SPECTRAL DISCRIMINATION AND SEPARABILITY ANALYSISOF AGRICULTURAL CROPS AND SOIL ATTRIBUTES USING

ASTER IMAGERYArmando Apan*, Rob Kelly#, Troy Jensen#, David Butler#, Wayne Strong#,

and Badri Basnet#

*Faculty of Engineering and SurveyingUniversity of Southern QueenslandToowoomba 4350 QLD Australia

Phone: (07) 4631-1386 Fax: (07) 4631-2526Email: apana@usq.edu.au

#Queensland Department of Primary IndustriesToowoomba 4350 QLD Australia

Phone: (07) 4688 1524 Fax: (07) 4688 1192Email: Rob.Kelly@dpi.qld.gov.au

AbstractThere are growing interests on how Advanced Spaceborne Thermal Emission andReflectance Radiometer (ASTER) image data could be used for various applications.ASTER offers improved spatial, spectral and radiometric resolutions. Hence, ourpurpose was to evaluate the utility of multispectral ASTER imagery in thediscrimination and mapping of agricultural crops, soil and related land cover types. Fouragricultural land cover attributes were specifically considered for spectral separabilityassessment and mapping: crop type, crop growth stages, soil colour and soil texture.

Two scenes of approximately 60 km x 60 km ASTER Level 1B imagery, covering theeastern Darling Downs region, Queensland, Australia, were selected for this study.Acquired on 24 September 2001, the imagery covers extensive agricultural regionencompassing the rural towns of Dalby, Brookstead, Jondaryan, Pittsworth andMillmerran. ASTER’s visible and near infrared (VNIR) and shortwave infrared (SWIR)bands, as well as selected image transformation layers (e.g. ratios, vegetation indices,and principal components), were utilised. Training areas were selected and supportedwith field information, soil sampling and cropping details obtained by interview withfarmers.

The results of separability analysis indicated that ASTER data provided adequatespectral discrimination of crop types (wheat/barley vs. chickpea), and to some extent,crop growth stages. Interaction between cover attributes (e.g. same wheat but in an earlystage or in a very wet patch) produced some classification errors. Despite that,ASTER’s sensitivity to changes in bio-physical conditions indicate that these data areuseful for mapping within-field variability where the focus is confined to a limited area.The three bands that produced the best average separability are the layers pertaining tovegetation indices: ratio NIR/R, Sqrt(NIR/R) and NIR-R.

On the other hand, two-level broad classes of soil colour and texture were adequatelymapped. Bands 2 (visible red), 8 (SWIR), and first principal component 1 (of bands 1 to9) are the best layers to use for discriminating soil features. However, some spectralconfusion between intermediate soil colour and texture class was observed. Thus,

detailed level mapping of soil attributes using ASTER does not appear achievable.Nevertheless, ASTER’s 15-m spatial resolution in the VNIR bands offers potential formapping within-in field soil variability at relatively broad attribute classes.

1. IntroductionThe use of remote sensing imagery for mapping, assessing and monitoring ofagricultural crop conditions and production has been steadily increasing in recent years.The late 1990s have been a time of rebirth for remote sensing for farm management(Moran, 2000) after the “revolution” began in 1970s that went through a period offailures at delivering promises (Johannsen, et al., 2000). Airborne and/or spaceborneimaging systems are now increasingly being used for various spatio-temporal scales atdifferent mapping objectives and implementation levels.

It is often regarded that spectral data, in tandem with knowledge of local bio-physicalsetting and crop calendar information, is useful in broad-level crop type discriminationand plant stress detection (e.g. Seelan, 2001; Cloutis, et al., 1999). Recently launchedsatellite based imaging sensors, equipped with improved spatial, spectral andradiometric resolutions, offer enhanced capabilities over those of previous systems.

