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«Mapping of several soil properties using DAIS-7915 hyperspectral scanner data—a case study over clayey soils in Israel E. BEN-DOR1, K. PATKIN1, A. ...»

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4.14 dsm cmÕ 1 (AVE). The relatively high EC values provide evidence that the soil surface areas along the study location were aVected by salinity contamination. This nding stands in good agreement with eld observations, which show signi cant soil degradation in several locations around agricultural elds. The soil saturated moisture (SM ) values are relatively lower than expected from clayey soils (AVE of 43.31%). However, because the nal moisture stage in this method is subjective, the most important issue is that all soils were treated equally. Other properties (soil eld moisture {FM}, and pH {PH}) represent normal values for the soils examined at this time of the year.

The VNIRA procedure was rst run on the laboratory spectral data (48 soil samples and their spectra) to obtain a correlation between the spectral and the chemical data (calibration stage). This step was taken in order to ensure that the selected populations have reliable chemical and spectral relationships to perform a con dent VNIRA analysis. Doing so revealed a signi cant ability to predict each soil property from its re ectance information. In table 2 some statistical parameters of the laboratory VNIRA results are provided (marked with @). In the next stage, the DAIS spectral data (over the 0.5–2.3 mm spectral range) were processed using the VNIRA approach and two spectral manipulations: the original DAIS re ectance (R) and its rst derivative (Rê ). The rst step for each spectral domain was to generate

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Std. Dev.=Standard deviation, CV (%)=CoeYcient of variation (Std. dev. *100/Average).

Table 2. The calibration equations obtained for each property (see text for more details).

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the correlogram spectrum in order to judge whether the highest correlated wavelengths consisted of reliable spectral assignments ( known from the literature). This step is extremely important because it is intended to prevent spectral noise from entering into the analyses (known as an over tting problem, Davies and Grant 1987). Figure 2 provides the correlograms used for all properties examined under the rst derivative spectral domain. As seen, a relatively high correlation exists in several wavelengths between the properties in question and their spectral readings (r $ 0.5­ 0.6). In the case of organic matter for example, all of these wavelengths can be assigned according to Ben-Dor et al. (1997) to remaining chlorophyll (around

0.7 mm), oil and cellulose (around 1 mm), pectin, starch and cellulose (around 1.6 mm), and lignin and humic acid (around 2.3 mm). The prediction equations extracted from these correlograms are given in table 2. These equations were generated by calculating a forward stepwise multiple analysis on the highest 38 spectral reliable bands. The next step was to run the best equation on a pixel-by-pixel basis on the DAIS re ectance cube in order to produce a spatial view of the property in question (see later discussion). In table 2, the prediction (calibration) equations for the examined soil properties are given along with some statistical parameters (R2, SEC, SEP, and m SEL; see de nitions in table 2) and possible spectral assignments. From table 2 it can be seen that in general, the prediction performances obtained for soil eld moisture, organic matter, saturated moisture, and soil salinity (EC) are favourable (R2 0.65). Both the organic matter and the eld moisture properties are ‘features’ m properties (having signi cant spectral assignments, which are also termed ‘chromophores’). In organic matter, many features across the VIS-NIR-SWIR regions are dominant because of the many functional groups active in this spectral region (see previous discussion).

In order to determine whether the wavelengths were spectrally reliable, we generated a pure spectra library of components representing the soil environment of Zvaim Valley resampled into the DAIS spectral con guration. Figure 3 (a, b, c) provides the spectra of the following components: silt-loam soil in six diVerent

