Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data

Abstract : The recent availability of high spatial and temporal resolution (HSTR) remote sensing data (Formosat-2, and future missions of Venμs and Sentinel-2) offers new opportunities for crop monitoring. In this context, we investigated the perspective offered by coupling a simple algorithmfor yield estimate (SAFY)with the Formosat-2 data to estimate crop production over large areas. With a limited number of input parameters, the SAFY model enables the simulation of time series of green area index (GAI) and dry aboveground biomass (DAM). From2006 to 2009, 95 Formosat-2 images (8 m, 1 day revisit) were acquired for a 24×24 km² area southwest of Toulouse, France. This study focused on two summer crops: irrigatedmaize (Zea mays) and sunflower (Helianthus annuus). Green area index (GAI) time serieswere deduced fromFormosat-2NDVI time series andwere used to calibrate six major parameters of the SAFY model. Four of those parameters (partition-to-leaf and senescence function parameters) were calibrated per crop type based on the very dense 2006 Formosat-2 data set. The retrieved values of these parameterswere consistentwith the in situ observations and a literature review. Two of themajor parameters of the SAFYmodel (emergence day and effective light-use efficiency)were calibrated per field relative to crop management practices. The estimated effective light-use efficiency values highlighted the distinction between the C4 (maize) and C3 (sunflower) plants, and were linked to the reduction of the photosynthesis rate due to water stress. The model was able to reproduce a large set of GAI temporal shapes, which were related to various phenological behaviours and to crop type. The biomass was well estimated (relative error of 28%), especially considering that biomass measurements were not used for the calibration. The grain yields were also simulated using harvest index coefficients and were compared with grain yield statistics from the French Agricultural Statistics for the department of Haute-Garonne. The inter-annual variation in the simulated grain yields of sunflowerwas consistentwith the reported variation. Formaize, significant discrepancieswere observed with the reported statistics.
Type de document :
Article dans une revue
Remote Sensing of Environment, Elsevier, 2012, pp.1-14. 〈10.1016/j.rse.2012.04.005〉
Liste complète des métadonnées

Littérature citée [28 références]  Voir  Masquer  Télécharger

http://hal.ird.fr/ird-00718813
Contributeur : Dominique Bruel <>
Soumis le : mercredi 12 décembre 2012 - 11:25:32
Dernière modification le : mercredi 19 septembre 2018 - 01:30:55
Document(s) archivé(s) le : mercredi 13 mars 2013 - 02:25:11

Fichier

rse2012-Martin-Revision_v5.PDF
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Collections

Citation

M. Claverie, V. Demarez, B. Duchemin, Olivier Hagolle, D. Ducrot, et al.. Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sensing of Environment, Elsevier, 2012, pp.1-14. 〈10.1016/j.rse.2012.04.005〉. 〈ird-00718813〉

Partager

Métriques

Consultations de la notice

411

Téléchargements de fichiers

613