A spectral-based method for reconstructing spatial distributions of soil surface temperature during simulated fire events

Remote Sensing of Environment 114 (2010) 322–331

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Remote Sensing of Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e

A spectral-based method for reconstructing spatial distributions of soil surface temperature during simulated fire events R. Lugassi a,b,⁎, E. Ben-Dor b, G. Eshel c a b c

The Porter School of Environmental Studies, Tel-Aviv University, Israel The Remote Sensing and GIS Laboratory, Department of Geography and Human Environment, Tel-Aviv University, Israel Soil Erosion Research Station, Rupin, Israel

a r t i c l e

i n f o

Article history: Received 10 September 2008 Received in revised form 16 September 2009 Accepted 17 September 2009 Keywords: Fire Maximum soil surface temperatures Soil spectroscopy Imaging spectroscopy Deformation of soil minerals

a b s t r a c t Heterogeneous heat was applied to a homogenous soil surface to simulate a natural fire event. Subsequently cooled soil samples were evaluated spectrally and a spectral–spatial cube was generated corresponding to the burned area. Heat-induced spectral changes associated with thermal effects on soil minerals were observed across the entire spectrum, including: soil color changes (iron-oxide transformation); shifting absorption bands (iron-oxide transformation and illite/calcite ratio); changes in spectral shape (illite/ montmorillonite ratio); disappearing absorption features (unknown); and changes in the overall brightness (soot). A model was developed using Partial Least Squares (PLS) to predict maximum soil surface temperatures, measured using thermocouples, from soil spectral reflectance. Thus, this proof-of-concept study demonstrates that soil spectroscopy reveals important information about soil temperature history and as such, represents a promising tool for viewing fire events retrospectively. © 2009 Elsevier Inc. All rights reserved.

1. Introduction Fire events generally affect soil properties and the surrounding environment. The high temperature induced by fire can alter physical, chemical, mineralogical, and biological soil properties, some of which can be changed irreversibly (Certini, 2005). Soils that have been exposed to fire events exhibit significantly reduced organic matter content, collapse of aggregates and subsequent loss of porosity and permeability (Certini, 2005), considerable loss of nutrients through volatilization, increased soil pH values, and altered soil minerals (Ghuman & Lal, 1989; Hatten et al., 2005). These changes can indirectly cause changes in hydrophobicity (water repellency), which consequently decrease the infiltration rate and hence increase runoff and soil erosion (Moench & Fusaro, 2000). Irreversible heat-induced changes in soil properties occur across a wide range of temperatures. For example, soil structure degradation occurs at 300 °C, organic matter loss occurs from 100 °C until 450 °C, dehydroxylation and structural breakdown of clay minerals occur between 460 °C and 980 °C (DeBano et al., 1998), and dehydroxylation of goethite occurs between 200 and 320 °C (Schwertmann, 1984). Summarily, fire can lead to significantly altered soil properties that need to be monitored.

⁎ Corresponding author. The Porter School of Environmental Studies, Tel-Aviv University, Israel. Tel.: +972 3 6405411; fax: +972 3 6406243. E-mail address: [email protected] (R. Lugassi). 0034-4257/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2009.09.015

The effect of heat on soil minerals during a controlled heating process has been studied extensively over the last few decades using Thermal Analysis (TA) techniques such as Differential Thermal Analysis (DTA), Differential Scanning Calorimetry (DSC), and Thermal Gravimetric Analysis (TGA) (Tan & Hajek, 1977). DTA determines the difference in temperature between a thermal inert reference and a sample during controlled heating. DSC measures the relationship between the amount of heat required to increase the temperature of a sample and the temperature of an inter reference during a controlled heating process. TGA determines the gravimetrical changes during temperature changes induced by a controlled process. In addition, X-Ray Diffraction (XRD) analyses (Schwertmann et al., 1987) and InfraRed (IR) spectroscopy (post-heating techniques) have been used to study the influence of heat on soil minerals. Thermal Analysis and other complementary techniques require complicated preparation, specific infrastructure, and previous knowledge. Moreover, they are not applicable in the field, and do not help understand spatial–structural changes. Therefore, there is a need for a new methodology that allows easier monitoring of heat-induced soil changes in both the laboratory and the field. Soil spectroscopy is an emerging, promising technique that can be used to assess certain properties of soils, rocks, and vegetation, from short and long distances (Ben-Dor et al., 1999; Clark, 1999). This technique has been applied successfully to evaluate soil attributes (e.g. cation exchange capacity, specific surface area, organic matter, organic carbon, pH), to identify soil minerals, estimate soil infiltration capacity, and more generally to provide a soil phenotype (Ben-Dor,