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moisture contents ranging from 0.8% to 20.2% (taken from Bowers and Hanks (1965) gure 3 (a)); montmorillonite, kaolinite, halite, illite and quartz (taken from JPL-spectral library, Grove et al. 1992 gure 3(b)) and pure (fresh-a and decomposedb) organic matter (taken from Ben-Dor et al. 1997, gure 3(c)). From gure 3(a) it can be postulated that in addition to peak intensity changes at around 1.9 mm (assigned to OH in water; see montomorillonite spectrum) and at 2.2 mm (assigned to OH in clay lattice; see montmorillonite spectrum), signi cant and consistent changes of the spectral slope along the VIS-NIR (0.5–1.3 mm), SWIR-I (1.55–1.8 mm) and SWIR-II (2.25–2.4 mm) regions also exist. As Ben-Dor and Banin (1994) pointed out, the strong OH bands at 1.4 mm and 1.9 mm may not be always correlated with soil/clay moisture. Ben-Dor and Banin (1994) showed that across the NIR-SWIR spectral region (using 25 bands), the 2.365 mm wavelength is highly correlated with hygroscopic moisture, which emerged from the slope changes. In this regard it is interesting to note that using 63 bands across this region with the same population, the 1.621 mm wavelength is best for predicting soil moisture status based on a similar slope assignment (Ben Dor 1992). As seen in table 2, the selected bands for predicting soil moisture are 0.739, 0.86, and 1.65 mm, which all fell within the spectral range of ‘VIS-NIR slope changes’ previously discussed. Because these slope changes (in the original spectra) are more pronounced in the rst derivative domain, these wavelengths can be assigned to the slope-water relationship. Nevertheless, we suspect that the 0.739 mm wavelength is also assigned to chlorophyll absorption that might occur because of organic matter/vegetation remaining in the soil (see the pure organic matter spectra in gure 3(c) or even to microphytes (Karnieli and Tsoar 1994). In general, relatively high organic matter content will be found along areas of relatively high moisture. In the current study the coeYcient of determination value obtained between organic matter and soil moisture (table 3) is relatively low (r=0.37 ), but still high enough to indicate that such a trend might exist. To validate the above discussion for the Zvaim soil samples, gure 4 gives laboratory, eld and airborne spectra of two representative soil samples. As can be clearly seen, the absorption features of OH in clay lattice (around 2.2 mm) and in adsorbed water (around 1.9 mm) are signi cant together with noticeable slopes at around the VIS (0.4–1.0 mm) and at the SWIR-1 (1.2–1.8 mm) spectral regions. Weak spectral features can be depicted around 0.7 mm and 0.83 mm, which can be attributed to both organic matter remaining and iron oxide components in these soils, respectively.

4. Discussion As Ben-Dor and Banin (1995b) pointed out, ‘featureless’ properties (properties without a direct chromophore) may also be predicted via internal correlation with

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SM=Saturated Moisture, FM=Field Moisture, OM=Organic Matter, PH=pH, EC= Electrical Conductivity of the soil extracted pasta liquids.

*, + Signi cance at the 0.001 and 0.01 probability level, respectively.

E. Ben-Dor et al.

‘chromophoric’ properties. In this case, neither the soil salinity nor the pH has any direct spectral assignments. However, soil salinity (EC) is signi cantly correlated with eld moisture content as seen in table 3 (r=0.58), and hence its prediction equation consists of the eld moisture assignments. From the correlation coeYcient matrix it is postulated that a negative correlation exists between pH and EC (r= ­ 0.61), whereas no direct correlation exists between pH and eld moisture or organic matter (‘chromophoric’) properties. If a more varied population containing acidic, alkaline and neutral soils was involved, it is possible that a prediction equation could be obtained for the pH property based on internal correlation. Also it may be possible that a secondary intercorrelation (pH via EC with FM) might be less eVective than the primary intercorrelation (EC with FM). The saturated moisture (SM) content is known to be signi cantly correlated with clay mineralogy and content (Banin and Amiel 1970). As the clay content and its speci c surface area increase (e.g. appearance of montmorillonite as the dominant clay mineral in these soils), more water molecules may enter into the nal stage of the soil-saturated mixture and hence aVect the saturated moisture content. Thus the assignment of the saturated moisture wavelengths in table 2 are of OH in clay mineral lattice at

