c), control sunflower (d). Direction of the photos taken is shown by arrows.
(a)
(b)
(c)
(d)Figure 6
Weed-infected (brown polygon) and control (green polygon) sunflower fields on IRS-P6 LISS (30/06/2012, R: NIR, G: SWIR, B: red) (a) and RADARSAT2 (17/06/2012, R:YAModd, G: SE, B: l2) satellite image composites (b) and photos taken on ground survey (3-4/7/2012) (c, d): weed-infected sunflower (c), control sunflower (d). Direction of the photos taken is shown by arrows.
(a)
(b)
(c)
(d)Figure 7
WCR-infected (brown polygon), storm (blue polygon) damaged, and control (green quadrangle) maize fields on SPOT4 (29/07/2012, R: NIR, G: SWIR, B: red) (a), SPOT5 (19/08/2012, R: NIR, G: SWIR, B: red) (b) optical and RADARSAT2 (26/07/2012, 19/08/2012, R:SEp, G: SEi, B: l1) (c, d) radar satellite image composites and photos taken on ground survey (6–8/08/2012) (e, f, g): WCR-infected (e), storm damaged (f), and control parcels (g). Direction of the photos taken is shown by arrows.
(a)
(b)
(c)
(d)
(e)
(f)
(g)


#### 2.3.1. Sunflower Hit by Drought

Figure5 represents a typical sunflower parcel hit by drought (delineated by brown) contrarily to a healthy one (delineated by green) visualized on optical satellite image composite (Geoeye 30/6/2012, R: NIR, G: red, B: blue) and on radar data composite (RADARSAT2 8/7/2012, R: SE, G: SEi, B: l3) as well.Difference between the sunflower parcels can be well discernible on photos taken on the spot. As sunflowers hit by drought have sagging leaves, they cannot cover the soil completely, whilst leaves of healthy sunflower are horizontal and cover the entire soil surface.Difference can be well identified on both optical and radar satellite composites. Healthy sunflower parcel appears orange on optical images, unlike those hit by drought showing bluish tinge which is typical to bare soil.On radar images appearance of healthy sunflower can be described with intense light colors, whilst damaged parcels are greenish-gray.


#### 2.3.2. Appearance of Weed Infection in Sunflower Parcels

Figure6 represents a typical sunflower parcel infected by weed (delineated by brown colour) contrarily to a control, weed-free one (delineated by green) visualized on optical satellite image composite (IRS-P6 LISS 30/6/2012, R: NIR, G: red, B: blue) and on radar data composite (RADARSAT2 17/06/2012, R: YAModd, G: SE, B: l2) as well, accompanied by photos taken on the spot on July 3 and 4, 2012.Difference between these two sunflower parcels can be well discernible on photos. As weed-infected sunflower is less developed, weed and soil are usually revealed between the rows of plants, while leaves of healthy sunflower stand horizontal, covering the soil surface fully, making it invisible from above.Difference can be well identified on radar satellite composite (17/6/2012), while it cannot be observed on the optical scene acquired on June 30, 2012.


#### 2.3.3. WCR and Storm Damage in Maize Parcels

A typical maize parcel attacked by Western Corn Rootworm (delineated by brown colour) and another damaged by storm (delineated by blue), contrarily to a healthy one (delineated by green) are visualized on optical satellite image composites (a; SPOT4 29/06/2012, R: NIR, G: SWIR, B: red) (b; SPOT5 19/08/2012, R: NIR, G: SWIR, B: red), on radar data composites RADARSAT2 (26/07/2012, 19/08/2012, R:SEp, G: SEi, B: l1) and photos taken on the spot (06–08/08/2012) as well (Figure 7).State of maize is obvious on the photos taken on the spot. WCR damaged maize parcel with its stems leaned to each other randomly shows a rather disordered scenery. Contrarily, all the stems on maize parcel damaged by storm leaned according to wind direction, whilst control maize parcel looks well structured.Difference between the three parcels can be detected on optical satellite composites acquired on 29/07/2012 and 19/08/2012, particularly in case of parcels damaged by storm. Difference between the 3 parcels is similarly apparent on the radar composite 26/07/2012, while no significant distinction appears any more on the radar composite 19/08/20.


### 2.4. Methodology Applied

Steps of preprocessing of optical satellite images are as follows:(i)
import,(ii)
geometric correction,(iii)
radiometric correction (ToA reflectance calculation),(iv)
generation of spectral indices (NDVI, NSI),(v)
intercalibration of spectral indices.The spectral indices applied for our study were calculated from ToA reflectance instead of using surface reflectance; consequently these indices are not comparable between different years.Reference fields’ spectral index features Normalized Difference Vegetation Index (NDVI) and normalized special index (NSI) derived from optical images (SPOT4/5, Landsat TM7, IRS-P6/R2 LISS III, IRS-P6 AWiFS) are calculated from radiometric calibrated ToA reflectance values.Definition of these indices is shown by(1)NDVI=100∗NIR-REDNIR+RED,NSI=100∗SWIR-NIRNIR-RED,where NIR is the ToA reflectance in near infrared band, RED is the ToA reflectance in red band, and SWIR is the ToA reflectance in short wave infrared band.The vegetation index, NDVI, describes the state and growth of vegetation, while NSI, based on our survey, serves characterization of spectral changes occurring in damaged fields. The different types of satellite images were intercalibrated, illustrated by Figure8.Figure 8
Intercalibration of NDVI values derived from IRS-R2 AWiFS and Landsat TM7.The well-known Landsat TM7 discontinuity (striping) errors were managed by intercalibrated spectral index derived from IRS P6/R2 AWiFS images having nearly similar acquisition date.Preprocessing steps of RADARSAT2 data and radar features (assigned by light yellow) applied for our study are shown on Figure9.Figure 9
Preprocessing of RADARSAT2 satellite data and radar features applied for our study.Calibrated backscattering coefficients (Sigma0) of standard quad-pol RADARSAT2 data were calculated by using NEST software. Polarimetric descriptors of these data were calculated by using Polsarpro software. Their definitions are described at [11]. Each of these types of software was developed by ESA. Two different types of decompositions (H/A/Alpha and Yamaguchi) and polarimetric features derived from those have been applied in our survey.In our study the so called Shannon entropy based on H/A/Alpha decomposition of the coherency matrix [12] is one of the most important polarimetric descriptors. Natural or agricultural damage often causes physical and visible disorders within the parcels. They can be characterized as follows: the polarization status of the transmitted and received pulses is known. The degree of polarization is influenced by the target. The so-called Shannon entropy (SE) can describe the ratio of “polarization scattering” above. Shannon entropy [13, 14] consists of two components:(2)SE=SEi+SEp,where SE is intrinsic degrees of coherence and SEp is degrees of polarization.Further important descriptors involved in our study are the components of Yamaguchi decomposition of the coherency matrix [15–17]. This type of decomposition technique can be applied for identification of scattering targets. It can account for the strength of different scattering mechanisms. Their components applied in this study are as follows: YAModd for single scattering, YAMdbl for double bounce scattering, and YAMvol for volume scattering.Efficiency and accuracy of these features were evaluated by statistical comparative analysis of reference crop fields. Based on the damaged (weed-infected, drought, WCR, or storm) and the healthy reference fields we analyzed the separability (significance) of the polarimetric descriptors of radar images mentioned above. Polarimetric descriptors of the reference fields are supposed to show normal distribution, so we derived the significance,t value calculated by the Welch test [18], and ranked the descriptors according to that. The Welch test is applicable here. The null hypothesis is the averaged values characterizing that damaged and control areas are equal.Figure10 shows the methodology applied. It is important to note that time series of radar and optical data have an equally significant role in identification of damage. Polarimetric descriptors derived from radar data contain a wide range of information. However they require adequate interpretation for each case. Therefore, we have to identify the crop type by the time series of optical satellite data. Interpretation of radar data should be implemented only for the mask generated based on the selected crop types. After that, only one question has to be answered: whether the plant given is damaged or not.Figure 10
Sketch of methodology applied.


## 2.1. Study Areas

Our survey was applied to three distinct study areas: Csongrád, Baranya, and Békés sites (Figure4).Figure 4
The location of the study areas and coverage of radar satellite images and location of reference parcels for Baranya site on SPOT5 (20/08/2012, R: NIR, G: SWIR, B: Red) satellite image composite.Csongrád site, located between the river Tisza and the Danube, can be characterized by small-size parcels (its average parcel size is 1–5 hectares), sandy soils with poor water holding capacity; therefore the area is extremely sensitive to drought.Baranya site, located on the southern part of Transdanubia, has medium-size parcels (20–30 hectares). The area, sunflower in particular, is extremely infected by ragweed and numerous other types of weeds.Békés site is located in the south-eastern part of the Hungarian Great Plain. Its average parcel-size takes 40–50 hectares. As being one of the most important corn-producing areas with maize monoculture, it opens the door to the spreading of WCR. The area was hit by serious storms several times in 2012. We analyzed the effects of storm which occurred on July 7, 2012.


