Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

AbstractDigital soil mapping for soil texture is mostly an understanding of how soil texture fractions vary in space as influenced by environmental variables mainly derived from the digital elevation model (DEM). In this study, topsoil texture models were generated and evaluated by multiple linear regression (MLR), ordinary kriging (OK), simple kriging (SK) and universal kriging (UK) using free and open-source R, System for Automated Geoscientific Analyses, and QGIS software. Comparing these models is the main objective of the study. The study site covers an area of 124 km2 of the Municipality of Barili, Cebu. A total of 177 soil samples were gathered and analyzed from irregular sample points. DEM derivatives and remote sensing data (Landsat 8) were used as environmental variables. Exploratory analyses revealed no outlier in the data. Skewness and kurtosis values of the untransformed data vary greatly between –3.85 to 7.20 and 1.8 to 70.7, respectively; an indication that variables are highly skewed with heavy tails. Thus, Tukey’s ladder of powers transformation was applied that resulted to normal or nearly normal distribution having skewness values close to zero and kurtosis values have lighter tails. All data analysis from MLR modeling, variography, kriging, and cross-validations of models were implemented using the transformed data. Forward selection, backward elimination, and stepwise selection methods were adapted for predictors selection in MLR. The MLR, OK, SK, and UK were applied and cross validated for topsoil texture prediction. Likewise, exponential, Gaussian, and spherical models were fitted for the experimental variograms. Backward elimination method for clay, sand, and silt have the lowest MAE and highest R2 in MLR. The UK fitted with exponential variogram model has the highest R2 of 0.878, 0.821, and 0.893 for clay, sand, and silt, respectively. These models can be adapted as a decision support for agricultural land use planning and crop suitability development in the area.

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Digital Mapping of Soil Particle Size Distribution in an Alluvial Plain Using the Random Forest Algorithm

10.5194/egusphere-egu21-7828 ◽

2021 ◽

Author(s):

Fuat Kaya ◽

Levent Başayiğit

Keyword(s):

Land Use ◽

Random Forest ◽

Land Use Planning ◽

Soil Texture ◽

Environmental Variables ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Data Sets ◽

Soil Maps ◽

Random Forest Algorithm

Soil maps are an important source of data in monitoring natural resources and land use planning. However, in many countries, soil maps were prepared at a reconnaissance level. This detail is not enough for land use planning. Soil texture is one of the most important soil physical properties that affect water holding capacity, nutrient availability, and crop growth. The spatial distribution of soil texture at a high resolution is essential for crop planning and management. Digital soil mapping is the method of spatial data generation with the advantages of current technologies. It supplies fast, accurate, and reproducible results.In this study, a soil texture map with 30 m spatial resolution was produced for an alluvial plain covering an area of approximately 10,000 ha. In the study, 11 Topographic Environmental Variables obtained from NASA's ASTER Global Digital Elevation model were used. Another input parameters were clay, silt, and sand values determined for 91 soil samples obtained through field studies.R Core Environment (3.6.1) and related packages were used for environmental variable extraction, modeling, and spatial mapping. For model building, 70 % of data was used and the rest of the data was used for validation. Random Forest Algorithm offers interpretability for pedological information extraction by determining the importance of environmental variables in digital soil mapping. Random Forest Algorithm is preferred because of working in small data sets, harmoniously. The most important topographic environmental variables for clay were elevation, aspect, and slope. For sand, it was the elevation, aspect, and topographic wetness index. And for silt, it was the elevation, slope length, and planform curvature. Root Mean Square Error (RMSE), was used as a model performance measure. In the train data, R2 values for clay, sand and silt were 0.84, 0.75, 0.85 and RMSE values were 5.23 %, 3.03 %, 5.48 % respectively. In the test data, R2 and RMSE values were 0.26, 0.11, 0.10 and 11.8 %, 6.74 %, 13.71 % respectively.There are high differences between RMSE values of training and test data sets. This event may be caused by the small sample size and to be discussed subject in different studies. High resolution (30 m) data of clay, silt, and sand contents can be useful for hydrological studies and for the preparation of land use plans. Digital soil maps can guide policymakers in creating site-specific land management plans. As well as it can be used for monitoring soil fertility and providing ecosystem services. This study revealed important results regarding the use of digital soil mapping in practice with its analytical and statistical accuracy.

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

Remote Sensing ◽

10.3390/rs11141683 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1683 ◽

Cited By ~ 6

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.