Our objective was to evaluate the utility of the Advanced Spaceborne Thermal Emissionand Reflectance Radiometer (ASTER) multispectral data in the discrimination andmapping of agricultural crops, soil and related land cover types. In this project, twocategories of land cover types were studied:

• specific crop and soil attribute – crop type, crop growth stage, soil colour andsoil texture; and

• broad cover type – woody vegetation, pasture/grazing land, agricultural crops,bare soil, stubble, and water.

2. Research Methods

2.1 Study Area

The study area covers part of the Darling Downs of Queensland, encompassing a totalarea of about 350,000 hectares (Figures 1 and 2). It is located west of the mountains ofthe Great Dividing Range, the central point of which is approximately 200 km fromQueensland’s capital city of Brisbane. The area includes the rural towns of Dalby,Brookstead, Jondaryan, Pittsworth and Millmerran.

The Darling Downs contains some of the richest farming lands in Australia and supportsone of the Queensland’s most important centres of agriculture. The economic activitiesin the region are mainly centred on grain growing and cattle grazing. Major agriculturalcrops include cereals (e.g. wheat, barley and sorghum, etc.), pulses (chickpea andmungbean) and fibre crops (cotton). Apart from intensive cultivation, the regionconsists of grasslands, remnant woodlands and revegetated areas, and settlement areas.

The climate in the region is predominantly sub-tropical, with summer dominant rainfall.In Pittsworth, for example, the average annual rainfall is 659 mm, and the temperatureranges from a mean minimum of 5.2°C in July to a mean maximum of 30.2°C inJanuary (DPI, 1988).

Figure 1. ASTER Scene 1 (“Dalby”)

Figure 2. ASTER Scene 2 (“Millmerran”)

Scene 1 (“Dalby”)

Scene 2 (“Millmerran”)

2.2. Data Acquisition

Two scenes of 60 km x 60 km ASTER Level 1B (calibrated radiance-at-the-sensor data)imagery were acquired on 24 September 2001, free of charge from the Earth ObservingSystem Data Gateway (EDG) (online search and order facility) at the web addresshttp://edcimswww.cr.usgs.gov/pub/imswelcome.

The ancillary data used in this study included hardcopy topographic maps (1:100,000scale), Landsat ETM+ (15 August 2001), and digital elevation model (DEM), whichwere used to assist the interpretation and analysis.

2.3 Ground-truthing and Field Data Gathering

Ancillary maps and plotted hardcopy images were used in identifying various landcover features (focusing on agricultural crops and soil attributes) and in describing theirspectral characteristics. Field reconnaissance survey was carried out in the initial stage,while intensive field data gathering were conducted during the middle and final stagesof project implementation. Interviews with farmers near Brookstead and Millmerrangenerated the relevant crop type and crop calendar (to determine crop growth stage)information needed in identifying and delineating sample areas.

Sample areas representing winter crops (wheat, barley, chickpea, etc.), cultivated/baresoil, pasture/grazing land, woody vegetation, stubble and water bodies (e.g. dams), weredelineated and coded on the hardcopy plots. The attributes (e.g. crop type, date planted,stubble materials, vegetation condition, management regime, etc.) pertaining to eachsample area were recorded in hardcopy field notebook.

Sites located some 20-30 km south-west of Jondaryan were identified as being useful toground-truth soil classes identified by imagery. These sites on the alluvial flood plainsupport predominantly grain and cotton-based farming systems. Dominating soil typesinclude a heavy-clay vertosol, both black and grey types, as well as medium-claychromosols (Isbell, 1996), and are classified within the Formartin and Waco soil series(Harris, et al., 1999).

Local information on soil variation and predominant vegetation type was initiallygathered from land managers. Sites of interest, where soil variation was visuallyobvious, were then identified, and mapped using a CSI differential global positioningsystem (dGPS) receiver capable of receiving positional corrections from the beaconsignal. Positional information, accurate to ±0.5 m, was collected as much as waspractical from the center of each soil patch. A composite sample of soil was collectedfrom 10-15 sites within a 20-30 m radius of the position from which a subsample wascollected for field processing.