## 2.2. Data Used

Coverage of satellite images and position of sample areas are presented on Figure4.Features of the applied optical and radar satellite images (acquisition data, date of ground reference data collection, and other characteristics) are shown in Tables1–3. Tables 4–9 contain the characterisation of the optical radar satellite images applied for the examinations. Each of the radar satellite images is coupled with optical satellite images acquired nearly at the same time. Although we tried our best, we did not always succeed in collecting ground reference data at the same time.Table 1
Data applied for Csongrád site.
Acquisition date RADARSAT2
Date of field survey
Optical satellite images and acquisition date
14/6/2012
14-15/06/2012
Landsat TM7 18/06/2012
8/7/2012
3–5/07/2012
Landsat TM7 04/07/2012,Geoeye: 30/06/2012, IRS-R2 AWiFS: 03/07/2012
1/8/2012
—
Landsat TM7 20/07/2012,SPOT5 29/07/2012, Landsat TM7 05/08/2012
25/8/2012
26/09/2012
SPOT5 19/08/2012,Landsat TM7 21/08/2012Table 2
Data applied for Baranya site.
Acquisition date of RADARSAT2
Date of field survey
Optical satellite images and acquisition date
17/6/2012
17/6/2012
IRS-R2 LISS III 1/5/2012Landsat TM7 31/5/2012Landsat TM7 16/6/2012
11/7/2012
3-4/7/2012
IRS-P6 LISS III: 30/6/2012,Landsat TM7 02/7/2012, Landsat TM7 11/7/2012
4/8/2012
—
SPOT4: 28/7/2012,Landsat TM7 3/8/2012, IRS-R2 LISS III 5/8/2012
28/8/2012
24–26/9/2012
Landsat TM7 19/08/2012,SPOT5 20/08/2012, Landsat TM7 28/08/2012, IRS-P6 LISS III 10/09/2012Table 3
Data applied for Békés site.
Acquisition date of RADARSAT2
Date of field survey
Optical satellite images and acquisition data
26/7/2012
6–8/8/2012
Landsat TM7 13/7/2012,Landsat TM7 20/7/2012, SPOT4 29/7/2012, IRS-P6 LISS III 8/8/2012
19/8/2012
—
Landsat TM7 14/8/2012,SPOT5 19/8/2012, Landsat TM7 30/8/2012Table 4
Characterisation of optical satellite images applied for Csongrád site.
Acquisition date
Satellite
Spatial resolution (m)
Spectral resolution
29/04/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIR
01/05/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
11/05/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIR
18/06/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
30/06/2012
GEOEYE
0.5
Blue, green, red, NIR
01/07/2012
IRS-P6 AWiFS
50
Green, red, NIR, SWIR
03/07/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIR
04/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
20/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
29/07/2012
SPOT5
10
Green, red, NIR, SWIR
05/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
05/08/2012
IRS-R2 AWiFS
50
Blue, green, red, NIR, SWIR1, SWIR2
19/08/2012
SPOT5
10
Green, red, NIR, SWIR
21/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
06/10/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIRTable 5
Characterisation of radar satellite images applied for Csongrád site.
Acquisition date
Satellite
Spatial resolution (m)
Polarisation
Wavelength (cm)
Incidence angle (°)
Beam
Pass
14/06/2012
RADARSAT2
20
Quad-pol
5.6
37.4–38.9
SQ18
Ascending
08/07/2012
RADARSAT2
20
Quad-pol
5.6
37.4–38.9
SQ18
Ascending
01/08/2012
RADARSAT2
20
Quad-pol
5.6
37.4–38.9
SQ18
Ascending
25/08/2012
RADARSAT2
20
Quad-pol
5.6
37.4–38.9
SQ18
AscendingTable 6
Characterisation of optical satellite images applied for Baranya site.
Acquisition date
Satellite
Spatial resolution (m)
Spectral resolution
01/05/2012
IRS-R2 LISS III
20
Green, red, NIR, SWIR
11/05/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIR
31/05/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
16/06/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
30/06/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIR
02/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
03/07/2012
IRS-R2 AWiFS
50
Green, red, NIR, SWIR
11/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
28/07/2012
SPOT4
20
Green, red, NIR, SWIR
03/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
05/08/2012
IRS-R2 LISS III
20
Green, red, NIR, SWIR
19/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
20/08/2012
SPOT5
10
Green, red, NIR, SWIR
28/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
10/09/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIRTable 7
Characterisation of radar satellite images applied for Baranya site.
Acquisition date
Satellite
Spatial resolution (m)
Polarisation
Wavelength (cm)
Incidence angle (°)
Beam
Pass
17/06/2012
RADARSAT2
20
Quad-pol
5.6
41.0–42.4
SQ22
Ascending
11/07/2012
RADARSAT2
20
Quad-pol
5.6
41.0–42.4
SQ22
Ascending
04/08/2012
RADARSAT2
20
Quad-pol
5.6
41.0–42.4
SQ22
Ascending
28/08/2012
RADARSAT2
20
Quad-pol
5.6
41.0–42.4
SQ22
AscendingTable 8
Characterisation of optical satellite images applied for Békés site.
Acquisition date
Satellite
Spatial resolution (m)
Spectral resolution
03/07/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIR
13/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
29/07/2012
SPOT4
20
Green, red, NIR, SWIR
29/07/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
08/08/2012
IRS-P6 LISS III
20
Green, red, NIR, SWIR
14/08/2012
Landsat TM7
30
Blue, green, red, NIR, SWIR1, SWIR2
19/08/2012
SPOT5
10
Green, red, NIR, SWIRTable 9
Characterisation of radar satellite images applied for Békés site.
Acquisition date
Satellite
Spatial resolution (m)
Polarisation
Wavelength (cm)
Incidence angle (°)
Beam
Pass
26/07/2012
RADARSAT2
20
quad-pol
5.6
35.4–37.0
SQ16
Descending
19/08/2012
RADARSAT2
20
quad-pol
5.6
35.4–37.0
SQ16
DescendingReference data was collected from weed-infected sunflower parcels, WCR and storm damaged maize parcels, and sunflower parcels hit by drought. Control data was collected from correspondent healthy parcels as well. Results of reference data collecting are given in Tables10–12.Table 10
Characterization of reference sunflower fields, Csongrád site.
Time period
Control
Hit by drought
Total
#field
Area (ha)
#field
Area (ha)
#field
Area (ha)
14-15/6/2012
5
79
5
79
3–5/7/2012
34
103
8
31.4
42
134
26/9/2012
5
11.8
5
12
Total
44
194
8
31
52
225Table 11
Characterization of reference sunflower fields, Baranya site.
Time period
Weed-free
Weed-infected
Total
#field
Area (ha)
#field
Area (ha)
#field
Area (ha)
17/6/2012
2
18
2
18
3–7/7/2012
17
204
10
80
27
284
24–26/9/2012
11
67
13
51
24
118
Total
30
289
23
131
53
420Table 12
Characterization of reference maize fields, Békés site.
Time period
Control
WCR damaged
Storm damaged
Total
#field
Area (ha)
#field
Area (ha)
#field
Area (ha)
#field
Area (ha)
6–8/8/2012
35
370
14
96
11
146
60
612
Total
35
370
14
96
11
146
60
612


