EARLY PREDICTION OF CROP PRODUCTIONS USING DROUGHT
INDICES AT DIFFERENT TIME SCALES AND REMOTE SENSING DATA:
APPLICATION IN THE EBRO VALLEY (NORTH-EAST SPAIN)
Sergio M. Vicente-Serrano1,2*, José M. Cuadrat-Prats2 and Alfredo Romo3
1
Centre d'Etudes Spatiales de la Biosphère (CESBIO), 18 avenue. Edouard Belin, bpi 2801, 31401
Toulouse cedex 9. France.
2
Departamento de Geografía. Universidad de Zaragoza. C/ Pedro Cerbuna 12. 50009. Zaragoza. Spain.
3
Laboratorio de Teledetección. Departamento de Física Aplicada I. Facultad de Ciencias. Universidad
de Valladolid. 47071. Valladolid. España.
* e-mail: sergio.vicente-serrano@cesbio.cnes.fr
Abstract. In this letter are shown the results of early crop prediction from combined use
of AVHRR-NDVI data and drought indices at different time scales. The study was
carried out in an agricultural municipality located in the Middle Ebro valley, one of the
most arid regions in Europe. The methodology proposed here has allowed the prediction
of wheat and barley productions in February, four months before harvest. Moreover, the
predictive models created have explained 88% and 82% of the temporal variability of
wheat and barley productions, respectively. This procedure could be very useful to
manage crop productions at a municipal level. Moreover, insurance companies could
take advantage of the early prediction of crop loses, which are very frequent in this area
affected by droughts.
Key-words: Crop prediction, NDVI, NOAA-AVHRR, Standardized Precipitation
Index, Mediterranean region, Ebro valley
1. Introduction
Remote sensing has been widely used for early prediction of crop productions
(Curran 1987) on the basis of predictive empirical models. Those models have been
derived from satellite sensor imagery and crop production data (Tucker and Sellers
1986; Basnyat et al. 2004). Different authors have shown the high reliability of early
crop predictions obtained by means of NDVI-AVHRR data (i.e., Rasmussen 1997;
Kalubarme et al. 2003) or other satellite sensor imagery such as Landsat-TM and ETM+
(Thenkabain, 2003; Baez-González et al. 2005), IRS (Ray and Pokharna, 1999) and
SPOT (Moulin et al. 1998).
Crop prediction models have been applied in large and homogeneous agricultural
regions of Africa, USA and South America (i.e., Seiler et al. 2000; Wannebo and
Rosenzweig 2003; Mkhabela et al. 2005). Few studies have analysed the usefulness of
NDVI-AVHRR data to predict crop productions in small, non-homogeneous areas (i.e.
at municipal level). Moreover, few works have predicted crop productions by means of
remotely sensed data in semi-arid Mediterranean regions (i.e., Quarmby et al. 1993;
Royo et al. 2003). Drought indices have been also used for crop prediction with good
results (Karl and Koscielny 1982; Yamoah et al. 2000; Quiring and Papakryiakou
2003).
These two approaches (remote sensing data and drought indices) have been useful
for crop prediction. However, until now both approaches have been used independently.
In this letter, the combined use of NDVI data and drought indices has been analysed to
predict crop productions at fine spatial resolution. The analysis has been done in the
northernmost semi-arid region of Europe, the Centre of the Ebro valley. In this area
droughts are very frequent and severe (Vicente-Serrano and Beguería 2003; VicenteSerrano et al. 2004), and crop productions have an important inter-annual variability
(Austin et al. 1998). Therefore, early prediction of crop production would be very
useful in this region.
2. Methodology
Data on wheat and barley production (proportion between sowed and harvested), in
Monegrillo municipality, between 1987 and 2000 have been used (Figure 1). In this
area, cereal is usually sowed in November and harvested during the last two weeks of
June. For details about the production data refer to Austin et al. (1998).
[Insert Figure 1 about here]
Full resolution (1 km2) NOAA-AVHRR images (bands 1 and 2) have also been used
during the same period. The images were processed by the LATUV (Remote Sensing
Laboratory of Valladolid University). Processing consisted on: a) atmospheric,
radiometric and geometric corrections (Illera et al. 1996 and 1997), b) NDVI
calculation, and c) creation of ten-day composites in order to reduce possible cloud and
remaining atmospheric disturbances (Holben 1986). A temporal filter was applied to the
ten-day composites NDVI in order to reduce even more residual atmospheric effects.
