Data/hora: 05/06/2024 05:37:28

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 09/02/2018
Data da última atualização: | 07/01/2020
Autoria: | MOLIJN, R. A.; LANNINI, L.; HANSSEN, R. F; LEIJEN, F. J. van; LAMPARELLI, R.; COUTINHO, A. C.
Afiliação: | L. LANNINI; R. F. HANSSEN; F. J. VAN LEIJEN; R. LAMPARELLI, Unicamp; ALEXANDRE CAMARGO COUTINHO, CNPTIA.
Título: | Integration of sar and troptical dense time series for land cover monitoring.
Ano de publicação: | 2017
Fonte/Imprenta: | In: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2017, Fort Worth. Proceedings. Piscataway: IEEE, 2017. p. 4330-4333.
DOI: | DOI: 10.1109/IGARSS.2017.8127960
Idioma: | Inglês
Notas: | IGARSS 2017.
Conteúdo: | Multi-temporal and multi-sensor solutions are essential to increase timeliness and reliability of land monitoring systems. This paper advocates the exploitation of the temporal contextual information provided by temporally dense SAR and optical data series series through the use of a Hidden Markov model (HMM)-based approach. An efficient strategy to incorporate the C-Band SAR data into the HMM framework, relying so far on Landsat, will be debated and assessed over a dynamic agricultural scenario, i.e. characterized by high temporal and spatial diversity in cropping practices. The site is located in the state of São Paulo (Brazil), where recent ground surveying activities has been conducted.
Palavras-Chave: | Land cover mapping; Sensor assimilation; Time series processing.
Thesaurus Nal: | Land cover; Landsat; Time series analysis.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (19615-1) - UPC - AA - DD -  -  -  - DIS