## 2.3. Presentation of the Damages Examined

This paragraph describes the main effects of the various damage impacts. On Figures5–7 we used special band combinations for colour composite to emphasize the difference between damaged and control areas.Figure 5
Sunflower hit by drought (marked by brown polygon) and control (marked by green polygon) sunflower fields on Geoeye (30/6/2012, R: NIR, G: red, B: blue) (a) and RADARSAT2 (8/7/2012, R: SE, G:SEi, B: l3) satellite image composites (b) and photos taken on ground survey (3–5/07/2012) (c, d): sunflower hit by drought (c), control sunflower (d). Direction of the photos taken is shown by arrows.
(a)
(b)
(c)
(d)Figure 6
Weed-infected (brown polygon) and control (green polygon) sunflower fields on IRS-P6 LISS (30/06/2012, R: NIR, G: SWIR, B: red) (a) and RADARSAT2 (17/06/2012, R:YAModd, G: SE, B: l2) satellite image composites (b) and photos taken on ground survey (3-4/7/2012) (c, d): weed-infected sunflower (c), control sunflower (d). Direction of the photos taken is shown by arrows.
(a)
(b)
(c)
(d)Figure 7
WCR-infected (brown polygon), storm (blue polygon) damaged, and control (green quadrangle) maize fields on SPOT4 (29/07/2012, R: NIR, G: SWIR, B: red) (a), SPOT5 (19/08/2012, R: NIR, G: SWIR, B: red) (b) optical and RADARSAT2 (26/07/2012, 19/08/2012, R:SEp, G: SEi, B: l1) (c, d) radar satellite image composites and photos taken on ground survey (6–8/08/2012) (e, f, g): WCR-infected (e), storm damaged (f), and control parcels (g). Direction of the photos taken is shown by arrows.
(a)
(b)
(c)
(d)
(e)
(f)
(g)


### 2.3.1. Sunflower Hit by Drought

Figure5 represents a typical sunflower parcel hit by drought (delineated by brown) contrarily to a healthy one (delineated by green) visualized on optical satellite image composite (Geoeye 30/6/2012, R: NIR, G: red, B: blue) and on radar data composite (RADARSAT2 8/7/2012, R: SE, G: SEi, B: l3) as well.Difference between the sunflower parcels can be well discernible on photos taken on the spot. As sunflowers hit by drought have sagging leaves, they cannot cover the soil completely, whilst leaves of healthy sunflower are horizontal and cover the entire soil surface.Difference can be well identified on both optical and radar satellite composites. Healthy sunflower parcel appears orange on optical images, unlike those hit by drought showing bluish tinge which is typical to bare soil.On radar images appearance of healthy sunflower can be described with intense light colors, whilst damaged parcels are greenish-gray.


### 2.3.2. Appearance of Weed Infection in Sunflower Parcels

Figure6 represents a typical sunflower parcel infected by weed (delineated by brown colour) contrarily to a control, weed-free one (delineated by green) visualized on optical satellite image composite (IRS-P6 LISS 30/6/2012, R: NIR, G: red, B: blue) and on radar data composite (RADARSAT2 17/06/2012, R: YAModd, G: SE, B: l2) as well, accompanied by photos taken on the spot on July 3 and 4, 2012.Difference between these two sunflower parcels can be well discernible on photos. As weed-infected sunflower is less developed, weed and soil are usually revealed between the rows of plants, while leaves of healthy sunflower stand horizontal, covering the soil surface fully, making it invisible from above.Difference can be well identified on radar satellite composite (17/6/2012), while it cannot be observed on the optical scene acquired on June 30, 2012.


### 2.3.3. WCR and Storm Damage in Maize Parcels

A typical maize parcel attacked by Western Corn Rootworm (delineated by brown colour) and another damaged by storm (delineated by blue), contrarily to a healthy one (delineated by green) are visualized on optical satellite image composites (a; SPOT4 29/06/2012, R: NIR, G: SWIR, B: red) (b; SPOT5 19/08/2012, R: NIR, G: SWIR, B: red), on radar data composites RADARSAT2 (26/07/2012, 19/08/2012, R:SEp, G: SEi, B: l1) and photos taken on the spot (06–08/08/2012) as well (Figure 7).State of maize is obvious on the photos taken on the spot. WCR damaged maize parcel with its stems leaned to each other randomly shows a rather disordered scenery. Contrarily, all the stems on maize parcel damaged by storm leaned according to wind direction, whilst control maize parcel looks well structured.Difference between the three parcels can be detected on optical satellite composites acquired on 29/07/2012 and 19/08/2012, particularly in case of parcels damaged by storm. Difference between the 3 parcels is similarly apparent on the radar composite 26/07/2012, while no significant distinction appears any more on the radar composite 19/08/20.


## 2.3.1. Sunflower Hit by Drought

Figure5 represents a typical sunflower parcel hit by drought (delineated by brown) contrarily to a healthy one (delineated by green) visualized on optical satellite image composite (Geoeye 30/6/2012, R: NIR, G: red, B: blue) and on radar data composite (RADARSAT2 8/7/2012, R: SE, G: SEi, B: l3) as well.Difference between the sunflower parcels can be well discernible on photos taken on the spot. As sunflowers hit by drought have sagging leaves, they cannot cover the soil completely, whilst leaves of healthy sunflower are horizontal and cover the entire soil surface.Difference can be well identified on both optical and radar satellite composites. Healthy sunflower parcel appears orange on optical images, unlike those hit by drought showing bluish tinge which is typical to bare soil.On radar images appearance of healthy sunflower can be described with intense light colors, whilst damaged parcels are greenish-gray.


## 2.3.2. Appearance of Weed Infection in Sunflower Parcels

Figure6 represents a typical sunflower parcel infected by weed (delineated by brown colour) contrarily to a control, weed-free one (delineated by green) visualized on optical satellite image composite (IRS-P6 LISS 30/6/2012, R: NIR, G: red, B: blue) and on radar data composite (RADARSAT2 17/06/2012, R: YAModd, G: SE, B: l2) as well, accompanied by photos taken on the spot on July 3 and 4, 2012.Difference between these two sunflower parcels can be well discernible on photos. As weed-infected sunflower is less developed, weed and soil are usually revealed between the rows of plants, while leaves of healthy sunflower stand horizontal, covering the soil surface fully, making it invisible from above.Difference can be well identified on radar satellite composite (17/6/2012), while it cannot be observed on the optical scene acquired on June 30, 2012.


## 2.3.3. WCR and Storm Damage in Maize Parcels

A typical maize parcel attacked by Western Corn Rootworm (delineated by brown colour) and another damaged by storm (delineated by blue), contrarily to a healthy one (delineated by green) are visualized on optical satellite image composites (a; SPOT4 29/06/2012, R: NIR, G: SWIR, B: red) (b; SPOT5 19/08/2012, R: NIR, G: SWIR, B: red), on radar data composites RADARSAT2 (26/07/2012, 19/08/2012, R:SEp, G: SEi, B: l1) and photos taken on the spot (06–08/08/2012) as well (Figure 7).State of maize is obvious on the photos taken on the spot. WCR damaged maize parcel with its stems leaned to each other randomly shows a rather disordered scenery. Contrarily, all the stems on maize parcel damaged by storm leaned according to wind direction, whilst control maize parcel looks well structured.Difference between the three parcels can be detected on optical satellite composites acquired on 29/07/2012 and 19/08/2012, particularly in case of parcels damaged by storm. Difference between the 3 parcels is similarly apparent on the radar composite 26/07/2012, while no significant distinction appears any more on the radar composite 19/08/20.


