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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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int. j. remote sensing, 2002, vol. 23, no. 6, 1043 –1062

Mapping of several soil properties using DAIS-7915 hyperspectral

scanner data—a case study over clayey soils in Israel


1Department of Geography, Tel-Aviv University, Ramat Aviv, Tel-Aviv

2Department of Soil and Water Sciences, Faculty of Agricultural, Food and

Environmental Quality Sciences, The Hebrew University, Rehovot, Israel

3J. Blaustein Institute for Desert Research Sde-Boker Campus, Negev, Israel (Received 28 September 1999; in nal form 5 June 2000) Abstract. The data acquired from the hyperspectral airborne sensor DAIS-7915 over Izrael Valley in northern Israel was processed to yield quantitative soil properties maps of organic matter, soil eld moisture, soil saturated moisture, and soil salinity. The method adopted for this purpose was the Visible and Near Infrared Analysis ( VNIRA) approach, which yields an empirical model for predicting the soil property in question from both wet chemistry and spectral information of a representative set of samples (calibration set). Based on spectral laboratory data that show a signi cant capability to predict the above soil properties and populations using the VNIRA strategy, the next step was to examine this feasibility under a hyperspectral remote sensing (HSR) domain. After atmospherically rectifying the DAIS-7915 data and omitting noisy bands, the VNIRA routine was performed to yield a prediction equation model for each property, using the re ectance image data. Applying this equation on a pixel-bypixel basis revealed images that described spatially and quantitatively the surface distribution of each property. The VNIRA results were validated successfully from a priori knowledge of the area characteristics and from data collected from several sampling points. Following these examinations, a procedure was developed in order to create a soil property map of the entire area, including soils under vegetated areas. This procedure employed a random selection of more than 80 points along nonvegetated areas from the quantitative soil property images and interpolation of the points to yield an isocontour map for each property. It is concluded that the VNIRA method is a promising strategy for quantitative soil surface mapping, furthermore, the method could even be improved if a better quality of HSR data were used.

1. Introduction Hyperspectral remote sensing (HSR) is an advanced technique that provides a near-laboratory-qualit y re ectance spectra of each single pixel. This capability allows the identi cation of targets based on their well-known spectral absorption features (Goetz et al. 1985). Under laboratory conditions, the spectral information of the visible, near-infrared and short wave infrared (VIS-NIR-SWIR; 0.4–2.5 mm) spectral regions provides a promising capability to identify soil, vegetation, rock and mineral materials (e.g. Stoner and Baumgardner 1981, Gao and Goetz 1990, Clark et al.

1990). Under HSR conditions, this spectral information enables semi-quantitative Internationa l Journal of Remote Sensing ISSN 0143-116 1 print/ISSN 1366-590 1 online © 2002 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/01431160010006962 E. Ben-Dor et al.

classi cation of large areas regarding such issues as composition of rocks and minerals (Kruse et al. 1990, Lorcher 1999, Hausknecht 1999, vegetation status (Martin and Aber, 1993, Gao and Goetz 1995), water body condition (Keller et al.

1998, Lazar et al. 1998, Pierson 1998) and atmospheric gas distribution (Gao and Goetz 1990, Richter and Ludeker 1998).

Because soil is a complex system, soil properties cannot be easily assessed directly from their re ectance spectra even under controlled (laboratory) conditions (BenDor and Banin 1994). Since under a remote sensing domain this capability could be even more problematic (Peng 1998), neither quantitative nor semi-quantitativ e spatial analysis of many soil properties from re ectance data have yet received proper attention in either the point or imaging spectroscopy domain. Nevertheless, in some cases, quantitative feasibility can be achieved using HSR data, mainly if the property in question is a well-known spectral property active across the re ectance region (e.g. organic matter, Ubelhoven et al. 1997 ).

