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| Registros recuperados : 3 | |
| 1. |  | URBINA SALAZAR, D. F.; DEMATTÊ, J. A. M.; VICENTE, L. E.; GUIMARAES, C. C. B.; SAYÃO, V. M.; CERRI, C. E. P.; PADILHA, M. C. de C.; MENDES, W. de S. Emissivity of agricultural soil attributes in southeastern Brazil via terrestrial and satellite sensors. Geoderma, v. 361, article 114038, 2020.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 2. | | PADILHA, M. C. C.; VICENTE, L. E.; DEMATTÊ, J. A. M.; LOEBMANN, D. G. dos S. W.; URBINA SALAZAR, D.; KOGA-VICENTE, A.; ARAUJO, L. S. de; MANZATTO, C. V. Prediction statistical model for soil organic carbon mapping in crop areas using the Landsat/OLI sensor. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos. Anais... São José dos Campos: INPE, 2019. Ref. 96042. p. 1-4.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 3. | | 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. Using Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil. Geoderma Regional, v. 21, e00253, 2020.| Biblioteca(s): Embrapa Meio Ambiente. |
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| Registros recuperados : 3 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
15/10/2020 |
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Data da última atualização: |
25/08/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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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. |
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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. |
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Título: |
Using Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Geoderma Regional, v. 21, e00253, 2020. |
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ISSN: |
2352-0094 |
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DOI: |
https://doi.org/10.1016/j.geodrs.2020.e00253 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Digital soil mapping; Multiple linear regression. |
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Thesagro: |
Argissolos; Carbono; Latossolo; Oxisol; Satélite; Sensoriamento Remoto. |
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Thesaurus NAL: |
Landsat; Oxisols; Soil organic carbon; Soil properties. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
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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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