The temporal filter developed by Quarmby et al. (1993) was applied. This substitutes
abnormally low NDVI values according to equation (1):
NDVInew (n)=MAX{NDVI(n), (NDVI(n-1)+NDVI(n+1))/2}
(1)
where NDVInew (n) is the definitive NDVI value for a ten-day composite of each pixel.
High NDVI values are not changed because they are less affected by atmospheric
effects and usually are collected on optimal conditions (near nadir).
The pixels corresponding to cereal areas in the municipality were identified using the
CORINE land cover map (CLC, 1990). Every ten days, a mean value of NDVI was
calculated as the average NDVI over these pixels (88 in total).
Drought was evaluated by means of the Standardized Precipitation Index (SPI,
McKee et al. 1993). This index has several advantages over others. Calculation of the
SPI is easier than on more complex indices. Moreover, the SPI is comparable in both
time and space, and is not affected by geographical or topographical differences. The
SPI allows for the determination of duration, magnitude and intensity of droughts and
only requires precipitation data (Wu et al., 2005). Its main advantages are that it can be
calculated for several time scales (McKee et al., 1995) and identifies various drought
types: hydrological, agricultural or environmental.
The SPI has been extensively used for drought analysis at country and continental
level in Europe (Lloyd-Hughes and Saunders, 2002), South Africa (Rouault and
Richard, 2003), Greece (Tsakiris and Vangelis, 2004) and Spain (Vicente-Serrano et al.,
2004b).
Data from the Monegrillo weather-station were used for the calculation of the SPI.
SPI was obtained each ten days at four time scales (1, 3, 6 and 12 months) (Hayes et al.
1999) using the Pearson III distribution and the L-moment method, as recommended by
Vicente-Serrano (2005) for the same region. Negative values of SPI indicate dry
conditions. Moderate, severe and extreme droughts correspond to SPI values under 0.84, -1.28 and –1.65, respectively. The complete procedure using the Pearson III
distribution and the L-moment method can be consulted in detail in Vicente-Serrano
(2005).
The correlations among cereal production, NDVI and SPI were calculated at the four
time scales every ten days between January and April. Linear multiple regression
analysis was used to predict barley and wheat productions. The accuracy of the
predictions was evaluated by means of the following error statistics: Root Mean Square
Error (RMSE), Mean Absolute Error (MAE) and Willmot’s D (Willmot 1982).
3. Results
Table 1 shows correlations among wheat and barley productions, NDVI and SPI at
the four time scales, for each ten days period, from January to April. Strong correlations
have been found considering the 1-months and 3-months SPI in the different ten-day
periods of February, both for barley and wheat production. Moreover, for some ten-day
periods of April, correlations higher than 0.8 have been found with the 6-months SPI.
Correlations with the NDVI were also significant the first two ten-day periods of
February, the second and third ten days of March and the first ten days of April.
[Insert Table 1 about here]
In view of the high correlations found among the wheat and barley productions, the
1-month SPI and the NDVI in the first ten days of February, a predictive model for the
cereal production 4 months before harvest was calculated. Stepwise regression analysis
was used for this purpose to avoid colinearity between variables (Hair et al. 1998). The
highest correlations between NDVI and crop yield were obtained in March,
corresponding to flowering and maximum leaf area index (LAI). Nevertheless, also high
correlations were obtained in February. Therefore, only variables previous to the second
week of February were included in the models. Table 2 shows the results for barley and
wheat production. Included variables were the 1-month SPI and the NDVI in the first
ten days of February, being both of them significant at  = 0.05. The models have
predicted 88% and 82% of the temporal variability of wheat and barley production,
respectively. SPI has had a higher explanatory role than NDVI (higher Beta
standardised coefficients) but the inclusion of NDVI has increased significantly the
accuracy of the final prediction. Although water availability is the main constraint for
crop development, the NDVI can summarise other factors related to the crop
management and development that also affect inter-annual variability of crop
productions.
[Insert Table 2 about here]
Figure 2 shows the evolution of observed and predicted productions of barley and
wheat between 1987 and 2000 in Monegrillo. Little differences have been found
between both series.