## 2.4. Methodology Applied

Steps of preprocessing of optical satellite images are as follows:(i)
import,(ii)
geometric correction,(iii)
radiometric correction (ToA reflectance calculation),(iv)
generation of spectral indices (NDVI, NSI),(v)
intercalibration of spectral indices.The spectral indices applied for our study were calculated from ToA reflectance instead of using surface reflectance; consequently these indices are not comparable between different years.Reference fields’ spectral index features Normalized Difference Vegetation Index (NDVI) and normalized special index (NSI) derived from optical images (SPOT4/5, Landsat TM7, IRS-P6/R2 LISS III, IRS-P6 AWiFS) are calculated from radiometric calibrated ToA reflectance values.Definition of these indices is shown by(1)NDVI=100∗NIR-REDNIR+RED,NSI=100∗SWIR-NIRNIR-RED,where NIR is the ToA reflectance in near infrared band, RED is the ToA reflectance in red band, and SWIR is the ToA reflectance in short wave infrared band.The vegetation index, NDVI, describes the state and growth of vegetation, while NSI, based on our survey, serves characterization of spectral changes occurring in damaged fields. The different types of satellite images were intercalibrated, illustrated by Figure8.Figure 8
Intercalibration of NDVI values derived from IRS-R2 AWiFS and Landsat TM7.The well-known Landsat TM7 discontinuity (striping) errors were managed by intercalibrated spectral index derived from IRS P6/R2 AWiFS images having nearly similar acquisition date.Preprocessing steps of RADARSAT2 data and radar features (assigned by light yellow) applied for our study are shown on Figure9.Figure 9
Preprocessing of RADARSAT2 satellite data and radar features applied for our study.Calibrated backscattering coefficients (Sigma0) of standard quad-pol RADARSAT2 data were calculated by using NEST software. Polarimetric descriptors of these data were calculated by using Polsarpro software. Their definitions are described at [11]. Each of these types of software was developed by ESA. Two different types of decompositions (H/A/Alpha and Yamaguchi) and polarimetric features derived from those have been applied in our survey.In our study the so called Shannon entropy based on H/A/Alpha decomposition of the coherency matrix [12] is one of the most important polarimetric descriptors. Natural or agricultural damage often causes physical and visible disorders within the parcels. They can be characterized as follows: the polarization status of the transmitted and received pulses is known. The degree of polarization is influenced by the target. The so-called Shannon entropy (SE) can describe the ratio of “polarization scattering” above. Shannon entropy [13, 14] consists of two components:(2)SE=SEi+SEp,where SE is intrinsic degrees of coherence and SEp is degrees of polarization.Further important descriptors involved in our study are the components of Yamaguchi decomposition of the coherency matrix [15–17]. This type of decomposition technique can be applied for identification of scattering targets. It can account for the strength of different scattering mechanisms. Their components applied in this study are as follows: YAModd for single scattering, YAMdbl for double bounce scattering, and YAMvol for volume scattering.Efficiency and accuracy of these features were evaluated by statistical comparative analysis of reference crop fields. Based on the damaged (weed-infected, drought, WCR, or storm) and the healthy reference fields we analyzed the separability (significance) of the polarimetric descriptors of radar images mentioned above. Polarimetric descriptors of the reference fields are supposed to show normal distribution, so we derived the significance,t value calculated by the Welch test [18], and ranked the descriptors according to that. The Welch test is applicable here. The null hypothesis is the averaged values characterizing that damaged and control areas are equal.Figure10 shows the methodology applied. It is important to note that time series of radar and optical data have an equally significant role in identification of damage. Polarimetric descriptors derived from radar data contain a wide range of information. However they require adequate interpretation for each case. Therefore, we have to identify the crop type by the time series of optical satellite data. Interpretation of radar data should be implemented only for the mask generated based on the selected crop types. After that, only one question has to be answered: whether the plant given is damaged or not.Figure 10
Sketch of methodology applied.


## 3. Results and Discussion

### 3.1. Drought in Sunflower Fields, Csongrád Site

Figure11 shows temporal progress of sunflower’s average crop development curve derived from time series of optical satellite data, referred to as control parcels and parcels hit by drought. Horizontal lines stand for indicating standard deviation referring to each of the acquisition dates. The most significant difference takes place in the first decade of July. Difference decreases gradually by the first decade of August and then disappears. Therefore, it can be established that from the beginning of August, healthy and arid sunflower parcels are no longer separable by optical satellite images.Figure 11
Average crop development curve based on NDVI values derived from time series of optical satellite data for sunflower hit by drought and control fields, Csongrád site. Vertical lines represent single standard deviation.The temporal behaviour of NSI values (Figure12) is reverse of NDVI.Figure 12
Average crop development curve based on NSI values derived from time series of optical satellite data for sunflower hit by drought and control fields, Csongrád site. Vertical lines represent single standard deviation.Figure13 shows the temporal development of sigma0 values of different polarization bands. In all bands the sigma0 values are lower in case of hit by drought. The higher difference between control and hit by drought areas is at the beginning of July in all bands particularly in HV one.Figure 13
The temporal behavior of sigma0 bands of backscattered beam from sunflower (control and hit by drought area).Figure14 represents the separation of ellipses featured by control and hit-drought areas in two-dimensional space of sigma0 bands. We can see well the distinguishing is highest at the beginning of July like in Figure 11. The ellipses of controls are significantly upper-right at all dates. We can see similar distinguishing in H-Alpha space (Figure 15).Figure 14
Separability of hit-drought and control areas in two-dimensional space of sigma0 co-pol. bands at different dates.Figure 15
Separability of hit-drought and control areas in two-dimensional space of H-Alpha polarimetric descriptors at different dates.Figure16 shows the result of separability analysis of polarimetric descriptors derived from RADARASAT2 satellite images acquired at different dates regarding reference sunflower parcels hit by drought and control ones. The one acquired on 8/7/2012 proved to be the most efficient in distinction. Although in case of each of the four acquisitions there exist such polarimetric descriptors which can distinguish these two states significantly.Figure 16
Result of separability analysis of sunflower hit by drought and control fields, Csongrád site.It can be seen as well that not the same polarimetric descriptors show the highest efficiency at the dates given. Table13 shows the ranking of polarimetric descriptors based on efficiency of separability or level of significance. It can be established that during the period of June to August components of Yamaguchi decomposition (mainly YAMvol and YAModd) have notable role in distinguishing between sunflower parcels hit by drought and the control ones.Table 13
Ranking of polarimetric descriptors based on significance (t) calculated by Welch test, referred to as sunflower hit by drought and control fields, Csongrád site.
Sign. (t) and P value
Date of polarimetric descriptors
14/06/2012
08/07/2012
01/08/2012
25/08/2012
>3.8<0.001
—
l3, l1, YAMvol, SE, l2, SEi
YAMvol
YAModd, l1
2.9–3.80.001–0.01
—
YAModd
l2, l3
—
2.1–2.90.01–0.05
YAModd, l1, SEi ped, p3, rvi, polfrac, SE, SEp, p1, l2, anilu
—
SE
SE, p1 anilu,SEpIt is worth mentioning that component of “vol” is the one showing the most significant deviation (P value < 0.001) between July 8 and August 1, while the “odd” one has that in mid-June and end of August. It means that distinguishing these two states is dominated by volumetric scattering during the middle of the growing season (July-August), while single scattering has more important role at the beginning and end of the growing season (mid-June and August).By evaluating both the optical and radar satellite time series data it can be established that the first decade of July is the most suitable date for distinguishing the sunflower parcels hit by drought from the control ones. According to Figure17 those pixels of sunflower were identified as hit by drought, whose NDVI was less than 52 and l3 was less than 0.035.Figure 17
Average deviation of damaged (hit by drought) and healthy (control) reference parcels based on l3 derived from RADARSAT2 (08/07/2012),P<0.001 (a). Average deviation of damaged (hit by drought) and healthy (control) reference sunflower fields based on NDVI derived from Landsat TM7 (04/07/2012), P<0.001 (b). Vertical lines represent single standard deviation.
(a)
(b)The drought map was derived from Landsat TM7 (4/7/2012), from RADARSAT2 (8/7/2012) and from using both of them (Figure18). The accuracy of these maps validated by reference data was almost the same (Figure 19).Figure 18
Drought map of sunflower parcels derived from integrated evaluation of Landsat TM7 (4/7/2012) and RADARSAT2 (8/7/2012) and the reference sunflower parcels.Figure 19
Result of accuracy assessment of sunflower’s drought map.


