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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
26/07/2023 |
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Data da última atualização: |
26/07/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BASTOS, B. P.; PINHEIRO, H. S. K.; FERREIRA, F. J. F.; CARVALHO JUNIOR, W. de; ANJOS, L. H. C. dos. |
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Afiliação: |
BLENDA PEREIRA BASTOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; HELENA SARAIVA KOENOW PINHEIRO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; FRANCISCO JOSÉ FONSECA FERREIRA, UNIVERSIDADE FEDERAL DO PARANÁ; WALDIR DE CARVALHO JUNIOR, CNPS; LÚCIA HELENA CUNHA DOS ANJOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO. |
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Título: |
Could airborne geophysical data be used to improve predictive modeling of agronomic soil properties in tropical hillslope area? |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Remote Sensing, v. 15, n. 15, 3719, 2023. |
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DOI: |
https://doi.org/10.3390/rs15153719 |
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Idioma: |
Inglês |
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Conteúdo: |
Airborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0-30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model's performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R2 values ranging from 0.15 to 0.23), as well as the SVM model (R2 values ranging from 0.08 to 0.23) when compared to RF (R2 values ranging from 0.10 to 0.20) and SVM (R2 values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping. MenosAirborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0-30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model's performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient ... Mostrar Tudo |
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Palavras-Chave: |
Digital soil mapping; Gamma-ray spectrometry data; Hillslope areas; Machine learning; Magnetic data; Mapeamento digital do solo; Parent material. |
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Thesagro: |
Sensoriamento Remoto. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155276/1/Could-airborne-geophysical-data-be-used-to-improve-predictive-modeling-2023.pdf
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Marc: |
LEADER 03095naa a2200277 a 4500
001 2155276
005 2023-07-26
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs15153719$2DOI
100 1 $aBASTOS, B. P.
245 $aCould airborne geophysical data be used to improve predictive modeling of agronomic soil properties in tropical hillslope area?$h[electronic resource]
260 $c2023
520 $aAirborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0-30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model's performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R2 values ranging from 0.15 to 0.23), as well as the SVM model (R2 values ranging from 0.08 to 0.23) when compared to RF (R2 values ranging from 0.10 to 0.20) and SVM (R2 values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping.
650 $aSensoriamento Remoto
653 $aDigital soil mapping
653 $aGamma-ray spectrometry data
653 $aHillslope areas
653 $aMachine learning
653 $aMagnetic data
653 $aMapeamento digital do solo
653 $aParent material
700 1 $aPINHEIRO, H. S. K.
700 1 $aFERREIRA, F. J. F.
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aANJOS, L. H. C. dos
773 $tRemote Sensing$gv. 15, n. 15, 3719, 2023.
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Registro original: |
Embrapa Solos (CNPS) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agroenergia. Para informações adicionais entre em contato com cnpae.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agroenergia. |
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Data corrente: |
13/11/2013 |
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Data da última atualização: |
21/09/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
LIRA, G. M.; PASCOAL, J. C. M.; TORRES, E. A. F. S.; SOARES, R. A. M.; MENDONCA, S.; SAMPAIO, G. R.; CORREIA, M. S.; CABRAL, C. C. V. Q.; JÚNIOR, C. R. C.; LÓPES, A. M. Q. |
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Afiliação: |
Giselda M. Lira, Universidade Federal de Alagoas; Jadna C. M. Pascoal, Universidade Federal de Alagoas; Elizabeth A. F. S. Torres, Universidade de São Paulo; Rosana A. M. Soares, Universidade de São Paulo; SIMONE MENDONCA, CNPAE; Geni R. Sampaio, Universidade de São Paulo; Meiryellen S. Correia, Universidade Federal de Alagoas; Caterine C. V. Q. Cabral, Universidade Federal de Alagoas; Cyro R. Cabral Júnior, Universidade Federal de Alagoas; Ana M. Q. López, Universidade Federal de Alagoas. |
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Título: |
Influence of seasonality on the chemical composition of oysters (Crassostrea rhizophorae). |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Food Chemistry, v. 138, n. 2-3, p. 786-790, 2013. |
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DOI: |
10.1016/j.foodchem.2012.11.088 |
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Idioma: |
Inglês |
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Conteúdo: |
This paper aimed to evaluate the influence of seasonality on the chemical composition of oysters (Crassostrea rhizophorae). Samples were collected during summer and winter from the estuary and lagoon complex of the municipality of Barra de São Miguel, Alagoas, Brazil. Statistical differences (p< 0.05) between summer and winter were observed in relation to chemical composition. The oysters cultivated in the winter presented some nutritional advantages because of the higher levels of proteins and functional nutrients, such as the eicosapentaenoic?docosahexaenoic acid combination and percentages of polyunsaturated fatty acids (n3 and n6), and the lower levels of saturated fatty acids. Therefore, the animals in winter presented a higher content of cholesterol oxides. The levels of cholesterol oxides found in these products during winter may encourage researchers to investigate the composition of oysters cultivated in different climates all over the world. |
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Palavras-Chave: |
Nutritional quality index; Seasonality. |
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Thesaurus NAL: |
chemical composition; oysters. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01832naa a2200289 a 4500
001 1971065
005 2017-09-21
008 2013 bl uuuu u00u1 u #d
024 7 $a10.1016/j.foodchem.2012.11.088$2DOI
100 1 $aLIRA, G. M.
245 $aInfluence of seasonality on the chemical composition of oysters (Crassostrea rhizophorae).$h[electronic resource]
260 $c2013
520 $aThis paper aimed to evaluate the influence of seasonality on the chemical composition of oysters (Crassostrea rhizophorae). Samples were collected during summer and winter from the estuary and lagoon complex of the municipality of Barra de São Miguel, Alagoas, Brazil. Statistical differences (p< 0.05) between summer and winter were observed in relation to chemical composition. The oysters cultivated in the winter presented some nutritional advantages because of the higher levels of proteins and functional nutrients, such as the eicosapentaenoic?docosahexaenoic acid combination and percentages of polyunsaturated fatty acids (n3 and n6), and the lower levels of saturated fatty acids. Therefore, the animals in winter presented a higher content of cholesterol oxides. The levels of cholesterol oxides found in these products during winter may encourage researchers to investigate the composition of oysters cultivated in different climates all over the world.
650 $achemical composition
650 $aoysters
653 $aNutritional quality index
653 $aSeasonality
700 1 $aPASCOAL, J. C. M.
700 1 $aTORRES, E. A. F. S.
700 1 $aSOARES, R. A. M.
700 1 $aMENDONCA, S.
700 1 $aSAMPAIO, G. R.
700 1 $aCORREIA, M. S.
700 1 $aCABRAL, C. C. V. Q.
700 1 $aJÚNIOR, C. R. C.
700 1 $aLÓPES, A. M. Q.
773 $tFood Chemistry$gv. 138, n. 2-3, p. 786-790, 2013.
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Embrapa Agroenergia (CNPAE) |
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