[Insert Figure 2 about here]
Table 3 shows the error statistics obtained from the comparison between observed
and predicted production. Mean absolute error was very small in comparison with the
variable range (Barley = 0-16.6, Wheat = 0-13.1). The high D values also have
indicated little differences between observed and predicted data, which has
demonstrated the predictive capabilities of the statistical models. The error statistics
obtained from a simple regression model, which only considers the SPI in the first tendays of February, have also been reported in table 3. It can be seen that the performance
is lower compared to the model including also the NDVI.
[Insert Table 3 about here]
4. Conclusions
Cereals are very important for the economy of large rural areas in Spain. The interannual variability of crop production is very high due to aridity and frequent droughts
(Austin et al. 1998). This letter has shown the high potential of the combined use of
high temporal resolution remote sensing images and drought indices to predict cereal
productions four months prior harvest in a semi-arid region of North-East Spain. The
results obtained here only correspond to a municipality and further analysis are needed
in other semiarid regions to confirm the potential of this method. Nevertheless, the
combined use of remote sensing and drought indices seems to be a very useful approach
for early crop prediction, having a good potential to reduce crop uncertainty to farmers
and insurance companies.
Acknowledgements
This work has been supported by the projects: BSO2002-02743 and REN2003-07453
financed by the Spanish Comission of Science and Technology (CICYT) and FEDER,
and “Programa de grupos de investigación consolidados” (grupo Clima, Cambio Global
y Sistemas Naturales, BOA 48 of 20-04-2005), financed by the Aragón Government.
We want to thank Pascual Cano for the readiness of the crop data used in this work.
Research of the first author was supported by a postdoctoral fellowship by the
Ministerio de Educación y Ciencia (Spain).
References
Austin, R.B., Cantero-Martínez, C., Arrúe, J.L., Playán, E. and Cano-Marcellán, P.,
1998, Yield-rainfall relationships in cereal cropping systems in the Ebro river
valley of Spain. European Journal of Agronomy, 8, 239-248.
Baez-Gonzalez, A.D., Kiniry, J.R., Maas, S.J., Tiscareno, M., Macias, J., Mendoza, J.L.,
Richardson, C.W., Salinas, J. and Manjarrez, J.R., (2005): Large-area maize
yield forecasting using leaf area index based yield model. Agronomy Journal,
97, 418-425.
Basnyat, P., McConkey, B., Lafond, G.R., Moulin, A. and Pelcat, Y., 2004, Optimal
time for remote sensing to relate to crop grain yield on the Canadian prairies.
Canadian Journal of Plant Science, 84, 97-103.
CLC, 1990, Proyecto Ocupación del suelo del Programa CORINE: Definiciones de la
clasificación Española. Comisión Técnica del Proyecto Ocupación del Suelo de
España. Instituto Geográfico Nacional.
Curran, P.J., 1987, Remote sensing in agriculture: an introductory review. Journal of
Geography, 86, 147-156.
Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C., 1998, Multivariate data
analysis. Prentice Hall International, Inc. 799 pp.
Hayes, M., Wilhite, D.A., Svoboda, M. and Vanyarkho, O., 1999, Monitoring the 1996
drought using the Standardized Precipitation Index. Bulletin of the American
Meteorological Society, 80, 429-438.
Holben, B., 1986, Characteristics of maximum value composite images from temporal
AVHRR data. International Journal of Remote Sensing, 6, 1271-1328.
Illera, P., Delgado, J.A. and Calle, A., 1996, A navigation algorithm for satellite images.
International Journal of Remote Sensing, 17, 577-588.
Illera, P., El-Kaouri, A., and Vegas, A., 1997, Influencia de las condiciones
atmosféricas sobre el NDVI-AVHRR. In Teledetección. Usos y aplicaciones.
Universidad de Valladolid: 415-422. Valladolid. Spain.
Karl, T.R. and Koscielny, A.J., 1982, Drought in the United States: 1895-1981. Journal
of Climatology, 2, 313-329.
Kalubarme, M.H., Potdar, M.B., Manjunath, K.R., Mahey, R.K. and Siddhu, S.S., 2003,
Growth profile based crop yield models: a case of large area wheat yield
modelling and its extendibility using atmospheric corrected NOAA AVHRR
data. International Journal of Remote Sensing, 24, 2037-2054.
