Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data

Chagas, C. da S.; Júnior, Waldir de Carvalho; Pinheiro, Helena Saraiva Koenow; Xavier, Pedro Armentano Mudado; Bhering, S. B.; Pereira, N. R.; Filho, B. Calderano

doi:10.1590/18069657rbcs20170183

Cited by 11 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[20] reported lower correlation (<−0.38) between DEM and soil attributes including CEC, and moderate to strong correlation (<0.60) between satellite bands of synthetic soil image and soil attributes. The legacy soil map showed better correlation (0.5) with CEC compared to Landsat 5 TM derived attributes (<0.44) in the flat landscape of a semiarid region of Brazil [14]. Similar to [17], we found that based on the whole dataset, SOM has a strong correlation with CEC (0.73).…”

Section: Spatial Trend Modelingmentioning

confidence: 53%

“…Linear and multiple linear regressions (MLR) models have been widely used for spatial prediction of soil organic carbon due to their simplicity in application and ease of interpretation [7,10]. For example, [13] applied regression kriging (MLR and kinging of residuals) to predict and map field-scale variability of soil organic carbon using terrain attributes as covariates while [14] utilized legacy soil map and Landsat 5 TM variables for predicting CEC in a flat landscape based on random forest and geostatistical (i.e., Cokriging) models. Other studies used generalized linear models [15], and machine learning algorithms such as artificial neural networks [15][16][17][18], random forest [11,19,20], and Cubist [8,17,19,20] for predicting SOM and CEC.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Rahmani

Ackerson²,

Schulze

et al. 2022

Agronomy

View full text Add to dashboard Cite

Soil organic matter content (SOM) and cation exchange capacity (CEC) are important agronomic soil properties. Accurate, high-resolution spatial information of SOM and CEC are needed for precision farm management. The objectives of this study were to: (1) map SOM and CEC in a low relief area using only lidar elevation-based terrain attributes, and (2) compare the prediction accuracy of SOM and CEC maps created by universal kriging, Cubist, and random forest with Soil Survey Geographic (SSURGO) database. For this study, 174 soil samples were collected from a depth from 0 to 10 cm. The topographic wetness index, topographic position index, multi resolution valley bottom flatness, and multi resolution ridge top flatness indices generated from the lidar data were used as covariates in model predictions. No major differences were found in the prediction performance of all selected models. For SOM, the predictive models provided results with coefficient of determination (R2) (0.44–0.45), root mean square error (RMSE) (0.8–0.83%), bias (0–0.22%), and concordance correlation coefficient (ρc) (0.56–0.58). For CEC, the R2 ranged from 0.39 to 0.44, RMSE ranged from 3.62 to 3.74 cmolc kg−1, bias ranged from 0–0.17 cmolc kg−1, and ρc ranged from 0.55 to 0.57. We also compared the results to the USDA Soil Survey Geographic (SSURGO) data. For both SOM and CEC, SSURGO was comparable with our predictive models, except for few map units where both SOM and CEC were either under or over predicted.

show abstract

Section: Spatial Trend Modelingmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Rahmani

Ackerson²,

Schulze

et al. 2022

Agronomy

View full text Add to dashboard Cite

show abstract

“…In general, combining the micro-dam potential map by the AHP+RF method generates better spatial results, reducing the effect of underestimating areas for micro-dam allocation in restricted areas when applying the methods in isolation. Notably, the advantage of ML is the inclusion of covariates in prediction studies (KUHN E JOHNSON, 2013;CHAGAS et al, 2018;SOUZA et al, 2018;GOMES et al, 2019). The insertion of covariates opens up vast possibilities for future studies on areas for micro-dam allocation, especially by inserting covariates of hydrological and climatic factors, which are important factors in micro-dam allocation (SCHIETTECATTE et al, 2005;HIPÓLITO et al, 2019;MESHRAM et al, 2020).…”

Section: Prediction Of Potential Areas For Dams (Ahp and Rf)mentioning

confidence: 99%

Multicriteria Analysis and Machine Learning Algorithm for Definition of Areas for Micro-Dam, Southeastern Brazil

Souza

Veloso

Faria

et al. 2021

RCG

View full text Add to dashboard Cite

As barraginhas são eficientes no controle da erosão e aumentam a taxa de infiltração de água no solo. Este estudo visa prever áreas potenciais para alocação de barraginhas. Preparamos o mapa de áreas potenciais para alocação de barraginhas a partir da análise multicritério (AHP) usando dados topográficos e uso do solo. Além disso, desenvolvemos uma estrutura metodológica com algoritmo Random Forest (RF) para prever áreas potenciais de barraginhas a partir de pontos extraídos do mapa gerado por AHP e auxiliados pela inserção de covariáveis; e também aplicamos a sobreposição dos dois mapas (AHP + RF). O método AHP superestima as áreas de alto potencial em zonas sem potencialidade. O modelo de RF usou sete covariáveis topográficas, e elas estão relacionadas aos fluxos hidrológicos. O desempenho de R2 no RF foi de 0,43, estaticamente satisfatório, mas espacialmente subestimou as classes de potencial muito baixo e muito alto. A superposição da classe de baixo potencial do método AHP no mapa de RF favoreceu uma redução de 99% das áreas de muito alto potencial em zonas não adequadas para alocação de micro-barragens. Portanto, a combinação do método AHP e ML gera resultados espaciais mais consistentes.

show abstract

“…The model also provides statistical data that shows accuracy (coeffi cient of determination R 2 ) and error (root mean squared error RMSE, and mean absolute error MAE), usual procedures in ML (KUHN & JOHNSON, 2013;CHAGAS et al, 2018;SOUZA et al, 2018;GOMES et al, 2019). The values of R 2 RMSE and MAE are obtained with equations 6, 7 and 8, respectively.…”

Section: Spatial Prediction Sl and K Sn Indicesmentioning

confidence: 99%

Machine Learning Algorithm in the Prediction of Geomorphic Indices for Appraisal the Influence of Landscape Structure on Fluvial Systems, Southeastern - Brazil

Souza

Figueredo

Costa

et al. 2020

Rev. Bras. Geomorfol.

View full text Add to dashboard Cite

The Abaeté hydrographic basin was influenced by sediments (Proterozoic and Cretaceous) and by volcanism (Upper Cretaceous). The objective was to identify whether the drainage configuration is due to structural factors or by neotectonics. We use geomorphic indices (Basin Asymmetry Factor, Stream Length-gradient Index - SL, and Channel Steepness Index - ksn). We elaborate longitudinal profiles in the rivers of sub-basins. We apply the Random Forest (RF) Machine Learning algorithm in the prediction of the SL and ksn indices, with the selection of relevant covariates. The RF was efficient in predicting, with better performance in the ksn (R2 0.38), and indicating the areas of influence of the indices. The highest values of the indices are in zones of lithological contact with different resistances, favoring a sharp change in channel slope (knickpoints). In these zones, there is also a predominance of sub-basins tilted, and the longitudinal profiles of the rivers show uplift or subsidence. Therefore, structural factors conditioned the drainage of the basin.

show abstract

Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data

Abstract: Mapping soil cation exchange capacity in a semiarid region through predictive models and covariates from remote sensing data. Rev Bras Cienc Solo. 2018;42:e0170183.

Cited by 11 publications

References 23 publications

Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Multicriteria Analysis and Machine Learning Algorithm for Definition of Areas for Micro-Dam, Southeastern Brazil

Machine Learning Algorithm in the Prediction of Geomorphic Indices for Appraisal the Influence of Landscape Structure on Fluvial Systems, Southeastern - Brazil

Contact Info

Product

Resources

About