Vegetation Indices (VIs) have been widely used for supporting archaeological investigations [1–5]. Some studies aim at monitoring archaeological sites and monuments using satellite images [6,7], while some other studies are focused on the detection of buried archaeological relics (i.e., crop marks) [8–11]. The latest have been used in archaeology since the first grayscale aerial images were available to researches. Crop marks are usually formed in areas where vegetation overlays near-surface archaeological remains. In this case, archaeological features can retain soil moisture with a different percentage of moisture compared to non-archaeological areas [12].

VIs intend to explore vegetation’s spectral signature characteristics, both in the visible and near-infrared part of the spectrum. Vegetation stress associated with sub-surface soil disturbance may be observed from visual symptoms, such as stunted growth and sparse vegetation cover [13]. Moreover, as [14] argue, canopy reflectance in the visible and near infrared is strongly dependent on both structural (i.e., amount of leaves per area, leaf orientation, canopy structure) and biochemical properties (i.e., chlorophylls, carotenoids) of the canopy. Hence, it is very difficult to develop a “unique” VI exclusively sensitive to one plant variable. Methods for extraction information from hyperspectral data related to vegetation characteristics have been discussed in detail [15]. Indeed, the use of hyperspectral data for monitoring vegetation characteristics is increasingly needed in order to characterize, model, classify and map agricultural crops and natural vegetation, specifically in the study in [16].

VIs can be either retrieved using multispectral or hyperspectral satellite images. Moreover, airborne sensors are also used to support archaeological investigations (e.g., [17]). Nevertheless, as the study in [14] has shown, airborne and satellite reflectance signals may be additionally influenced by soil, illumination, atmospheric conditions and the sensor observation geometry [18]. Recently, [2,7] have demonstrated that ground spectroscopy can also be used for the retrieval of vegetation characteristics. In this case, several errors can be minimized, since the measurements are taken from a close distance from the canopy, while at the same time, calibrated Lambertian panels are being used (see [19]). As [16] highlighted, the advances of hyperspectral sensors have contributed to the study of spectral signatures in controlled environments.

As [20] argue, a growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. Despite the fact that several indices exist in the literature—both broadband and narrowband—only a small number of these indices are practically used or evaluated for remote sensing archaeology applications. The Normalized Differential Vegetation Index (NDVI) is considered to be the most widely used index for archaeological studies (e.g., [1–3]).

This study aims to analyze the performance of several indices, both broadband and narrowband, that can be used for archaeological investigations. More than seventy different VIs were evaluated (71 in total) and tested for their performance during the detection of archaeological crop marks. The equations of these indices are presented in a fully updated table (Table 1). For the needs of the study, several ground spectroradiometric data retrieved from a “controlled archaeological area” were used. These ground data were extracted during a whole phenological cycle of the barley crops (2011–2012) in the study area of Alampra, Cyprus.

Several vegetation indices have been presented in the relevant literature. In their study, [21] have reported that more than fifty different remote sensing VIVIs have been used in the literature since 1995. However, since then, especially after the deployment of hyperspectral sensors, several other VIs have been developed (see, for instance, Table 1). Indeed, the use of hyperspectral data has significantly contributed to the further study of vegetation characteristics. Hyperspectral data may be used in order to remove highly correlated bands, to create physically meaningful VIs, to create new VIs not sampled by broad bands and, moreover, to create multi-linear indices (i.e., indices involving more than two bands) [16].

As [14] report, VIs can be divided into five main categories according to their formula (equation) or according to the potential use of each index: (a) broadband indices that use broadband reflectance; (b) narrowband indices that use narrowband reflectance (hyperspectral); (c) leaf pigment indices that were developed in order to examine several pigments in the leaf (e.g., Cl-a etc.); (d) stress indices, which were developed for monitoring stress conditions in the canopy; and (e) water stress indices. Moreover, [22] report that narrowband VIs can be divided into three main categories: (a) structure; (b) biochemistry; and (c) plant physiology/stress. However, VIs can be simply divided according to the wavelength characteristics used in their formula (broadband and narrowband indices).

Table 1 provides the list of all VIs used in this study (71 different indices). It should be noted that all indices mentioned in Table 1 have a range from 400 up to 1,050 nm, since this was the spectral range of the ground spectroradiometer used in order to collect the ground measurements (see next Chapter). Due to the numerous indices used in this study, only the formula is shown in Table 1. Additional details for the development and the characteristics of each index can be found in the relative reference shown in Table. 1.

As it is shown in the following table, similar broadband and narrowband indices exist in the literature (e.g., NDVI, SR, etc.). Furthermore, the same narrowband vegetation index can be found several times in the literature with a slightly different wavelength (e.g., see SR1–SR5). Moreover, some other indices might be modifications of other known indices (e.g., Modified Simple Ratio, Modified Triangular Vegetation Index, etc.). The majority of indices shown in Table 1 tend to explore the red and near infrared part of the spectrum, while some others may use either blue or green wavelengths (e.g., SARVI, Greenness Index). The variability of these VIs shows that each VI was initially developed in order to enhance a specific canopy reflectance parameter or to minimize atmospheric errors, soil background scattering, etc.

A controlled archaeological site was developed in the Alampra village, located in central Cyprus (WGS 84, 36o N: 535051, 3870818). In this field, a 5 × 5 m square was constructed in order to simulate ancient “tombs” at a depth of 25 cm (Figure 1) [70,71]. During the construction of the controlled field, particular attention was paid to maintain the original stratigraphy of the soil (i.e., the original ground surface was placed at the top). Moreover, the creation of the “tomb” aimed to create a trapped underground gas pocket. Since the whole area was then cultivated similarly with barley crop, Global Position Systems (GPS) were used in order to trace the “tombs”.

