| Texto completo | |
| Autor(es): |
Molijn, Ramses A.
[1]
;
Iannini, Lorenzo
[1]
;
Dekker, Paco Lopez
[1]
;
Magalhaes, Paulo S. G.
[2]
;
Hanssen, Ramon F.
[2]
Número total de Autores: 5
|
| Afiliação do(s) autor(es): | [1] Delft Univ Technol, Geosci & Remote Sensing, NL-2628 CN Delft - Netherlands
[2] Univ Estadual Campinas, Fac Engn Agr FEAGRI, BR-13083875 Campinas, SP - Brazil
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | REMOTE SENSING; v. 10, n. 10 OCT 2018. |
| Citações Web of Science: | 3 |
| Resumo | |
Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account. (AU) | |
| Processo FAPESP: | 13/50943-9 - Incremento do mapeamento do uso da terra utilizando sensoriamento remoto uma contribuicao para a expansao sustentavel do setor do bio-etanol no brasil. (fapesp/be-basic) |
| Beneficiário: | Rubens Augusto Camargo Lamparelli |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa BIOEN - Regular |