Descriptive data was collected at each site using a palmtop computer-based geographicinformation system (GIS), including approximate dimensions of the patch, surface soilcolour, surface soil texture, stubble level/age/type, surrounding vegetation. Soil colourwas found by comparing a wetted-up soil ped with standard soil colour charts (Munsell,1994), while soil texture was determined using a simple soil-wetting technique(McDonald, et al., 1990).

2.4 Image Processing and Analysis

The flowchart of the techniques employed is given in Figure 3.

Training Area

Processing of ASTER Data for Spectral Separability Analysis andMapping of Crops and Soil

ASTER Data(VNIR & SWIR)

Training Area Selection

Ground Truthing /Field Data Gathering

Interpret

Ancillary Data

Field Information

Geometric Correction

RelateDerivatives ofASTER Data

Stack

Statistics and Plots onSpectral Separability

Signature Evaluation and Refinement

Supervised Classification

Classified Image

Accuracy Assessment

Classification AccuracyOutput

Knowledge on SpectralSeparability and

Mapping Implications

Figure 3. Flowchart of major steps in the processing of ASTER data

2.4.1. Spectral Bands Used

All the visible and infrared bands (ASTER spectral bands 1 to 9), except the thermalinfrared (bands 10 to 14), were included in the analysis. In addition, selected imagetransforms (vegetation indices, ratios and principal components) were produced forinclusion in the analysis. All layers were standardised to unsigned 8-bit (256 range) datatype. Below are the lists of layers used:

Crop Type and Growth Stage Discrimination and Mapping1 = ASTER Band 1 (Green Band), 0.52 - 0.62 µm, 15 m spatial resolution2 = ASTER Band 2 (Red Band) 0.63 - 0.69 µm, 15 m spatial resolution3 = ASTER Band 3 (Near Infrared Band) 0.78 - 0.86 µm, 15 m spatial resolution4 = ASTER Band 4 (Short Wave Infrared Band) 1.6 - 1.7 µm, 30 m spatial resolution5 = ASTER Band 5 (Short Wave Infrared Band) 2.145 - 2.185 µm, 30 m spatial resolution6 = ASTER Band 6 (Short Wave Infrared Band) 2.185 - 2.2225 µm, 30 m spatial resolution7 = ASTER Band 7 (Short Wave Infrared Band) 2.235 - 2.285 µm, 30 m spatial resolution8 = ASTER Band 8 (Short Wave Infrared Band) 2.295 - 2.365 µm, 30 m spatial resolution

9 = ASTER Band 9 (Short Wave Infrared Band) 2.36 - 2.43 µm, 30 m spatial resolution10 = Ratio NIR/R11 = Sqrt(NIR/R)12 = VIDIFF (NIR - R)13 = NDVI (NIR-R/NIR+R)14 = TNDVI (Sqrt ((NIR-R/NIR+R)+ 0.5)15 = Principal Component 1(of ASTER 1 to 9)16 = Principal Component 2(of ASTER 1 to 9)17 = Principal Component 3(of ASTER 1 to 9)18 = Principal Component 4(of ASTER 1 to 9)

Soil Colour and Texture Discrimination and Mapping(ASTER Bands 1 to 9)10 = Ratio 2/111 = Ratio 4/312 = Ratio 4/513 = Ratio 4/614 = Ratio 4/715 = Ratio 4/816 = Ratio 5/617 = Ratio 6/718 = Ratio 8/919 = Principal Component 1 (of ASTER 1 to 9)20 = Principal Component 2 (of ASTER 1 to 9)21 = Principal Component 3 (of ASTER 1 to 9)22 = Principal Component 4 (of ASTER 1 to 9)

Broad Cover Type Discrimination and Mapping(ASTER Bands 1 to 9)10 = Ratio NIR/R11 = NDVI12 = Ratio 4/313 = Principal Component 1 (of ASTER 1 to 9)14 = Principal Component 2 (of ASTER 1 to 9)15 = Principal Component 3 (of ASTER 1 to 9)16 = Principal Component 4 (of ASTER 1 to 9)

2.4.2 Training Area Selection and Signature Evaluation

Training areas for each four crop and soil attributes (crop type, crop growth stage, soilcolour and soil texture) under study were selected from the information generated byground-truthing and interviews. On-screen digitising of samples from the display ofenhanced image composites was implemented to generate five signature files.