During the phenological cycle of the crops (2011–2012), more than sixteen (16) in situ spectroradiometric campaigns took place in the study area. In detail, the dates for these cloud-free campaigns were: (1): 10/17/2011, (2): 10/26/2011, (3): 10/31/2011, (4): 11/9/2011, (5): 11/16/2011, (6): 11/23/2011, (7): 11/28/2011, (8): 12/13/2011, (9): 12/20/2011, (10): 1/3/2012, (11): 2/11/2012, (12): 2/21/2012, (13): 3/4/2012, (14): 3/17/2012, (15): 3/29/2012 and (16): 4/17/2012.

In each campaign, several ground spectroradiometric measurements were taken over the “archaeological” area and also to the non-archaeological area. For this purpose, a calibrated GER 1500 handheld spectroradiometer was used (Figure 2(a)). This instrument has the ability to record electromagnetic radiation from visible to near infrared spectrum (400–1,050 nm) using 512 different channels with a range of ∼1.5 nm. Moreover, a calibrated Lambertian spectralon panel was used (Figure 2(b)) in order to measure the incoming solar radiation and calibrate all the measurements taken over the crops. The field of view (FOV) of the instrument was set to 4 degrees (≈0.02 m² from a height of 1.2 m). Initially, the incoming radiance was calculated based on the reference measurement at the spectralon panel, while the following measurements were taken over the area of interest (either the archaeological or non archaeological area) (Figure 3).

Altogether, more than 1,600 ground spectroradiometric measurements were taken during the whole phenological cycle of the crops (≈800 measurements over the “archaeological” area and ≈800 measurements over the non archaeological” area). All these measurements were used in order to assess the performance of VIs.

In order to examine the use of broadband vegetation indices, as indicated in Table 1, it is essential to recalculate the ground hyperspectral measurements according to the spectral characteristics of a specific satellite sensor. For this purpose, the authors decided to simulate these data with Landsat TM /ETM+ satellite imagery based on its Relative Spectral Response (RSR) filters. RSR filters describe the instrument relative sensitivity to radiance at various parts of the electromagnetic spectrum [72]. These spectral responses have a value of 0 to 1 and unit-less, since they are relative to the peak response (see Figure 4). Bandpass filters are used in the same way in spectroradiometers in order to transmit a certain wavelength band and to block others [73]. The reflectance from the spectroradiometer was calculated based on the wavelength of each sensor and the RSR filter as follows: (1) [ R band = ∑ ( Ri * RSRi ) / ∑ RSRi ]where:

R_band = reflectance at a range of wavelength (e.g., Band 1);

For narrowband Vis, the initial hyperspectral measurements taken from the GER 1500 were used, while for the broadband Vis, the measurements were re-calculated as described above. The performance of each index was examined in comparison to the other indices. The difference in values that occurred from the barley crops over the “tomb” and from the surrounding area was also calculated (“archaeological” and non archaeological area).

This study aims to evaluate several Vis, both broadband and narrowband, for monitoring crop marks. Several ground spectroradiometric measurements were collected from a controlled archaeological site at the Alampra village (Cyprus), and different VI values for each campaign were calculated. The results showed that beyond the most widely used VIs in archaeological investigations (e.g., NDVI, SR), several other indices can assist in the detection of crop marks.

Specifically, it was proven that hyperspectral VIs can further maximize the contrast between the archaeological and non-archaeological areas. Compared with the broadband indices, the narrowband indices can maximize the contrast difference up to 20%, with some minor exceptions, especially when crops photosynthesize. Some of the most promising vegetation indices found from this study are the broadband SARVI, SR and PVI, while from the narrowband indices, these are SR₇₀₅, mSR_3,MTCI, SR−SR₆ and VOG.

The above results also applied to multispectral and hyperspectral images for the detection of archaeological remains. This study has highlighted the benefits of the use of hyperspectral images and hyperspectral VIs for supporting archaeological investigations. Indeed, as it was found, only a small number of multispectral VIs have been able to detect neolithic tells, such as SARVI, NDVI and SR, in contrast to several other hyperspectral VIs applied to the Hyperion dataset.

Moreover, VIs based only on the visible part of the spectrum (such as BRI) have also shown some potential use for the detection of crop marks. In this way, a significant amount of data (e.g., traditional aerial images in the visible part of the spectrum) can be further exploited in order to assist archaeological research.

The results denoted the fact that for each phenological stage, a different vegetation index may be used in order to enhance the archaeological area of interest compared to a non-archaeological area. Despite that the results may not be similar in a different archaeological environment (i.e., a non-Mediterranean region or a different type of crop), the overall outcomes of this study can be applied in any archeological environment. The researchers can select those VIs that better maximize the contrast of the archaeological environment with its surroundings and contribute substantially to the detection of archaeological relics. Further evaluation of such indices will be performed and analyzed using satellite remote sensing data.

The overall results from this study or from similar studies [74–78] can assist in the design of the first satellite sensor intended for archaeological studies. Indeed, the evaluation of ground spectral signatures and VIs over buried archaeological remains, along with the study of the formation of crop marks, can define the spectral regions of the spectrum most suitable for archaeological research.

Recent advantages in the near future regarding hyperspectral spaceborne missions, such as the multi-angular hyperspectral observation capability [79], is one important aspect that needs to be focused on by researchers. Indeed, as [16] highlight, hyperspectral data can be used and exploited in order to find the specific wavebands that are most suitable to study particular biophysical and/or biochemical properties. Such attempts will be performed by the authors for monitoring crop marks.