Selection of terrain attributes and its scale dependency on soil organic carbon prediction

Geoderma ◽  
2019 ◽  
Vol 340 ◽  
pp. 303-312 ◽  
Author(s):  
Zhixing Guo ◽  
Kabindra Adhikari ◽  
Menaka Chellasamy ◽  
Mette B. Greve ◽  
Phillip R. Owens ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Scale Dependency ◽  
Terrain Attributes ◽  
Selection Of

Mapping Soil Organic Carbon Using Local Terrain Attributes: A Comparison of Different Polynomial Models

Pedosphere ◽  
2017 ◽  
Vol 27 (4) ◽  
pp. 681-693 ◽  
Author(s):  
Xiaodong SONG ◽  
Feng LIU ◽  
Ganlin ZHANG ◽  
Decheng LI ◽  
Yuguo ZHAO ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Terrain Attributes ◽  
Polynomial Models

scholarly journals Terrain attributes and forage productivity predict catchment-scale soil organic carbon stocks

Geoderma ◽  
2020 ◽  
Vol 368 ◽  
pp. 114286
Author(s):  
Scott M. Devine ◽  
Anthony T. O'Geen ◽  
Han Liu ◽  
Yufang Jin ◽  
Helen E. Dahlke ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Stocks ◽  
Catchment Scale ◽  
Terrain Attributes ◽  
Soil Organic Carbon Stocks

Measuring soil organic carbon: which technique and where to from here?

Soil Research ◽  
2015 ◽  
Vol 53 (7) ◽  
pp. 717 ◽  
Author(s):  
Timothy J. Johns ◽  
Michael J. Angove ◽  
Sabine Wilkens
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Time Budget ◽  
Analytical Techniques ◽  
Wet Oxidation ◽  
Spectroscopic Techniques ◽  
Research Needs ◽  
Important Health ◽  
Definition Of ◽  
Selection Of

This review compares and contrasts analytical techniques for the measurement of total soil organic carbon (TOC). Soil TOC is seen to be a highly important health and quality indicator for soils, as well as having the potential to sequester atmospheric carbon. Definition of the form of organic carbon measured by a given method is vital to the selection of appropriate methodology, as well as the understanding of what exactly is being measured. Historically, studies of TOC have ranged from basic measures, such as colour and gravimetric analyses, to dry and wet oxidation techniques. In more recent times, various spectroscopic techniques and the application of remote or mobile approaches have gained prominence. The different techniques, even the oldest ones, may have their place in current research depending on research needs, the available time, budget and access to wider resources. This review provides an overview of the various methods, highlights advantages, limitations and research opportunities and provides an indication of what the method actually measures so that meaningful comparisons can be made.

scholarly journals Influence of the Selection of Interpolation Method on Revealing Soil Organic Carbon Variability in the Red Soil Region, China

2018 ◽  
Vol 10 (7) ◽  
pp. 2290 ◽  
Author(s):  
Zhongqi Zhang ◽  
Dongsheng Yu ◽  
Xiyang Wang ◽  
Yue Pan ◽  
Guangxing Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Interpolation Method ◽  
Red Soil ◽  
Selection Of

scholarly journals Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

2019 ◽  
Vol 11 (11) ◽  
pp. 1298 ◽  
Author(s):  
Ahmed Laamrani ◽  
Aaron A. Berg ◽  
Paul Voroney ◽  
Hannes Feilhauer ◽  
Line Blackburn ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Least Squares Regression ◽  
Agricultural Field ◽  
Southern Ontario ◽  
Spectral Bands ◽  
Selection Of

The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors.

scholarly journals Spatial distribution of soil organic carbon by digital mapping: the case of the Medio Aguanaval river sub-basin

2021 ◽  
Vol 13 (2) ◽  
pp. 227-245
Author(s):  
Georgina Pérez-Rodríguez ◽  
◽  
Armando López-Santos ◽  
Miguel Agustín Velásquez-Valle ◽  
José Villanueva-Díaz ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Horizontal Distribution ◽  
Digital Mapping ◽  
Lack Of Information ◽  
Static Modeling ◽  
Vertical And Horizontal Distribution ◽  
Soc Stocks ◽  
Selection Of

ntroduction: Carbon is found mainly in geological reservoirs, oceans, atmosphere and land. Soil organic carbon (SOC) is determined by the quantity and vertical distribution of vegetation, intrinsic soil properties and climate, but variability is influenced by anthropogenic interference. SOC stocks are not static; modeling their spatial, vertical and horizontal distribution involves the creation of baseline estimates to quantify these stocks. Objective: To estimate the magnitude of SOC stocks in the Medio Aguanaval River sub-basin (ScRMA) and to analyze the sensitivity of four interpolation methods to minimize the error of digital mapping for the ScRMA. Methodology: The study consisted of five stages: 1) search, download and analysis of soil data, 2) data processing, 3) selection of verification sites, 4) laboratory analysis and 5) processing of data from verification sites. Results: SOC values ranged from 9 to 133 t·ha-1, with a mean of 36.31 t·ha-1 and standard deviation of 23.83 t·ha-1. The ordinary exponential Kriging interpolator was the best representation for SOC of the ScRMA based onstatistics. The results of the analysis of the verification sites yielded a mean SOC of 24.4 t·ha-1. Limitations of the study: Soil profile density for the region and the lack of information on bulk density. Originality: The baseline distribution of SOC at the sub-basin level was used to analyze its dynamics. Conclusions: The highest concentration of SOC (61 to 129 t·ha-1) was found in the municipalities of Cuencamé and Santa Clara, while the lowest records (10 to 30 t·ha-1) were located in the municipalities of Torreón and Viesca.

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