Signature evaluation was done to examine the spectral properties of individual trainingsample class and its separability over others. The statistics contained in the signature fileaid in determining whether the classes are “good” (no or little overlap) in terms of theirseparability in the multi-dimensional attribute space. As provided by the softwareERDAS IMAGINE 8.5, various signature separability assessment tools, e.g. histograms,ellipses, descriptive statistics, error matrix and divergence measures, were utilised.

2.4.3 Supervised Classification and Accuracy Assessment

The goal of supervised classification in this part is not to produce a land cover map thatwill be used for thematic mapping or for other purposes. Instead, the conduct ofclassification was to determine how the various training samples will perform, and thento identify potential misclassifications.

The supervised classification in this study utilised the minimum distance to meansalgorithm. With this classifier, training data is used to determine class means;classification is then implemented by assigning a pixel in the class of the nearest mean.While other classifier, e.g. maximum likelihood, may give better classification accuracyin theory, the limitations of the number of verified training pixels and the many layersinvolved (18 and 22 layers) disallowed the generation of covariance information neededin using such a classifier. Thus, it is more effective to resort to a classifier (e.g.minimum distance to means) that does not make use of covariance information butinstead depends only upon the mean positions of the spectral classes, noting that for agiven number of samples these can be more accurately estimated than covariances(Richards, 1993, p. 189).

At this stage, the conventional accuracy assessment procedure and presentation by usingerror matrix (e.g. Congalton, 1991) was not implemented. The evaluation of theclassification results was done by visual comparison and qualitative assessment of theclassified images and the field/ancillary data. Misclassified areas were noted andevaluated.

3. Results

3.1. Spectral Separability and Mapping of Agricultural Crops

Statistics from the training pixels indicate that wheat and barley have close oroverlapping values for most bands and their derivatives. On the other hand, chickpea isquite separable from the two crops in layers corresponding to vegetation indices and the4th principal component (Figure 4).

ASTER and Crop Type (Brookstead)

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AST1AST2

AST3AST4

AST5AST6

AST7AST8

AST9NIR

/R

SQTNIR

/RVID

IFF NDVI

TNDVI

PC1PC2

PC3PC4

Layers

Mea

n

Wheat Barley Chickpea

Figure 4. Mean of spectral values for crop type training samples

From the signature separability analysis, the three bands that produced the best averageseparability are layer 10 (ratio NIR/R), layer 11 (Sqrt(NIR/R)) and layer 12 (VIDIFF).The raw ASTER bands in the VNIR and SWIR regions (Bands 1 to 9) exhibited poorseparability.

These close to overlapping spectral values of wheat and barley were further revealed bythe results of the training set pixel classification. From the error matrix of sample pixels,only 67% of wheat training pixels where correctly classified as wheat. When the entirescene was classified, the spectral confusion between the two winter crops became moreevident.

Although the chickpea signatures indicate potential for separability from other croptypes, the classification of the entire scene revealed substantial misclassifications.Comparisons between crops of different ages appeared to be the main reason formisclassification.

The analysis of spectral data comparing crops of different growth stage disclosed that“younger” and “older” (3-week apart) wheat crops (Figure 5) have better spectralseparability when compared to the “younger” and “older” (3-week apart) chickpeas(Figure 6). As for crop type discrimination, the three bands that produced the bestaverage separability for crop growth stage were the vegetation indices layer 10 (ratioNIR/R), layer 11 (Sqrt(NIR/R)) and layer 12 ((NIR - R)).