Lloyd-Hughes, B. and Saunders, M.A., 2002, A drought climatology for Europe,
International Journal of Climatology 22, 1571- 1592.
McKee, T.B.N., Doesken, J. and Kleist, J., 1993, The relationship of drought frecuency
and duration to time scales. Eight Conf. On Applied Climatology. Anaheim, CA,
Amer. Meteor. Soc. 179-184.
McKee, T.B.N., Doesken, J. and Kleist, J., 1995, Drought monitoring with multiple
time scales, Ninth. Conf. On Applied Climatology, Dallas, TX, Amer. Meteor.
Soc., 233-236.
Moulin, S., Bondeau, A. and Delecolle, R., 1998, Combining agricultural crop models
and satellite observations: from field to regional scales. International Journal of
Remote Sensing, 19, 1021-1036.
Mkhabela, M.S., Mkhabela, M.S. and Mashinini, N.N., 2005, Early maize yield
forecasting in the four agro-ecological regions of Swaziland using NDVI data
derived from NOAAs-AVHRR. Agricultural and Forest Meteorology, 129, 1-9.
Quarmby, N.A., Milnes, M., Hindle, T.L. and Silleos, N., 1993, The use of
multitemporal NDVI measurements from AVHRR data for crop yield estimation
and prediction. International Journal of Remote Sensing, 14, 199-210.
Quiring, S.M. and Papakryiakou, T.N., 2003, An evaluation of agricultural drought
indices for the Canadian praires. Agricultural and Forest Meteorology, 118, 4962.
Rasmussen, M.S., 1997, Operational yield forecast using AVHRR NDVI data:
reduction of environmental and inter-annual variability. International Journal of
Remote Sensing, 18, 1059-1077.
Ray, S.S. and Pokharna, S.S., 1999, Cotton yield estimation using agrometeorological
model and satellite-derived spectral profile. International Journal of Remote
Sensing, 20, 2693-2702.
Royo, C., Aparicio, N., Villegas, D., Casadesus, J., Monneveux, P. and Araus, J.L.,
2003, Usefulness of spectral reflectance indices as durum wheat yield predictors
under contrasting Mediterranean conditions. International Journal of Remote
Sensing, 24, 4403-4419.
Rouault, M. and Richard, Y., 2003, Intensity and spatial extension of droughts in South
Africa at different time scales, Water SA. 29, 489-500.
Seiler, R.A., Kogan, F. and Wei, G., 2000, Monitoring weather impact and crop yield
from NOAA-AVHRR data in Argentina. Advances in Space Research, 26, 11771185.
Thenkabail, P.S., 2003, Biophysical and yield information for precision farming from
near-real-time and historical Landsat TM images. International Journal of
Remote Sensing, 24, 2879-2904.
Tsakiris, G. and Vangelis, H., 2004, Towards a drought watch system based on spatial
SPI, Water Resources Management, 18, 1-12.
Tucker, C.J. and Sellers. J, 1986, Satellite Remote Sensing of primary production.
International Journal of Remote Sensing, 7, 1395-1416.
Vicente Serrano, S.M. and Beguería, S., 2003, Estimating extreme dry-spell risk in the
middle Ebro valley (Northeastern Spain): A comparative analysis of partial
duration series with a General Pareto distribution and Annual maxima series
with a Gumbel distribution. International Journal of Climatology, 23, 11031118.
Vicente Serrano, S.M., Pons, X. and Cuadrat, J.M., 2004, Using Landsat and NOAA
satellites on soil moisture mapping and comparison with meteorological data.
Application in the central Ebro river valley (NE of Spain). International Journal
of Remote Sensing, 25, 4325-4350.
Vicente Serrano, S.M., González-Hidalgo, J.C., de Luis, M. and Raventós, J., 2004b,
Drought patterns in the Mediterranean area: the Valencia region (eastern Spain).
Climate Research, 26, 5-15.
Vicente Serrano, S.M., 2005, Differences in spatial patterns of drought on different time
scales: an analysis of the Iberian Peninsula. Water Resources Management. in
press.
Wannebo, A. and Rosenzweig, C., 2003, Remote sensing of US cornbelt areas sensitive
to the El Niño-Southern Oscillation. International Journal of Remote Sensing,
24, 2055-2067.
