scholarly journals Digital soil mapping of arable land in Sweden – Validation of performance at multiple scales

Geoderma ◽  
2019 ◽  
Vol 352 ◽  
pp. 342-350 ◽  
Author(s):  
Kristin Piikki ◽  
Mats Söderström
Keyword(s):  
Multiple Scales ◽  
Arable Land ◽  
Soil Mapping ◽  
Digital Soil Mapping

Digital soil mapping workflow for forest resource applications: a case study in the Hearst Forest, Ontario

2021 ◽  
Vol 51 (1) ◽  
pp. 59-77
Author(s):  
Christopher Blackford ◽  
Brandon Heung ◽  
Ken Baldwin ◽  
Robert L. Fleming ◽  
Paul W. Hazlett ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Soil Mapping ◽  
Environmental Data ◽  
Digital Soil Mapping ◽  
Moisture Regime ◽  
Forest Resource ◽  
Forest Management Planning ◽  
Point Data ◽  
Data Layers

Accurate soil information is critically important for forest management planning and operations but is challenging to map. Digital soil mapping (DSM) improves upon the limitations of conventional soil mapping by explicitly linking a variety of environmental data layers to spatial soil point data sets to continuously predict soil variability across a landscape. Thus far, much DSM research has focussed on the development of ultrafine-resolution soil maps within agricultural systems; however, increasing availability of light detection and ranging (LiDAR) data presents new opportunities to apply DSM to support forest resource applications at multiple scales. This project describes a DSM workflow using LiDAR-derived elevation data and machine learning models (MLMs) to predict key forest soil attributes. A case study in the Hearst Forest in northeastern Ontario, Canada, is used to illustrate the workflow. We applied multiple MLMs to the Hearst Forest to predict soil moisture regime and textural class. Both qualitative and quantitative assessment pointed to the random forest MLM producing the best maps (63% accuracy for moisture regime and 66% accuracy for textural class). Where error occurred, soils were typically misclassified to neighbouring classes. This standardized, flexible workflow is a valuable tool for practitioners that want to undertake DSM as part of forest resource management and planning.

Application of legacy soil data in digital soil mapping for the elaboration of novel, countrywide maps of soil conditions

2014 ◽  
Vol 63 (1) ◽  
pp. 79-88 ◽  
Author(s):  
László Pásztor ◽  
E. Dobos ◽  
G. Szatmári ◽  
A. Laborczi ◽  
K. Takács ◽  
...  
Keyword(s):  
Object Oriented ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Conditions ◽  
Related Information ◽  
Soil Information

The main objective of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project is to significantly extend the potential, how demands on spatial soil related information could be satisfied in Hungary. Although a great amount of soil information is available due to former mappings and surveys, there are more and more frequently emerging discrepancies between the available and the expected data. The gaps are planned to be filled with optimized digital soil mapping (DSM) products heavily based on legacy soil data, which still represent a valuable treasure of soil information at the present time. The paper presents three approaches for the application of Hungarian legacy soil data in object oriented digital soil mapping.

scholarly journals Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

2019 ◽  
Vol 11 (14) ◽  
pp. 1683 ◽  
Author(s):  
Yangchengsi Zhang ◽  
Long Guo ◽  
Yiyun Chen ◽  
Tiezhu Shi ◽  
Mei Luo ◽  
...  
Keyword(s):  
Time Series ◽  
Organic Carbon ◽  
Time Series Data ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Series Data ◽  
Landsat 8 ◽  
Auxiliary Variables ◽  
Jianghan Plain ◽  
Ndvi Time Series

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

Digital soil mapping of compositional particle-size fractions using proximal and remotely sensed ancillary data

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB201-WB211 ◽  
Author(s):  
S. Buchanan ◽  
J. Triantafilis ◽  
I. O. A. Odeh ◽  
R. Subansinghe
Keyword(s):  
Particle Size ◽  
High Resolution ◽  
Soil Mapping ◽  
Additive Models ◽  
Digital Soil Mapping ◽  
Unbiased Estimation ◽  
Ancillary Data ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Log Ratio

The soil particle-size fractions (PSFs) are one of the most important attributes to influence soil physical (e.g., soil hydraulic properties) and chemical (e.g., cation exchange) processes. There is an increasing need, therefore, for high-resolution digital prediction of PSFs to improve our ability to manage agricultural land. Consequently, use of ancillary data to make cheaper high-resolution predictions of soil properties is becoming popular. This approach is known as “digital soil mapping.” However, most commonly employed techniques (e.g., multiple linear regression or MLR) do not consider the special requirements of a regionalized composition, namely PSF; (1) should be nonnegative (2) should sum to a constant at each location, and (3) estimation should be constrained to produce an unbiased estimation, to avoid false interpretation. Previous studies have shown that the use of the additive log-ratio transformation (ALR) is an appropriate technique to meet the requirements of a composition. In this study, we investigated the use of ancillary data (i.e., electromagnetic (EM), gamma-ray spectrometry, Landsat TM, and a digital elevation model to predict soil PSF using MLR and generalized additive models (GAM) in a standard form and with an ALR transformation applied to the optimal method (GAM-ALR). The results show that the use of ancillary data improved prediction precision by around 30% for clay, 30% for sand, and 7% for silt for all techniques (MLR, GAM, and GAM-ALR) when compared to ordinary kriging. However, the ALR technique had the advantage of adhering to the special requirements of a composition, with all predicted values nonnegative and PSFs summing to unity at each prediction point and giving more accurate textural prediction.

Harmonizing legacy soil data for digital soil mapping in Indonesia

Geoderma ◽  
2013 ◽  
Vol 192 ◽  
pp. 77-85 ◽  
Author(s):  
Yiyi Sulaeman ◽  
Budiman Minasny ◽  
Alex B. McBratney ◽  
Muhrizal Sarwani ◽  
Atang Sutandi
Keyword(s):  
Soil Mapping ◽  
Digital Soil Mapping

Integrating Different Types of Knowledge for Digital Soil Mapping

2009 ◽  
Vol 73 (5) ◽  
pp. 1682-1692 ◽  
Author(s):  
X. Shi ◽  
R. Long ◽  
R. Dekett ◽  
J. Philippe
Keyword(s):  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Types Of Knowledge ◽  
Different Types

scholarly journals Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches

CATENA ◽  
2021 ◽  
Vol 207 ◽  
pp. 105702
Author(s):  
Sanaz Zare ◽  
Ali Abtahi ◽  
Seyed Rashid Fallah Shamsi ◽  
Philippe Lagacherie
Keyword(s):  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Laboratory Measurements ◽  
Sensing Data ◽  
Proximal Soil Sensing

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
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