### 3.2. Weed Infection in Sunflower Fields, Baranya Site

Figure20 shows temporal progress of sunflower’s average NDVI curve derived from time series of optical satellite data, referred to as the control (weed-free) parcels and parcels infected by weed. Horizontal lines indicate standard deviation referred to as each of the acquisition dates. In July deviation decreases appreciable while in August it starts to increase again. The largest deviation takes place in 2nd half of August. Therefore, it can be established that optical images acquired during that period are the most suitable for sorting out infected sunflower parcels from the control ones.Figure 20
NDVI derived from optical satellite data for weed-infected and control sunflower fields, Baranya site. Vertical lines represent single standard deviation. LISS-R2: IRS-R2 LISS III, AWiFS: IRS-R2 AWiFS, TM7: Landsat TM7, LISS-P6: IRS-P6 LISS III, SP4: SPOT4, and SP5: SPOT5.From Figures20 and 21 we can see the NSI values have reverse behaviour as NDVI similar to the case above. The difference between weed-infected and weed-free areas is highest at end of August.Figure 21
NSI derived from optical satellite data regarded to weed-infected and control sunflower fields, Baranya site. Vertical lines represent single standard deviation. LISS-R2: IRS-R2 LISS III, AWiFS: IRS-R2 AWiFS, TM7: Landsat TM7, LISS-P6: IRS-P6 LISS III, SP4: SPOT4, and SP5: SPOT5.Figure22 shows the temporal variation of sigma0 in different polarization cases weed-infected and weed-free areas. The graphs are similar in all polarization bands. The backscattered intensity of weed-infected and weed-free areas is changing at beginning of August. The weed-free is higher earlier and the weed-infected increased later. The highest difference between weed-free and weed-infected areas is at mid-June in particularly co-pol. bands. The trend of Shannon entropy (SE) is very similar (Figure 23).Figure 22
Temporal development of sigma0 values in different polarization bands, Baranya site.Figure 23
Temporal development of SE polarimetric descriptor, Baranya site.The separability is well seen in the two-dimensional space of sigma0 co-pol. bands and H-Alpha bands too (Figures24 and 25).Figure 24
Separability of weed-infected and weed-free areas in two-dimensional space of sigma0 co-pol. bands at different dates, Baranya site.Figure 25
Separability of weed-infected and weed-free areas in two-dimensional space of H-Alpha polarimetric descriptors at different dates, Baranya site.We have very interesting results from analysis above. We can find the weed-infected sunflower areas at mid-June by radar features. While we can distinguish the weed-infected areas by optical images only two months later, at the end of August.Figure26 represents the result of separability analysis of polarimetric descriptors derived from multitemporal radar satellite data referred to as weed-free and weed-infected sunflower parcels. According to the graph, it is evidence that radar data acquired on 17/6/2012 is the most efficient in discrimination, while these two different statuses cannot be distinguished by analyzing radar data with other dates of acquisition about the area.Figure 26
Result of separability analysis of discriminating weed-infected and weed-free sunflower fields, Baranya site.Hierarchy of polarimetric descriptors by their discrimination efficiency and level of significance is represented by Table14. It is shown that components of Shannon entropy (SE, SEi) (P<0.001), odd and vol components of Yamaguchi decomposition (0.001<P<0.01), and eigenvalues of coherence matrix, l2 in particular (on level P<0.001), have significant role in discrimination of the two distinct statuses for mid-June. However we could not find such polarimetric descriptors applicable in order to distinguish parcels of these two statuses in mid-July and late August. In case of radar satellite scene acquired in early August anisotropy and p2 descriptors (0.01<P<0.05) seemed to be significant in discrimination.Table 14
Ranking of polarimetric descriptors based on significance calculated by Welch test, weed-infected and weed-free sunflower fields, Baranya site.
Sign. andP value
Polarimetric descriptors
17/06/2012
11/07/2012
04/08/2012
28/08/2012
>3.8<0.001
SE, l2SEi
—
—
—
2.9–3.80.001–0.01
YAMvol, YAModd l3, l1
—
—
—
2.1–2.90.01–0.05
S
E
p
—
Anisotropy, p2
—Based on evaluation of time series of both optical and radar satellite images it is evidence that weed infection in sunflower can be detected most efficiently by synergistic evaluation of optical data acquired in middle of August and radar data acquired in mid-June. According to Figure27 sunflower parcels having value of NDVI > 35 in middle of August and Shannon entropy <-1 in mid-June are expected to be infected.Figure 27
Average discrimination of weed-infected and weed-free reference sunflower fields based on SE derived from RADARSAT2 (17/06/2012),P<0.001 (a). Average discrimination of weed-infected and weed-free reference sunflower fields based on NDVI derived from SPOT5 (20/08/2012), P<0.001 (b). Vertical lines represent single standard deviation.
(a)
(b)We made weed maps in many different ways shown in Table15. The overall accuracy of these maps is shown in the last column of Table 15. Figure 28 shows the map detailed in bold row of Table 15, and the accuracy of the map is almost 90%.Table 15
Characterization of weed maps derived from radar and optical satellite data for sunflower parcels.
Basis of weed map
Type
#dates
Last date
Conditions for identification of weed infection
Overall (%)
RS2, 17/6/2012
Radar
1
17/6/2012
SE < −1
61.1
SPOT4, 28/7/2012
Optical
1
28/7/2012
NDVI > 60
72.8
LISS, 5/8/2012
Optical
1
5/8/2012
NDVI > 50
78.4
SPOT5, 20/8/2012
Optical
1
20/8/2012
NDVI > 35
83.0
SPOT4, 28/7/2012 + LISS, 5/8/2012
Optical
2
5/8/2012
NDVI1 > 60 and NDVI2 > 50
78.8
RS2_17/6/2012 + RS2_11/7/2012
Radar
2
11/7/2012
SE1 < −1 and SE2 < −1
78.9
RS2_17/6/2012 + RS2_11/7/2012 + SPOT4, 28/7/2012 + LISS, 5/8/2012
Radar + optical
2-2
5/8/2012
SE1  < −1  and  SE2  < −1  and  NDVI1  >  60  and  NDVI2  >  50
89.7
RS2_17/6/2012 + SPOT4_28/7/2012
Radar + optical
1-1
28/7/2012
SE < −1 and NDVI > 60
83.9
RS2_17/6/2012 + LISS_5/8/2012
Radar + optical
1-1
5/8/2012
SE < −1 and NDVI > 50
86.7
RS2_17/6/2012 + SPOT5_20/8/2012
Radar  +  optical
1-1
20/8/2012
SE  < −1  and  NDVI  >  35
91.3Figure 28
Weed infection map of sunflower parcels, created from optical and radar images.The accuracy of the processes was validated with reference data collection by field visit. The overall accuracy (%) and the Kappa value (%) [19] of different weed-infection maps are shown by Figure 29.Figure 29
Result of general accuracy assessment of weed maps of sunflower parcels.The accuracy of identification of weed-infected and weed-free areas was studied by averages of Hellden and Short measures [20], Figure 30.Figure 30
Result of accuracy assessment of the categories of weed maps indicated by average of measures defined by Hellden and Short.