ASTER and Crop Grow th Stage (Wheat)

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AST1

AST2AST3

AST4AS

T5AS

T6AS

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/R

SQTNIR

/R

VIDIFF

NDVI

TNDVI

PC1

PC2

PC3

PC4

Layer

Mea

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Wheat (younger) Wheat (older)

Figure 5. Mean of spectral values for crop growth (wheat) stage training samples

ASTER and Crop Growth Stage (Chickpea)

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AST1AST2

AST3

AST4AS

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/R

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PC2

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Chickpea (younger) Chickpea (older)

Figure 6. Mean of spectral values for crop growth (chickpea) stage training samples

3.2 Spectral Separability and Mapping of Soil Attributes

Results of the soil colour discrimination show that the spectral signatures have goodseparability (Figure 7). On particular layers, dark red brown soil is spectrally distinctfrom the black soil. ASTER band 2 (visible red), band 8 (SWIR), and layer 19(Principal Component 1 (of ASTER 1 to 9) are the best layers to use for discriminatingsoil colour features, although most of the ASTER raw bands provide good separation.The ratios of SWIR did not produce good separability, except for layer 18 (ratio 8/9)and layer 10 (ratio 2/1). The principal component 1 (PC1) has a very good separabilityfor soil colour.

Supervised classification of the area based on soil colour produced good results.Although only visual checks (i.e. comparing the unclassified and classified imagery)were made at this stage, selected regions expected to be classified as “red soil” werecorrectly classified as “red soil”. The same trend was obtained for selected “black soil”regions.

ASTER and Soil Colour

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AST1AST2

AST3AST4

AST5AST6

AST7AST8

AST9

RATIO21

RATIO43

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RATIO47

RATIO48

RATIO56

RATIO67

RATIO89 PC1

PC2PC3

PC4

Layer

Mea

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Dark red brown Black Very dark brown

Figure 7. Mean of spectral values for soil colour training samples

The spectral data on soil texture generally followed the same patterns as the soil colour(Figure 8). Loamy sand and heavy clay (the upper and lower “limit” of the range)offered good separation in the ASTER raw bands and Principal Component 1. Similarly,ASTER bands 2 (visible red), band 8 (SWIR), and layer 19 (Principal Component 1)offer the highest separability. The ratios of SWIR did not produce good separability.

Regions of known loamy sand and heavy clay soils were correctly classified. However,some areas of identified “intermediate” classes of soil texture (e.g. medium clay andsandy clay loam) were incorrectly classified. This is supported by the results of the errormatrix of the training pixels, where percentage correctly classified was only – 58%,68%, and 64% for light clay, medium clay, and sandy clay, respectively. Loamy sandand heavy clay were more accurately classified with 83% and 94% correctclassification.

ASTER and Soil Texture

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AST3AST4

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AST9

RATIO21

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RATIO67

RATIO89 PC1

PC2PC3

PC4

Layer

Mea

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Light clay Medium clay 1 (VDG) Medium clay 2 (B)

Heavy Clay Sandy clay loam Loamy sand

Figure 8. Mean of spectral values for soil texture training samples

3.3 Spectral Separability and Mapping of Broad Cover Types

The training pixels show good spectral separability measures in ASTER layer 10 (ratioNIR/R), layer 13 (principal component 1) and layer 15 (principal component 3) for mostclasses. As expected, vegetation areas (crops and woody vegetation) are spectrallydistinct from the non-vegetation area (stubble, cultivated/bare soil, and water) (Figure9). However, the following classes have very close or overlapping spectral values evenin the best bands identified above:

• pasture/grazing (with dry vegetation), bare soil, and stubble are very close oroverlapping in band 3 (red) and ratio NIR/R;

• water and woody vegetation are close in principal component 1; and• pasture and woody vegetation are close in principal component 3. Similar case

holds true for bare soil and stubble in this layer.

The supervised classification confirmed these spectral overlaps – there are classificationerrors between pasture and stubble; and between woody vegetation and crops.

