Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions
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- 作者：Cé ; sar da Silva Chagas cesar.chagas@embrapa.br" class="auth_mail" title="E-mail the corresponding author ; Waldir de Carvalho Junior ; waldir.carvalho@embrapa.br" class="auth_mail" title="E-mail the corresponding author ; Silvio Barge Bhering silvio.bhering@embrapa.br" class="auth_mail" title="E-mail the corresponding author ; Braz Calderano Filho braz.calderano@embrapa.br" class="auth_mail" title="E-mail the corresponding author
- 关键词：Pedometrics ; Landsat dataset ; Digital soil mapping ; Granulometric composition ; Grain size
- 刊名：Catena
- 出版年：2016
- 出版时间：April 2016
- 年：2016
- 卷：139
- 期：Complete
- 页码：232-240
- 全文大小：1530 K
文摘
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DSM techniques were evaluated in a semiarid environment using remote sensed data.
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Random forest and linear regression can be used to map soil texture.
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The most significant covariates were b3/b7 for sand and clay and GSI for silt.
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RFM had higher R2 and lower RMSE than MLR for sand and clay.
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For silt, the MLR was better than RFM with higher R2 and lower RMSE.