Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation

Abstract The objective of this study was to assess the efficiency of the assimilation of the fraction of intercepted photosynthetically active radiation (fIPAR) data derived from Satellite Pour l’Observation de la Terre SPOT images into the MOSICAS sugarcane crop growth model for estimating the yield at field scale on Reunion Island. Over 3 years, time series of SPOT satellite imagery were used to estimate the daily evolution of NDVI for 60 plots located on two climatically contrasted farms. Ground measurements of the fIPAR were performed on 5 reference fields and used to calibrate a relationship with the corresponding NDVI values. Forced and not forced simulations were run and compared with respect to their ability to predict the final observed yield. Forcing MOSICAS with fIPAR values derived from SPOT images improved the accuracy of the model for the yield estimation (RMSE = 12.2 against 14.8 t ha−1) closer to the 1:1 line. However, underestimations of the yield by the forced model suggest that some of the model parameters were not optimal. The maximal radiation use efficiency parameter (RUEm) was optimised for each field, and an analysis of variance showed the significant effect of the ratoon number of the field, of its cultivar and of the farm where it is planted. Accordingly, the RUEm was recalibrated for each cultivar for the number of ratoons and farms. New RUEm values ranged from 3.09 to 3.77 g MJ−1, and new computations were run using the optimised values of RUEm. The results indicate that recalibrating the maximal radiation use efficiency according to the number of ratoons improved the yield estimation accuracy by as much as 10.5 t ha−1 RMSE. This study highlights the potential of time series of satellite images to enhance the estimation of the yield by a forced ecophysiological model and to obtain better knowledge about the ecophysiological processes that are involved in crop dynamics with the recalibration method.

[1]  Guang Zheng,et al.  Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors , 2009, Sensors.

[2]  N. Inman-Bamber.  A growth model for sugar-cane based on a simple carbon balance and the CERES· Maize water balance , 1991 .

[3]  M. Guérif,et al.  Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications , 2005 .

[4]  Jean-François Martiné.  Analysis and forecasting of the sucrose content of sugarcane crops during the harvest period in Reunion Island. , 2007 .

[5]  R. C. Muchow,et al.  GROWTH OF SUGARCANE UNDER HIGH INPUT CONDITIONS IN TROPICAL AUSTRALIA. I. RADIATION USE, BIOMASS ACCUMULATION AND PARTITIONING , 1996 .

[6]  Yuan Shen,et al.  Large-area rice yield forecasting using satellite imageries , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Jean-François Martiné,et al.  Modélisation de la production potentielle de la canne à sucre en zone tropicale, sous conditions thermiques et hydriques contrastées. Applications du modèle , 2003 .

[8]  R. Bonhomme.  Beware of comparing RUE values calculated from PAR vs solar radiation or absorbed vs intercepted radiation , 2000 .

[9]  A. Wood,et al.  Radiation interception and biomass accumulation in a sugarcane crop grown under irrigated tropical conditions , 1994 .

[10]  G. O'Leary.  A review of three sugarcane simulation models with respect to their prediction of sucrose yield , 2000 .

[11]  Jan G. P. W. Clevers,et al.  Using SPOT data for calibrating a wheat growth model under mediterranean conditions , 2002 .

[12]  Benoit Gabrielle,et al.  Analysis and Field Evaluation of the Ceres Models Water Balance Component , 1995 .

[13]  Richard H. Waring,et al.  Remote Sensing of Leaf Area Index and Radiation Intercepted by Understory Vegetation , 1994 .

[14]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[15]  J. Chopart,et al.  Relations entre l'altitude et la température mensuelle de l'air dans l'ouest de la Réunion , 2002 .

[16]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[17]  V. Lebourgeois,et al.  Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI , 2010 .

[18]  M. Claverie,et al.  Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data , 2012 .

[19]  Douglas K. Bolton,et al.  Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics , 2013 .

[20]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[21]  D. Bartlett,et al.  Use of vegetation indices to estimate indices to estimate intercepted solar radiation and net carbon dioxide exchange of a grass canopy , 1989 .

[22]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[23]  Stephan J. Maas,et al.  Remote sensing and crop production models: present trends , 1992 .

[24]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[25]  Frédéric Baret,et al.  Forcing a wheat crop model with LAI data to access agronomic variables: Evaluation of the impact of model and LAI uncertainties and comparison with an empirical approach , 2012 .

[26]  William L. Goffe,et al.  SIMANN: FORTRAN module to perform Global Optimization of Statistical Functions with Simulated Annealing , 1992 .

[27]  C. N. Bezuidenhout,et al.  Operational forecasting of South African sugarcane production: Part 1 – System description , 2007 .

[28]  W. Verhoef,et al.  Reconstructing cloudfree NDVI composites using Fourier analysis of time series , 2000 .

[29]  Ronghua Ma,et al.  [Preliminary study on ecological footprint in Bashang region of Zhangjiakou city]. , 2003, Ying yong sheng tai xue bao = The journal of applied ecology.

[30]  A. Bégué.  Leaf area index, intercepted photosynthetically active radiation, and spectral vegetation indices: A sensitivity analysis for regular-clumped canopies , 1993 .