Spatial prediction of soil organic carbon at different depths using digital soil mapping

GlobalSoilMap ◽  
2014 ◽  
pp. 181-184
Author(s):  
F Collard ◽  
N Saby ◽  
A de Forges ◽  
S Lehmann ◽  
J Paroissien ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Prediction ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Different Depths

scholarly journals The effectiveness of digital soil mapping with temporal variables in modeling soil organic carbon changes

Geoderma ◽  
2022 ◽  
Vol 405 ◽  
pp. 115407
Author(s):  
Ren-Min Yang ◽  
Li-An Liu ◽  
Xin Zhang ◽  
Ri-Xing He ◽  
Chang-Ming Zhu ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Temporal Variables

Digital soil mapping of organic carbon and it’s spatial distribution uncertainty in field scale

2020 ◽  
Author(s):  
Yan Guo ◽  
Ting Liu ◽  
Zhou Shi ◽  
Laigang Wang
Keyword(s):  
Standard Deviation ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Conditional Simulation ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Distance Weighting ◽  
Fine Resolution ◽  
Inverse Distance

<p>     Soil organic carbon (SOC) is a key property that affects soil quality and the assessment of soil resources. However, the spatial distribution of SOC is very heterogeneous and existing soil maps have considerable uncertainty. Traditional polygon-based soil maps are less useful for fine-resolution soil maps modeling and monitoring because they do not adequately characterize and quantify the spatial variation of continuous soil properties. And recently, digital soil mapping of organic carbon is the main source of information to be used in natural resource assessment and soil management. In this study, we collected 100 soil samples on a 50 m grid to conduct soil maps of topsoil (0-20 cm) organic carbon in a 500×500m field and evaluate the uncertainty by spatial stochastic simulation. The map of soil organic carbon generated by inverse distance weighting interpolation indicated that the average topsoil SOC is 11.59±0.61g/kg with averaged standard deviation error is 0.61. In order to evaluate the uncertainties, numbers were defined as 50, 100, 200, 500, 1000, 5000, 10000 with interval of 2×2 m to conduct conditional simulation. The standard deviation error gradually declined from 0.74 to 0.51 g/kg. Then, the uncertainty of SOC was expressed as the range of the 95% confidence intervals of the standard deviation error. Maps of uncertainty showed fine spatial heterogeneity even the numbers of simulations reached 10000. Compared with inverse distance weighting interpolation method, conditional simulation approach can improve the fine-resolution SOC maps. For some points, the simulated values deviated from the averaged values while closed to the observed values. On the whole, the maps of uncertainty showed larger waves in the field-edge and different SOC contour border. Consideration of the sample distribution and sampling strategy, the uncertainty map provides a guide for decision-making in additional sampling.</p><p><strong>Key words</strong><strong>:</strong> Soil organic carbon (SOC); uncertainty assessment; conditional simulation; digital soil mapping</p><p><strong>Acknowledgements</strong></p><p>This material is based upon work funded by National Natural Science Foundation of China (No. 41601213), Major science and technology projects of Henan (171100110600), the Key Science and Technology Program of Henan (182102410024).</p>

Comparison of uncertainty quantification methods on the example of soil organic carbon stock mapping in Hungary

2020 ◽  
Author(s):  
Gábor Szatmári ◽  
László Pásztor
Keyword(s):  
Decision Making ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Uncertainty Quantification ◽  
Carbon Stock ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Organic Carbon Stock ◽  
Wide Range ◽  
Organic Carbon Stock