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2002; Demattê, 2002; Demattê et al., 2003; Malley et al., 2004; Viscarra-Rossel et al., 2006). Basically, soil spectroscopy reveals information about soil chromophores, the latter defined as chemical groups or physical properties of the soil that can be detected spectrally. For example, chemical groups such as water molecules, iron-oxides, OH− and CO2− in minerals (clay and carbonates, 3 respectively), along with functional groups such as C=O, COOH, and C–H in organic matter (Ben-Dor et al., 1997, 2008) influence the nature and shape of soil spectra. The direct mechanisms that enable chromophores to be detected spectrally are as follows: (1) electronic processes that produce features in the Visible (VIS) region and (2) overtone and combination modes of fundamental vibrations in the infrared region that produce features in the Near InfraRed–Short Wave InfraRed (NIR-SWIR) region. Hunt (1979) and later Ben-Dor et al. (1999) have cataloged the chemical chromophores in rocks and soils associated with spectral features across the VNIR-SWIR (Visible Near InfraRed–Short Wave Infrared) spectral regions. It has been established that absorption features at certain wavelengths reflect the presence of specific chemical chromophores. In addition, it has become clear that physical properties of soil, such as particle size, modify scattering, impacting the depth of absorption features and overall soil spectra. Over the past two decades a new technology for remote sensing, termed Imaging spectroscopy (IS), has been developed whereby an image consisting of hundreds of continuous bands per pixel (spectral cube) is generated, enabling a spatial–spectral view of a selected area (Goetz, 1991). As pointed out by Ben-Dor et al. (2008), IS has the potential to advance our understanding of soil spectral properties as they vary spatially. Moreover, as this technology can be used from afar, when combined with the cumulative knowledge about heat-induced changes in soil properties it could prove invaluable to the study of post fire changes in the environment. Generally, when using remote sensing methods to evaluate fire damage, heat-induced changes in the appearance of vegetation, litter, and soil are classified into four categories: unburned, low, moderate, and high burn severity (Lewis et al., 2006; Robichaud et al., 2007; White et al., 1996). Recently, Kokaly et al. (2007) derived a more detailed categorization of heat damage using AVIRIS (Airborne Visible/ Infrared Imaging Spectrometer) data collected three months after the Cerro Grande fire near Los Alamos, New Mexico. They used a Tetracorder (Clark et al., 2003), which is a set of algorithms that score how well an observed spectrum (the unknown) compares with a large library of spectra of well-characterized materials, to define 10 burn severity classes. The classes were based on the following surface characteristics: ash/char, soil/rock, iron-oxide, clay minerals, dry conifer, and straw mulch with/without chlorophyll. Importantly, this study indicates that spectral information can be used to evaluate the extent of heat damage after the event. Furthermore, it raises the possibility that such methods could be used to reconstruct maximum soil temperatures experienced during a wildfire event retrospectively, which is currently difficult to assess (Lewis et al., 2006; Shakesby & Doerr, 2006). Specifically, since changes in soil properties correlate strongly with the maximum temperature during a fire event (DeBano et al., 1998) and the spatial pattern of temperature distribution at the soil surface is largely dependent on fuel distribution and load (Gimeno-Carcia et al., 2004), reflectance spectroscopy represents a promising tool for reconstructing maximum soil surface temperatures. Recently, Guerrero et al. (2007) reported that NIR spectral reflectance can be used for estimating retrospectively (after cooling) the maximum temperature experienced by soil samples exposed to oven heating (70–700 °C). We hypothesize that the maximum surface temperature of a soil exposed to fire can be reconstructed by analyzing the reflectance spectra (VIS-NIR-SWIR) of the cooled soil. The objective of this study is twofold: 1. to relate spectral changes to chemical changes associated with heating soil; and 2. to use the spectral changes to reconstruct a spatial distribution of maximal soil surface temperatures after a fire event.

