Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Autor(es)

Molijn, Ramses A. ^[1] ; Iannini, Lorenzo ^[1] ; Dekker, Paco Lopez ^[1] ; Magalhaes, Paulo S. G. ^[2] ; Hanssen, Ramon F. ^[2]

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

• 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 contribuição para a expansão sustentável do setor do bio-etanol no Brasil
• Beneficiário: Rubens Augusto Camargo Lamparelli
• Modalidade de apoio: Auxílio à Pesquisa - Programa BIOEN - Regular