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Progress in soil geography I: Reinvigoration

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133319889048 ◽

2019 ◽

Vol 43 (6) ◽

pp. 827-854 ◽

Cited By ~ 3

Author(s):

Bradley A Miller ◽

Eric C Brevik ◽

Paulo Pereira ◽

Randall J Schaetzl

Keyword(s):

Land Use Planning ◽

Precision Agriculture ◽

Critical Role ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Science ◽

Accurate Information ◽

Soil Maps ◽

Geospatial Technologies ◽

Soil Geography

The geography of soil is more important today than ever before. Models of environmental systems and myriad direct field applications depend on accurate information about soil properties and their spatial distribution. Many of these applications play a critical role in managing and preparing for issues of food security, water supply, and climate change. The capability to deliver soil maps with the accuracy and resolution needed by land use planning, precision agriculture, as well as hydrologic and meteorologic models is, fortunately, on the horizon due to advances in the geospatial revolution. Digital soil mapping, which utilizes spatial statistics and data provided by modern geospatial technologies, has now become an established area of study for soil scientists. Over 100 articles on digital soil mapping were published in 2018. The first and second generations of soil mapping thrived from collaborations between Earth scientists and geographers. As we enter the dawn of the third generation of soil maps, those collaborations remain essential. To that end, we review the historical connections between soil science and geography, examine the recent disconnect between those disciplines, and draw attention to opportunities for the reinvigoration of the long-standing field of soil geography. Finally, we emphasize the importance of this reinvigoration to geographers.

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A GIS-Based Multicriteria Analysis of Land Suitability for Groundnut Crop in Nghe An Province, Vietnam

International Journal of Geoinformatics ◽

10.52939/ijg.v17i6.2071 ◽

2021 ◽

pp. 85-95

Keyword(s):

Land Use ◽

Land Use Planning ◽

Soil Texture ◽

Soil Type ◽

Agricultural Land ◽

Soil Depth ◽

Land Suitability ◽

Agricultural Land Use ◽

Total Rainfall ◽

Average Temperature

This study focuses on identifying the potential lands for growing groundnut in Dien Chau district of Nghe An province (Vietnam), where groundnut is one of the major crops and brings high income for farmers. Based on the ecological requirements of groundnut, six criteria, including Soil Type, Soil Texture, Soil Depth, Slope, Average Temperature, and Average Total Rainfall in the planting season, were used. The Analytic Hierarchy Process method, commonly used in agricultural land use planning, was utilized to determine each criterion's weights via experts’ opinions. A pairwise comparison matrix was established to support this assessment process. The results revealed that Soil Texture showed the highest weight (0.31727) for groundnut farming, which was followed by Average Temperature (0.21131), Soil Type (0.17426), and Soil Depth (0.13982). Slope and Average Total Rainfall were the lowest weight factors, with 0.08122 and 0.07612, respectively. The weighted sum overlay analysis was implemented by ArcGIS software to generate the spatial distribution of land suitability of groundnut. The land suitability map indicated that 6830.07 ha (22.26%) of the studied area was highly suitable (S1), 10413.85 ha (33.95%) was moderately suitable (S2), 4336.76 ha (14.14%) was marginally suitable (S3), and 424.99 ha (1.39%) was not suitable (N). The total area of constrained area, including Waterbody and Built-up Land, was 8671.39 ha, accounting for 28.27% of the total area. Finally, the proposed land for groundnut cultivation was 12928.69 ha. The outcomes of this study may be regarded as a good reference for local government in agricultural land use planning.

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Decision trees for digital soil mapping on subtropical basaltic steeplands

Scientia Agricola ◽

10.1590/s0103-90162011000200006 ◽

2011 ◽

Vol 68 (2) ◽

pp. 167-174 ◽

Cited By ~ 31

Author(s):

Elvio Giasson ◽

Eliana Casco Sarmento ◽

Eliseu Weber ◽

Carlos Alberto Flores ◽

Heinrich Hasenack

Keyword(s):

Decision Tree ◽

Decision Trees ◽

Land Use Planning ◽

Spatial Information ◽

Soil Mapping ◽

Soil Survey ◽

Digital Soil Mapping ◽

Tree Algorithms ◽

Mapping Techniques ◽

Soil Surveys

When soil surveys are not available for land use planning activities, digital soil mapping techniques can be of assistance. Soil surveyors can process spatial information faster, to assist in the execution of traditional soil survey or predict the occurrence of soil classes across landscapes. Decision tree techniques were evaluated as tools for predicting the ocurrence of soil classes in basaltic steeplands in South Brazil. Several combinations of types of decicion tree algorithms and number of elements on terminal nodes of trees were compared using soil maps with both original and simplified legends. In general, decision tree analysis was useful for predicting occurrence of soil mapping units. Decision trees with fewer elements on terminal nodes yield higher accuracies, and legend simplification (aggregation) reduced the precision of predictions. Algorithm J48 had better performance than BF Tree, RepTree, Random Tree, and Simple Chart.

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Landslide Risk Analysis on Agriculture Area in Pacitan Regency in East Java Indonesia Using Geospatial Techniques

Environment and Natural Resources Journal ◽

10.32526/ennrj/19/2020167 ◽

2021 ◽

Vol 19 (2) ◽

pp. 141-152

Author(s):

Aditya Nugraha Putra ◽

◽

Istika Nita ◽

Muhammad Rifqi Al Jauhary ◽

Shofie Rindi Nurhutami ◽

...

