«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. ...»
performed on a large scale, the current results do indicate that the VNIRA methodology is a feasible tool for quantitatively assessing soil properties using a remotesensing means. It is important to note that the quality of the DAIS-7915 data still lags (in terms of signal-to-noise ratio, radiometric calibration and sensors’ stability) far behind that of laboratory data and even other airborne HSR data, such as the AVIRIS 97 or HyMap data (Green et al. 1997, Cocks et al. 1998 ). Although the results obtained in this study are promising, we strongly believe that using better HSR data could improve the VNIRA’s accuracy and could enable it to be used as an alternative tool for soil surface mapping. Another limitation is the fact that optical remote sensing can directly assess only the soil surface area. Because full and detailed soil mapping must consist of the entire pro le, this tool is not optimally suYcient for traditional soil mapping. Nevertheless, it is a most useful vehicle for assessing the properties of surface conditions (e.g. physical crust) or signi cant properties on the surface (e.g. soil organic matter or surface moisture). In conclusion it can be summarized that in spite of the above-mentioned limitations, the current DAIS-7915 enabled reliable and quantitative assessment of soil properties on the soil surface.
The feasibility of the DAIS-7915 data to be processed by the VNIRA methodology demonstrates that this analytical step can be practically used on other HSR data, which is acquired by a better HSR sensor.
Case study over clayey soils in Israel 1057
5. Soil property maps
Two major limitations were encountered with optical remote sensing of soils:
(1) it is impossible to sense the entire soil pro le (see previous discussion); and (2) soil vegetation (dry or green) masks out Sun photons, preventing interaction with the soil. Taking the second limitation into account, it appears that along densely vegetated areas (temporary or permanent ) no soil information can be extracted from the HSR images in general and from quantitative VNIRA images in particular (Murphy and Wadge 1994, Zhang et al. 1998). In advanced agriculture it is important to know the soil status in order to improve decision making from one season to another. Because the soil surface is not always clear of vegetation coverage, a reliable spatial mapping technique for soil properties is strongly required. In this regard, we suggest application of an interpolation process on non-vegetate d sites in order to estimate the entire area (vegetated and non-vegetated). For that purpose and to increase spatial accuracy, it is important to have a large number of soil samples for the analysis. Traditionally, preparation of such a set (based on eld and laboratory work) is a time- and money-consuming process and is not always possible.
Alternatively, the VNIRA images oVer a favourable database from which large numbers of soil samples and their corresponding properties can be rapidly extracted.
Accordingly, and based on the quantitative images created in the previous stage, we randomly selected approximately 80 soil targets (pixels from the VNIRA image with their corresponding soil property values) from an area measuring 49 km2. Figure 7 shows the exact locations of these sites with polygons overlain to represent areas of vegetation coverage. Examining the histogram of the chosen soil population (Gaosian like) along with its spatial distribution (homogeneous like) suggests that the selected group is a favourable database within which the selected interpolation processes can be run. The nal product of this stage is intended to be geocoded maps with isovalue
vectors for each property. The interpolation procedure selected for this stage was the Inverse Distance Weighting Interpolation (IDW). This technique serves as a model in the MapInfo software (MapInfo User’s Guide 1996) and is a type of moving average interpolative usually applied to highly variable data. For certain data types it is possible to return to the collection site and record a new value that is statistically diVerent from the original reading, but within the general trend for the area. Because this method is recommended for soil chemistry results, bedrock assays and monitoring environmental data we used it in this study. The IDW technique calculates a value for each grid node by examining surrounding data points that lie within a user-de ned search radius. The node value is calculated by averaging the weighted sum of all the points. Data points that lie progressively farther from the node in uence the computed value far less than those closer to the node. Using the 80 soil samples, the IDW technique was run to provide the soil property maps that are presented in gure 8(a, b, c, d). In general, good spatial agreement exists between the EC (soil salinity) and the eld moisture content maps ( gures 8(a) and (b), respectively). This was expected based on the relationships already obtained between the laboratory values of these properties (table 3) as well as the positive agreement that occurred between their spectral assignments (table 2). Comparing the organic matter map ( gure 8(c)) with both the EC and the eld moisture maps reveals that some areas are highly correlated (e.g. at the north-west edge) and some areas are not (e.g. at the centre and south-east edge). A partial validation check of the EC (salinity) on the IDW+VNIRA map discovered new saline spots, as seen in gure 8(b) and represented by three yellow polygons north of Hamadia farms (situated in the south-east corner of the area), which were veri ed on the ground.
