1280 WWW.CROPS.ORG CROP SCIENCE, VOL. 55, MAY– JUNE 2015
RESEARCH
I
n the Mediterranean environment, durum wheat (Triticum
turgidum var. durum) exhibits an unstable grain size and weight,
and an increase in the grain weight is generally accompanied by a
pronounced decrease in the grain N concentration (Cossani et al.,
2011). The early assessment of harvest characteristics and the mon-
itoring of crop production are being researched for many years at
Integration of Remote Sensing and Crop
Modeling for the Early Assessment of Durum
Wheat Harvest at the Field Scale
Francesca Orlando,* Anna Dalla Marta, Marco Mancini,
Ray Motha, John J. Qu, and Simone Orlandini
ABSTRACT
In the Mediterranean, durum wheat is one of
the major crops, but a high variability of grain
yield and protein concentration (GPC) prevents
an adequate agronomic planning at the farm or
consortium level. Although there are many stud-
ies on monitoring of crop production and early
prediction of yields, little has been done at the
local scale. The aim of this study was to assess
simplifed integration algorithms (SIAs) for inte-
grating remote sensing information with a crop
model, to forecast the GPC and grain yield at
the feld scale. To this end, the CERES-Wheat
model was run to simulate the seasonal aver-
age of grain yield (AVE) and GPC in Val d’ Orcia
(Tuscany Region, Italy) during the 2009–2010
and 2010–2011 growing seasons. The perfor-
mances of different vegetation indices from
MODIS imagery in harvest forecasting were
assessed and compared. The SIA formulation
was based on the simulated AVE and GPC, and
on their spatialization in relation to the intraan-
nual variability between the felds described by
vegetation indices. The simulated AVE traced
the observed trend. The fraction of absorbed
photosynthetically active radiation (fPAR) was
the best index in describing grain yield, and the
related SIA showed at validation good perfor-
mance at the feld scale (r
2
= 0.74). Conversely,
the SIA was unable to predict GPC due to the
low performance of CERES-Wheat in capturing
the interannual variability and to the failure of the
fPAR in describing the GPC inter felds variability
at intermediate canopy refectance values.
F. Orlando, Dep. of Agricultural and Environmental Sciences, Produc-
tion, Landscape, and Agroenergy, CASSANDRA Lab., Univ. of Milan,
Via Celoria 2 – 20133 Milan, Italy; A. Dalla Marta, Dep. of Agrifood
Production and Environmental Sciences, Univ. of Florence, Piazzale
delle Cascine 18 – 50144 Florence, Italy; M. Mancini, and S. Orlan-
dini, Foundation for Climate and Sustainability, Via Caproni 8 – 50145
Florence, Italy; R. Motha, and J. Qu, Global Environment and Natu-
ral Resources Institute (GENRI), George Mason Univ., Fairfax, VA
22030. Received 9 July 2014. Accepted 11 Dec. 2014. *Corresponding
author (francesca.orlando@unimi.it).
Abbreviations: AVE, seasonal average grain yield simulated by
CERES-Wheat initialized without detailed input data; CF, correc-
tion factor for the under/overestimation error; CRM, coefcient of
residual mass; DM, dry matter; EF, model efciency; fPAR, fraction of
absorbed photosynthetically active radiation; EVI, enhanced vegetation
index; GPC, grain protein concentration; LAI, leaf area index; NDVI,
normalized diference vegetation index; RRMSE, relative root mean
square error; SIA, simplifed integration algorithms between remotely
sensed vegetation index and CERES-Wheat output; VId, the vegeta-
tion index deviation of each feld from the vegetation index spatial aver-
age; Yd, grain yield deviation of each feld from the seasonal grain yield
average; YIELD, forecasted grain yield at the feld scale; YIELDcorr,
forecasted grain yield at the feld scale corrected for the under/overesti-
mation error; YIELD
obs
, observed grain yield at the feld scale.
Published in Crop Sci. 55:1280–1289 (2015).
doi: 10.2135/cropsci2014.07.0479
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