ASSESSMENT OF RELATIVE EFFICIENCY OF USING MODIS DATA TO WINTER WHEAT YIELD FORECASTING IN UKRAINE

Olga Kussul1, Nataliia Kussul1,2, Sergii Skakun1,2,

Oleksii Kravchenko2, Andrii Shelestov1,2,3, Andrii Kolotii1,2

1National Technical University of Ukraine “Kyiv Polytechnic Institute”; 2Space Research Institute NAS Ukraine and SSA Ukraine; 3National University of Life and Environmental Sciences of Ukraine

ABSTRACT

Wheat is one of the most important and grown crops in Ukraine. Enabling reliable and accurate winter wheat yield forecasts several months in advance of the harvest is an important problem. In this paper we assess relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine at oblast level. Relative efficiency is defined as the ratio between the variance of the sample (in our case the winter wheat yield official statistics) and the variance of the estimate that has been made with the aid of satellite data. Performance of the forecasting models in terms of relative efficiency was dependant on the agro-climatic zone being on average 1.2 for Plane-Polissya, 1.5 for Forest-Steppe, and 1.9 for Steppe.

Index Terms— Remote sensing, agriculture, yield, relative efficiency, wheat

1. INTRODUCTION In recent years food security became a problem of great importance worldwide. This problem is addressed within the Global Agricultural Monitoring System of Systems (GLAM) that aims to integrate multiple satellite datasets and in situ observations to provide services for monitoring crop production, agro-meteorological parameters, and water resources. The two main components of crop production monitoring are crop yield forecasting and crop area estimation [1]-[5]. Therefore, providing accurate crop yield forecasts several months in advance of the harvest is crucial at global, national and region scales.

Satellite data play important role in crop yield forecasting since they can provide timely, repeatable, continuous, human-independent information for large territories [6]. Satellite-derived products such as leaf area index (LAI), green area index (GAI), vegetation index (Normalized Difference Vegetation Index - NDVI), meteorological parameters can be assimilated into crop growth models to improve their performance [7]-[8]. They can also be used as a predictor parameter in empirical regression-based models. In particular, NDVI, and

Vegetation Health Index (VHI) have been successfully applied to forecast crop yield [9]-[12]. In many cases, performance of empirical models based on satellite data is assessed only against the root mean square error (RMSE) when comparing to official statistics while no improvement comparing to the benchmark model (for example, trend model) is assessed. In this paper we introduce relative efficiency of using remote sensing data to yield forecasting similar to the approach used to crop area estimation [13]. We assess relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine. We show that NDVI-based models can be used to predict winter wheat in Ukraine two to three months prior to harvest, while relative efficiency of models varies within agro-climatic zones.

2. STUDY AREA AND MATERIALS DESCRIPTION Ukraine is composed of 24 oblasts and the Autonomous Republic of Crimea with the area of oblasts ranging from 8,097 to 33,310 sq. km (average oblast area is approximately 24,000 sq. km). In general, Ukraine can be divided into the following agro-climatic zones: Plane-Polissya in the north (mixed forest zone, 26% of the entire Ukrainian territory), Forest-Steppe in the centre (34%) and Steppe in the south (the most intensive cultivated area, 40%).

Wheat is one of the most important and grown crops in Ukraine. For past five years wheat accounted on average for almost 45% of production of all grains, and 48% of all crop areas. Winter wheat is typically sown in the autumn, and harvested in July next year.

The following datasets were used in the study: MOD13Q1 product at the 250 m resolution for 2000-2011; ESA Global Land Cover map (GlobCover) at the 300 m resolution for 2009; official statistics of winter wheat yield for Ukraine at oblast level for 2000-2011. NDVI values for Ukraine were extracted using the MOD13Q1 product generated from the MODIS instrument on board the Terra satellite. MOD13Q1 data are provided every 16 days at the 250 m resolution in the sinusoidal projection. NDVI images are composited over a 16-days interval to create a cloud-free map with minimal atmospheric and sun-surface-sensor

3235978-1-4799-1114-1/13/$31.00 ©2013 IEEE IGARSS 2013

angular effects [14]. Data for the territory of Ukraine and 2000-2011 years were downloaded from the Land Processes Distributed Active Archive Center (LP DAAC) of the USGS.

In order to derive a map of crop lands for the territory of Ukraine, the ESA GlobCover land cover map was used. This map was produced at the 300 m spatial resolution using as input observations from MERIS sensor on board the ENVISAT satellite mission [15]. In particular, we used the GlobCover map version 2.3 that covers the period of January-December 2009. Only areas identified in the GlobCover land cover map as a Rainfed croplands class (class value 14) were used for averaging NDVI values derived from the MOD13Q1 product. For this purpose, NDVI maps and GlobCover map were transformed to the Albers Equal Area projection.