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2. Materials and methods 2.1. Soil and minerals Loess soil (Camborthids, according to the local and USDA 1993 classification system) from the Besor basin area, Negev, Israel, was selected for this study. Soil was collected from the upper 10 cm and air-dried, then plant remains were removed before the soil was ground gently and sieved through a 2 mm sieve. The soil texture is sandy loam (0.9% coarse sand, 76% fine sand, 8.7% silt, and 14.4% clay), which is formed mainly as desert dust sediments. The content of calcite, montmorillonite, and illite in the clay fraction, is about 20%, 40%, and 15%, respectively (Ravikovitch, 1981). In order to study the effect of heat on the spectral properties of particular soil minerals, we burned in a controlled laboratory furnace samples (2 g each) of the common minerals found in this soil (montmorillonite SWY-1 and calcium carbonate from Ashkalit Chemiprod Ltd., Israel) at temperatures ranging from 300 to 500 °C (100 °C intervals). Twenty-four hours after the heating process, when the samples had cooled to room temperature, an ASD (Analytical Spectral Devices) spectrometer was used to monitor the reflectance spectra of the samples, as described in Section 2.3. 2.2. Fire simulation Sixteen K-type thermocouples (NiCr-Ni) were constructed in an aluminum tray (30 cm wide, 36 cm long and 7 cm deep). The thermocouples were installed in a 4 × 4 grid in such a way that a matrix covered the entire area of the tray (the horizontal and vertical spacing between the thermocouples was 9 and 7 cm, respectively). The thermocouple joint tips were fixed such that after spreading 4 kg of cleaned soil in the tray, only 1–2 mm of soil covered the tips (Fig. 1a). The aluminum tray was exposed to a heterogeneous butane flame directed at the soil surface from a height of 20 cm for about 10min, under standard atmospheric pressure. This arrangement induces a heterogeneous distribution of temperatures over the tray area (Fig. 1a). During heating, the temperature of each thermocouple was recorded every 5 s, using a Data Logger-21X (Campbell Scientific, Inc., UK). Because of a technical malfunction, the temperatures of two thermocouples (out of 16) were not recorded (Fig. 1a). The maximum temperatures measured by the thermocouples ranged from 189 °C to 439 °C. 2.3. Spectral measurements Spectral reflectance was measured of undisturbed cooled samples from multiple grid positions at the surface (14× 10, the horizontal and vertical spacing was 2.5 and 3 cm, respectively, Fig. 1a), using an ASD field-Pro spectrometer furnished with a contact probe. The ASD measures spectra in 2151 bands at 1-nm intervals across the VNIR-SWIR (350– 2500 nm) spectral region (ASD, Inc. Boulder, CO, USA). The spectra were standardized against a white Halon reflectance panel (Spectralon, Labsphere Inc., www.labsphere.com) using the same geometry and conditions employed to measure the soil. Each spectral measurement represents an average of 40 spectral readings. In total, 140 spectra were collected in order to monitor systematically all tray areas and enable generation of a soil spectral cube (14 rows, 10 columns, and 2151 bands). Similarly the spectral reflectance of heated mineral samples was measured using the ASD field-Pro spectrometer. Samples were cooled, mixed well and then placed in a box with a diameter identical to that of the contact probe. 2.4. Creation of surface temperature map Based on the maximum soil temperatures measured by each thermocouple we generated a regular grid of temperature isolines using the Kriging interpolation technique (Papritz & Stein, 1999)

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Fig. 1. (a) Map of temperature isolines of the burned soil based on 14 thermocouples' maximum temperature measurements, (b) temperature gradient at eight points along the cross section, as they are marked in (a), and (c) the corresponding spectra.

(Fig. 1a). Using this map of the burned area, we extrapolated a temperature for each spectral pixel not measured directly by the thermocouples. Subsequently, the entire temperature distribution across the soil spectral cube, comprising a combination of direct measurements and extrapolated predictions, was used to correlate surface temperature with spectral readings. 2.5. Calculation of burned soil indices The following burned soil indices were calculated: (1) Redness index RI = R/G where R and G are the reflectance values of red and green bands (640 nm and 550 nm respectively). This index is based on the demonstration that the visible range of diffuse reflectance spectra of 56 soil samples can be used to model iron-oxide content (hematite and goethite) (Madeira et al., 1997). (2) Slope index SI = R1 − R2 where R1 and R2 are the reflectance values at 600 nm and 540 nm. The slope index calculation is performed on Continuum Removal (CR) spectral units (Clark, 1999). Continuum Removal enables reflectance spectra to be normalized to a common baseline to allow comparison of particular absorption features. 2.6. Data analysis The data set used to generate the spectral model comprised 95 samples: the temperatures of fourteen samples were measured directly by the thermocouples and the temperatures of the other 81 samples were estimated indirectly by the “surface temperature map” extrapolation. In order to detect possible outlying values in the entire spectral domain and to assess the sources of variance, Principal Component Analysis (PCA) was performed using Unscrambler software Version 9.2 (Esbensen, 2002). Based on the PCA results and visual examination of

