Characterization of Soil Erosion Indicators Using Hyperspectral Data From a Mediterranean Rainfed Cultivated Region

Schmid, Thomas; Rodríguez-Rastrero, Manuel; Escribano, Paula; Palacios-Orueta, Alicia; Ben‐Dor, Eyal; Milewski, Robert; Huesca, Margarita; Bracken, Ashley; Cicuéndez, Víctor; Pelayo, M.; Chabrillat, Sabine

doi:10.1109/jstars.2015.2462125

Cited by 42 publications

(40 citation statements)

References 38 publications

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“…Each of erosion classes was consequently characterized by a range of values of distinctive soil surface properties (soil erosion indicators, [30]) having specific spectral reflectance characteristics that can be obtained using imaging spectroscopy (SOC, texture classes, CaCO 3 and iron oxides). We presume that in ME and SE, the removal of topsoil and its mixture with subsoil leads to qualitative and quantitative changes in surface soil properties manifested in spectral reflectance characteristics [30,33,41,42,73].…”

Section: Assessment Of Soil Properties For Erosion Classes Distinguismentioning

confidence: 99%

“…We presume that in ME and SE, the removal of topsoil and its mixture with subsoil leads to qualitative and quantitative changes in surface soil properties manifested in spectral reflectance characteristics [30,33,41,42,73]. However, the soil properties of deposited material can vary significantly (both topsoil and subsoil material can be accumulated), strongly depending on site-specific conditions (topography, parent material, soil properties of parental soil, type, intensity and frequency of erosion events).…”

Section: Assessment Of Soil Properties For Erosion Classes Distinguismentioning

confidence: 99%

“…In this study, PLS models reveal slightly lower accuracy than other methods, however not significantly worse. More recently, machine learning techniques have been used in imaging spectroscopy [30,76,[88][89][90][91]. Whereas ANN method is not so frequently used due to the need of a large training dataset, training demands and a tendency to over-fitting, the SVM has become the most frequent method overcoming the difficulties of the ANN technique [27].…”

Section: Prediction Of Soil Properties By Imaging Spectroscopymentioning

confidence: 99%

“…These methods are based on classification techniques or use different mathematical and statistical procedures to assess the correlation between erosion signs and their spectral reflectance [15,26,27]. Soil erosion and accumulation affect chemical and physical properties of upper layer of soils; thus, spectral reflectance is changed and eroded soils have different spectral response from non-eroded "healthy" soils [28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…Thus far, only few studies have employed this method in soil erosion assessment [47][48][49], a majority of them in semi-arid conditions [30,33,45,[50][51][52]. Several studies dealt with the monitoring of specific soil properties related to soil erosion, such as development of soil crust [50] or changes in soil moisture [47,48].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

2017

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Abstract:The assessment of the soil redistribution and real long-term soil degradation due to erosion on agriculture land is still insufficient in spite of being essential for soil conservation policy. Imaging spectroscopy has been recognized as a suitable tool for soil erosion assessment in recent years. In our study, we bring an approach for assessment of soil degradation by erosion by means of determining soil erosion classes representing soils differently influenced by erosion impact. The adopted methods include extensive field sampling, laboratory analysis, predictive modelling of selected soil surface properties using aerial hyperspectral data and the digital elevation model and fuzzy classification. Different multivariate regression techniques (Partial Least Square, Support Vector Machine, Random forest and Artificial neural network) were applied in the predictive modelling of soil properties. The properties with satisfying performance (R 2 > 0.5) were used as input data in erosion classes determination by fuzzy C-means classification method. The study was performed at four study sites about 1 km 2 large representing the most extensive soil units of the agricultural land in the Czech Republic (Chernozems and Luvisols on loess and Cambisols and Stagnosols on crystalline rocks). The influence of site-specific conditions on prediction of soil properties and classification of erosion classes was assessed. The prediction accuracy (R 2 ) of the best performing models predicting the soil properties varies in range 0.8-0.91 for soil organic carbon content, 0.21-0.67 for sand content, 0.4-0.92 for silt content, 0.38-0.89 for clay content, 0.73-089 for Fe ox , 0.59-0.78 for F ed and 0.82 for CaCO 3 . The performance and suitability of different properties for erosion classes' classification are highly variable at the study sites. Soil organic carbon was the most frequently used as the erosion classes' predictor, while the textural classes showed lower applicability. The presented approach was successfully applied in Chernozem and Luvisol loess regions where the erosion classes were assessed with a good overall accuracy (82% and 67%, respectively). The model performance in two Cambisol/Stagnosol regions was rather poor (51%-52%). The results showed that the presented method can be directly and with a good performance applied in pedologically and geologically homogeneous areas. The sites with heterogeneous structure of the soil cover and parent material will require more precise local-fitted models and use of further auxiliary information such as terrain or geological data. The future application of presented approach at a regional scale promises to produce valuable data on actual soil degradation by erosion usable for soil conservation policy purposes.

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Section: Assessment Of Soil Properties For Erosion Classes Distinguismentioning

confidence: 99%

Section: Assessment Of Soil Properties For Erosion Classes Distinguismentioning

confidence: 99%

Section: Prediction Of Soil Properties By Imaging Spectroscopymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

2017

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Point and Imaging Spectroscopy in Geospatial Analysis of Soils

Rizzo

Mendes

Silvero

et al. 2020

Geospatial Technologies for Crops and Soils

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Imaging Spectroscopy for Soil Mapping and Monitoring

et al. 2019

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There is a renewed awareness of the finite nature of the world's soil resources, growing concern about soil security and significant uncertainties about the carrying capacity of the planet. Regular assessments of soil conditions from local through to global scales are requested, and there is a clear demand for accurate, up-to-date and spatially referenced soil information by the modelling scientific community, farmers and land users, and policy-and decision-makers. Soil and imaging spectroscopy, based on visible-near-infrared and shortwave infrared (400-2500 nm) spectral reflectance, has been shown to be a proven method for the quantitative prediction of key soil surface properties. With the upcoming launch of the next generation of hyperspectral satellite sensors in the next years, a high potential to meet the demand for global soil mapping and monitoring is appearing. In this paper, we briefly review the basic concepts of soil spectroscopy with a special attention to the effects of soil roughness on reflectance and then provide a review of state of the art, achievements and perspectives in soil mapping and monitoring based on imaging spectroscopy from airand spaceborne sensors. Selected application cases are presented for the modelling of soil organic carbon, mineralogical composition, topsoil water content and characterization of soil crust, soil erosion and soil degradation stages based on airborne and simulated spaceborne imaging spectroscopy data. Further, current challenges, gaps and new directions toward enhanced soil properties modelling are presented. Overall, this paper highlights the potential and limitations of multiscale imaging spectroscopy nowadays for soil mapping and monitoring, and capabilities and requirements of upcoming spaceborne sensors as support for a more informed and sustainable use of our world's soil resources.

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Characterization of Soil Erosion Indicators Using Hyperspectral Data From a Mediterranean Rainfed Cultivated Region

Cited by 42 publications

References 38 publications

Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

Point and Imaging Spectroscopy in Geospatial Analysis of Soils

Imaging Spectroscopy for Soil Mapping and Monitoring

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