Keyword(s):

Land Use ◽

Land Use Planning ◽

Agricultural Land ◽

Landslide Risk ◽

Landsat 8 ◽

Data Mapping ◽

Land Use Classification ◽

Geospatial Techniques ◽

Landslide Hazards ◽

High Level

Pacitan is one of the regencies in East Java Province, dominated by hills and mountain landforms covering 85% of its area. Since 2011, more than 16 landslides have occurred significantly in this area. These disasters have engulfed more than 350 ha of agricultural land in Pacitan. This study analyzed the risk of future landslides due to land use change. The parameters used were rainfall, slope, topography, geology, soil, and land use which were assessed and weighed by the Paimin method. Land-use classification from Landsat 8 OLI in 1998, 2008, and 2018 were analyzed using regression formula to calculate the trend of change in 2030. Land use was also classified from the land capability classification (LCC) and regional spatial planning (RSP) as land use options in 2030. The results showed that land use changed over time due to the changes in landslide hazards, which increased three-foldfrom 1998 to 2018 and will peak tremendously in 2030. There are 29.47 ha of agricultural land in 2018 that have a high potential landslide hazard if no intervention is made. The accuracy for prediction of the 2018 data mapping was 82%. The LCC strategy suggests land use planning to reduce a high level of the landslides.

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

10.5194/egusphere-egu2020-18480 ◽

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

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 km2 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.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.

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The Generation of Soil Spectral Dynamic Feedback Using Landsat 8 Data for Digital Soil Mapping

Remote Sensing ◽

10.3390/rs12101691 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1691

Author(s):

Canying Zeng ◽

Lin Yang ◽

A-Xing Zhu

Keyword(s):

Spatial Resolution ◽

Temporal Resolution ◽

High Spatial Resolution ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Landsat 8 ◽

Soil Drying ◽

Dynamic Feedback ◽

Drying Process ◽

Fusion Data

The soil spectral dynamic feedback captured from high temporal resolution remote sensing data, such as MODIS, during the soil drying process after a rainfall could assist with digital soil mapping. However, this method is ineffective in utilizing the images with a relatively high spatial resolution. There are an insufficient number of images in the soil drying process since those high spatial resolution images tend to have a low temporal resolution. This study is aimed at generating soil spectral dynamic feedback by integrating the feedback captured from the images with a high spatial resolution during the process of multiple drying after different rainfall events. The Landsat 8 data with a temporal resolution of 16 day was exemplified. Each single spectral feedback obtained from Landsat 8 was first adjusted to eliminate the impact of different rainfall magnitudes. Then, the soil spectral dynamic feedback was reorganized and generated based on the adjusted feedback. Finally, the soil spectral dynamic feedback generated based on Landsat 8 was used for mapping topsoil texture and compared with the mapping results based on the MODIS data and the fusion data of MODIS and Landsat 8. As revealed by the results, not only could the generated soil spectral dynamic feedback based on Landsat 8 data improve the details of the spatial distribution of soil texture, but it also enhances the accuracy of mapping. The mapping accuracy based on Landsat 8 data is higher than that based on the MODIS data and fusion data. The improvements of accuracy are more obvious in the areas with more complex surface conditions. This study widens the scope of application for soil spectral dynamic feedback and provides support for large-scale and high-precision digital soil mapping.

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Evaluating near infrared spectroscopy for field prediction of soil properties

Soil Research ◽

10.1071/sr09005 ◽

2009 ◽

Vol 47 (7) ◽

pp. 664 ◽

Cited By ~ 25

Author(s):

Budiman Minasny ◽

Alex B. McBratney ◽

Leo Pichon ◽

Wei Sun ◽

Michael G. Short

Keyword(s):

Soil Properties ◽

Near Infrared ◽

Agricultural Land ◽

Diffuse Reflectance Spectroscopy ◽

Soil Samples ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Conditions ◽

Spectral Library ◽

Field Samples

This paper demonstrates the application of near infrared diffuse reflectance spectroscopy (NIR-DRS) measurements as part of digital soil mapping. We also investigate whether calibration functions developed from a spectral library can be used for rapid characterisation of soil properties in the field. Soil samples were collected along 24 toposequences in the Pokolbin irrigation district, ~7 km2 of predominantly agricultural land in the Hunter Valley, NSW, Australia. Soil samples at 2 depths: 0–0.10 and 0.40–0.50 m were collected. The soil samples were scanned using NIR under 3 different conditions: field condition, dried unground, and dried ground. A separate spectral library containing soil laboratory measurements was used to develop functions to predict 3 main soil properties from NIR spectra (total C content, clay content, and sum of exchangeable cations). The absorbance spectra were found to be different for the 3 soil conditions. The field spectra appear to have higher absorbance, followed by dried unground samples and then dried ground samples. Although most spectral signatures or peaks were similar for the 3 soil conditions, field samples appear to have higher absorbance, particularly at 1400 nm and 1900 nm. The convex hull of the first 2 principal components of the soil spectra is an easy tool to evaluate the similarity of spectra from a calibration set to an observation. For field prediction, samples need to be calibrated using field samples. Finally, this study shows that NIR-DRS measurement is a useful part of digital soil mapping.

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