spectra, the entire data set (95 samples) was divided into two sub-sets: the calibration set (a training set), comprised 83 spectra (12 directly measured and 71 extrapolated) used to generate the model; and a prediction set, comprised 12 spectra samples (2 directly measured and 10 extrapolated) used to validate the model. The samples were divided such that the entire temperature range was represented in each subset. The temperatures of the calibration and prediction sets ranged from 189 to 439 °C and 225 to 432 °C, respectively. Partial Least Squares (PLS) regression and a multivariate calibration procedure were performed under the assumption that the spectral properties of the post-heated soil are independent variables (X variable — VNIR-SWIR spectral values) and the maximum surface temperatures are dependent variable (Y variable). A VIS-NIR-InfraRed Analysis (VNIRA) approach was used. To establish the calibration model we used the "full-cross validation" method. The prediction accuracy of the constructed model was then assessed using the external test set (e.g. 12 samples). The VNIRA method assumes that the concentration of a given constituent can be represented by a linear combination of several absorption features (Dalal & Henry, 1986; BenDor & Banin, 1995; Chudnovsky & Ben-Dor, 2008). The correlation between reflectance values and maximum surface temperatures was calculated using Unscrambler software Version 9.2 (Esbensen, 2002). The reflectance data were transformed to apparent absorbance (Log (1/R), Kubelka–Munk ((1 − R)2/2R), and Continuum Removal (denoted henceforth in the text as Abs⁎, KM and CR, respectively) (Esbensen, 2002; Clark, 1999; Duckworth, 2004; Mark, 2000). In addition, the first order derivative was calculated for reflectance and Abs⁎ data (symbolized as Ref', Abs⁎′, respectively) using Savitsky– Golay (Mark, 2000) smoothing filter of three. Then PLS analysis was performed on the reflectance and transformed data and on the derivative data. The best performing model was selected based on the smallest Root Mean Square Error of calibration and prediction sets (RMSECV and RMSEP, respectively), the smallest number of PLS components (LV — Latent Variables meaning number of PLS factors), the highest coefficient of determination (R2) (Esbensen, 2002), and a

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high Residual Predictive Deviation (RPD). The latter is the ratio of the RMSECV/RMSEP to the standard deviation of the original data. The RPD statistic provides a basis for standardizing the RMSEP and RMSECV (Williams and Sobering, 1993). 3. Results and discussion 3.1. Spectral and visual changes of the heated Loess soil The fire simulation left a color fingerprint on the soil surface that evidenced non-uniform burning (Fig. 1a). At the center of the tray a reddish island was visible that was hardly marked by soot. This reddish island was surrounded by a wide ring of soot, which in turn was surrounded by ring of brighter color (similar to the color of unheated soil) (Fig. 1a). The thin sooty layer results from incomplete burning of organic matter such as small pieces of dry plant tissue not removed from the soil. The spectral values (VNIR-SWIR) derived from a horizontal cross section of the tray vary considerably (Fig. 1b, c). Four types of spectral change can be observed: (1) changes in the overall brightness (albedo); (2) changes in the VIS-NIR spectral region (color); (3) a shift in the absorption peaks in the VIS and SWIR regions; and (4) changes in the shape of the curve (mainly in the SWIR region). 3.1.1. Spectral albedo changes in the VNIR-SWIR region The albedo can be defined as a fraction of the incident electromagnetic radiation that reflects from an object (Post et al., 2000). In general, the albedo encapsulates overall reflectance values, which in turn are governed by the particle size distribution of a sample and other general factors such as color, shade and moisture (Baumgardner et al., 1985). Below 250 °C the albedo was observed to decrease with increasing heat relative to the albedo of unheated soil spectra (Fig. 2a, b). This observation can be explained by the thin layer of soot (Fig. 1a) being deposited due to incomplete burning of organic matter. Above 250 °C, the albedo increases with temperature (Fig. 2a, b), as the burning process is completed and most of the organic matter is volatized, making the soil color brighter. Accordingly, the overall albedo value between 350 and 2500 μm of the unheated soil sample is

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0.49 whereas the overall reflectance of the heated soil sample at 251 °C is 0.41 (dark pixel, numbered as 122 in Fig. 2b) and at 432 °C is 0.55 (bright pixel, numbered as 62 in Fig. 2b). 3.1.2. VNIR changes — redness factor The reddish color observed in the central area of the soil tray corresponds to the soil exposed to the highest temperatures (N320 °C) (Fig. 1a). Plotting the redness index (RI) versus temperature results in two linear trend lines (Fig. 3a): line a, with a slope of 0 for samples between 189 and 320 °C indicating that redness index is not changing with temperature; and line b, with a slope of 0.0029, for all samples above 320 °C, indicating that at higher temperatures the redness index changes. Kämpf et al. (2000) have proposed that reddish soil color (5YR according to Munsell color system) is due to hematite masking the yellow color of goethite (between 7.5YR and 2.5Y). Moreover Schwertmann (1984) have shown using Differential Thermal Analysis (DTA) that goethite dehydroxylation occurs at 320 °C. Therefore, we surmise that the heat-induced color changes (redness) are most likely the consequence of goethite transforming into hematite (Cornell, & Schwertmann, 2003; Ketterings et al., 2000). This issue will be discussed later on (Section 3.1.3). 3.1.3. Absorption shifts and slope changes in the VNIR-SWIR region 3.1.3.1. VNIR. In order to uncover spectral changes in the VNIR region, we applied Continuum Removal (CR) to reflectance values in the VIS region for the entire population of soil samples (n = 95). This analysis revealed two key changes, a shift in peak absorption (around 485 nm) and changes in the slope between 540 and 600 nm (Fig. 3b). Specifically, the peak absorption band shifts towards longer wavelengths with increasing temperature (Fig. 3b); the peak is at 486 nm in spectra of unheated soil and in spectra of soil exposed to lower temperatures (189 °C) whereas the peak is at 495 nm in the spectra of soil exposed to higher temperatures (439 °C). In light of this observed shift, we analyzed the effect of temperature on the slope index (SI) (Fig. 3c), the latter defined in Section 2.5. Similar to what was seen when the redness index was plotted against temperature, a plot of SI against temperature results in a trend line indicating that higher

