scholarly journals Downscaling digital soil maps using electromagnetic induction and aerial imagery

Geoderma ◽  
2021 ◽  
Vol 385 ◽  
pp. 114852
Author(s):  
Anders Bjørn Møller ◽  
Triven Koganti ◽  
Amélie Beucher ◽  
Bo V. Iversen ◽  
Mogens Humlekrog Greve
Keyword(s):  
Electromagnetic Induction ◽  
Aerial Imagery ◽  
Soil Maps ◽  
Digital Soil Maps

scholarly journals Downscaling digital soil maps using electromagnetic induction and aerial imagery

2020 ◽  
Author(s):  
Anders Møller ◽  
Triven Koganti ◽  
Amélie Beucher ◽  
Bo Iversen ◽  
Mogens Greve
Keyword(s):  
Electromagnetic Induction ◽  
Aerial Imagery ◽  
Soil Maps ◽  
Digital Soil Maps

Digital soil maps can perform as well as large-scale conventional soil maps for the prediction of catchment baseflows

Geoderma ◽  
2021 ◽  
Vol 400 ◽  
pp. 115230
Author(s):  
Zisis Gagkas ◽  
Allan Lilly ◽  
Nikki J. Baggaley
Keyword(s):  
Large Scale ◽  
Soil Maps ◽  
Digital Soil Maps

scholarly journals Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools

2015 ◽  
Vol 64 (1) ◽  
pp. 49-64 ◽  
Author(s):  
László Pásztor ◽  
Annamária Laborczi ◽  
Katalin Takács ◽  
Gábor Szatmári ◽  
Endre Dobos ◽  
...  
Keyword(s):  
Data Mining ◽  
Soil Maps ◽  
Mining Tools ◽  
Digital Soil Maps

Validation of digital soil maps at different spatial supports

Geoderma ◽  
2015 ◽  
Vol 241-242 ◽  
pp. 238-249 ◽  
Author(s):  
T.F.A. Bishop ◽  
A. Horta ◽  
S.B. Karunaratne
Keyword(s):  
Soil Maps ◽  
Digital Soil Maps

Sensitivity of digital soil maps based on FCM to the fuzzy exponent and the number of clusters

Geoderma ◽  
2012 ◽  
Vol 171-172 ◽  
pp. 24-34 ◽  
Author(s):  
Xiao-Lin Sun ◽  
Yu-Guo Zhao ◽  
Hui-Li Wang ◽  
Lin Yang ◽  
Cheng-Zhi Qin ◽  
...  
Keyword(s):  
Soil Maps ◽  
Number Of Clusters ◽  
Digital Soil Maps

Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

2020 ◽  
Author(s):  
Nada Mzid ◽  
Stefano Pignatti ◽  
Irina Veretelnikova ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Central Italy ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Field Scale ◽  
Umbria Region ◽  
Digital Soil Maps

<p>The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).</p>

scholarly journals Mobile Proximal Sensing with Visible and Near Infrared Spectroscopy for Digital Soil Mapping

Soil Systems ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 40
Author(s):  
Masakazu Kodaira ◽  
Sakae Shibusawa
Keyword(s):  
Soil Properties ◽  
Regression Models ◽  
Near Infrared ◽  
Soil Property ◽  
Coefficient Of Determination ◽  
Clay Loam ◽  
Soil Maps ◽  
Local Regression ◽  
Predicted Values ◽  
Digital Soil Maps

The objective of this study was to estimate multiple soil property local regression models, confirm the accuracy of the predicted values using visible near-infrared subsurface diffuse reflectance spectra collected by a mobile proximal soil sensor, and show that digital soil maps predicted by multiple soil property local regression models are able to visualize empirical knowledge of the grower. The parent materials in the experimental fields were light clay, clay loam, and sandy clay loam. The study was conducted in Saitama Prefecture, Japan. To develop local regression models for the 30 chemical and 4 physical properties, a total of 231 samples were collected; to evaluate accuracy of prediction, 65 samples were collected. The local regression models were developed using 2nd derivative pretreatment by the Savitzky–Golay algorithm and partial least squares regression. The local regression models were evaluated using the coefficient of determination (R2), residual prediction deviation (RPD), range error ratio (RER), and the ratio of prediction error to interquartile range (RPIQ). The R2 accuracy of the 34 local regression models was 0.81 or higher. In the predicted values for 65 unknown samples, the local regression models could ‘distinguish between high and low’ for 3 of the 34 soil properties, but were ‘not useful’ as absolute quantitative values for the other 31 soil properties. However, it was confirmed that the predicted values followed the transition in measured values, and thus that the developed 34 regression models could be used for generating digital soil maps based on relative quantitative values. The grower changed the ridge direction in the field from east–west to north–south just looking at the digital soil maps.

Digital Soil Maps for Iowa

1992 ◽  
Author(s):  
Gerald A. Miller ◽  
Robert C. Mortensen
Keyword(s):  
Soil Maps ◽  
Digital Soil Maps

Digital soil mapping of soil fertility index for agricultural lands in tropical environment

2021 ◽  
Author(s):  
Ozias Hounkpatin ◽  
Aymar Bossa ◽  
Mouinou Igué ◽  
Yacouba Yira ◽  
Brice Sinsin
Keyword(s):  
Random Forest ◽  
Soil Fertility ◽  
Soil Properties ◽  
Management Practices ◽  
Fertility Level ◽  
Soil Maps ◽  
Fertility Index ◽  
Bioclimatic Variables ◽  
Soil Fertility Index ◽  
Digital Soil Maps

<p>Indicators of soil production function such as soil fertility index can potentially be a key decision tool in spatial planning for sustainable land management. The establishment of such soil fertility index requires basic soil properties which can be modelled for spatial mapping. The objective of this study was to take advantage of the soil legacy data of Benin to produce a digital soil map of soil fertility index at a national scale based on 8 soil properties (soil organic carbon matter, nitrogen, pH, exchangeable potassium, assimilable phosphorus, sum of base, cation exchange capacity and base saturation). Specific research aims were: (1) to model and develop digital soil maps; (2) to identify important factors influencing soil nutrients; (3) to establish soil fertility potentials using digital soil maps. For each soil property, modelling procedures involved the use of different covariates including soil type, topographic, bioclimatic and spectral data along with the comparative assessment of the Cubist and Quantile Random Forest model. Results revealed that apart from N and exchangeable K, significant models can be produced for most of the soil properties with R-square varying between 28% and 72% with the Quantile Random Forest presenting a more accurate prediction interval coverage probability. The analysis revealed that the distance to the nearest stream has strong predictive ability for all the soil properties along with the bioclimatic variables. Visualisation of the soil fertility map showed that most of the soils in Benin have low fertility level suggesting that the use of fertilizers and organic materials will be critical in sustaining crop productivity. A limited number of high and average fertility level soils were found in the low elevation areas of southern Benin and policy could advocate for their sole use for agriculture purpose as well as promote sustainable management practices.</p>

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