Legacy data-based national-scale digital mapping of key soil properties in India

Geoderma ◽  
2021 ◽  
Vol 381 ◽  
pp. 114684 ◽  
Author(s):  
Nagarjuna N. Reddy ◽  
Poulamee Chakraborty ◽  
Sourav Roy ◽  
Kanika Singh ◽  
Budiman Minasny ◽  
...  
Keyword(s):  
Soil Properties ◽  
National Scale ◽  
Digital Mapping ◽  
Legacy Data

scholarly journals Harmonisation of a large-scale historical database with the actual Czech soil classification system

2020 ◽  
Vol 15 (No. 2) ◽  
pp. 101-115 ◽  
Author(s):  
Tereza Zádorová ◽  
Daniel Žížala ◽  
Vít Penížek ◽  
Aleš Vaněk
Keyword(s):  
Soil Properties ◽  
Classification System ◽  
Large Scale ◽  
Soil Classification ◽  
Study Region ◽  
Taxonomic Distance ◽  
Historical Database ◽  
Legacy Data ◽  
Soil Units ◽  
Data Harmonisation

The possibility of the adequate use of data and maps from historical soil surveys depends, to a large measure, on their harmonisation. Legacy data originating from a large-scale national mapping campaign, “Systematic soil survey of agricultural soils in Czechoslovakia (SSS, 1961–1971)”, were harmonised and converted according to the actual system of soil classification and descriptions used in Czechia – the Czech taxonomic soil classification system (CTSCS). Applying the methods of taxonomic distance and quantitative analysis and reclassification of the selected soil properties, the conversion of two types of mapping soil units with different detailed soil information (General soil representative (GSR), and Basic soil representative (BSR)) to their counterparts in the CTSCS has been effectuated. The results proved the good potential of the used methods for the soil data harmonisation. The closeness of the concepts of the two classifications was shown when a number of soil classes had only one counterpart with a very low taxonomic distance. On the contrary, soils with variable soil properties were approximating several related units. The additional information on the soil skeleton content, texture, depth and parent material, available for the BSR units, showed the potential in the specification of some units, though the harmonisation of the soil texture turned out to problematic due to the different categorisation of soil particles. The validation of the results in the study region showed a good overall accuracy (75% for GSR, 76.1% for BSR) for both spatial soil units, when better performance has been observed in BSR. The conversion accuracy differed significantly in the individual soil units, and ranged from almost 100% in Fluvizems to 0% in Anthropozems. The extreme cases of a complete mis-classification can be attributed to inconsistencies originating in the historical database and maps. The study showed the potential of modern quantitative methods in the legacy data harmonisation and also the necessity of a critical approach to historical databases and maps.

Digital soil mapping: the challenge to obtain the best soil dataset and create a precise environmental model to support land use management at a national level (Ecuador).

2020 ◽  
Author(s):  
Daphne Armas ◽  
Mário Guevara ◽  
Fernando Bezares ◽  
Rodrigo Vargas ◽  
Pilar Durante ◽  
...  
Keyword(s):  
Land Use ◽  
Information System ◽  
Soil Properties ◽  
Soil Profile ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Land Use Management ◽  
National Scale ◽  
Global Soil ◽  
Soil Information

<p>One of the biggest challenges for digital soil mapping is the limited of field soil information (e.g., soil profile descriptions, soil sample analysis) for representing soil variability across scales. Global initiatives such as the Global Soil Partnership (GSP) and the development of a <strong>Global Soil Information System</strong> (GloSIS), World Soil Information Service (WoSis) or SoilGrids250m for global pedometric mapping highlight new opportunities but the crescent need of new and better soil datasets across the world. Soil datasets are increasingly required for the development of soil monitoring baselines, soil protection and sustainable land use strategies, and to better understand the response of soils to global environmental change.  However, soil surveys are a very challenging task due to their high acquisition costs such data and operational complexity. The use of legacy soil data can reduce these sampling efforts.</p><p>The main objective of this research was the rescue, synthesis and harmonization of legacy soil profile information collected between 2009 and 2015 for different purposes (e.g., soil or natural resources inventory) across Ecuador. This project will support the creation of a soil information system at the national scale following international standards for archiving and sharing soil information (e.g., GPS or the GlobalSoilMap.net project). This new information could be useful to increase the accuracy of current digital soil information across the country and the future development of digital soil properties maps.</p><p>We provided an integrated framework combining multiple data analytic tools (e.g., python libraries, pandas, openpyxl or pdftools) for the automatic conversion of text in paper format (e.g., pdf, jpg) legacy soil information, as much the qualitative soil description as analytical data,  to usable digital soil mapping inputs (e.g., spatial datasets) across Ecuador. For the conversion, we used text data mining techniques to automatically extract the information. We based on regular expressions using consecutive sequences algorithms of common patterns not only to search for terms, but also relationships between terms. Following this approach, we rescued information of 13.696 profiles in .pdf, .jpg format and compiled a database consisting of 10 soil-related variables.</p><p>The new database includes historical soil information that automatically converted a generic tabular database form (e.g., .csv) information.</p><p>As a result, we substantially improved the representation of soil information in Ecuador that can be used to support current soil information initiatives such as the WoSis, Batjes et al. 2019, with only 94 pedons available for Ecuador, the Latin American Soil Information System (SISLAC, http://54.229.242.119/sislac/es),  and the United Nations goals  towards increasing soil carbon sequestration areas or decreasing land desertification trends.  In our database there are almost 13.696 soil profiles at the national scale, with soil-related (e.g., depth, organic carbon, salinity, texture) with positive implications for digital soil properties mapping. </p><p>With this work we increased opportunities for digital soil mapping across Ecuador. This contribution could be used to generate spatial indicators of land degradation at a national scale (e.g., salinity, erosion).</p><p>This dataset could support new knowledge for more accurate environmental modelling and to support land use management decisions at the national scale.</p><p> </p>

scholarly journals A comparison between three legacy soil maps of Zambia at national scale: The spatial patterns of legend units and their relation to soil properties

