Soil organic carbon assessment by field and airborne spectrometry in bare croplands: accounting for soil surface roughness

Denis, Antoine; Stevens, Antoine; Wesemael, Bas van; Udelhoven, Thomas; Tychon, Bernard

doi:10.1016/j.geoderma.2014.02.015

Cited by 44 publications

(31 citation statements)

References 18 publications

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“…Denis et al [29] obtained the best results (R 2 = 0.96 and RPD = 3.13) by correcting the soil shadow effect on the image reflectance through using a wavelength-dependent correction factor calculated from soil vertical photographs taken in the field. Stevens et al [27] used a large dataset with 400 samples and obtained good results (R 2 = 0.79 and RPD = 2.33) by performing local models for the different regions and soil textures found in the study area.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

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Abstract:The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R 2 = 0.60-0.62, while models for the Hyperion sensor showed R 2 = 0.49-0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600-700 nm), and the short wave infrared region betweeñ 2000-2250 nm. The use of SMLR and spectral indices based on reference channels at~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method.

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Section: Discussionmentioning

confidence: 99%

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

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“…Therefore, excluding low-sampled (<25 samples) single fields from further discussion, for some single fields, the clay (AOI1b: R 2 = 0.72) or/and sand (AOI1b, AOI3b: R 2 ≥ 0.65) contents may also significantly influence (linear correlation) the soil reflectance pattern. Spielvogel et al (2004) [26] stated that soil spectral reflectance could be affected by (in order of priority) OM (principal determinant), CaCO3, clay, silt, and sand contents, even when no significant correlations between PCst1 and the texture parameters existed. The glaciomorphological and pedological genesis of the young morainic soil landscape of Northern Germany result in the soil reflectance not showing the darkening effect of Fe-oxides [49].…”

Section: Relationships Between Soil Properties and Pcst1 Of Multitempmentioning

confidence: 99%

“…With field spectrometry using a narrowband spectroradiometer in the VIS, NIR and SWIR range, Denis et al (2014) [26] could improve field scale-based measurement results of soil organic carbon from R 2 0.77 to 0.89 and 0.89 to 0.91 by soil shadow corrections. In general, airborne hyperspectral imagery provide more accurate OM modeling results (e.g., R 2 ≥ 0.93 [26]).…”

Section: Field Compositesmentioning

confidence: 99%

“…In general, airborne hyperspectral imagery provide more accurate OM modeling results (e.g., R 2 ≥ 0.93 [26]). …”

Section: Field Compositesmentioning

confidence: 99%

“…Numerous studies have investigated the relation between soil reflectance characteristics (e.g., soil color and soil brightness) and soil properties, particularly organic matter (OM), soil moisture, and texture [1,[20][21][22][23][24]. As proximal and remote sensing methods, diffuse reflectance spectroscopic techniques in the visible, near-and shortwave infra-red range were applied for OM measurements [25,26]. During the attempt to determine the optimal content of soil organic matter of agriculturally-used soils, Wessolek et al (2008) [27] pointed out that optimal organic matter supply is generally site-specific and strongly influenced by climate (e.g., temperature, precipitation), soil texture and land management/land use.…”

Section: Introductionmentioning

confidence: 99%

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Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

et al. 2015

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This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data analysis and GIS spatial data modeling, which is based on the methodology of Blasch et al. The results demonstrate (i) the potential of MSPA to predict OM for single fields and field composites with varying geomorphological, topographical, and pedological backgrounds and (ii) the method conversion of MSPA from the field scale to the multifield landscape scale. For single fields, as well as for field composites, significant correlations between OM and the soil pattern detecting first standardized principal components were found. Thus, high-quality functional OM soil maps could be produced after excluding temporal effects by applying modified MSPA analysis steps. A regional OM prediction model was developed using four representative calibration test sites. The MSPA-method conversion was realized applying the transformation parameters of the soilpattern detection algorithm used at the four calibration test sites and the developed regional prediction model to a multi-field, multitemporal, bare soil image mosaic of all agrarian fields of the Demmin study area in Northeast Germany. Results modeled at the landscape scale were validated at an independent test site with a resulting prediction error of 1.4 OM-% for the main OM value range of the Demmin study area.

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A Review on Soil Organic Carbon Estimation via Remote Sensing Approaches

Batool¹

2022

Preprint

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Soil organic carbon assessment by field and airborne spectrometry in bare croplands: accounting for soil surface roughness

Cited by 44 publications

References 18 publications

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

A Review on Soil Organic Carbon Estimation via Remote Sensing Approaches

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