ASTER and Broad Cover Types

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AST1AST2

AST3AST4

AST5AST6

AST7AST8

AST9NIR

/RNDVI

Ratio4

3PC1

PC2PC3

PC4

Layer

Mea

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Stubble Pasture/Grazing Crops

Bare/Cultivated Woody Veg Water

Figure 9. Mean of spectral values for broad cover type training samples

4. Discussion

4.1. Spectral Separability and Mapping of Agricultural Crops

The expectation, that at least chickpea and wheat/barley can be adequately mappedusing spectral data, is flawed. This study has realised a concept we termed as the“spectral separability paradox” ? even if land cover classes are highly separable in thespectral domain, there will be varying conditions in nature that will inevitably lead tospectral misclassification between classes. For instance, while chickpea is spectrallydifferent from wheat and barley, the presence of relatively young (newly planted) wheator barley paddock where black soil is still exposed, could result to significantclassification errors. This spectral mixing of young crops and black soil approximatesthe spectral response of chickpeas that render them inseparable.

The results of this study suggest that crop type-level (e.g. wheat vs. barley vs. chickpea)differentiation and mapping using digital processing of ASTER data has two mainpathways in terms of yielding mapping accuracy. On one side, accurate mapping at thecrop type level is possible when ideal conditions are met:

• the study area is limited to a relatively small region where bio-physicalconditions (mainly soil, water regimes and topography) are homogenous.

• the study area contains no crops planted at significantly different planting dates.

Of course, these two conditions are rare in nature and perhaps could only be found onexperimental plots, or when the focus of the study is on within-field variabilitymapping.

On the other side, the mapping of crops at detailed “species” level, using spectral dataalone, seems to be prone to errors and spectral confusions. This is where the “spectralseparability paradox” case holds true. Unfortunately, this is often the case in nature dueto the heterogeneity of the natural environment and the variations in farming practices.The implication is that generating accurate maps at the regional scaled level usingsingle-date spectral data alone would be very difficult. The feasible solutions to reduceclassification errors (assuming that variations in planting dates could not be eliminated)under this condition, will include:

• the use of multi-temporal imagery• the use of ancillary data for spatial masking (“cut-and-paste”) of areas under

confusion.

These spectral overlaps are not unique to agricultural crops. Niemann (1993), Bensonand deGloria (1985), and others, found that digital classification of satellitemultispectral data alone can not be used to determine species composition in foreststands. In their study of tropical secondary forest, Lucas et al. (1993) also found similardifficulty because they could not adequately discriminate plantations (oil palm) fromsecondary forest using Landsat TM data. Using SPOT data, Passini and Lacaze (1995)found that some vegetation (species) classes are very close in spectral feature space, andrecommended that a simplified typology of vegetation units should be used.

Neither these spectral overlaps are unique to ASTER data. In a separate study ofagricultural crops (Potgieter and Apan, 2002, this volume) of the same region usingLandsat 7 ETM+, there were significant errors observed when crop type level (wheat vs.barley vs. chickpeas vs. oat) mapping was attempted.

Does it mean that ASTER data and other multispectral imagery are not useful for croptype mapping? On the contrary, the results of separability analysis indicated thatASTER data provides adequate spectral discrimination of crop types (wheat/barley vs.chickpea). It is when certain conditions become “different” (e.g. same wheat but in anearly stage or in a very wet patch) that ASTER could not adequately categorise featuresat the crop type level. On the other side, in fact, this is a strength – ASTER’s sensitivityto changes in physical conditions would mean that they are useful for mapping within-field variability where the focus is confined to a limited area.

4.2 Spectral Separability and Mapping of Soil Attributes

The statistics generated from training areas, as well as the classification results, indicatethat ASTER imagery is good in mapping variations in soil colour when a two-classcategory “red soil” vs. “black soil” is used. For mapping of variability within a field,this broad category level may be sufficient for most practical farming requirements. It iswhen other intermediate classes are added, e.g. “grey soil”, where classification errorsmay likely to occur.

