Determining iron content in Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging spectroscopy

Bartholomeus, Harm; Epema, G.F.; Schaepman, Michael E.

doi:10.1016/j.jag.2006.09.001

Cited by 68 publications

(42 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A high variability of model performances was reported in several studies dealing with texture prediction by RS [80][81][82][83][84][85]. [84] or even better [86,87] in comparison to other studies. In conclusion, the analysis showed that the distribution of soil properties in eroded landscapes can be successfully predicted using the spectroscopic data.…”

Section: Prediction Of Soil Properties By Imaging Spectroscopymentioning

confidence: 98%

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

2017

View full text Add to dashboard Cite

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.

show abstract

Section: Prediction Of Soil Properties By Imaging Spectroscopymentioning

confidence: 98%

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

2017

View full text Add to dashboard Cite

show abstract

“…Although it may not be as important for soil fertility as e.g., phosphorus, nitrogen and organic matter, its absence would be detrimental to plant growth. Iron is thus an indicator for soil fertility and the usability of an area for cultivation of crops [27]. A relative high spectral resolution is needed for mapping iron contents with reflectance data [26], and remote sensing is the only suitable tool for surveying large areas at a high temporal and spatial interval.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2 for Mapping Iron Absorption Feature Parameters

Werff

Meer

2015

Remote Sensing

View full text Add to dashboard Cite

Iron is an indicator for soil fertility and the usability of an area for cultivating crops. Remote sensing is the only suitable tool for surveying large areas at a high temporal and spatial interval, yet a relative high spectral resolution is needed for mapping iron contents with reflectance data. Sentinel-2 has several bands that cover the 0.9 µm iron absorption feature, while space-borne sensors traditionally used for geologic remote sensing, like ASTER and Landsat, had only one band in this feature. In this paper, we introduce a curve-fitting technique for Sentinel-2 that approximates the iron absorption feature at a hyperspectral resolution. We test our technique on library spectra of different iron bearing minerals and we apply it to a Sentinel-2 image synthesized from an airborne hyperspectral dataset. Our method finds the wavelength position of maximum absorption and absolute absorption depth for minerals Beryl, Bronzite, Goethite, Jarosite and Hematite. Sentinel-2 offers information on the 0.9 µm absorption feature that until now was reserved for hyperspectral instruments. Being a satellite mission, this information comes at a lower spatial resolution than airborne hyperspectral data, but with a large spatial coverage and frequent revisit time.

show abstract

“…The use of hyperspectral techniques has also demonstrated the quantification, which is attractive for soil scientists due to the reduction in the need for time-and cost-intensive soil laboratory analyses and field campaigns [1]. Several studies have demonstrated that such soil characteristics can be quantified and predicted statistically via their spectral signatures in the commonly used/accessible VNIR-SWIR wavelength region [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Applicability of the Thermal Infrared Spectral Region for the Prediction of Soil Properties Across Semi-Arid Agricultural Landscapes

et al. 2012

View full text Add to dashboard Cite

In this study we tested the feasibility of the thermal infrared (TIR) wavelength region (within the atmospheric window between 8 and 11.5 µm) together with the traditional solar reflective wavelengths for quantifying soil properties for coarse-textured soils from the Australian wheat belt region. These soils have very narrow ranges of texture and organic carbon contents. Soil surface spectral signatures were acquired in the laboratory, using a directional emissivity spectrometer (µFTIR) in the TIR, as well as a bidirectional reflectance spectrometer (ASD FieldSpec) for the solar reflective wavelengths (0.4-2.5 µm). Soil properties were predicted using multivariate analysis techniques (partial least square regression). The spectra were resampled to operational imaging spectroscopy sensor characteristics (HyMAP and TASI-600). To assess the relevance of specific wavelength regions in the prediction, the drivers of the PLS models were interpreted with respect to the spectral characteristics of the soils' chemical and physical composition. The study

show abstract

Determining iron content in Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging spectroscopy

Cited by 68 publications

References 21 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

Sentinel-2 for Mapping Iron Absorption Feature Parameters

Applicability of the Thermal Infrared Spectral Region for the Prediction of Soil Properties Across Semi-Arid Agricultural Landscapes

Contact Info

Product

Resources

About