Abstract. The study aims to analyze the ability of the most popular and widely used vegetation indices (VI’s), including NDVI, SAVI, EVI and TDVI, to discriminate and map soil salt contents compared to the potential of evaporite mineral indices such as SSSI and NDGI. The proposed methodology leverages on two complementary parts exploiting simulated and imagery data acquired over two study areas, i.e. Kuwait-State and Omongwa salt-pan in Namibia. In the first part, a field survey was conducted on the Kuwait site and 100 soil samples with various salinity levels and contents were collected; as well as, herbaceous vegetation cover canopy (alfalfa and forage plants) with various LAI coverage rates. In a Goniometric-Laboratory, the spectral signatures of all samples were measured and transformed using the continuum removed reflectance spectrum (CRRS) approach. Subsequently, they were resampled and convolved in the solar-reflective spectral bands of Landsat-OLI, and converted to the considered indices. Meanwhile, soil laboratory analyses were accomplished to measure pHs, electrical conductivity (EC-_Lab), the major soluble cations and anions; thereby the sodium adsorption ratio was calculated. These elements support the investigation of the relationship between the spectral signature of each soil sample and its salt content. Furthermore, on the Omongwa salt-pan site, a Landsat-OLI image was acquired, pre-processed and converted to the investigated indices. Mineralogical ground-truth information collected during previous field work and an accurate Lidar DEM were used for the characterization and validation procedures on this second site. The obtained results demonstrated that regardless of the data source (simulation or image), the study site and the applied analysis methods, it is impossible for VI's to discriminate or to predict soil salinity. In fact, the spectral analysis revealed strong confusion between signals resulting from salt-crust and soil optical properties in the VNIR wavebands. The CRRS transformation highlighted the complete absence of salt absorption features in the blue, red and NIR wavelengths. As well as the analysis in 2D spectral-space pointed-out how VI’s compress and completely remove the signal fraction emitted by the soil background. Moreover, statistical regressions (p ˂ 0.05) between VI's and EC-_Lab showed insignificant fits for SAVI, EVI and TDVI (R² ≤ 0.06), and for NDVI (R² of 0.35). Although the Omongwa is a natural flat salt playa, the four derived VI’s from OLI image are completely unable to detect the slightest grain of salt in the soil. Contrariwise, analyses of spectral signatures and CRRS highlighted the potential of the SWIR spectral domain to distinguish salt content in soil regardless of its optical properties. Likewise, according to Kuwait spectral data and EC-_Lab analysis, NDGI and SSSI incorporating SWIR wavebands have performed very well and similarly (R² of 0.72) for the differentiation of salt-affected soil classes. These statistical results were also corroborated visually by the maps derived from these evaporite indices over the salt-pan site, as well as by their consistency with the validation points representing the ground truth. However, although both the indices have mapped the salinity patterns almost similarly, NDGI further highlights the gypsum content. While the SSSI show greater sensitivity to salt crusts present in the pan area that are formed from different mineral sources (i.e., halite, gypsum, etc.).

中文翻译：

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植被指数与蒸发矿物指数在土壤盐分鉴别和制图方面的潜力和限制

摘要。该研究旨在分析最流行和广泛使用的植被指数 (VI)，包括 NDVI、SAVI、EVI 和 TDVI，与 SSSI 和 NDGI 等蒸发矿物指数的潜力相比，区分和绘制土壤盐分含量的能力。提议的方法利用两个互补部分，利用在两个研究区域（即科威特州和纳米比亚的 Omongwa 盐田）获得的模拟和图像数据。第一部分在科威特现场进行了实地调查，采集了100个不同盐度和含量的土壤样品；以及具有不同 LAI 覆盖率的草本植被覆盖冠层（苜蓿和饲料植物）。在测角实验室中，使用连续谱去除反射光谱 (CRRS) 方法测量和转换所有样品的光谱特征。随后，它们被重新采样并在 Landsat-OLI 的太阳反射光谱带中进行卷积，并转换为所考虑的指数。同时，完成土壤实验室分析以测量 pH 值、电导率（EC-_实验室)，主要的可溶性阳离子和阴离子；从而计算钠吸附率。这些元素支持调查每个土壤样品的光谱特征与其盐含量之间的关系。此外，在 Omongwa 盐田现场，Landsat-OLI 图像被采集、预处理并转换为调查指标。在之前的实地工作中收集的矿物学地面实况信息和准确的激光雷达 DEM 被用于第二个站点的表征和验证程序。获得的结果表明，无论数据来源（模拟或图像）、研究地点和应用的分析方法如何，VI 都不可能区分或预测土壤盐分。事实上，光谱分析揭示了 VNIR 波段中盐壳和土壤光学特性产生的信号之间的强烈混淆。CRRS 转换突出了在蓝色、红色和 NIR 波长中完全没有盐吸收特征。以及 2D 光谱空间中的分析指出 VI 如何压缩和完全去除土壤背景发出的信号部分。此外，统计回归（ VI's 和 EC- _Lab之间的p ˂ 0.05)对 SAVI、EVI 和 TDVI (R ²  ≤ 0.06) 和 NDVI (R ²为 0.35) 的拟合不显着。虽然 Omongwa 是一个天然的平坦盐滩，但来自 OLI 图像的四个衍生 VI 完全无法检测到土壤中最细微的盐粒。相反，光谱特征和 CRRS 的分析突出了 SWIR 光谱域区分土壤中盐含量的潜力，而不管其光学特性如何。同样，根据科威特光谱数据和 EC- _Lab分析，结合 SWIR 波段的 NDGI 和 SSSI 表现非常好，并且类似（R ²0.72）用于区分受盐分影响的土壤类别。这些统计结果也得到了从盐田上这些蒸发岩指数得出的地图的视觉证实，以及它们与代表地面实况的验证点的一致性。然而，尽管这两个指数几乎相似地绘制了盐度模式，但 NDGI 进一步突出了石膏含量。而 SSSI 对盘区中存在的由不同矿物来源（即岩盐、石膏等）形成的盐壳表现出更大的敏感性。