Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data

Mondal, Arun; Khare, Deepak; Kundu, Sananda; Mondal, Surajit; Mukherjee, Sandip; Mukhopadhyay, Anirban

doi:10.1016/j.ejrs.2016.06.004

Cited by 68 publications

(33 citation statements)

References 48 publications

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“…For coarse fragments (CRFVOL), a value of 0 % was used for missing information prior to the mass-preservative spline modeling. SOC estimates (0 to 30 cm) were derived following a standardized SOC calculation method (Nelson and Sommers, 1982) (Eq. 2):…”

Section: Soc Observationsmentioning

confidence: 99%

“…Data mining techniques have been successfully used to model and predict the spatial variability of soil properties (Rossel and Behrens, 2010;Hengl et al, 2017; and generate site-specific and country-specific SOC maps (Viscarra Rossel et al, 2014;Adhikari et al, 2014). The combination of regression modeling approaches with geostatistics of independent model residuals (i.e., regression kriging) is a combined strategy that has been widely used to map SOC (Hengl et al, 2004;Mishra et al, 2009;Marchetti et al, 2012;Kumar et al, 2012;Peng et al, 2013;Adhikari et al, 2014;Yigini and Panagos, 2016;Nussbaum et al, 2014;Mondal et al, 2017). Machine learning algorithms such as random forests or support vector machines have also been used to increase statistical accuracy of soil carbon models (Martin et al, 2011;Hashimoto et al, 2017;Hengl et al, 2017) including applications for SOC mapping (Grimm et al, 2008;Sreenivas et al, 2016;Yang et al, 2016;Hengl et al, 2017;Delgado-Baquerizo et al, 2017;Ließ et al, 2016;Viscarra Rossel et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America

Guevara

Olmedo

Stell

et al. 2018

SOIL

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Abstract. Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but 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 (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 × 5 km pixel resolution) were obtained from ISRIC -World Soil Information. 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 datasets and models, and were able to explain SOC variability in a range between ∼ 1 and ∼ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ∼ 39 % of SOC variability from repeated 5-fold cross-validation. We report a combined SOC stock of 77.8 ± 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 ± 16.5 Pg) and croplands (13 ± 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 ± 42.2 and 76.8 ± 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates.

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Section: Soc Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America

Guevara

Olmedo

Stell

et al. 2018

SOIL

View full text Add to dashboard Cite

show abstract

“…These predictions will be representative of the time period (t) when soil available data was collected. Therefore, the prediction factors ideally should represent, the conditions of the soil forming environment for the same period of time (as much 10 as possible) when soil available data was collected. In Eq.…”

Section: Introductionmentioning

confidence: 99%

“…Data mining techniques have been successfully used to model and predict the spatial variability of soil properties (Rossel and Behrens, 2010;Hengl et al, 2017;Shangguan et al, 2017) and generate country-specific SOC maps (Viscarra Rossel et al, 2014;Adhikari et al, 2014). The combination of regression modeling approaches with geostatistics of model residuals (i.e., regression Kriging) is a combined strategy that 30 has been widely used to map SOC (Hengl et al, 2004;Mishra et al, 2009;Marchetti et al, 2012;Kumar et al, 2012;Peng et al, 2013;Adhikari et al, 2014;Yigini and Panagos, 2016;Nussbaum et al, 2014;Mondal et al, 2017). Machine learning algorithms such as random forests or support vector machines have also been used to increase statistical accuracy of soil 3 SOIL Discuss., https://doi.org /10.5194/soil-2017-40 Manuscript under review for journal SOIL Discussion started: 25 January 2018 c Author(s) 2018.…”

mentioning

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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“…[42] [43]. The first three features, which are brightness index, greenness index, and wetness index, respectively usually account for the most variation in a single-date image [44]. [45] analyzed Landsat data for environmental studies and found the Tasseled Cap transformation to be a consistent indicator of assessing forest change as it captures Shortwave Infrared (SWIR).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of Spatial Autocorrelation of Land-Use and Land-Cover Data within Pipestem Creek in North Dakota

Rozario¹,

Oduor²,

Kotchman³

et al. 2017

GEP

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A major threat to biodiversity in North Dakota is the conversion of forested land to cultivable land, especially those that act as riparian buffers. To reverse this trend of transformation, a validation and prediction model is necessary to assess the change. Spatial prediction within a Geographic Information System (GIS) using Kriging is a popular stochastic method. The objective of this study was to predict spatial and temporal transformation of a small agricultural watershed-Pipestem Creek in North Dakota; USA using satellite imagery from 1976 to 2015. To enhance the difference between forested land and non-forested land, a spectral transformation method-Tasseled-Cap's Greenness Index (TCGI) was used. To study the spatial structure present in the imagery within the study period, semivariograms were generated. The Kriging prediction maps were post-classified using Remote Sensing techniques of change detection to obtain the direction and intensity of forest to non-forest change. TCGI generated higher values from 1976 to 2000 and it gradually reduced from 2000 to 2011 indicating loss of forested land.

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Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data

Cited by 68 publications

References 48 publications

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America

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

Uncertainty Analysis of Spatial Autocorrelation of Land-Use and Land-Cover Data within Pipestem Creek in North Dakota

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