Fig. 2. The overall reflectance spectral changes with temperatures (a) across the entire ASD spectral region of 10 selected spectra (b).

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Fig. 3. The spectral changes of iron-oxides in the VIS region: (a) the Redness index as a function of the temperatures of 95 soil spectra, (b) Continuum Removal (CR) of five selected soil spectra and peak position shifts of three selected samples, (c) changes in the slope (540–600 nm) of normalized spectra (CR) as a function of temperature, (d) the correlation between the slope index and the redness index, and (e) a spectral mixture of goethite and hematite CR spectra (taken from JPL laboratory spectra).

temperatures (320 °C and above) change the slope index (line b with R2 = 0.79 in Fig. 3c.). Accordingly, the redness and slope indices (Fig. 3d) exhibit high correlation (R2 = 0.97). This correlation likely reflects that heat-induced changes in color and peak absorption which are caused by the same phenomenon, namely, the aforementioned transformation of goethite into hematite. In agreement with this premise, in the first derivative spectra of mixtures containing hematite, calcite, and quartz, the Fe3+ absorption peak was reported to shift towards longer wavelengths with increasing hematite (Deaton & Balsam, 1991). To provide more support to the idea that heat-induced transformation of goethite into hematite causes a shift in the Fe3+ absorption band, we used the spectra of pure goethite and hematite available from the JPL (Jet Propulsion Laboratory) spectral library (Research Systems, 2000) to generate spectra as synthetic mixtures of pure constituents. This analysis (Fig. 3e) indicates that as the proportion of hematite to goethite increases, the absorption peak shifts from 485 nm (goethite peak) towards 535 nm (hematite peak), which mirrors the absorption peak shift reported to occur upon dehydroxylation of goethite to hematite (2FeOOH → Fe2O3 + H2O, Murad & Wagner, 1998). Taken together, our data evidence that reflectance spectra echo the effect of temperature on iron-oxides in the soil, and

thus spectroscopy, like DTA and TGA, can be used to monitor such changes. 3.1.3.2. SWIR. In the spectra of unheated soil samples the calcite absorption peak is at 2341 nm whereas in the spectra of heated (439 °C) soil samples it shifts towards shorter wavelengths around 2335 nm (Fig. 4a). Of note, no absorption peak shift is seen in this region in the spectra of pure calcite powder heated under controlled conditions from 300 to 500 °C (at 100 °C intervals) (Fig. 4b). Since calcite decarboxylation usually takes place at temperatures around 800 °C (Ben-Dor & Banin, 1990) far above the temperatures reached in this experiment (439 °C), we surmise that the shifting peak is not reflecting decarboxylation of calcite in the soil. However Loess soil contains both illite and calcite and the absorption peaks corresponding to these minerals are adjacent (calcite around 2340 nm and illite around 2345 nm) (Hunt & Salisbury, 1971a; Post & Noble, 1993). Moreover, synthetic mixtures of illite/calcite spectra (based on pure spectra from IGCP laboratory spectra, Research Systems, 2000) (Fig. 4c), suggest that as the ratio of calcite increases, the absorption peak shifts from 2343 nm (illite-dominated peak) to 2340 nm (calcite-dominated peak). Notably, dehydroxylation of illite occurs between 350 and 900 °C (Murad & Wagner, 1996). Therefore, we

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Fig. 4. Spectral changes in the SWIR region of calcite/illite features: (a) the Continuum Removal (CR) spectra at 2270–2400 nm of the 95 samples, (b) the CR spectra of unheated and heated pure calcite, (c) the CR spectra of calcite/illite mixtures (IGCP laboratory spectra), and (d) the correlation between SAM classification (θ) and temperature.