### 3.3. WCR and Storm Damage in Maize Fields, Békés Site

The average temporal change of NDVI time series of healthy reference maize parcels to the ones damaged by Western Corn Rootworm and the storm which occurred on July 7 can be seen in Figure31. Vertical lines represent standard deviation relevant to definite data. Figure 31 shows that no remarkable deviation among the three diverse states can be detected. Deviation from the control parcels increases gradually during the period between July 10 and mid-August, particularly after the storm occurred. Therefore, maize parcels damaged by storm and Western Corn Rootworm can be separated from the control ones most efficiently by evaluation of optical satellite data acquired between the end of July and mid-August.Figure 31
NDVI derived from optical satellite data for WCR and storm damaged and control maize fields, Békés site. Vertical lines represent single standard deviation. LISS-R2: IRS-R2LISS III, TM7: Landsat TM7, LISS-P6: IRS-P6 LISS III, SP4: SPOT4, and SP5: SPOT5.Analogue can be observed in Figure32 on which identification of the three different states can be followed by the help of NSI index derived from optical satellite data. Based on that the most efficient feature is the one derived from optical satellite image acquired in mid-August.Figure 32
NSI derived from optical satellite data for WCR and storm damaged and control maize fields, Békés site. Vertical lines represent single standard deviation. LISS-R2: IRS-R2LISS III, TM7: Landsat TM7, LISS-P6: IRS-P6 LISS III, SP4: SPOT4, and SP5: SPOT5.The mentioned three states of the maize parcels are represented in the two-dimensional space of co-pol bands of sigma0 (Figure33).Figure 33
The ellipse positions of three states of the maize parcels in the co-pol. bands sigma0 space at different date.Figure34 represents result of separability analysis of polarimetric descriptors derived from multitemporal radar satellite data referred to as maize parcels damaged by larvae of Western Corn Rootworm. According to Figure 34, radar data acquired on July 26, 2012 is proved to be more efficient in discrimination to the one acquired on August 19, 2012. Table 16 shows ranking of polarimetric descriptors based on discriminating efficiency of level of significance. It reveals that odd component of Yamaguchi decomposition and p component of Shannon entropy (SEp) have the most important role in it (on significance level 0.001<P<0.01). Although, in case of mid-August radar data there are such polarimetric descriptors (YAMdbl, pol.frac., rvi, and p3), suitable to separate different states significantly. However, this application is confined to significance level 0.01<P<0.05.Table 16
Ranking of polarimetric descriptors based on significance calculated by Welch test, WCR damaged and control maize fields, Békés site.
Sign. andP value
Date of polarimetric descriptors
26/07/2012
19/08/2012
3–3.90.001–0.01
YAModd, SEp
—
2.1–30.01–0.05
entropy praks, l1, depol.index, p1,SEi pedestal, anilu, SE
YAMdbl, pol.frac., rvi, p3Figure 34
Result of separability analysis of WCR damaged and control fields, Békés site.Figure35 represents result of separability analysis of polarimetric descriptors derived from multitemporal radar satellite data referred to as maize parcels damaged by storm and control parcels. According to the figure, radar data acquired on July 26, 2012 is proved to be more efficient in discrimination compared to the one acquired on August 19, 2012. Table 17 shows ranking of polarimetric descriptors based on discriminating efficiency of level of significance. The table reveals that the first eigenvalue of coherence matrix (l1) and i component of Shannon entropy (SEi) have the most important role (on P<0.001 significance level). Although in case of radar data acquired in mid-August there are such polarimetric descriptors (YAMdbl, l2, SE, and SEi), suitable to separate these two different states significantly; however their application is confined to significance level 0.001<P<0.01.Table 17
Ranking of polarimetric descriptors based on significance calculated by Welch test, storm damaged and control maize fields, Békés site.
Sign. andP value
Date of polarimetric descriptors
26/07/2012
19/08/2012
>3.9<0.001
l1,SEi
—
2.8–3.90.001–0.01
YAModd, SE,  l2, SEp
YAMdbl, l2, SE, SEi
2.1–2.80.01–0.05
Entropy praks, depol.index, p1, anilu, pedestal, pol.frac., rvi, p3
l3, l1Figure 35
Result of separability analysis of discriminating storm damaged and control maize fields, Békés site.Figure36 represents result of separability analysis of polarimetric descriptors derived from multitemporal radar satellite data referred to as maize parcels damaged by storm parallel to those damaged by larvae of Western Corn Rootworm. According to the figure it is obvious that based on radar data acquired on July 26, 2012 these two different cases cannot be separated significantly. We were able to find a few polarimetric descriptors which were able to separate these cases from each other, although only restricted to significance level 0.01<P<0.005. Table 18 shows ranking of polarimetric descriptors based on discriminating efficiency level of significance referred to as certain dates of acquisition. The table reveals that there exist such polarimeric descriptors (l3, l2, SE, and YAMvol), eligible to separate significantly these two kinds of conditions, although these can be applicable only restricted to significance level 0.01<P<0.05. However, in case of radar satellite data acquired during the last decade of July, none of the polarimetric descriptors were able to separate these cases at least by significance level P=0.05.Table 18
Ranking of polarimetric descriptors based on significance calculated by Welch test, WCR and storm damaged maize fields, Békés site.
Sign. andP value
Date of polarimetric descriptors
26/07/2012
19/08/2012
2.1–2.80.01–0.05
—
l3, l2, SE, YAMvolFigure 36
Result of separability analysis of storm and WCR damaged maize fields, Békés site.Based on evaluation of time series of optical and radar satellite data alike, it can be established that identification of maize damaged by larvae of Western Corn Rootworm can be the most efficient by synergistic evaluation of those optical and radar satellite data acquired around the end of July. Taking Figures37(a) and 38(a) into consideration those maize parcels are likely to be damaged where NDVI < 53 derived from IRS-P6 LISS III (08/08/2012) and YAModd>-7.7 derived from RADARSAT2 (26/07/2012). Similarly, maize parcels damaged by storm to healthy control parcels can be separated most efficiently based on synergistic evaluation of optical and radar satellite data acquired between the end of July and mid-August. Based on Figures 37(b) and 38(b) those maize parcels are the most likely to be damaged by storm where NSI <-3 derived from SPOT5 (19/08/2012) and l1>0.24 derived from RADARSAT2 (26/07/2012).Figure 37
Average discrimination of WCR damaged and control reference maize fields based onYAModd derived from RADARSAT2 (26/07/2012), P<0.001 (a). Average discrimination of storm damaged and control reference maize fields based on l1 derived from RADARSAT2 (26/07/2012), P<0.001 (b). Average discrimination of WCR and storm damaged reference maize fields based on l3 derived from RADARSAT2 (19/08/2012), 0.01<P<0.05 (c). Vertical lines represent single standard deviation.
(a)
(b)
(c)Figure 38
Average discrimination of WCR, storm damaged and control reference maize fields based on NDVI derived from IRS-P6 LISS III (08/08/2012),P<0.001 (a). Average discrimination of WCR, storm damaged and control reference maize fields based on NSI derived from SPOT5 (19/08/2012), P<0.001 (b). Vertical lines represent single standard deviation.
(a)
(b)Based on Figures37(c) and 38(b) differently damaged corn fields can be separated by NSI index derived from SPOT5 (19/08/2012) and l3 value derived from RADARSAT2 (19/08/2012) data. In case NSI >-3 and l3<0.022, damage by storm is more likely, while in NSI <-3 or l3>0.022 maize field is damaged by Western Corn Rootworm otherwise.The identification of damaged maize parcels was carried out by coevaluating the odd component of Yamaguchi decomposition derived from the RADARSAT2 (26/7/2012) and the NDVI calculated from the IRS-P6 LISS III (8/8/2012). The separation of the two damaged states was made by the coevaluation of l3 derived from RADARSAT2 (19/8/2012) and NSI derived from SPOT5 (19/8/2012). The created damaged map is shown in Figure39.Figure 39
Damage map of maize parcels derived from integrated evaluation of radar and optical images.The damage map was compared to the reference data from field visit; we found the overall accuracy is about 75%.


## 3.1. Drought in Sunflower Fields, Csongrád Site

Figure11 shows temporal progress of sunflower’s average crop development curve derived from time series of optical satellite data, referred to as control parcels and parcels hit by drought. Horizontal lines stand for indicating standard deviation referring to each of the acquisition dates. The most significant difference takes place in the first decade of July. Difference decreases gradually by the first decade of August and then disappears. Therefore, it can be established that from the beginning of August, healthy and arid sunflower parcels are no longer separable by optical satellite images.Figure 11
Average crop development curve based on NDVI values derived from time series of optical satellite data for sunflower hit by drought and control fields, Csongrád site. Vertical lines represent single standard deviation.The temporal behaviour of NSI values (Figure12) is reverse of NDVI.Figure 12
Average crop development curve based on NSI values derived from time series of optical satellite data for sunflower hit by drought and control fields, Csongrád site. Vertical lines represent single standard deviation.Figure13 shows the temporal development of sigma0 values of different polarization bands. In all bands the sigma0 values are lower in case of hit by drought. The higher difference between control and hit by drought areas is at the beginning of July in all bands particularly in HV one.Figure 13
The temporal behavior of sigma0 bands of backscattered beam from sunflower (control and hit by drought area).Figure14 represents the separation of ellipses featured by control and hit-drought areas in two-dimensional space of sigma0 bands. We can see well the distinguishing is highest at the beginning of July like in Figure 11. The ellipses of controls are significantly upper-right at all dates. We can see similar distinguishing in H-Alpha space (Figure 15).Figure 14
Separability of hit-drought and control areas in two-dimensional space of sigma0 co-pol. bands at different dates.Figure 15
Separability of hit-drought and control areas in two-dimensional space of H-Alpha polarimetric descriptors at different dates.Figure16 shows the result of separability analysis of polarimetric descriptors derived from RADARASAT2 satellite images acquired at different dates regarding reference sunflower parcels hit by drought and control ones. The one acquired on 8/7/2012 proved to be the most efficient in distinction. Although in case of each of the four acquisitions there exist such polarimetric descriptors which can distinguish these two states significantly.Figure 16
Result of separability analysis of sunflower hit by drought and control fields, Csongrád site.It can be seen as well that not the same polarimetric descriptors show the highest efficiency at the dates given. Table13 shows the ranking of polarimetric descriptors based on efficiency of separability or level of significance. It can be established that during the period of June to August components of Yamaguchi decomposition (mainly YAMvol and YAModd) have notable role in distinguishing between sunflower parcels hit by drought and the control ones.Table 13
Ranking of polarimetric descriptors based on significance (t) calculated by Welch test, referred to as sunflower hit by drought and control fields, Csongrád site.
Sign. (t) and P value
Date of polarimetric descriptors
14/06/2012
08/07/2012
01/08/2012
25/08/2012
>3.8<0.001
—
l3, l1, YAMvol, SE, l2, SEi
YAMvol
YAModd, l1
2.9–3.80.001–0.01
—
YAModd
l2, l3
—
2.1–2.90.01–0.05
YAModd, l1, SEi ped, p3, rvi, polfrac, SE, SEp, p1, l2, anilu
—
SE
SE, p1 anilu,SEpIt is worth mentioning that component of “vol” is the one showing the most significant deviation (P value < 0.001) between July 8 and August 1, while the “odd” one has that in mid-June and end of August. It means that distinguishing these two states is dominated by volumetric scattering during the middle of the growing season (July-August), while single scattering has more important role at the beginning and end of the growing season (mid-June and August).By evaluating both the optical and radar satellite time series data it can be established that the first decade of July is the most suitable date for distinguishing the sunflower parcels hit by drought from the control ones. According to Figure17 those pixels of sunflower were identified as hit by drought, whose NDVI was less than 52 and l3 was less than 0.035.Figure 17
Average deviation of damaged (hit by drought) and healthy (control) reference parcels based on l3 derived from RADARSAT2 (08/07/2012),P<0.001 (a). Average deviation of damaged (hit by drought) and healthy (control) reference sunflower fields based on NDVI derived from Landsat TM7 (04/07/2012), P<0.001 (b). Vertical lines represent single standard deviation.
(a)
(b)The drought map was derived from Landsat TM7 (4/7/2012), from RADARSAT2 (8/7/2012) and from using both of them (Figure18). The accuracy of these maps validated by reference data was almost the same (Figure 19).Figure 18
Drought map of sunflower parcels derived from integrated evaluation of Landsat TM7 (4/7/2012) and RADARSAT2 (8/7/2012) and the reference sunflower parcels.Figure 19
Result of accuracy assessment of sunflower’s drought map.