Geoderma ◽  
2021 ◽  
Vol 402 ◽  
pp. 115193
Author(s):  
I. Mukumbuta ◽  
L.M. Chabala ◽  
S. Sichinga ◽  
C. Miti ◽  
R.M. Lark
Keyword(s):  
Soil Properties ◽  
Spatial Patterns ◽  
Soil Maps ◽  
National Scale

scholarly journals Optimal scaling of predictors for digital mapping of soil properties

Geoderma ◽  
2022 ◽  
Vol 405 ◽  
pp. 115453
Author(s):  
Andrei Dornik ◽  
Marinela Adriana Cheţan ◽  
Lucian Drăguţ ◽  
Daniel Dorin Dicu ◽  
Andrei Iliuţă
Keyword(s):  
Soil Properties ◽  
Optimal Scaling ◽  
Digital Mapping

scholarly journals Spatial Prediction of the Forest Soil Water Holding Capacities in Temperate Region on a National Scale With Random Forest Models

2021 ◽  
Author(s):  
Hyunje Yang ◽  
Honggeun Lim ◽  
Hyung Tae Choi
Keyword(s):  
Random Forest ◽  
South Korea ◽  
Soil Properties ◽  
Water Content ◽  
Spatial Prediction ◽  
Random Forest Model ◽  
National Scale ◽  
Environmental Covariates ◽  
Forest Model ◽  
Water Holding

Soil water holding capacities (SWHCs) is important input factor in hydrological simulation models for sustainable water management. Forests that covered 63% of South Korea are the main source of clean water, and it is essential to estimate SWHCs on a nationwide scale for effective forest water resources management. However, there are a few studies estimating SWHCs on a nationwide scale in the temperate regions especially in South Korea. Fortunately, forest spatial big data have been collected on a national scale, and the nationwide prediction of the SWHC can be possible with this dataset. In this study, spatial prediction of forest SWHCs (saturated water content, water content at pF1.8 and 2.7) was conducted with 953 forest soil samples and forest spatial big dataset. 4 soil properties and 14 environmental covariates were used for predicting SWHCs. Simple linear regression and random forest model were compared for selecting the optimal predictive model. From the variable importance analysis, environmental covariates had as big importance as soil properties had. And prediction performance of the model with environmental covariates as the input data was higher than that of the model with soil properties. Comparing two models, the random forest model could accurately and stably predict SWHCs than the simple linear model. As a result of spatial prediction of SWHCs at the national scale through the random forest model and the forest spatial big dataset, it was confirmed that higher SWHCs were distributed along with the Baekdudaegan, the watershed-crest-line in South Korea.

Creation of detailed soil properties maps of the Czech Republic based on national legacy data and digital soil mapping

2020 ◽  
Author(s):  
Robert Minařík ◽  
Daniel Žížala ◽  
Anna Juřicová
Keyword(s):  
Czech Republic ◽  
Soil Properties ◽  
Large Scale ◽  
Agricultural Land ◽  
Prediction Models ◽  
External Validation ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
The Czech Republic ◽  
Legacy Data

<p>Legacy soil data arising from traditional soil surveys are an important resource for digital soil mapping. In the Czech Republic, a large-scale (1:10 000) mapping of agricultural land was completed in 1970 after a decade of field investigation mapping. It represents a worldwide unique database of soil samples by its national extent and detail. This study aimed to create a detailed map of soil properties (organic carbon, ph, texture, soil unit) by using state-of-the-art digital soil mapping (DSM) methods. For this purpose we chose four geomorphologically different areas (2440 km<sup>2</sup> in total). A selected ensemble machine learning techniques based on bagging, boosting and stacking with random hyperparameters tuning were used to model each soil property. In addition to soil sample data, a DEM and its derivatives were used as common covariate layers. The models were evaluated using both internal repeated cross-validation and external validation. The best model was used for prediction of soil properties. The accuracy of prediction models is comparable with other studies. The resulting maps were also compared with the available original soil maps of the Czech Republic. The new maps reveal more spatial detail and natural variability of soil properties resulting from the use of DEM. This combination of high detailed legacy data with DSM results in the production of more spatially detailed and accurate maps, which may be particularly beneficial in supporting the decision-making of stakeholders.</p><p>The research has been supported by the project no. QK1820389 " Production of actual detailed maps of soil properties in the Czech Republic based on database of Large-scale Mapping of Agricultural Soils in Czechoslovakia and application of digital soil mapping" funding by Ministry of Agriculture of the Czech Republic.</p>

Robust analysis of soil properties at the national scale: cadmium content of French soils

2010 ◽  
Vol 61 (1) ◽  
pp. 144-152 ◽  
Author(s):  
B. P. Marchant ◽  
N. P. A. Saby ◽  
R. M. Lark ◽  
P. H. Bellamy ◽  
C. C. Jolivet ◽  
...  
Keyword(s):  
Soil Properties ◽  
Cadmium Content ◽  
National Scale ◽  
Robust Analysis

Re-tooling of regression kriging in R for improved digital mapping of soil properties

2014 ◽  
Vol 19 (1) ◽  
pp. 157-165 ◽  
Author(s):  
Christian T. Omuto ◽  
Ronald R. Vargas
Keyword(s):  
Soil Properties ◽  
Regression Kriging ◽  
Digital Mapping
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