Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data

Campbell, Patrícia Morais da Matta; Filho, Elpı́dio Inácio Fernandes; Francelino, Márcio Rocha; Demattê, José Alexandre Melo; Pereira, Marcos Gervásio; and, Clécia Cristina Barbosa Guimarães; Pinto, Luiz Alberto da Silva Rodrigues

doi:10.1590/18069657rbcs20170413

Cited by 8 publications

(6 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although parental material is critical in the soil texture spatial distribution, the covariates selection identified that the et al, 2014) or based on the spectral response in the visible and near-infrared spectrum (Vis-NIR), medium infrared (MIR), and Vis-NIR-MIR (Campbell et al, 2019). In the PSF predictions (in specific, the ALR components), the importance of the climatic covariates did not have apparent changes with depth.…”

Section: Discussionmentioning

confidence: 99%

Colombian soil texture: building a spatial ensemble model

Varón-Ramírez

Carrillo²,

Guevara³

2022

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. Texture is a fundamental soil property for multiple applications in environmental and earth sciences. Knowing its spatial distribution allows a better understanding of the response of soil conditions to changes in the environment, such as land use. This paper describes the technical development of Colombia's first texture maps, obtained via a spatial ensemble of national and global digital soil mapping products. This work compiles a new database with 4203 soil profiles, which were harmonized at five standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm) and standardized with additive log ratio (ALR) transformation. A compilation of 83 covariates was developed and harmonized at 1 km2 of spatial resolution. Ensemble machine learning (EML) algorithms (MACHISPLIN and landmap) were trained to predict the distribution of soil particle size fractions (PSFs) (clay, sand, and silt), and a comparison with SoilGrids (SG) products was performed. Finally, a spatial ensemble function was created to identify the smallest prediction errors between EML and SG. Our results are the first effort to build a national texture map (clay, sand, and silt fractions) based on digital soil mapping in Colombia. The results of EML algorithms showed that their accuracies were very similar at each standard depth, and were more accurate than SG. The largest improvement with the spatial ensemble was found at the first layer (0–5 cm). EML predictions were frequently selected for each PSF and depth in the total area; however, SG predictions were better when increasing soil depth in some specific regions. The final error distribution in the study area showed that sand presented higher absolute error values than clay and silt fractions, specifically in eastern Colombia. The spatial distribution of soil texture in Colombia is a potential tool to provide information for water-related applications, ecosystem services, and agricultural and crop modeling. However, future efforts need to improve aspects such as treating abrupt changes in the texture between depths and unbalanced data. Our results and the compiled database (https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db, Varón-Ramírez and Araujo-Carrillo, 2022, https://doi.org/10.6073/pasta/d6c0bf5847aa40836b42dcc3e0ea874e, Varón-Ramírez et al., 2022) provide new insights to solve some of the aforementioned issues.

show abstract

Section: Discussionmentioning

confidence: 99%

Colombian soil texture: building a spatial ensemble model

Varón-Ramírez

Carrillo²,

Guevara³

2022

Earth Syst. Sci. Data

View full text Add to dashboard Cite

show abstract

“…Furthermore, the relationship between the covariates and BAC of PTEs in soil-rice systems is highly complex and difficult to model using linear relations alone, which further lower the performance of MLR. SVMs have also previously been employed to predict soil SOC stock [84], phosphorus [85], and clay [85]. They were reported to be more suitable for classification than quantitative prediction [86].…”

Section: Model Performance Employing the Different Methods And Elementsmentioning

confidence: 99%

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

Xie

Zhu³

et al. 2021

Land

View full text Add to dashboard Cite

Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R2) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg−1. Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.

show abstract

“…Juiz de Fora (21º41'20" S and 43º20'40" W) is a medium-sized municipality with 573.285 inhabitants (IBGE, 2020) located in the southeast of the state of Minas Gerais, Brazil, in the geographical mesoregion of Zona da Mata (Figure 1). It is situated in a landform called Mar de Morros, which is characterized by a large number of hills and valleys at altitudes ranging from 600 to 1000 msm, where are found clayey soils, mostly of the Latosol type, with high levels of organic carbon (NUNES et al, 2001;CAMPBELL et al, 2019). The climate has a welldefined seasonality, classified as subtropical in altitude (Cwa, sensu Köeppen) defined by two distinct seasons, with lower temperatures and precipitation between the months of May to September, and warmer and with greater precipitation between october and April.…”

Section: Study Areamentioning

confidence: 99%

An estimation of ecosystem services provided by urban and peri-urban forests: a case study in Juiz de Fora, Brazil

2023

View full text Add to dashboard Cite

Urban expansion has led to the replacement of natural landscapes and environmental degradation, making cities and their urban and peri-urban forests (UPFs) vulnerable to climate change, especially on the formation of heat islands. Using i-Tree Canopy program (v. 7.0), we estimate the ecosystem services provided by UPFs in Juiz de Fora (Minas Gerais State, Southeastern Brazil), through the analysis of the (1) annual removal of atmospheric pollutants, (2) annual removal of atmospheric carbon, (3) total carbon stock in vegetation, and (4) the monetary benefits of sequestered and stocked carbon, based on Future Carbon Credit (CFI2Z1) as a monetary proxy. The results showed an average total amount of removal of 4.45 thousand tons of air pollution annually. The average annual total carbon storage was 158 thousand tons and the equivalent CO2 was 580 thousand tons, with an estimated total value of R$ 173 million per year. Significant values of the gross carbon stock (3.98 million tons) and equivalent CO2 (14.59 million tons) were found, being valued at R$ 4.35 billion. We concluded that the Juiz de Fora UPFs have a great potential for socio-environmental and economic benefits.

show abstract

Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data

Cited by 8 publications

References 80 publications

Colombian soil texture: building a spatial ensemble model

Colombian soil texture: building a spatial ensemble model

Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China

An estimation of ecosystem services provided by urban and peri-urban forests: a case study in Juiz de Fora, Brazil

Contact Info

Product

Resources

About