Willmott, C.J., 1982, Some comments on the evaluation of model performance. Bulletin
of the American Meteorological Society, 63, 1309-1313.
Wu, H., Hayes, M.J., Wilhite, D.A. and Svoboda, M.D., 2005, The effect of the length
of record on the standardized precipitation index calculation. International
Journal of Climatology, 25, 505-520.
Yamoah, C.F., Walters, D.T., Shapiro, C.A., Francis, C.A. and Hayes, M.J., 2000,
Standardized Precipitation Index and nitrogen rate effects on crop yields and risk
distribution in maize. Agriculture, Ecosystems & Environment, 80, 113-120.
FIGURE CAPTIONS:
Figure 1. Landsat image showing the location of the study area
Figure 2. Time evolution of observed and predicted annual production of barley (a) and
wheat (b), in the period 1986-2000. White squares: observed; black circles: predicted
Table 1. Pearson correlations between crop production of barley and wheat, NDVI and
SPI at three time scales in the different ten days periods (three every month), in the
period 1987-2000. * indicates correlations significant at  = 0.05. SPIn refers to the
Standardized Precipitation Index (SPI) at the time scale of n months (n = 1, 3 and 6).
Month
January
February
February
February
Ten-day period
3
1
2
3
March
1
March
2
March
April
April
April
3
1
2
3
Variable
Correlation coefficients
Barley
Wheat
SPI1
0.83*
0.82*
SPI3
0.75*
0.80*
SPI6
0.60*
0.52
NDVI
0.67*
0.68*
SPI1
0.89*
0.84*
SPI3
0.73*
0.79*
NDVI
0.63*
0.64*
SPI1
0.59*
0.58*
SPI3
0.80*
0.82*
SPI6
0.62*
0.52
SPI3
0.83*
0.83*
SPI6
0.63*
0.55*
SPI3
0.81*
0.83*
SPI6
0.66*
0.60*
NDVI
0.64*
0.69*
SPI3
0.61*
0.74*
SPI6
0.66*
0.66*
NDVI
0.60*
0.68*
SPI3
0.54*
0.64*
SPI6
0.74*
0.78*
NDVI
0.47
0.56*
SPI3
0.53
0.56*
SPI6
0.76*
0.80*
SPI1
0.74*
0.5
SPI3
0.60*
0.59*
SPI6
0.84*
0.81*
SPI1
0.63*
0.47
SPI6
0.83*
0.86*
Table 2. Results of the stepwise models for barley and wheat productions. R: Multiple
correlation coefficient, R2: Coefficient of determination, St. Error: Standard Error, Cte:
Constant of the multiple regression model. SPI1 (1-FEB): Standardized Precipitation
Index at the time scale of 1 month in the first ten days of February. NDVI (1-FEB):
Normalized Difference Vegetation Index in the first ten days of February. Beta
standardized coefficients are between brackets.
Barley
Wheat
R
R2
St. Error
0.94
0.9
0.88
0.82
1.98
1.85
Regression coefficients
Cte.
Independent variables
SPI1 (1-FEB) NDVI (1-FEB)
-8.53
5.25 (0.74)
57.37 (0.34)
-8.81
3.85 (0.67)
50.56 (0.37)
Table 3. Error statistics for Barley and Wheat productions. R2: Coefficient of
determination, MAE: Mean absolute error, RMSE: Root Mean Square Error, D:
Agreement Index. SPI-NDVI: Results obtained from a multiple regression model that
considers NDVI and SPI. SPI: Results obtained from a simple regression model that
only considers the SPI.
R2
MAE
RMSE
D
SPI-NDVI
Barley
Wheat
0.88
0.82
1.47
1.37
1.74
1.64
0.97
0.95
SPI
Barley
0.79
1.93
2.34
0.94
Wheat
0.71
1.74
2.12
0.91
Figure 1. Landsat image showing the location of the study area
Crop Production
(harvested/sowed)
18
16
14
12
10
8
6
4
2
0
a
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Crop Production
(harvested/sowed)
14
12
b
10
8
6
4
2
0
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Figure 2. Time evolution of observed and predicted annual production of barley (a) and
wheat (b), in the period 1986-2000. White squares: observed; black circles: predicted