All the ASTER SWIR bands, particularly band 8 (2.295 - 2.365 µm), exhibit goodability to discriminate differences in soil colour. While this may not be unique toASTER (as Landsat 7 ETM+ has band 7 that corresponds to ASTER’s band 5-9), themultiple and fine spectral range of ASTER bands in the SWIR region may havecaptured more variability, thus more information. It will be interesting to know howLandsat 7 ETM+ will perform in mapping soil colour in the same field/paddock.

The spectral data on soil texture generally follow similar patterns with soil colour.When image classification was conducted, better results were obtained when only twocontrasting classes were included, i.e. heavy clay vs. loamy sand. Loamy sandconsistently has a higher reflectance values in the raw ASTER bands than heavy clay.On the other hand, inclusion of “in-between” transitional classes yielded significantclassification errors. Again, this broad category level may be sufficient for mostmapping within field variability in soil texture.

4.3 Spectral Separability and Mapping of Broad Cover Types

The delineation of areas as either vegetated or non-vegetated is highly possible, due tothe contrasting reflectance properties of bare areas and vegetation in the visible andinfrared bands and their transforms. However, classification errors occur when moredetailed classes (pasture vs. stubble; crops vs. woody vegetation) were attempted. Inhere, the fundamental difference between land cover type (physical attribute) and landuse type (socio-economic attribute) is becoming more evident. Satellite sensors capturea physically-based parameter: radiance. Sensors can record the radiance of dry organicmaterial, but no distinction is made if it is stubble or in pasture land. Dobson (1993, p.22) echoed the distinction: “The scientifically correct solution is to create two separateclassification schemes. Employ land cover to study physical processes and land use tostudy cultural and economic processes. But do not look for land use with remotesensing.”

With this, land use information should be acquired from other mapping procedures andmethods that are highly reliable. One way to segregate these land cover features is touse the GIS overlay (Boolean or otherwise) technique between a land use map and aland cover map. Logical queries and display could be used to produce “combinations”,e.g. vegetation (land cover) in cropping regions (land use), which could help refine theclassification process. Other ancillary data, e.g. digital elevation model, may be alsoused if strong relationships exist between land cover types and the thematic informationon the ancillary data. For instance, a DEM may be used to filter out misclassifiedagricultural crops in areas above 45% slope.

5. ConclusionsASTER data provides adequate spectral differentiation between different crop types andbetween crop growth stages based on selected training areas. The three bands thatproduced the best average separability are the layers pertaining to vegetation indices:ratio NIR/R, Sqrt(NIR/R) and NIR-R. However, classification errors become apparentwhen the whole, bigger study area was classified due to the presence of certainconditions that confuse crop type and growth stage discrimination. The “spectralseparability paradox” was realised: even if land cover classes are highly separable inthe spectral domain, there will be varying conditions in nature that will inevitably leadto spectral misclassification between classes. However, for applications confined to aparticular farm or paddock, such as mapping variability of crop’s vigour or biomasswithin-field, ASTER data will be useful due to its better spatial resolution (e.g. thanLandsat 7) in the visible and near infrared bands.

Broad-level classes of soil colour and texture were adequately mapped using ASTERdata. Bands 2 (visible red), 8 (SWIR), and first principal component 1 (of bands 1 to 9)are the best layers to use for discriminating soil features, although most of the ASTER

raw bands also provide good separation. Because spectral confusions appear betweenintermediate transitional soil colour and texture classes, ASTER data was not able toprovide detailed classes of soil attributes under the condition prevailing (and techniquesapplied) on this study. Nevertheless, the broad category level may be sufficient for mostmapping of within field variability of soil colour and texture.

AcknowledgmentThe authors gratefully acknowledge the help of Mr. Andries Potgieter and Mr. OwenChandler for allowing us to use some of the field data they collected for another project.Many thanks to NASA’s ASTER project for providing ASTER data free of charge.

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