Although a comprehensive validation check has not been performed, the previous ground validation checks strongly suggest that the IDW + VNIRA methodology is a feasible tool for agriculture applications. It is assumed that improved data quality and improved data processing would provide even better results. Accordingly, it is hoped that this paper can act as a precursor to further implementation of the VNIRA methodology in soil mapping applications using many varieties of HSR data. The VNIRA approach can take place together with the ongoing development of the HSR technology, which aims at providing an advanced spatial sensor with relatively high spectral, spatial and temporal resolutions.
5. Summary and conclusions This study employs the laboratory approach known as VNIRA for soil mapping applications by using DAIS-7915 hyperspectral data. The VNIRA method uses a spectral-chemical empirical model to predict soil properties from their re ectance spectra only. This is done by using a well-known set of calibration data and an unknown set of validation data to check the results. Under remote sensing conditions this approach has never been examined for soil applications. This paper could therefore serve as a case study from which other HSR users can start in order to create quantitative soil surface maps. In this regard many problems arose, such as atmospheric contamination of the raw data, low signal-to-noise ratios, unreliable spectral band response and positioning of the sample on the ground. Although eVort was made to overcome all of these diYculties, the results were still aVected by these obstacles and the process thus lagged in comparison with laboratory accuracy. Using the DAIS spectral information it was possible to obtain reliable prediction equations for the following soil properties: soil moisture, soil salinity (EC), soil saturated Case study over clayey soils in Israel Figure 8. A mosaic image (recti ed to local Israeli net coordinates) providing the spatial distribution of each property after application of the IDW interpolation technique (see text for more details). (a=Electrical Conductivity (EC), b=Field Moisture (FM), c=Organic Matter (OM), d= Saturated Moisture (PM)).
E. Ben-Dor et al.
moisture and organic matter content. It was found that the intercorrelation between properties is as important a parameter as the spectral information. This is because the intercorrelation enlarges the envelope of spectral assignments and provides a greater physical basis for the spectral prediction model. In this regard it was found that although the soil salinity (EC) is a featureless property, it can be spectrally explained via eld moisture assignments.
There was an indication that organic matter assignments played a role in the soil eld moisture assignments. A validation stage using ve independent samples yielded reasonable results (except for one outlier, which was questionable in terms of its ground positioning). This stressed the fact that a careful positioning of ground targets using the VNIRA approach under a remote-sensing domain is essential. An attempt to estimate soil property distribution under vegetation coverage using the VNIRA results was made. For that, we employed a random selection of 80 soil samples from the quantitative images and applied an interpolation technique to provide an isocontour map for each of the studied soil properties. It was shown that merging the quantitative remote sensing (VNIRA) technique with a spatial interpolation algorithm (IDW) provides a useful tool for soil mapping applications. Although the results are still far from what can be achieved in the laboratory, the study showed that the VNIRA technique is a feasible tool for mapping soil properties using HSR data. Better HSR data, more soil samples and sharpening the VNIRA approach could be the combination that makes this method fully applicable.
Acknowledgments This study was supported by the KKL, Land Development Authority (under CHOSEN internal fund) and by the German Israel Foundation (GIF). The DAISover ight was funded by the European Community. We are grateful to the DLR Optoelectronics Department for their eVorts in bringing the sensor to Israel and for conducting the air campaign under very high standards.
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