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Registro Completo |
Biblioteca(s): |
Embrapa Meio Ambiente. |
Data corrente: |
15/10/2020 |
Data da última atualização: |
25/08/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
PADILHA, M. C. de C.; VICENTE, L. E.; DEMATTÊ, J. A. M.; LOEBMANN, D. G. dos S. W.; VICENTE, A. K.; URBINA SALAZAR, D. F.; GUIMARÃES, C. C. B. |
Afiliação: |
MANUELA CORRÊA DE CASTRO PADILHA, ESALQ-USP; LUIZ EDUARDO VICENTE, CNPMA; JOSÉ ALEXANDRE MELO DEMATTÊ, ESALQ-USP; DANIEL GOMES DOS SANTOS W LOEBMANN, CNPMA; ANDREA KOGA VICENTE; DIEGO FERNANDO URBINA SALAZAR, ESALQ-USP; CLÉCIA CRISTINA BARBOSA GUIMARÃES, ESALQ-USP. |
Título: |
Using Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Geoderma Regional, v. 21, e00253, 2020. |
ISSN: |
2352-0094 |
DOI: |
https://doi.org/10.1016/j.geodrs.2020.e00253 |
Idioma: |
Inglês |
Conteúdo: |
Abstract: Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, São Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model was applied to the C spatial distribution using the predictive variables as layers, predominant concentrations of 0.65 to 0.79 g. Kg-1 in 51 (23.4%) soil samples. The analysis presented here offer possibilities for SOC prediction using Geographic Information Systems (GIS) tools. MenosAbstract: Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, São Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model... Mostrar Tudo |
Palavras-Chave: |
Digital soil mapping; Multiple linear regression. |
Thesagro: |
Argissolos; Carbono; Latossolo; Oxisol; Satélite; Sensoriamento Remoto. |
Thesaurus Nal: |
Landsat; Oxisols; Soil organic carbon; Soil properties. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 02849naa a2200361 a 4500 001 2125532 005 2021-08-25 008 2020 bl uuuu u00u1 u #d 022 $a2352-0094 024 7 $ahttps://doi.org/10.1016/j.geodrs.2020.e00253$2DOI 100 1 $aPADILHA, M. C. de C. 245 $aUsing Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil.$h[electronic resource] 260 $c2020 520 $aAbstract: Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, São Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model was applied to the C spatial distribution using the predictive variables as layers, predominant concentrations of 0.65 to 0.79 g. Kg-1 in 51 (23.4%) soil samples. The analysis presented here offer possibilities for SOC prediction using Geographic Information Systems (GIS) tools. 650 $aLandsat 650 $aOxisols 650 $aSoil organic carbon 650 $aSoil properties 650 $aArgissolos 650 $aCarbono 650 $aLatossolo 650 $aOxisol 650 $aSatélite 650 $aSensoriamento Remoto 653 $aDigital soil mapping 653 $aMultiple linear regression 700 1 $aVICENTE, L. E. 700 1 $aDEMATTÊ, J. A. M. 700 1 $aLOEBMANN, D. G. dos S. W. 700 1 $aVICENTE, A. K. 700 1 $aURBINA SALAZAR, D. F. 700 1 $aGUIMARÃES, C. C. B. 773 $tGeoderma Regional$gv. 21, e00253, 2020.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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Registro Completo
Biblioteca(s): |
Embrapa Pecuária Sudeste. |
Data corrente: |
02/03/2005 |
Data da última atualização: |
30/08/2021 |
Autoria: |
KEMENES, P. A.; COUTINHO, L. L.; REGITANO, L. C. de A. |
Afiliação: |
USP; USP; LUCIANA CORREIA DE ALMEIDA REGITANO, CPPSE. |
Título: |
Quantificação das frequências dos alelos A e B do gene de Kappa-caseina em diferentes raças bovinas. |
Ano de publicação: |
1996 |
Fonte/Imprenta: |
Revista Brasileira de Genética, Ribeirão Preto, SP, v.19, n.3 supl, p.198, 1996. |
Idioma: |
Português |
Palavras-Chave: |
Alelos A e B; Frequências; Kappa-caseina. |
Thesagro: |
Gene. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/110418/1/Quantificacao-das-frequencias-dos-alelos.pdf
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Marc: |
LEADER 00574nam a2200169 a 4500 001 1044837 005 2021-08-30 008 1996 bl uuuu u00u1 u #d 100 1 $aKEMENES, P. A. 245 $aQuantificação das frequências dos alelos A e B do gene de Kappa-caseina em diferentes raças bovinas.$h[electronic resource] 260 $aRevista Brasileira de Genética, Ribeirão Preto, SP, v.19, n.3 supl, p.198$c1996 650 $aGene 653 $aAlelos A e B 653 $aFrequências 653 $aKappa-caseina 700 1 $aCOUTINHO, L. L. 700 1 $aREGITANO, L. C. de A.
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