suspect that the slight absorption peak shift (around 2340 nm) observed in the spectra of heated soils reflects the process of illite dehydroxylation that, in turn, increases the ratio of calcite to illite in the soil. In addition, a weak absorption at 2295 nm seen in the SWIR region of untreated soil spectra disappears in the spectra of heated soils (Fig. 4a). This weak absorption corresponds to a part of an asymmetric absorption around 2300 nm associated with the carbonate group (CO2− 3 ) (Hunt & Salisbury 1971a). In order to quantify this spectral change, we applied Spectral Angle Mapper (SAM) (Boardman & Huntington, 1996) to Continuum Removal data from the 2275– 2315 nm region of 95 soil spectra (Fig. 4a). SAM uses an algorithm to find spectral similarity between two spectra. Specifically, SAM compares the angle between the reference spectrum (unheated Loess soil) and each pixel spectrum (heated Loess soil in tray) in an n-dimensional space. Smaller angles (θ) represent closer matches to the reference spectrum. Using this analysis, a good correlation (R2 = 0.73, n = 95) was found between θ values and soil maximum temperatures (Fig. 4d), suggesting that this weak spectral change is in fact significant. However, this peak is still seen in the spectra of heated pure calcite (Fig. 4b). Summarily, at present, it is not clear to what physical phenomenon the heat-induced disappearance of this peak corresponds; further investigation is needed. Another heat-induced change in soil spectra occurs around 2200 nm, an absorption that typically reflects OH− in aluminosilicate minerals, in particular, the shoulder on the side of longer wavelengths diminishes with increasing temperature (Fig. 5a). Since Loess soil is composed mainly of montmorillonite and illite (clay minerals), their ratio influences the spectral response of soil that has undergone a fire (Fig. 5a). Based on spectra measured by the USGS (United States Geologic Survey) spectroscopy laboratory (Research Systems, 2000), it is evident that montmorillonite (SWY-1) has a sharp symmetric absorption peak around 2205 nm, whereas illite has an asymmetric

absorption peak at 2215 nm, with a shoulder at 2255 nm (Fig. 5b). Thus, the heat-induced changes around 2200 nm are most likely due to changes occurring to illite. This is further supported by spectra of pure montmorillonite mineral (SWY-1) heated from 300 °C to 500 °C (in 100 °C increments) that do not exhibit any heat-associated changes in absorption peak or symmetry around 2200 nm (Fig. 5c). Furthermore, synthetic mixtures of illite/montmorillonite spectra support that the illite shoulder disappears with increasing concentrations of montmorillonite (Fig. 5b) such that the absorption around 2200 nm resembles that seen in the spectra of heated soil samples (Fig. 5a). Since dehydroxylation of montmorillonite occurs above 600 °C (Blahoslav & Gunther, 1981) whereas dehydroxylation of illite begins above 350 °C (Murad & Wagner, 1996), we consider that the heat-associated absorption changes in the soil spectra around 2200 nm reflect dehydroxylation of illite and consequent dominance of montmorillonite, particularly at the highest temperature (439 °C). As with the other heat-induced spectral changes, these data highlight that reflectance spectroscopy can serve as a sensitive tool for monitoring thermally induced changes in soil minerals. 3.1.4. Changes in the spectral curve shape in the SWIR region Hitherto, we dealt with specific spectral features (e.g. peak absorption). An alternative way to examine heat-induced spectral changes is to follow changes in the spectral curve shape. In order to quantify this spectral change, we applied again SAM (Spectral Angel Mapper) to the first derivative data from the 2138–2250 nm region of 95 soil spectra, a region that reflects the OH− absorption features of aluminosilicates (Fig. 6a). Applying SAM to every pixel of the spectral cube with the unheated soil spectrum as the reference resulted in a spatial view of SAM angles (θ) (Fig. 6b) that could be related to the maximum soil surface temperature map (Fig. 6c). Plotting θ as a function of the maximum soil surface temperature (Fig. 6d) generated two linear trend lines: line a, which indicates that spectral shape does not change

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Fig. 5. The SWIR spectra of the montmorillonite/illite features at around 2200 nm: (a) the Continuum Removal (CR) of 95 spectra at 2105–2275 nm, (b) the CR spectra of montmorillonite/illite mixtures (USGS laboratory spectra), and (c) the CR spectra of heated pure montmorillonite.

at temperatures below 320 °C (R2 = 0.23), and line b, with R2 = 0.76, which evidences that at temperatures above 320 °C the spectral curve shape changes. Importantly, these SAM data can be used as a spectral index to assess soil that has been exposed to fire, a further validation that reflectance spectroscopy represents a useful tool for monitoring thermally induced changes of soil minerals. 3.2. Developing the temperature prediction model Having related spectral information to the chemical/physical behavior of burned soil, we searched for the best model to enable soil temperatures to be predicted solely from spectral information. Initially, we applied PLS regression analysis to the entire spectrum with no a priori knowledge of soil chromophores. Different derivatives of spectral units were tested to discover an optimal PLS model; the best results are summarized in Table 1. The first derivative of apparent absorbance (Abs⁎′) and of reflectance (Ref′) data yielded the best models. In particular, the Abs⁎′ model has a lower RMSEP value (13.1), a higher R2

Fig. 6. Changes in the spectral curve shape in the SWIR region: (a) a first derivative across the SWIR region of unheated soil at three selected temperatures, (b) the SAM classification map, (c) the temperature map, and (d) SAM classification (θ) as a function of maximum soil temperature.