3. METHODOLOGY

A linear regression model was developed for each oblast separately. For each oblast, NDVI values from the MOD13Q1 product at the 250 m resolution were spatially averaged for each 16-day composite. The spatial averaging within oblast was done for a cropland map (Rainfed croplands class) that was extracted from the ESA GlobCover map at the 300 m resolution. As a result of this procedure, we had a single NDVI value for each oblast and for each 16-day NDVI composite for the period 2000-2011.

Let us denote the obtained NDVI value with NDVIіj, where i is the year, and j is the day of the year (DOY) for which an NDVI composite is available, i.e j = 1, 17, 33,…. It should be noted that winter wheat yield exhibited a positive linear trend for all oblasts due to the improvement in agricultural technology [10]. Therefore, yields can be represented using the following equation:

Yі = Tі + dYі, (1) where Yі is the yield for the year i, Tі is the trend component corresponding to the agriculture technology improvements, dYі is the component dependant on weather conditions and crop state.

The trend component was approximated using a linear regression [10]:

Ті = а0 + а1*i, (2) where i is the year.

Therefore, the regression model connects winter wheat yield deviation from the trend (dYі), and the NDVI value for particular DOY j:

dYі = Yі – Tі = f(NDVIі) = b0 + b1*NDVIіj, (3) A robust linear regression was applied for estimating b0

and b1 values to reduce the influence of outliers in the regression model [16]-[17]. The following approach was applied in order to find DOY for which the NDVI value is selected to predict winter wheat yield. We applied a leave-one-out cross-validation (LOOCV) procedure when the model is routinely calibrated based on data for all years

except the one (testing data). Therefore, for each oblast and for each DOY for which NDVI values were available, we had n predicted values of the winter wheat yield for testing data (n=10 when calibrating models for 2000-2009, and n=11 for 2000-2010). These values along with observed values from official statistics were used to estimate the root mean square error (RMSE):

( )21∑ -=

iii OP

nRMSE . (4)

where Pi and Oi are predicted and observed winter wheat yields, respectively.

An NDVIіj value for DOY j that provided the minimum RMSE value (Eq. 4) was selected as a predictor in the regression model defined by Eq. (3).

Two criteria were used to estimate the efficiency of the proposed model: relative efficiency estimated within the LOOCV procedure, and model performance on independent data for 2010 and 2011. Relative efficiency [13] is the ratio between the variance of the sample (in our case the winter wheat yield official statistics) and the variance of the estimate that has been made with the aid of satellite data using Eq. (1) and (3):

rel. eff. = V(Ysample) / V(Ysatellite) = 1/n∑i(dYі)2 / RMSE2. (5)

4. RESULTS

For each oblast, the following steps were carried out to enable winter wheat forecasting from MODIS data:

(i) Identification of a trend in the winter wheat yield and estimation of parameters а0 and а1 in Eq. (2).

(ii) Derivation of the NDVI values for each DOY by spatially averaging NDVI maps over a cropland map (Rainfed croplands class) that was extracted from the ESA GlobCover map.

(iii) Running the LOOCV procedure to find the DOY value that provides minimum RMSE value (Eq. 4) and to assess relative efficiency (Eq. 5).

(iv) Estimation of parameters b0 and b1 in the linear regression model (Eq. 3).

Results showed that for most oblasts the NDVI value from April and May is selected to predict winter wheat yield, i.e. 2-3 months prior to harvest. The performance of the models was evaluated using two metrics: R-square of the regression model (Eq. 3), and relative efficiency (Eq. 5, Fig. 1). Coefficient of determination R-square was 0.4 to 0.8. The obtained relative efficiency values averaged by agro-climatic zones were as follows (Table 1): for Plane Polissya was 1.2; for Forest Steppe was 1.5; and for Steppe was 1.9. The proposed approach and a simple trend model (Eq. 2) were used to forecast winter wheat yield for independent data sets for 2010 and 2011, i.e. on data that were not used within model calibration process (Table 2). These forecasts were compared to official statistics and corresponding RMSE values were estimated. RMSE values

3236

for NDVI-based approach and the trend model were 0.794 t/ha and 1.346 t/ha in 2010, and 0.625 t/ha and 1.050 t/ha in 2011, respectively.

