Spectroscopic measurements and imaging of soil colour for field scale estimation of soil organic carbon

Geoderma ◽  
2020 ◽  
Vol 357 ◽  
pp. 113972 ◽  
Author(s):  
Asa Gholizadeh ◽  
Mohammadmehdi Saberioon ◽  
Raphael A. Viscarra Rossel ◽  
Lubos Boruvka ◽  
Ales Klement
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Field Scale ◽  
Spectroscopic Measurements ◽  
Scale Estimation

Strategies for the efficient estimation of soil organic carbon at the field scale with vis-NIR spectroscopy: Spectral libraries and spiking vs. local calibrations

Geoderma ◽  
2019 ◽  
Vol 354 ◽  
pp. 113856 ◽  
Author(s):  
Michael Seidel ◽  
Christopher Hutengs ◽  
Bernard Ludwig ◽  
Sören Thiele-Bruhn ◽  
Michael Vohland
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Nir Spectroscopy ◽  
Efficient Estimation ◽  
Field Scale ◽  
Spectral Libraries

Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale

Geoderma ◽  
2012 ◽  
Vol 175-176 ◽  
pp. 21-28 ◽  
Author(s):  
Christine Hbirkou ◽  
Stefan Pätzold ◽  
Anne-Katrin Mahlein ◽  
Gerhard Welp
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Hyperspectral Imaging ◽  
Field Scale

scholarly journals A Parsimonious Approach to Estimate Soil Organic Carbon Applying Unmanned Aerial System (UAS) Multispectral Imagery and the Topographic Position Index in a Heterogeneous Soil Landscape

2021 ◽  
Vol 13 (18) ◽  
pp. 3557
Author(s):  
Marc Wehrhan ◽  
Michael Sommer
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Errors ◽  
Field Scale ◽  
Local Approach ◽  
Topographic Position ◽  
Topographic Position Index ◽  
Soil Landscape ◽  
Position Index

Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils’ SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R²) = 0.91; root mean square error (RMSE) = 0.11% and R² = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R² = 0.88, RMSE = 0.07%; R² = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.

scholarly journals Redistribution of Soil Organic Carbon Triggered by Erosion at Field Scale Under Subhumid Climate, Hungary

Pedosphere ◽  
2016 ◽  
Vol 26 (5) ◽  
pp. 652-665 ◽  
Author(s):  
Zoltán SZALAI ◽  
Judit SZABÓ ◽  
József KOVACS ◽  
Erzsébet MÉSZÁROS ◽  
Gáspár ALBERT ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Field Scale

Field-Scale Mapping of Surface Soil Organic Carbon Using Remotely Sensed Imagery

2000 ◽  
Vol 64 (2) ◽  
pp. 746-753 ◽  
Author(s):  
Feng Chen ◽  
David E. Kissel ◽  
Larry T. West ◽  
Wayne Adkins
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Field Scale ◽  
Remotely Sensed Imagery

Simulating Field-Scale Soil Organic Carbon Dynamics Using EPIC

2007 ◽  
Vol 71 (4) ◽  
pp. 1174-1185 ◽  
Author(s):  
Hector J. Causarano ◽  
Joey N. Shaw ◽  
Alan J. Franzluebbers ◽  
D. Wayne Reeves ◽  
Randy L. Raper ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Dynamics ◽  
Field Scale ◽  
Soil Organic Carbon Dynamics

Predicting Soil Organic Carbon at Field Scale Using a National Soil Spectral Library

2013 ◽  
Vol 21 (3) ◽  
pp. 213-222 ◽  
Author(s):  
Yi Peng ◽  
Maria Knadel ◽  
René Gislum ◽  
Fan Deng ◽  
Trine Norgaard ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spectral Library ◽  
Field Scale
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