Table 1 Calibration and prediction; statistical parameters obtained by each of the spectra preprocessing techniques. Model

a

Refl Abs⁎b KMc Abs⁎′d Refl′e a b c d e

Cross validation (83 samples) 2

RMSEC

R

20.3 17.6 20.7 17.0 15.0

0.90 0.93 0.90 0.93 0.95

Prediction (12 samples)

Slope

RPD

LV

RMSEP

R2

Slope

RPD

0.91 0.94 0.91 0.93 0.95

3.24 3.74 3.16 3.87 4.38

7 8 5 5 4

20.2 19.0 22.8 13.1 15.8

0.92 0.94 0.92 0.96 0.95

0.86 0.79 0.71 0.89 0.93

3.25 3.46 2.90 5.02 4.16

Reflectance. Apparent absorbance (Log (1/R)). Kubelka–Munk function. First derivative of apparent absorbance. First derivative of reflectance.

R. Lugassi et al. / Remote Sensing of Environment 114 (2010) 322–331 Table 2 The best two pre-processing techniques and their prediction errors. Sample number

Measured temperature (°C)

spec29b spec41b spec53b spect62c spec77c spec87b spec99b spec105b spec120b spec141b spec144b spec149b

392.0 380.0 361.0 432.0 263.2 341.0 290.0 402.0 329.0 310.0 225.0 246.0

a b c

Predicted temperature

Relative prediction error a

(Refl)′ (°C)

(Abs)′ (°C)

(Refl)′ (%)

(Abs)′ (%)

403.2 389.2 358.2 431.1 305.2 341.9 285.4 405.3 320.3 325.7 219.5 271.2

388.1 374.0 331.9 436.3 277.5 340.0 284.0 401.9 331.7 318.7 235.1 273.2 Average(%) Average(%)b Average(%)c

2.9 2.4 0.8 0.2 16.0 0.3 1.6 0.8 2.6 5.1 2.5 10.2 3.8 2.92 8.1

1.0 1.6 8.1 1.0 5.4 0.3 2.1 0.0 0.8 2.8 4.5 11.0 3.2 3.2 3.22

(|Measured − Predicted| / Measured) ⁎ 100. Interpolated temperature. Direct measured temperature.

value (0.96) and an excellent RPD value (5.02) as well as a low number of LV components (5) relative to the other models (Table 1). Previous studies have found that when modeling multivariate regression analysis of soils, selecting important wavelengths yields better models (Ben-Dor et al., 1997; Chang et al., 2001). Indeed, according to Martens' significance test selection (Esbensen, 2002), the first derivative of apparent absorbance (Abs⁎′) and of reflectance (Ref′) data yielded better models when 300 and 338 spectral bands, respectively, were selected. The prediction accuracy of the constructed model when run on the external test set showed that average prediction error was slightly lower for Abs*′ data relative to Ref′ data (3.2% and 3.8%, respectively) (Table 2). Moreover, when we calculated the average prediction error separately for directly measured temperatures and then separately for extrapolated values, the Abs⁎′ average prediction errors improved. For Abs⁎′ the average prediction error was 3.2% for directly measured and 3.22% for extrapolated values whereas for Ref′ it was 8.1% for directly measured and 2.92% for extrapolated values (Table 2). Based on these parameters, we selected the Abs⁎′ model for reconstructing the maximum soil surface temperature during the burning event. The first two components in the best PLS model (Abs⁎′) explain 74% of X variance (spectra) and 84% of Y variance (maximum surface temperature). Plotting the first two PLS components of the Abs⁎′ model revealed that there is a temperature gradient along the axis of the first compo-