A new approach for analysing soil properties from laboratory re ectance information has been developed by Dalal and Henry (1986) and later expanded by Ben-Dor and Banin (1995a, 1995b). The method (termed VNIRA; Visible and Near Infrared Analysis) was originally developed for use in food science for rapidly determining chemical constituents directly from their laboratory re ectance spectra in the NIRSWIR spectral region (1.0–2.5 mm) (Norris 1988). This approach employs a statistical model that draws a correspondence between ‘wet chemistry’ and re ectance data to yield a tool for empirically predicting the constituent in question solely from its re ectance information. The VNIRA method is widely used in elds such as food science, tobacco and oil industries, pharmacology, vegetation monitoring and medicine (Stark et al. 1986). In the eld of remote sensing, extracting re ectance values from a pixel is a complicated task as compared with the process under controlled laboratory conditions, because of illumination and terrain changes, atmospheric attenuation, low signal-to-noise ratio and more. However, if the airborne sensor is sensitive enough and the atmospheric eVects can be properly removed from the original data, this technique might be useful for rapid quantitative mapping of large areas. In this regard Curan et al. (1992 ) and LaCapra et al. (1996) were able to demonstrate that the VNIRA approach is capable of assessing canopy chemistry by using AVIRIS (Airborne Visible and Infrared Imaging Scanner; Vane et al. 1993 ) HSR data. Soil is a more heterogeneous material than vegetation, which eventually results in greater diYculties in applying quantitative analyses to HSR soil data. BenDor and Banin (1990, 1994, 1995a, 1995b) have shown that the VNIRA approach is useful for assessing soil properties if careful laboratory conditions and spectral manipulation techniques are employed. Moreover, they showed that for several soil properties, a large number of spectral channels is not always required to accurately predict the property in question (number of channels required ranged between 15 and 313). Because airborne HSR technology enables band numbers around this range (e.g. AVIRIS-224, DAIS-79), the VNIRA approach should be examined for soil applications using HSR data. To the best of our knowledge, this approach has never been applied to a soil environment in a remote sensing domain. This study is therefore aimed at examining the HSR-VNIRA capability under such conditions.

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Figure 1. Location of the study area.

From the four ight overpasses acquired in this DAIS mission, the shaded polygon represents the study area of Zvaim Heights. (The small map is provided in a geographical coordination system for global positioning, where the major map is provided in the internal old Israel coordination system for local positioning).

E. Ben-Dor et al.

Germany (Muller and Ortel 1997). The sensor is sensitive to the VIS-NIR-SWIRTIR spectral regions (0.4–14 mm), consisting of 79 channels across, with a bandwidth ranging from 0.9 nm to 60 nm. The instantaneous- eld-of-view (IFOV) is 3.3 mrad, and the eld-of view (FOV) is 52°. For this study only the refractive portion of the electromagnetic radiation was taken covering the VIS-NIR-SWIR (0.4–2.5 mm) spectral region with 72 spectral bands. The sensor was mounted onboard a DLR Dornier 228 aircraft and own over several Israel locations during the summer of 1997 from an altitude of 10 000 feet (providing a pixel size of about 8 m×8 m). The area selected for this study is in northern Israel (Izrael Valley) on a relatively at terrain called Zvaim Heights ( gure 1). This area is heavily cultivated and intensively used to grow agricultural crops. The soil texture is heavy clay (mostly vertisol in the USDA classi cation system), which causes many related problems, such as poor drainage, salinity and heavy structure.

2.2. Data acquisition The over ight took place on 2 August, 1997, at 15:00 local time (12:00 GMT).

On the ground, several teams measured eld spectra, using a eld portable spectrometer (Analytical Spectral Devices—ASD), and surface temperature, using a thermal radiometer gun. Also, 62 soil samples were collected from throughout the area during the overpass. The soil sampling was carefully done as follows: for each soil sample, a uniform area measuring about four pixels (~30 m×30 m) was selected. Each target area was described in detail in the eld, accurately georeferenced, using a GPS device, and photographicall y documented. Four to ve samples from the upper layer of the selected 30 m×30 m area were mixed to yield a representative soil composite for further analysis. The selection of sample areas was based on minimal variation between airborne and eld spectra, which was visually detected during the sampling time. The soil samples were stored in plastic bags in order to preserve the in- eld soil moisture and were brought into the laboratory for chemical and physical analyses.

