Integration of remote sensing derived parameters in crop models: application to the PILOTE model for hay production

El Hajj, M. ; Baghdadi, N. ; Cheviron, B. ; Belaud, G. ; Zribi, M.

Type de document
Article de revue scientifique à comité de lecture
Langue
Anglais
Affiliation de l'auteur
IRSTEA MONTPELLIER UMR TETIS FRA ; IRSTEA MONTPELLIER UMR TETIS FRA ; IRSTEA MONTPELLIER UMR G-EAU FRA ; SUPAGRO UMR G-EAU MONTPELLIER FRA ; IRD UNIVERSITE DE TOULOUSE III CNRS UMR 5126 CESBIO FRA
Année
2016
Résumé / Abstract
The aim of this study is to assess the effects and interests of integrating remote-sensing-derived param-eters (LAI, harvest and irrigation dates) in a crop model (PILOTE) that simulates vegetation growth forhay crops. The target variable is the prediction of Total Dry Matter (TDM) production in each of the threegrowth cycles.Two scenarios are employed to process the available remotely sensed LAI values, predicting TDM valueswhen forcing in PILOTE either the initial and maximal optical LAI-values, or the initial, maximal and dailyinterpolated LAI values. The predictions show low deviations compared with the in situ TDM values (RMSEof 0.44 t/ha, MAPE of 23%).The feasibility of using harvest dates that are derived from optical data is examined by feeding themodel with randomly perturbed harvest dates. The magnitude of the perturbations is equal to the revisittimes of the current optical sensors. Optical images with revisit times lower than 16 days are adequateto feed PILOTE with remotely sensed harvest dates.Emphasis is placed on the forcing of 'uncertain' irrigation dates, derived from Synthetic Aperture Radarimages either replacing all true irrigation dates by randomly perturbed dates (using 3-day perturbationmagnitudes) or hypothesizing one or several irrigations are 'missed' (undetected). The results shownegligible errors for the TDM predictions when noisy irrigation dates are used (RMSE of 0.17 t/ha andMAPE of 4.2%). Disregarding one or two irrigations within a period with important rainfalls does notinduce significant errors for the predicted TDM values; however, it causes noticeable underestimationsin drier periods (maximum of 1.55 t/ha, reference TDM of 3.43 t/ha).This study enables the identification of a series of conditions in which remote-sensing-derived param-eters are suitable to feed the PILOTE model without endangering the reliability of its predictions.
Source
Agricultural Water Management, vol. 176, p. 67 - 79

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