Data/hora: 17/06/2024 02:15:29

Biblioteca(s): | Embrapa Meio Ambiente.
Data corrente: | 15/10/2020
Data da última atualização: | 25/08/2021
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.
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
Registro original: | Embrapa Meio Ambiente (CNPMA)
Exemplares: | CNPMA - (16827-1) - UPC - AP - DD -  -  -  - DIS