|
|
|
| Registros recuperados: 479 | |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
|
| Soares,Dênis de Moura; Galvão,Lênio Soares; Formaggio,Antônio Roberto. |
| Images acquired at the same day by the ETM+/Landsat-7 (30 m of spatial resolution) and MODIS/Terra (250 m) sensors were used to estimate areas of three major crops (soybean, sugarcane, and corn) with different landscape patterns in Southeastern Brazil. Majority filtering of ETM + classification results was applied to describe the behavior of 15 selected landscape metrics at distinct simulated spatial resolutions (90, 150, 210 and 270 m). By using regression models, the performance of MODIS and derived metrics to predict adequately the crop area, considering ETM+ data as reference, were analyzed. Results showed that the MODIS instrument overestimated the areas of soybean (15%) and sugarcane (1%), and underestimated the area of corn (12%). Multiple... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: MODIS; Remote sensing; Regression; Soybean; Sugarcane. |
| Ano: 2008 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000500003 |
| |
|
|
| Sayago,S; Bocco,M. |
| Development of models for crop yield prediction using remote sensing allows accurate, reliable and timely estimations over large areas. Particularly, this information is necessary to ensure the adequacy of a nation's food supply as well as to aid policy makers and farmers. In Argentina, soybean (Glycine max (L.) Merr.) and corn (Zea mays L.) are the most important crops. The goal of this research was to develop and evaluate linear and non-linear models to estimate crop yield from satellite data. Particularly, we proposed and applied those models to obtain soybean and corn yield in the central region of Córdoba (Argentina) using Landsat and SPOT images. The models were designed taking into account all or some bands included in the images from one or both... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Neural networks; Multiple linear regression; Soybean; Corn; Modelling. |
| Ano: 2018 |
URL: http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S1668-298X2018000100001 |
| |
|
| |
| Registros recuperados: 479 | |
|
|
|