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nent (Fig. 7). This supports that most spectral variation relates to surface temperature changes and that this variation is modeled reliably by the PLS model. Importantly, the significant wavelengths selected by the model (300 wavelengths) represent spectral regions corresponding to four well-known soil chromophores (Fig. 8). The first spectral region around 450–550 nm relates to iron oxide absorption, in particular, to the transition from ground state to electronic excited states by iron-oxides (Hunt & Salisbury, 1971b). The second spectral region relates to hygroscopic water present in soil: at 1400 nm to overtones (2ν3) of asymmetric stretch vibrations, and at 1900 nm to the combination mode (ν2 + ν3) of bend (ν2) and asymmetric stretch (ν3) vibrations (Hunt & Salisbury, 1971b). The third spectral region around 2200 nm relates to clay absorption, assigned to the OH− group (in mineral lattice) in combination mode (νOH− + δOH−, where ν, δ denote the stretching and bending vibrations, respectively) (Ben-Dor, 2002). The last spectral region (2300–2350 nm) relates to the CO2− 3 functional group associated to a strong calcite asymmetric absorption band with a shoulder towards the short wavelengths due to overtones (3ν3) of CO2− asymmetric 3 stretch (Hunt & Salisbury, 1971a). When a specific spectral feature was used to generate the model, temperatures from 320 °C to 440 °C could be reconstructed. However, when the entire spectral region was used to generate the model (Abs⁎′ model), the temperature range that could be reconstructed was extended, from 190 to 440 °C. Finally, we examined the ability of the Abs⁎′ model to predict surface temperature using the spectra not used in the calibration stage (the spectral measurements shown outside the white internal frame box in Fig. 9). We applied the Kriging interpolation technique to the temperatures predicted by the model and to the 14 directly measured temperatures that were used in the validation stage. For the most part, the new map of temperature isolines (green lines in Fig. 9) overlaps the isolines of the previous map (shown in Fig. 1a; cyan color in Fig. 9); the difference between old and new isolines ranges from 5 °C to 40 °C. 4. Summary Heterogeneous heating of the soil surface is associated with spatial spectral changes across the entire VNIR-SWIR region, most of which can be attributed to heat-induced changes in the chemical/physical properties of the soil. Specifically, the following changes are evident: color changes (iron-oxide transformation); shifts of absorption bands in VIS (iron-oxide transformation) and SWIR regions (calcite peak shift induced by gradually dehydroxylating illite); changes in spectral shape (disappearance of illite shoulder); the disappearance of a very weak absorption feature (unknown cause); and changes in the overall brightness (soot). At temperatures above 320 °C, most of these spectral indices were found to correlate with temperature significantly,

Fig. 7. PC1 and PC2 scores of best model (Abs⁎′) and temperature across PC1.

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Fig. 8. The regression coefficient versus selected wavelengths by best PLS model (Abs⁎′).

whereas between room temperature and 320 °C none of these spectral changes could be related to temperature. These data validate the potential of reflectance spectroscopy to serve as a useful tool for analyzing the effect of temperature on soil minerals. Importantly, reflectance spectroscopy enables thermal processes to be monitored after the heating event. Indeed, application of the VNIRA approach to soil samples without prior information enabled PLS modeling to predict soil temperatures several days after the fire event.

The optimal PLS model was derived using the Abs⁎′ of selected wavelengths. Analyses of LV components highlight the main spectral changes resulting from the effects of the heating process on soil minerals. Remarkably, the high accuracy of predictions (R2 = 0.96; RMSEP = 13.1 °C; RPD = 5.02) demonstrates that the maximum surface temperature can be accurately predicted from soil reflectance spectra. Of note, incompletely burned organic matter present on the soil surface did not affect appreciably the accuracy of our model. The nature of wildfires inhibits the installation of appropriate instruments for recording soil temperatures. Our novel approach of reflectance spectroscopy combined with PLS modeling should address this challenge, as it allows the spatial distribution of soil surface temperatures to be reconstructed even several days after the fire event. Doing this on a large scale could be achieved using current and future high spatial resolution hyperspectral airborne sensors (1–5 m), such as AisaDUAL (Hauer and Lorang, 2004) and AVIRIS (Green et al., 1998). Summarily, this study demonstrates the power of reflectance spectroscopy to monitor the spatial distribution of peak soil temperatures several days after the soil was exposed to fire. However, soil temperatures drop rapidly with distance from the surface (Campbell et al., 1995; Ghuman & Lal, 1989) and therefore heat footprints are found mainly on the soil surface. Consequently the time frame available after a fire event when spectral changes reliably reflect soil temperatures is limited by the degree and rate of soil surface disturbance (either due to erosion or human activity). Furthermore, it is important to note that soil properties are affected not only by the temperature level but also by the duration of heating. For example, DTA indicates that goethite typically dehydroxylates at around 320 °C but when subjected to extended heating, dehydroxylation is evident even at 200 °C (Murad and Wagner, 1998). In light of these and other considerations, in order for this approach to be more widely applicable, future research should explore in detail the following: the spectral behavior of different soil types after fire exposure; a wider range of temperatures; and the influence of heat duration on soil spectral reflectance. 5. Conclusions

Fig. 9. Map of extended temperature isolines of the burned soil: the cyan lines are identical to the isothermal lines presented in Fig. 1a. The green lines calculation, using the Abs⁎′ model, based on the 14 measured values (as in Fig. 1a), is combined with the extrapolated 45 temperature values taken from the region not used in the model (outside the white internal frame).

Soil spectroscopy reveals important information about soil temperature history and hence can be used as a tool to view fire events retrospectively. Having applied this spectral approach successfully to one selected soil sample in the present proof-of-concept study we are confident that it can be applied to other soils. Nonetheless, future study is required to evaluate the effects of fire

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