## 3.2. Weed Infection in Sunflower Fields, Baranya Site

Figure20 shows temporal progress of sunflower’s average NDVI curve derived from time series of optical satellite data, referred to as the control (weed-free) parcels and parcels infected by weed. Horizontal lines indicate standard deviation referred to as each of the acquisition dates. In July deviation decreases appreciable while in August it starts to increase again. The largest deviation takes place in 2nd half of August. Therefore, it can be established that optical images acquired during that period are the most suitable for sorting out infected sunflower parcels from the control ones.Figure 20
NDVI derived from optical satellite data for weed-infected and control sunflower fields, Baranya site. Vertical lines represent single standard deviation. LISS-R2: IRS-R2 LISS III, AWiFS: IRS-R2 AWiFS, TM7: Landsat TM7, LISS-P6: IRS-P6 LISS III, SP4: SPOT4, and SP5: SPOT5.From Figures20 and 21 we can see the NSI values have reverse behaviour as NDVI similar to the case above. The difference between weed-infected and weed-free areas is highest at end of August.Figure 21
NSI derived from optical satellite data regarded to weed-infected and control sunflower fields, Baranya site. Vertical lines represent single standard deviation. LISS-R2: IRS-R2 LISS III, AWiFS: IRS-R2 AWiFS, TM7: Landsat TM7, LISS-P6: IRS-P6 LISS III, SP4: SPOT4, and SP5: SPOT5.Figure22 shows the temporal variation of sigma0 in different polarization cases weed-infected and weed-free areas. The graphs are similar in all polarization bands. The backscattered intensity of weed-infected and weed-free areas is changing at beginning of August. The weed-free is higher earlier and the weed-infected increased later. The highest difference between weed-free and weed-infected areas is at mid-June in particularly co-pol. bands. The trend of Shannon entropy (SE) is very similar (Figure 23).Figure 22
Temporal development of sigma0 values in different polarization bands, Baranya site.Figure 23
Temporal development of SE polarimetric descriptor, Baranya site.The separability is well seen in the two-dimensional space of sigma0 co-pol. bands and H-Alpha bands too (Figures24 and 25).Figure 24
Separability of weed-infected and weed-free areas in two-dimensional space of sigma0 co-pol. bands at different dates, Baranya site.Figure 25
Separability of weed-infected and weed-free areas in two-dimensional space of H-Alpha polarimetric descriptors at different dates, Baranya site.We have very interesting results from analysis above. We can find the weed-infected sunflower areas at mid-June by radar features. While we can distinguish the weed-infected areas by optical images only two months later, at the end of August.Figure26 represents the result of separability analysis of polarimetric descriptors derived from multitemporal radar satellite data referred to as weed-free and weed-infected sunflower parcels. According to the graph, it is evidence that radar data acquired on 17/6/2012 is the most efficient in discrimination, while these two different statuses cannot be distinguished by analyzing radar data with other dates of acquisition about the area.Figure 26
Result of separability analysis of discriminating weed-infected and weed-free sunflower fields, Baranya site.Hierarchy of polarimetric descriptors by their discrimination efficiency and level of significance is represented by Table14. It is shown that components of Shannon entropy (SE, SEi) (P<0.001), odd and vol components of Yamaguchi decomposition (0.001<P<0.01), and eigenvalues of coherence matrix, l2 in particular (on level P<0.001), have significant role in discrimination of the two distinct statuses for mid-June. However we could not find such polarimetric descriptors applicable in order to distinguish parcels of these two statuses in mid-July and late August. In case of radar satellite scene acquired in early August anisotropy and p2 descriptors (0.01<P<0.05) seemed to be significant in discrimination.Table 14
Ranking of polarimetric descriptors based on significance calculated by Welch test, weed-infected and weed-free sunflower fields, Baranya site.
Sign. andP value
Polarimetric descriptors
17/06/2012
11/07/2012
04/08/2012
28/08/2012
>3.8<0.001
SE, l2SEi
—
—
—
2.9–3.80.001–0.01
YAMvol, YAModd l3, l1
—
—
—
2.1–2.90.01–0.05
S
E
p
—
Anisotropy, p2
—Based on evaluation of time series of both optical and radar satellite images it is evidence that weed infection in sunflower can be detected most efficiently by synergistic evaluation of optical data acquired in middle of August and radar data acquired in mid-June. According to Figure27 sunflower parcels having value of NDVI > 35 in middle of August and Shannon entropy <-1 in mid-June are expected to be infected.Figure 27
Average discrimination of weed-infected and weed-free reference sunflower fields based on SE derived from RADARSAT2 (17/06/2012),P<0.001 (a). Average discrimination of weed-infected and weed-free reference sunflower fields based on NDVI derived from SPOT5 (20/08/2012), P<0.001 (b). Vertical lines represent single standard deviation.
(a)
(b)We made weed maps in many different ways shown in Table15. The overall accuracy of these maps is shown in the last column of Table 15. Figure 28 shows the map detailed in bold row of Table 15, and the accuracy of the map is almost 90%.Table 15
Characterization of weed maps derived from radar and optical satellite data for sunflower parcels.
Basis of weed map
Type
#dates
Last date
Conditions for identification of weed infection
Overall (%)
RS2, 17/6/2012
Radar
1
17/6/2012
SE < −1
61.1
SPOT4, 28/7/2012
Optical
1
28/7/2012
NDVI > 60
72.8
LISS, 5/8/2012
Optical
1
5/8/2012
NDVI > 50
78.4
SPOT5, 20/8/2012
Optical
1
20/8/2012
NDVI > 35
83.0
SPOT4, 28/7/2012 + LISS, 5/8/2012
Optical
2
5/8/2012
NDVI1 > 60 and NDVI2 > 50
78.8
RS2_17/6/2012 + RS2_11/7/2012
Radar
2
11/7/2012
SE1 < −1 and SE2 < −1
78.9
RS2_17/6/2012 + RS2_11/7/2012 + SPOT4, 28/7/2012 + LISS, 5/8/2012
Radar + optical
2-2
5/8/2012
SE1  < −1  and  SE2  < −1  and  NDVI1  >  60  and  NDVI2  >  50
89.7
RS2_17/6/2012 + SPOT4_28/7/2012
Radar + optical
1-1
28/7/2012
SE < −1 and NDVI > 60
83.9
RS2_17/6/2012 + LISS_5/8/2012
Radar + optical
1-1
5/8/2012
SE < −1 and NDVI > 50
86.7
RS2_17/6/2012 + SPOT5_20/8/2012
Radar  +  optical
1-1
20/8/2012
SE  < −1  and  NDVI  >  35
91.3Figure 28
Weed infection map of sunflower parcels, created from optical and radar images.The accuracy of the processes was validated with reference data collection by field visit. The overall accuracy (%) and the Kappa value (%) [19] of different weed-infection maps are shown by Figure 29.Figure 29
Result of general accuracy assessment of weed maps of sunflower parcels.The accuracy of identification of weed-infected and weed-free areas was studied by averages of Hellden and Short measures [20], Figure 30.Figure 30
Result of accuracy assessment of the categories of weed maps indicated by average of measures defined by Hellden and Short.