1.563 mm, 1.538 mm (u+2d ) and 2.183 mm (u+d ) and of water OH at 2.085 mm. In summary it can be said that reliable spectral models for soil eld moisture, organic matter content, soil saturated moisture and soil salinity were achieved from the DAIS data. The reliability is based on both statistical parameters and spectral assignments. In general, quanti cation (and detection) of soil salinity is a diYcult and challenging task using re ectance data (Csillage et al. 1993) or images based on sun radiation if the eVect is not signi cant to the human eye (Metternicht and Zinck 1997). This is because possible salts in the soil (e.g. NaCl), do not consist of signi cant absorption peaks across the relevant spectral region (see for example the spectrum of halite in gure 3(b)). In this case an indirect correlation with soil eld moisture (and less with organic matter) enables the VNIRA-salinity measurements to be eVective. The correlation between soil eld moisture and soil salinity in this area has to be considered: in the study area, soil salinity emerges because of a high groundwater table causing a capillary rise driven by the evaporation process. This causes the formation of salt crusts at the soil/atmosphere interface (visible or invisible).

Along salinity-infected areas, the eld moisture is relatively high, and hence, the VNIRA analysis signi cantly picks its location via the eld moisture assignments.

In reality, the groundwater level may change from one season to another, and the saline crust might serve as an indicator for determining its spatial dynamics.

Figure 5 illustrates the ‘property images’ as generated by applying the prediction equations (see table 2) on a pixel-by-pixe l basis. Basically it is assumed that an 8 m×8 m pixel can show mixed eVects of the property in question. However, although this area may be represented by a diverse distribution, the calculated value may be a fair average to demonstrate as precisely as possible the spatial distribution of the soil property.

In general it can be seen that a reliable image of each property is depicted (excluding the covered vegetation pixels, which are masked out of the image). This conclusion is based on a priori knowledge of the area as well as on a careful validation check of ve independent soil samples. These samples were analysed in the laboratory, just like the samples used for the calibration step, and are termed the validation set. In this set, the VNIRA-based values were extracted from the quantitative images obtained in the previous step. The predicted values were then Case study over clayey soils in Israel 1053 Figure 3. Several pure materials suspected to be in the soil samples resampled into the DAIS spectral con guration (a) a silt-loam soil with varying hygroscopic moisture, taken from Bowers and Hanks 1965, (b) minerals taken from Grove et al. 1992 and (c) organic matter at two diVerent composition stages (a=fresh, b=decomposed after 355 days), after Ben-Dor et al. 1997.

E. Ben-Dor et al.

Figure 4. Representative spectra ( laboratory, eld and airborne) of two soil samples, representing typical spectral features emerging from a mixture of suggested pure chromophores given in gure 3.

compared with the actual ( laboratory) values, and the results are presented in gure 6.

It appears that a favourable relationship occurs between the two values except for sample b26. For practical reasons, the DAIS spectrum of sample b26 could not be properly spatially extracted. This sample was located between two cotton plots signi cantly in uenced by a mixed (soil and vegetation) pixel problem (a problem might arise in any non-homogeneou s pixel environment). It is obvious that the b26 sample is an outlier sample among the validation set population. In general, heterogeneity in the population examined by the VNIRA approach may produce outliers (Ben-Dor and Banin 1990). In this regard it is very important to identify the outliers prior the calibration stage so that the selected model is stable and reliable. This step was taken for sample b26 in the calibration stage, which is independent of the validation stage. The poor validation results obtained from sample b26 demonstrate that exact spatial identi cation and positioning of samples in the VNIRA technique are critical. It should be noted that the prediction equations developed in this study Case study over clayey soils in Israel 1055 Figure 5. A mosaic image providing the spatial distribution of soil properties after applying the prediction VNIRA equation given in table 2 to the DAIS re ectance cube. Each image is a spatial subset representing the intensive agriculture areas along the selected ight line. (a=Electrical Conductivity ( EC), b=Field Moisture (FM), c=Organic Matter (OM), d=Saturated Moisture (SM), e=Reference base map {channel #12

0.767 mm}) are adequate only for the soil population examined in this study, i.e. representing the soil types of the calibration set.

It can be concluded that although a vast eld validation check has not been E. Ben-Dor et al.

Figure 6. Validation plots of each examined property, showing the actual values of selected soil samples along the study area against the predicted values extracted from the VNIRA image (a=EC (Ds cmÕ 1); b=FM (fraction); c=OM (fraction); d=SM (fraction)).

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