Application of an automatic rice mapping system to extract phenological information from time series of MODIS imagery in African environment: first results of Senegal case study (Contributo in atti di convegno)

Type
Label
  • Application of an automatic rice mapping system to extract phenological information from time series of MODIS imagery in African environment: first results of Senegal case study (Contributo in atti di convegno) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Alternative label
  • Manfron G., M. Boschetti, R. Confalonieri, V. Pagani, F. Nutini, F.Filipponi, A. Crema, P.A. Brivio (2013)
    Application of an automatic rice mapping system to extract phenological information from time series of MODIS imagery in African environment: first results of Senegal case study
    in 33rd EARSeL symposium, Matera, 3-6 June 2013
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Manfron G., M. Boschetti, R. Confalonieri, V. Pagani, F. Nutini, F.Filipponi, A. Crema, P.A. Brivio (literal)
Pagina inizio
  • 81 (literal)
Pagina fine
  • 88 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.earsel.org/symposia/2013-symposium-Matera/proceedings.php (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 8 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • IREA-CNR, IREA-CNR, DISAA-UNIMI, DISAA-UNIMI, IREA-CNR, UNIPV, IREA-CNR, IREA-CNR (literal)
Titolo
  • Application of an automatic rice mapping system to extract phenological information from time series of MODIS imagery in African environment: first results of Senegal case study (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-88-89693-34-6 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Lasaponara R., Masini N., Biscione M. (literal)
Abstract
  • Among the three main cereals harvested in the world, rice it is the most important staple crop in terms of human consumption, especially in low- and lower-middle-income countries of Africa and Asia. The availability of up-to-date information on the crop season results a very important task for supporting food security initiative. In this contest the contribution of Remote Sensing images and technics could provide a strong contribution for near-real time agro-ecosystem monitoring system to retrieve spatial distribution information on large scale. The present paper aims to (i) evaluate the reliability of an automatic image processing methodology developed for rice detection and rice seasonal monitoring, and (ii) quantify remote sensed phenological metrics contribution to describe rice yields variability. The algorithm \"PhenoRice\", was applied and tested on Senegal (West Africa), producing rice cropped areas maps and estimations of four phenological metrics: crop seeding/transplanting (MIN), start of season (SoS), peak/flowering (MAX) and maturity (EoS). These indices, together with the maximum value of NDVI in the season (NDVI-max), were estimated for each year of the period 2001 ÷ 2010 using temporal series of vegetation indices from MOD09A1 data. Remote sensing estimations were aggregated at regional and national level and used as independent variables in a multivariate model to explain yearly variability of rice production. Results demonstrate that: i) despite errors due to the well know low-resolution bias, the mapping method was able to detect the crop in the main rice districts of the study area, ii) the remote sensed seasonal indices were able to explain up to 75% of annual yield variability at regional level. The proposed approach can be of fundamental support for early warning monitoring system and for crop modelling simulation in areas where information on crop calendar are absent or not reliable. (literal)
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