## 3.3. WCR and Storm Damage in Maize Fields, Békés Site

The average temporal change of NDVI time series of healthy reference maize parcels to the ones damaged by Western Corn Rootworm and the storm which occurred on July 7 can be seen in Figure31. Vertical lines represent standard deviation relevant to definite data. Figure 31 shows that no remarkable deviation among the three diverse states can be detected. Deviation from the control parcels increases gradually during the period between July 10 and mid-August, particularly after the storm occurred. Therefore, maize parcels damaged by storm and Western Corn Rootworm can be separated from the control ones most efficiently by evaluation of optical satellite data acquired between the end of July and mid-August.Figure 31
NDVI derived from optical satellite data for WCR and storm damaged and control maize fields, Békés site. Vertical lines represent single standard deviation. LISS-R2: IRS-R2LISS III, TM7: Landsat TM7, LISS-P6: IRS-P6 LISS III, SP4: SPOT4, and SP5: SPOT5.Analogue can be observed in Figure32 on which identification of the three different states can be followed by the help of NSI index derived from optical satellite data. Based on that the most efficient feature is the one derived from optical satellite image acquired in mid-August.Figure 32
NSI derived from optical satellite data for WCR and storm damaged and control maize fields, Békés site. Vertical lines represent single standard deviation. LISS-R2: IRS-R2LISS III, TM7: Landsat TM7, LISS-P6: IRS-P6 LISS III, SP4: SPOT4, and SP5: SPOT5.The mentioned three states of the maize parcels are represented in the two-dimensional space of co-pol bands of sigma0 (Figure33).Figure 33
The ellipse positions of three states of the maize parcels in the co-pol. bands sigma0 space at different date.Figure34 represents result of separability analysis of polarimetric descriptors derived from multitemporal radar satellite data referred to as maize parcels damaged by larvae of Western Corn Rootworm. According to Figure 34, radar data acquired on July 26, 2012 is proved to be more efficient in discrimination to the one acquired on August 19, 2012. Table 16 shows ranking of polarimetric descriptors based on discriminating efficiency of level of significance. It reveals that odd component of Yamaguchi decomposition and p component of Shannon entropy (SEp) have the most important role in it (on significance level 0.001<P<0.01). Although, in case of mid-August radar data there are such polarimetric descriptors (YAMdbl, pol.frac., rvi, and p3), suitable to separate different states significantly. However, this application is confined to significance level 0.01<P<0.05.Table 16
Ranking of polarimetric descriptors based on significance calculated by Welch test, WCR damaged and control maize fields, Békés site.
Sign. andP value
Date of polarimetric descriptors
26/07/2012
19/08/2012
3–3.90.001–0.01
YAModd, SEp
—
2.1–30.01–0.05
entropy praks, l1, depol.index, p1,SEi pedestal, anilu, SE
YAMdbl, pol.frac., rvi, p3Figure 34
Result of separability analysis of WCR damaged and control fields, Békés site.Figure35 represents result of separability analysis of polarimetric descriptors derived from multitemporal radar satellite data referred to as maize parcels damaged by storm and control parcels. According to the figure, radar data acquired on July 26, 2012 is proved to be more efficient in discrimination compared to the one acquired on August 19, 2012. Table 17 shows ranking of polarimetric descriptors based on discriminating efficiency of level of significance. The table reveals that the first eigenvalue of coherence matrix (l1) and i component of Shannon entropy (SEi) have the most important role (on P<0.001 significance level). Although in case of radar data acquired in mid-August there are such polarimetric descriptors (YAMdbl, l2, SE, and SEi), suitable to separate these two different states significantly; however their application is confined to significance level 0.001<P<0.01.Table 17
Ranking of polarimetric descriptors based on significance calculated by Welch test, storm damaged and control maize fields, Békés site.
Sign. andP value
Date of polarimetric descriptors
26/07/2012
19/08/2012
>3.9<0.001
l1,SEi
—
2.8–3.90.001–0.01
YAModd, SE,  l2, SEp
YAMdbl, l2, SE, SEi
2.1–2.80.01–0.05
Entropy praks, depol.index, p1, anilu, pedestal, pol.frac., rvi, p3
l3, l1Figure 35
Result of separability analysis of discriminating storm damaged and control maize fields, Békés site.Figure36 represents result of separability analysis of polarimetric descriptors derived from multitemporal radar satellite data referred to as maize parcels damaged by storm parallel to those damaged by larvae of Western Corn Rootworm. According to the figure it is obvious that based on radar data acquired on July 26, 2012 these two different cases cannot be separated significantly. We were able to find a few polarimetric descriptors which were able to separate these cases from each other, although only restricted to significance level 0.01<P<0.005. Table 18 shows ranking of polarimetric descriptors based on discriminating efficiency level of significance referred to as certain dates of acquisition. The table reveals that there exist such polarimeric descriptors (l3, l2, SE, and YAMvol), eligible to separate significantly these two kinds of conditions, although these can be applicable only restricted to significance level 0.01<P<0.05. However, in case of radar satellite data acquired during the last decade of July, none of the polarimetric descriptors were able to separate these cases at least by significance level P=0.05.Table 18
Ranking of polarimetric descriptors based on significance calculated by Welch test, WCR and storm damaged maize fields, Békés site.
Sign. andP value
Date of polarimetric descriptors
26/07/2012
19/08/2012
2.1–2.80.01–0.05
—
l3, l2, SE, YAMvolFigure 36
Result of separability analysis of storm and WCR damaged maize fields, Békés site.Based on evaluation of time series of optical and radar satellite data alike, it can be established that identification of maize damaged by larvae of Western Corn Rootworm can be the most efficient by synergistic evaluation of those optical and radar satellite data acquired around the end of July. Taking Figures37(a) and 38(a) into consideration those maize parcels are likely to be damaged where NDVI < 53 derived from IRS-P6 LISS III (08/08/2012) and YAModd>-7.7 derived from RADARSAT2 (26/07/2012). Similarly, maize parcels damaged by storm to healthy control parcels can be separated most efficiently based on synergistic evaluation of optical and radar satellite data acquired between the end of July and mid-August. Based on Figures 37(b) and 38(b) those maize parcels are the most likely to be damaged by storm where NSI <-3 derived from SPOT5 (19/08/2012) and l1>0.24 derived from RADARSAT2 (26/07/2012).Figure 37
Average discrimination of WCR damaged and control reference maize fields based onYAModd derived from RADARSAT2 (26/07/2012), P<0.001 (a). Average discrimination of storm damaged and control reference maize fields based on l1 derived from RADARSAT2 (26/07/2012), P<0.001 (b). Average discrimination of WCR and storm damaged reference maize fields based on l3 derived from RADARSAT2 (19/08/2012), 0.01<P<0.05 (c). Vertical lines represent single standard deviation.
(a)
(b)
(c)Figure 38
Average discrimination of WCR, storm damaged and control reference maize fields based on NDVI derived from IRS-P6 LISS III (08/08/2012),P<0.001 (a). Average discrimination of WCR, storm damaged and control reference maize fields based on NSI derived from SPOT5 (19/08/2012), P<0.001 (b). Vertical lines represent single standard deviation.
(a)
(b)Based on Figures37(c) and 38(b) differently damaged corn fields can be separated by NSI index derived from SPOT5 (19/08/2012) and l3 value derived from RADARSAT2 (19/08/2012) data. In case NSI >-3 and l3<0.022, damage by storm is more likely, while in NSI <-3 or l3>0.022 maize field is damaged by Western Corn Rootworm otherwise.The identification of damaged maize parcels was carried out by coevaluating the odd component of Yamaguchi decomposition derived from the RADARSAT2 (26/7/2012) and the NDVI calculated from the IRS-P6 LISS III (8/8/2012). The separation of the two damaged states was made by the coevaluation of l3 derived from RADARSAT2 (19/8/2012) and NSI derived from SPOT5 (19/8/2012). The created damaged map is shown in Figure39.Figure 39
Damage map of maize parcels derived from integrated evaluation of radar and optical images.The damage map was compared to the reference data from field visit; we found the overall accuracy is about 75%.


## 4. Conclusions

In most cases, damage in croplands does not induce any notable spectral changes; therefore, they cannot be traceable in optical range. However, polarimetric radar observation is applicable to identify changes in case they are accompanied by structural alterations. It is detectable that, in case of WCR and storm damage in corn, drought or weed infection in sunflower can be identified by polarimetric radar satellite images. We determined the most suitable polarimetric descriptors for characterising the abovementioned types of damage and their optimal time periods.Synergistic application of radar and optical satellite data brings significant improvement in accuracy of identification of detecting damage accompanied by geometrical structure changes of vegetation. The most important output of our study is to emphasize the importance and to select those polarimetric descriptors which fit most of all the task given. For this express purpose a statistical method has already been developed. Importance of the method lies in the numbers of polarimetric descriptors; it is difficult to find the most suitable one for the situation. An additional considerable output is that temporal development of changes both spectrally and geometrically differs. Therefore, better result requires usage of time series of radar and optical images as well.



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*Source: 548506-2015-07-26.xml*