Cluster-based spectral models for a robust assessment of soil properties

Ogen, Yaron; Zaluda, Jacqueline; Francos, Nicolas; Goldshleger, Naftaly; Ben‐Dor, Eyal

doi:10.1016/j.geoderma.2019.01.022

Cited by 26 publications

(14 citation statements)

References 48 publications

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“…We used descriptive statistics to compare our soil characteristics with other previous studies in the literature. We then employed the following non-parametric statistical techniques: Pearson's correlation and principal component analysis (PCA) [33,34]. PCA is often used to determine the soil attributes that better explain the dataset and reduce the data dimensionality [10].…”

Section: Discussionmentioning

confidence: 99%

Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

et al. 2021

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Geotechnologies allow natural resources to be surveyed more quickly and cheaply than traditional methods. This paper aimed to produce a digital soil map (DSM) based on Landsat time series data. The study area, located in the eastern part of the Brazilian Federal District (Rio Preto hydrographic basin), comprises a representative basin of the Central Brazil plateau in terms of pedodiversity. A spectral library was produced based on the soil spectroscopy (from the visible to shortwave infrared spectral range) of 42 soil samples from 0–15 cm depth using the Fieldspec Pro equipment in a laboratory. Pearson’s correlation and principal component analysis of the soil attributes revealed that the dataset could be grouped based on the texture content. Hierarchical clustering analysis allowed for the extraction of 13 reference spectra. We interpreted the spectra morphologically and resampled them to the Landsat 5 Thematic Mapper satellite bands. Afterward, we elaborated a synthetic soil/rock image (SySI) and a soil frequency image (number of times the bare soil was captured) from the Landsat time series (1984–2020) in the Google Earth Engine platform. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to model the SySI, using the endmembers as the input and generating a DSM, which was validated by the Kappa index and the confusion matrix. MESMA successfully modeled 9 of the 13 endmembers: Dystric Rhodic Ferralsol (clayic); Dystric Rhodic Ferralsol (very clayic); Dystric Haplic Ferralsol (loam-clayic); Dystric Haplic Ferralsol (clayic); Dystric Petric Plinthosol (clayic); Dystric Petric Plinthosol (very clayic); Dystric Regosol (clayic); Dystric Regosol (very clayic); and Dystric, Haplic Cambisol (clayic). The root mean squared error (RMSE) varied from 0 to 1.3%. The accuracy of DSM achieved a Kappa index of 0.74, describing the methodology’s effectiveness to differentiate the studied soils.

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Section: Discussionmentioning

confidence: 99%

Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

et al. 2021

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“…33 We hope that this study will be a precursor to further research in this direction with more soil samples, as previously done with this data set for other soil attributes. 11,34 As the forthcoming optical hyperspectral remote sensors are being prepared for orbit: NASA's Earth Surface…”

Section: Discussionmentioning

confidence: 99%

“…Ninety soil samples from the Israel SSL, 11 representing seven soil orders of the USDA classification system, were selected for this study. The samples were collected from the upper surface zone (0–5 cm depth) and were brought to the laboratory, air-dried, gently crushed, and sieved to pass a ≤2 mm sieve.…”

Section: Methodsmentioning

confidence: 99%

“…9,10 This direction is crucial to harnessing the spectral imaging technology from orbit to efficiently assess soil mineralogy from afar (amongst other spectral imaging missions). Therefore, we used the legacy soil spectral library (SSL) 11 of Israel, which includes a comprehensive analytical evaluation of soil mineralogy, chemistry, and spectroscopy across both the Vis-NIR-SWIR and LWIR regions, to study all possible relationships that might be used to estimate quartz content from the optical region.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Relative Abundance of Quartz to Clay Minerals Using the Visible–Near-Infrared–Shortwave-Infrared Spectral Region

