Assessment of the 1.75 μm absorption feature for gypsum estimation using laboratory, air- and spaceborne hyperspectral sensors

Milewski, Robert; Chabrillat, Sabine; Brell, Maximillian; Schleicher, Anja M.; Guanter, Luis

doi:10.1016/j.jag.2018.12.012

Cited by 10 publications

(12 citation statements)

References 59 publications

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“…This fact was observed by other authors in gypsiferous areas, and lead them to establish that gypsum concentration, lack of SOC and high salts concentration can be considered as an index of desertification intensity [74]. Recent studies [52] have set up gypsum prediction in laboratory models using the Normalized Differenced Gypsum Index (NDGI) and the Half-Area and Continuum Removed Absorption Depth (CRAD) spectral parameters. These models yielded high prediction features R 2 = 0.84 for NDGI and R 2 = 0.86 for CRAD.…”

Section: Discussionmentioning

confidence: 82%

“…Khayamim et al [51] used VIS/NIR spectroscopy and PLSR to predict soil gypsum content with high accuracy (R 2 = 0.86). More recently, quantitative estimates of gypsum content have been established from hyperspectral remote sensing [52]. However, these spectral signatures are a linear combination of different soil components such as salts of calcium, sodium or magnesium, quartz, etcetera, that are contributing in a local specific manner.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Marques

Álvarez

Carral

et al. 2020

Water

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Contents of soil organic carbon (SOC), gypsum, CaCO3, and quartz, among others, were analyzed and related to reflectance features in visible and near-infrared (VIS/NIR) range, using partial least square regression (PLSR) in ParLes software. Soil samples come from a sloping olive grove managed by frequent tillage in a gypsiferous area of Central Spain. Samples were collected in three different layers, at 0–10, 10–20 and 20–30 cm depth (IPCC guidelines for Greenhouse Gas Inventories Programme in 2006). Analyses were performed by C Loss-On-Ignition, X-ray diffraction and water content by the Richards plates method. Significant differences for SOC, gypsum, and CaCO3 were found between layers; similarly, soil reflectance for 30 cm depth layers was higher. The resulting PLSR models (60 samples for calibration and 30 independent samples for validation) yielded good predictions for SOC (R2 = 0.74), moderate prediction ability for gypsum and were not accurate for the rest of rest of soil components. Importantly, SOC content was related to water available capacity. Soils with high reflectance features held c.a. 40% less water than soils with less reflectance. Therefore, higher reflectance can be related to degradation in gypsiferous soil. The starting point of soil degradation and further evolution could be established and mapped through remote sensing techniques for policy decision making.

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Section: Discussionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Marques

Álvarez

Carral

et al. 2020

Water

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“…CC BY 4.0 License. characterize specifically the target under investigation (Rouse et al, 1974;Peon et al, 2017, Milewski et al, 2019.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, they proposed the Soil Salinity and Sodicity Index (SSSI) integrating the SWIR bands of ALI-EO or Landsat-OLI sensors. Recently, based on the gypsum absorption feature in 1.75 m and following the same concept behind the development of normalized difference vegetation index (NDVI), Milewski et al (2019) proposed the normalized difference gypsum index (NDGI). This new index exploits the most relevant narrow wavelengths characterizing the gypsum absorption features: 1690 and 1750 m.…”

Section: Introductionmentioning

confidence: 99%

“…It has been tested on Omongwa salt-pan area in Namibia, which is a natural flat salt playa dominated by evaporite minerals such as halite, gypsum, calcium carbonate, and minor content of clay (Mees, 1999;Fookes and Lee, 2018;Genderjahn et al, 2018). Using hyperspectral data acquired with diver sensors (space-borne Hyperion, airborne HySpex, and simulated space-borne EnMAP imagery), spectroradiometric measurements, XRD mineralogical analyses; as well as, applying continuum removed reflectance spectrum (CRRS), slop and half-area processing methods, the validation of NDGI provides satisfactory results (Milewski et al, 2019). Coincidentally, the NDGI is simply the SI-ASTER-4,5 proposed 16 years ago by Al-Khaier (2003).…”

Section: Introductionmentioning

confidence: 99%

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Potential and limits of vegetation indices compared to evaporite mineral indices for soil salinity discrimination and mapping

Bannari¹,

Abuelgasim

2021

Preprint

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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.).

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Spectral Analysis to Explore Gypsum Deposits and Its Association with Palaeochannels and Lithology Using PRISMA, Sentinel 2, and Dual-Polarimetric SAR Data in Bikaner, Rajasthan, Bharat

Biswas,

Rathore

2024

J Indian Soc Remote Sens

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Assessment of the 1.75 μm absorption feature for gypsum estimation using laboratory, air- and spaceborne hyperspectral sensors

Cited by 10 publications

References 59 publications

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Potential and limits of vegetation indices compared to evaporite mineral indices for soil salinity discrimination and mapping

Spectral Analysis to Explore Gypsum Deposits and Its Association with Palaeochannels and Lithology Using PRISMA, Sentinel 2, and Dual-Polarimetric SAR Data in Bikaner, Rajasthan, Bharat

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