Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach

Guo, Ping; Li, Mao-Fen; Luo, Wei; Qunfeng, Tang; Liu, Zhiwei; Lin, Zhongxu

doi:10.1016/j.geoderma.2014.08.009

Cited by 195 publications

(94 citation statements)

References 68 publications

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“…To overcome this disadvantage, a hybrid method that combines RF and OK was developed and has been verified to generate much lower prediction errors and to yield a more realistic spatial distribution than the RF model [29]. RFRK is an extension of RF and is very similar to RK.…”

Section: Random Forests Residuals Kriging (Rfrk)mentioning

confidence: 99%

“…The motivation for utilizing geostatistical analysis is that geostatistical methods can exploit the presence of spatial autocorrelation and joint dependence in space and time, which occur in most natural resource variables, and can improve ecological interpretation and help to assess error spatially [21,28]. Moreover, several new hybrid prediction methods that combine regression methods with geostatistical interpolation, such as the random forests residuals kriging (RFRK) method [29], have also been proposed to account for the spatial structure of observed data and the environmental correlation.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Wang

Tang

et al. 2016

Remote Sensing

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Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m 2 /m 2 and 0.23 m 2 /m 2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m 2 /m 2 ), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m 2 /m 2 and 0.35 m 2 /m 2 for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m 2 /m 2 to 0.28 m 2 /m 2 for PLSR and from 0.33 m 2 /m 2 to 0.28 m 2 /m 2 for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m 2 /m 2 to 0.32 m 2 /m 2 ).

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Section: Random Forests Residuals Kriging (Rfrk)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Wang

Tang

et al. 2016

Remote Sensing

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“…A textura por afetar fortemente a retenção de água e os nutrientes, a infiltração de água, drenagem e aeração, o teor de carbono orgânico, a capacidade de troca de cátions e a porosidade, além de controlar muitas funções e propriedades mecânicas do solo. O carbono orgânico, que tem um papel importante no ecossistema terrestre, está intimamente associado à fertilidade do solo, por meio de seu controle sobre as propriedades físico-químicas e, além da relação direta e com as mudanças climáticas globais (Akpa et al, 2014;Guo et al, 2015).…”

Section: Introductionunclassified

“…Vários estudos tem mostrado a importância dos atributos morfométricos para a predição das frações granulométricas do solo (Ließ et al, 2012;Akpa et al, 2014) e do teor de carbono orgânico (Grimm et al, 2008;Guo et al, 2015;Bonfatti et al, 2016). O modelo RF tem mostrado algumas vantagens em relação à maioria dos métodos estatísticos de modelagem, conforme destacado por Breiman (2001) e Liaw & Wiener (2002), que são: a habilidade para a modelagem de relações dimensionais altamente não lineares; a utilização de variáveis categóricas e contínuas; a resistência ao overfitting; a relativa robustez ante a presença de "ruídos" nos dados; o fornecimento de uma medida imparcial da taxa de erro; a determinação da importância das variáveis utilizadas; e a exigência de poucos parâmetros para ser implementado.…”

Section: Introductionunclassified

“…A utilização de modelos RF, com diferentes combinações de covariáveis preditoras, obteve sucesso na predição espacial do carbono orgânico do solo (Grimm et al, 2008;Viscarra Rossel & Behrens, 2010;Wiesmeier et al, 2011;Guo et al, 2015;Vaysse & Lagacherie, 2015;Bonfatti et al, 2016), e na predição da composição granulométrica (Viscarra Rossel & Behrens, 2010;Ließ et al, 2012;Akpa et al, 2014;Behrens et al, 2014;Vaysse & Lagacherie, 2015).…”

Section: Introductionunclassified

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Mapeamento digital de areia, argila e carbono orgânico por modelos Random Forest sob diferentes resoluções espaciais

Bhering

Chagas

Carvalho

et al. 2016

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar a influência da resolução espacial do modelo digital de elevação e da eficiência de modelos Random Forest sobre a predição dos teores de areia, argila e carbono orgânico, com uso de número reduzido de amostras. O trabalho foi realizado em área de Cerrado com diversidade litológica, no Estado do Mato Grosso do Sul, tendo-se utilizado atributos morfométricos, dados do sensor TM Landsat 5 e litologia como covariáveis preditoras. Dados da camada superficial (0,0-0,2 m) de 175 perfis de solos (0,009 perfis km -2 ) e de 26 covariáveis preditoras foram utilizados com resolução espacial de 30 (conjunto 1) e 90 m (conjunto 2). A análise realizada pelo Random Forest mostrou que as covariáveis de nível de base do canal de drenagem, da elevação e da litologia foram as mais importantes para explicar a variabilidade. A validação dos modelos apresentou similaridade entre os conjuntos quanto à predição de areia, argila e carbono orgânico, o que explica os seguintes valores de variabilidade espacial, respectivamente: 44, 40 e 33%, para a resolução de 30 m; e de 45, 46 e 33%, para a resolução de 90 m. A resolução espacial das covariáveis preditoras tem pouca influência sobre a predição dos atributos, e a abordagem por Random Forest apresenta potencial de utilização para estimar atributos do solo.Termos para indexação: modelo digital de elevação, morfometria, pedometria, SRTM. Digital mapping of sand, clay, and soil carbon by Random Forest models under different spatial resolutionsAbstract -The objective of this work was to evaluate the effect of the digital elevation model spatial resolution and of the efficiency of Random Forest models on the prediction of sand, clay, and organic carbon contents, using few soil samples. The study was carried out in a Cerrado area with lithological diversity, in the state of Mato Grosso do Sul, Brazil, using morphometric attributes, TM Landsat 5 sensor data, and lithology as predictive covariates. The surface layer data (0.0-0.2 m) of 175 soil profiles (0,009 profiles km -2 ) and of 26 predictor covariates were used with 30 (set 1) and 90-m (set 2) spatial resolutions. The performed analysis by Random Forest models showed that channel base level, elevation, and lithology were the most important ones to explain the variability. The validation of the models showed similarity among sets for the prediction of sand, clay, and organic carbon contents, which explains the following values of spatial variability, respectively: 44, 40, and 33%, for the spatial resolution of 30 m; and 45, 46, and 33%, for the spatial resolution of 90 m. The spatial resolution of the predictive covariates has little effect on attribute predictions, and the Random Forest approach has potential use for estimating soil properties.Index terms: digital elevation model, morphometrics, pedometrics, SRTM. IntroduçãoO conhecimento da distribuição espacial das propriedades físicas e químicas é muito importante para a modelagem ambiental e o manejo adequado dos solos. Por essa razão, existe...

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Computational Intelligence in Agriculture

Gupta¹,

Chopade²,

Dutta³

2022

Emerging Computing Paradigms

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Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach

Cited by 195 publications

References 68 publications

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Mapeamento digital de areia, argila e carbono orgânico por modelos Random Forest sob diferentes resoluções espaciais

Computational Intelligence in Agriculture

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