Fig. 1. Distribution of relative efficiency of regression models based on NDVI values derived from MODIS data for 2000-2009 (A) and 2000-2010 (B)

Table 1. Relative efficiency and coefficient of determination of the regression model for different agro-climatic zones averaged by oblasts Model 2000-2009 Model 2000-2010 Agro-climatic zone

Rel. eff. R-square Rel. eff. R-square

Plane-Polissya

1.182 0.479 1.177 0.433

Forest-Steppe

1.576 0.667 1.532 0.680

Steppe 1.883 0.804 1.894 0.796

Table 2. Minimum RMSE values of winter wheat yield forecasts for 2010 and 2011 produced by different models

Model and time of forecast

RMSE for 2010, t ha-1

RMSE for 2011, t ha-1

MODIS NDVI (2000-2009), April-May

0.794 0.585

MODIS NDVI (2000-2010), April-May

- 0.625

Trend model (2000-2009)

1.346 0.944

Trend model (2000-2010)

- 1.050

5. CONCLUSIONS

In this paper we assessed the feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level. An empirical regression model was built for each oblast that connected 16-day NDVI composites derived from the MODIS sensor at the 250 m resolution and official statistics on winter wheat yield. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of RMSE was identified. For most oblasts, NDVI values taken in April-May provided the minimum RMSE value, thus enabling forecasts 2-3 months prior to harvest. Performance of the models in terms of relative efficiency and coefficient of determination was dependant on the agro-climatic zone being on average 1.2 and 0.4 for Plane-Polissya, 1.5 and 0.7 for Forest-Steppe, and 1.9 and 0.8 for Steppe, respectively.

6. REFERENCES [1] N. Kussul, S. Skakun, A. Shelestov, O. Kravchenko, J.F. Gallego, and O. Kussul, “Crop area estimation in Ukraine using satellite data within the MARS project,” in: IEEE International Geoscience and Remote Sensing Symposium IGARSS 2012, 22-27 July 2012, Munich, Germany, pp. 3756–3759. [2] A.Yu. Shelestov, A.N. Kravchenko, S.V Skakun., S.V. Voloshin, and N.N. Kussul, “Geospatial information system for agricultural monitoring,” Cybern. Syst. Anal., vol. 49, no. 1, pp. 124–132, 2013. [3] J. Gallego, A. Kravchenko, N. Kussul, S. Skakun, A. Shelestov, and Y. Grypych, “Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations,” J. Automation Inf. Sci., vol. 44, no. 5, pp. 67–80, 2012. [4] S. Skakun, E. Nasuro, A. Lavrenyuk, and O. Kussul, “Analysis of Applicability of Neural Networks for Classification of Satellite Data,” J. Automation Inf. Sci., vol. 39, no. 3, pp. 37–50, 2007. [5] N. Kussul, B. Sokolov, Y. Zyelyk, V. Zelentsov, S. Skakun, and A. Shelestov, “Disaster Risk Assessment Based on

3237

Heterogeneous Geospatial Information,” J. of Automation Inf. Sci., vol. 42, no. 12, pp. 32–45, 2010. [6] N Kussul, A. Shelestov, and S. Skakun, “Grid and sensor web technologies for environmental monitoring,” Earth Sci. Inf., 2(1), pp. 37–51, 2009. [7] G.J. Roerink, et al., “Evaluation of MSG-derived global radiation estimates for application in a regional crop model,” Agric. For. Meteorol., 160, pp. 36–47, 2012. [8] A.J.W. de Wit, G. Duveiller, and P. Defourny, “Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations,” Agric. For. Meteorol., 164, pp. 39–52, 2012. [9] I. Becker-Reshef, E. Vermote, M. Lindeman, and C. Justice, “A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data,” Remote Sens. Env., 114(6), pp. 1312–1323, 2010. [10] F. Kogan, L. Salazar, and L. Roytman, “Forecasting crop production using satellite-based vegetation health indices in Kansas, USA,” Int. J. Remote Sens., 33(9), pp. 2798–2814, 2012. [11] L. Kouadio, et al., “Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data,” Int. J. Appl. Earth Obs. Geoinf., 18, pp. 111-118, 2012. [12] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul and A. Lavrenyuk, “Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models,” Int. J. Appl. Earth Observ. Geoinf., vol. 23, pp. 192–203, 2013. [13] E. Carfagna, and F.J. Gallego, “Using remote sensing for agricultural statistics,” Int. Stat. Rev., 73(3), pp. 389–404, 2005. [14] A. Huete, C. Justice, and W. van Leeuwen, “MODIS Vegetation Index (MOD13) Algorithm Theoretical Basis Document (ATBD),” Version 3, 1999. Available at http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf. [15] O. Arino, et al., “GlobCover: ESA service for global land cover from MERIS,” in: IEEE International Geoscience and Remote Sensing Symposium IGARSS 2007, 23-27 July 2007, Barcelona, Spain, pp. 2412–2415. [16] A. Shelestov, and N. Kussul, ”Using the fuzzy-ellipsoid method for robust estimation of the state of a grid system node,” Cybern. Syst. Anal., vol. 44, no. 6, pp. 847–854, 2008. [17] G.M. Bakan, and N.N. Kussul, “Fuzzy ellipsoidal filtering algorithm of static object state,” Problemy Upravleniya I Informatiki (Avtomatika), no. 5, pp. 77–92, 1996.

3238