Digital Mapping of Soil Carbon

Minasny, Budiman; McBratney, Alex B.; Malone, Brendan; Wheeler, Ichsani

doi:10.1016/b978-0-12-405942-9.00001-3

Cited by 366 publications

(198 citation statements)

References 108 publications

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Section: Relative Importance Of Environmental Variablesmentioning

confidence: 99%

“…It implies that there is a potential of remote sensing in mapping SOC distribution in coastal mountain regions with humid climates. Minnasny et al [59] reported that such indices were not only excellent predictors, but also played positive roles in the microbial activities in soils affecting the distribution of SOC. The lower influence of NDVI on SOC in 2010 can be attributed to the heavy land-use change such as a large number of forests, grasslands and wetlands converted to agriculture from 1990 to 2010.…”

Section: 8 1154mentioning

confidence: 99%

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Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

Wang

Adhikari

et al. 2016

Sustainability

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Soil organic carbon (SOC) plays an important role in soil fertility and the global carbon cycle. A better understanding of spatial-temporal changes of SOC content is essential for soil resource management, emission studies, and carbon accounting. In this study, we used a boosted regression trees (BRT) model to map distributions of SOC content in the topsoil (0-20 cm) and evaluated its temporal dynamics from 1990-2010 in Wafangdian City, northeast of China. A set of 110 (1990) and 127 (2010) soil samples were collected and nine environment variables (including topography and vegetation) were used. A 10-fold cross-validation was used to evaluate model performance as well as predictive uncertainty. Accuracy assessments showed that R 2 of 0.53 and RMSE (Root-mean-square error) of 9.7 g·kg −1 for 1990, and 0.55, and 5.2 g·kg −1 for 2010. Elevation and NDVI (Normalized Difference Vegetation Index) were the two important variables affecting SOC distribution. Results showed that mean SOC content decreased from 19 ± 14 to 18 ± 8 g·kg −1 over a 20 year period. The maps of SOC represented a decreasing trend from south to north across the study area in both periods. Rapid urbanization and land-use changes were accountable for declining SOC levels. We believe predicted maps of SOC can help local land managers and government agencies to evaluate soil quality and assess carbon sequestration potential and carbon credits.

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Section: Relative Importance Of Environmental Variablesmentioning

confidence: 99%

Section: 8 1154mentioning

confidence: 99%

Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

Wang

Adhikari

et al. 2016

Sustainability

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“…Many previous studies have used legacy soil data (Aitkenhead and Coull, 2016;Minasny et al, 2013;Mulder et al, 2016 …”

mentioning

confidence: 99%

Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project

Wijewardane

Wills

et al. 2016

Soil Science Soc of Amer J

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Visible and near infrared reflectance spectroscopy (VisNIr) has been used across a number of spatial scales to predict soil organic carbon (OC) content. The rapid Carbon Assessment Project (raCA) is a nationwide project that collected 144,000+ soil samples from across the conterminous United States for C stock mapping using VisNIr. The objective of this study was to calibrate and validate the VisNIr soil OC and total C (TC) models with ~20,000 samples from raCA. Models were developed with either partial least squares regression (PlSr) or the Artificial Neural Network (ANN) model. Four auxiliary variables-raCA region, land Use land Cover, Master Horizon, and Textural Class-were tested to stratify the dataset and to develop local models. The results showed that OC and TC models calibrated with ANN (R 2 > 0.94; rPD > 4.0) outperformed those of PlSr (R 2 = 0.83; rPD = 2.4). For PlSr, local models developed with all four auxiliary variables exhibited improvement in prediction accuracy, and the improvement was only marginal for ANN models. Master Horizon and Textural Class appeared to be more effective in stratifying samples into homogeneous groups because they gave an overall lower root mean squared error of prediction (rMSE P ) for the validation samples. For the majority of the local Textural Class models, the rMSE P of OC prediction ranged from 0.5 to 1.5%. To maximize the applicability of the raCA spectral library on external soil samples, PlSr local models developed from Master Horizon or Textural Class appeared to be more favorable.Abbreviations: ANN, Artificial Neural Network; IC, inorganic carbon; LULC, land use land cover classes; OC, organic carbon; PLSR, partial least squares regression; RaCA, Rapid Carbon Assessment Project; RPD, ratio of performance to deviation; RPIQ, ratio of performance to interquartile range; TC, total carbon; VisNIR, visible and near infrared reflectance spectroscopy. S oil organic carbon (OC) is a key soil property that plays many critical roles, from agriculture production to biogeochemical cycling to ecosystems functioning (Adhikari and Hartemink, 2016;Chen et al., 2000;Lal, 2004; van Wesemael et al., 2010). The capability of soils to sequester C and therefore regulate atmospheric CO 2 concentration and mitigate climate change is widely recognized and has been an active area of research (Grunwald et al., 2011;Lal, 2004;West and Post, 2002). Up-to-date, baseline soil C stock maps across different scales are a very useful tool for researchers, stakeholders, and policymakers for a wide variety of applications, ranging from best land management practices to natural resource conservation to C auditing (de Gruijter et al., 2016;Minasny et al., 2011).Producing large-scale (national or continental) soil C maps (e.g., for the purpose of C inventory and understanding of soil C sourcing and sinking) is highly challenging. Many previous studies have used legacy soil data (Aitkenhead and Coull, 2016;Minasny et al., 2013;Mulder et al., 2016 Core Ideas• We modeled ~20,000 samples in raCA from...

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“…Thus, we argue that the DSM form of each country should assess and incorporate countryspecific available data and environmental predictors to select the best prediction algorithm. The FAO SOC mapping cookbook explores possibilities to derive country-specific SOC maps from a variety of prediction algorithms (Yigini et al, 2017), and multiple resources have described the state of the art of modeling methods focused on DSM of soil carbon (Minasny et al, 2013;10 Malone et al, 2017) including geostatistics (Hengl, 2009). Thus, data characteristics (e.g., spatial structure, representativeness)…”

Section: Discussionmentioning

confidence: 99%

No Silver Bullet for Digital Soil Mapping: Country-specific Soil Organic Carbon Estimates across Latin America

Guevara¹,

Olmedo²,

Stell³

et al. 2018

Preprint

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Abstract. Country-specific soil organic carbon (SOC) maps are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM). We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included: support vector machines, random forest, kernel weighted nearest neighbors, partial least squares regression, and regression-Kriging based on stepwise multiple linear models. Country-specific training data and SOC predictors (5 × 5 km pixel resolution) were obtained from ISRIC-World-Soil-Information-System. In general, temperature, soil type, vegetation indices and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific data scenarios and were able to explain ~ 53 % of SOC variability (range

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Digital Mapping of Soil Carbon

Cited by 366 publications

References 108 publications

Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project

No Silver Bullet for Digital Soil Mapping: Country-specific Soil Organic Carbon Estimates across Latin America

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