<p>Digital soil mapping (DSM) aims to provide spatial soil information for a wide range of studies (e.g. agro-environmental management, nature conservation, rural development, water and food security etc.). For this purpose, advanced statistical methods are in use for inferring the spatial variations of soil. Nowadays, there is a heap of evidences that researchers and stakeholders are not just interested in the maps of soil properties, functions and/or services but in their uncertainties as well. This is indispensable to support decision making process. In DSM various uncertainty quantification methods are in use, however, only a few studies have addressed the issue of comparing them. In this study, we compared the suitability of several commonly applied digital soil mapping methods to quantify uncertainty with regard to a survey of soil organic carbon stock in Hungary. To fairly represent the wide range of DSM methods, the followings were selected: universal kriging (UK), sequential Gaussian simulation (SGS), random forest plus kriging (RFK) and quantile regression forest (QRF). For RFK two uncertainty quantification methods were adopted based on kriging variance (RFK-1) and bootstrapping (RFK-2). We used a control dataset consisting of 200 independent SOC stock observations for validating not just the spatial predictions but their uncertainty quantifications as well. For validating the uncertainty quantifications we applied accuracy plots (a.k.a. prediction interval coverage probability plots) and a modified version of G-statistics. According to our results, QRF and SGS provided the best quantifications of uncertainty. UK and RFK-2 overestimated whereas RFK-1 underestimated the uncertainty. Based on our results we could draw a conclusion that there is a need to validate the uncertainty quantifications before using them for decision making. Furthermore, special attention should be paid to the assumptions made in uncertainty quantification.</p><p> </p><p>Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NRDI; Grant No: KH126725) and the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (Gábor Szatmári).</p>

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

SOIL ◽  
2018 ◽  
Vol 4 (3) ◽  
pp. 173-193 ◽  
Author(s):  
Mario Guevara ◽  
Guillermo Federico Olmedo ◽  
Emma Stell ◽  
Yusuf Yigini ◽  
Yameli Aguilar Duarte ◽  
...  
Keyword(s):  
Latin America ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Support Vector ◽  
Conditional Response ◽  
Predictive Algorithms ◽  
Soc Stocks ◽  
Country Specific

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.

Digital soil mapping of soil organic carbon stocks in Western Ghats, South India

2021 ◽  
pp. e00387
Author(s):  
S. Dharumarajan ◽  
B. Kalaiselvi ◽  
Amar Suputhra ◽  
M. Lalitha ◽  
R. Vasundhara ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Western Ghats ◽  
South India ◽  
Carbon Stocks ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Organic Carbon Stocks

scholarly journals Game theory interpretation of digital soil mapping convolutional neural networks

SOIL ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 389-397 ◽  
Author(s):  
José Padarian ◽  
Alex B. McBratney ◽  
Budiman Minasny
Keyword(s):  
Neural Networks ◽  
Game Theory ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Spatial Context ◽  
Topographic Wetness Index ◽  
Model Predictions ◽  
Complex Models

Abstract. The use of complex models such as deep neural networks has yielded large improvements in predictive tasks in many fields including digital soil mapping. One of the concerns about using these models is that they are perceived as black boxes with low interpretability. In this paper we introduce the use of game theory, specifically Shapley additive explanations (SHAP) values, in order to interpret a digital soil mapping model. SHAP values represent the contribution of a covariate to the final model predictions. We applied this method to a multi-task convolutional neural network trained to predict soil organic carbon in Chile. The results show the contribution of each covariate to the model predictions in three different contexts: (a) at a local level, showing the contribution of the various covariates for a single prediction; (b) a global understanding of the covariate contribution; and (c) a spatial interpretation of their contributions. The latter constitutes a novel application of SHAP values and also the first detailed analysis of a model in a spatial context. The analysis of a SOC (soil organic carbon) model in Chile corroborated that the model is capturing sensible relationships between SOC and rainfall, temperature, elevation, slope, and topographic wetness index. The results agree with commonly reported relationships, highlighting environmental thresholds that coincide with significant areas within the study area. This contribution addresses the limitations of the current interpretation of models in digital soil mapping, especially in a spatial context. We believe that SHAP values are a valuable tool that should be included within the DSM (digital soil mapping) framework, since they address the important concerns regarding the interpretability of more complex models. The model interpretation is a crucial step that could lead to generating new knowledge to improve our understanding of soils.

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