2021

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Quartz is the most abundant mineral on the earth’s surface. It is spectrally active in the longwave infrared (LWIR) region with no significant spectral features in the optical domain, i.e., visible–near-infrared–shortwave-infrared (Vis–NIR–SWIR) region. Several space agencies are planning to mount optical image spectrometers in space, with one of their missions being to map raw materials. However, these sensors are active across the optical region, making the spectral identification of quartz mineral problematic. This study demonstrates that indirect relationships between the optical and LWIR regions (where quartz is spectrally dominant) can be used to assess quartz content spectrally using solely the optical region. To achieve this, we made use of the legacy Israeli soil spectral library, which characterizes arid and semiarid soils through comprehensive chemical and mineral analyses along with spectral measurements across the Vis–NIR–SWIR region (reflectance) and LWIR region (emissivity). Recently, a Soil Quartz Clay Mineral Index (SQCMI) was developed using mineral-related emissivity features to determine the content of quartz, relative to clay minerals, in the soil. The SQCMI was highly and significantly correlated with the Vis–NIR–SWIR spectral region (R2 = 0.82, root mean square error (RMSE) = 0.01, ratio of performance to deviation (RPD) = 2.34), whereas direct estimation of the quartz content using a gradient-boosting algorithm against the Vis–NIR–SWIR region provided poor results (R2 = 0.45, RMSE = 15.63, RPD = 1.32). Moreover, estimation of the SQCMI value was even more accurate when only the 2000–2450 nm spectral range (atmospheric window) was used (R2 = 0.9, RMSE = 0.005, RPD = 1.95). These results suggest that reflectance data across the 2000–2450 nm spectral region can be used to estimate quartz content, relative to clay minerals in the soil satisfactorily using hyperspectral remote sensing means.

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“…Furthermore, spectroscopy offers an alternative method for soil monitoring, surveying and land‐use planning in combination with remote sensing data (Brown, Shepherd, Walsh, Dewayne Mays, & Reinsch, 2006; Chabrillat et al, 2019; Escadafal, Girard, & Courault, 1989; Hill & Schütt, 2000; Irons, Weismiller, & Peterson, 1989). These methods are currently undergoing further development for predictions of soil properties, and improved soil classifications using cluster‐based statistical calculations (Ogen, Zaluda, Francos, Goldshleger, & Ben‐Dor, 2019; Poppiel et al, 2018; Viscarra Rossel, Rizzo, Demattê, & Behrens, 2010). Several studies highlighted the relationships between the shapes and features of spectra and soil properties such as moisture, minerals, organic matter contents, colours, etc.…”

Section: Introductionmentioning

confidence: 99%

Relationships between spectral features, iron oxides and colours of surface soils in northern Jordan

Sahwan

Lücke

Sprafke

et al. 2020

European J Soil Science

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Ground data on spectral characteristics of Jordan's soils remain sparse, which makes the interpretation of remote sensing datasets challenging. These are, however, very useful for predicting soil properties and agricultural suitability. Previous studies have shown that soil colours correlate well with degrees of weathering intensity, as indicated by magnetic parameters and dithionite‐extractable iron (Fed) contents along a climosequence in northern Jordan. This study enlarges the database by the results of 160 bulk samples that were collected systematically from the soil surface at 40 locations. In addition to soil colour and contents of Fed, we explore mean soil reflectance spectra (MSRS) measured by analytical spectral devices (ASD) and analyse the morphological conditions of the spectra referring to the effects of iron oxides on spectral behaviours. Results show a high correlation of spectral behaviours and colour variations with Fe oxides, and no correlation with soil organic carbon Corg. The influence of the Fe oxide contents is clearly apparent in the visible range (VIS). The presence of CaCO3 increases the reflectance in all spectral ranges. Six soil groups (Gr. I ‐ Gr. VI) were discerned qualitatively and quantitatively in the study area, which largely mirror the intensity of red colour expressed by redness indices. Highlights In northwestern Jordan there is an evident connection between the spectral properties, chemistry and soil colour. This study established a preliminary spectral library of soils in NW Jordan to facilitate the use of remote sensing in soil studies. The morphological properties and statistical analysis of the spectral data show that spectra of soils in NW Jordan are dominated by iron oxides. Spectral properties can be used to characterize the soil colours and types of Fe oxides in soils of Jordan.

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Cluster-based spectral models for a robust assessment of soil properties

Cited by 26 publications

References 48 publications

Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

Estimation of the Relative Abundance of Quartz to Clay Minerals Using the Visible–Near-Infrared–Shortwave-Infrared Spectral Region

Relationships between spectral features, iron oxides and colours of surface soils in northern Jordan

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