2.3. Wet chemistry analyses The soil eld moisture was determined by the oven drying method after Gardner (1986) (weighing the samples before and after 24 hours in a 105°C environment).

The organic matter content was determined by using the loss-on-ignition method after Ben-Dor and Banin (1989) (heating the sample to 400°C for 8 hours and calculating the weight (organic) loss on a dry soil basis). The soil was brought to the saturated moisture condition using distilled water. After equilibration for 60 minutes, the soil solution was extracted using a vacuum of ~0.3 atmospheres. The extracted solutions were stored in glass bottles under refrigeration for further analysis.

The electrical conductivity (EC) at 25°C and the pH of the extracted solutions were analysed. The saturated moisture content was determined using the oven drying method (see above). In addition to all of the above measurements, the soils were identi ed by colour using a Munsell colour chart and measured for their re ectance under laboratory conditions using two spectrometers (ASD with 2100 channels across the 0.4–2.5 mm spectral region and LT-1200 with 1200 channels across the 1.2–2.4 mm spectral region). A comparison between eld and laboratory spectra revealed a good match at the known atmospheric windows, whereas better signalto-noise ratios were observed in the laboratory spectra recorded by the LT-1200 spectrometer at around 2.1–2.4 mm).

Case study over clayey soils in Israel 1047

2.4. DAIS-7915 data processing The DAIS data were converted into radiance data using a calibration le provided by the DLR (based on a laboratory calibration performed by the Optoelectronics Laboratory of the DLR before the ight). Whereas most of the DAIS channels visually provided sharp images, apparently channels 60–70 ( between 2.314 and

2.462 mm) were contaminated with nonsystemati c across-trac k noise. Using the Minimum Noise Fraction (MNF) technique (Green et al. 1988), the noise components were isolated from the spectral components and the data spectral cube was reconstructed to yield clean images of channels 60–67. Using this method, the noise from channels 68–70 could not be removed and therefore they were omitted from the entire reconstructed image cube.

Atmospheric eVects were removed by applying several methods and models on the radiance data as follows: ATREM (Gao et al. 1993), ATCOR (Richter 1996), MODTRAN (Berk et al. 1989), at eld; IARR (Kruse 1988) and Empirical Line (EL; Roberts et al. 1985) techniques. The best method for providing the most reliable results (as examined against eld soil spectra) was the EL technique with seven targets. Accordingly, the radiance data (MNF treated) were corrected for further analysis using this selected EL technique. Nevertheless, because spectral noise across the 2.2–2.5 mm wavelengths (channels 62–67) were still visible after the atmosphere recti cation, this range was gently smoothed by using a moving average reduction technique.

Locating each soil sample on the image was possible using DiVerential Global Position System (DGPS) information recorded during the data acquisition ( both in the air and on the ground) and by using the detailed information collected for each of the targets during the time of acquisition. DAIS re ectance spectra (resulting from the EL correction) of each sample (generated from 5–10 pixels around a well-de ned location of each target as obtained either by using the DGPS information or relying on the detail eld description of each selected area) were extracted and transferred to a new environment in order to perform the VNIRA procedure independently.

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where C stands for the laboratory values and n for the number of samples involved a in the analysis. In general equation (1) is empirically extracted from a spectrally and chemically known population and is known as a calibration set.

3. Results Table 1 provides general information about the selected soil population as obtained from the laboratory analytical data (minimum {MIN} maximum {MAX}, standard deviation {SD}, and the coeYcient of variance {CV }). From this table it can be seen that a wide range of both organic matter and EC (and hence soil salinity) values does exist. The relatively high values of organic matter (MIN=3.56%) occur because most of the analysed soils were characterized by high contamination of dry vegetation debris (the soils were not run through a 2 mm sieve as is routinely done in soil science prior to soil analysis). The electrical conductivity (EC) values range from 0.59 dsm cmÕ 1 (MIN ) to 27.4 dsm cmÕ 1 (MAX) with a mean value of

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