# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
## 2 Materials and Methodology Applied to the Forecast and Estimation Model  
The target of the present study was to analyze vegetation indices (NDVI) obtained by a MODIS satellite and to construct models for forecasting and explanatory purposes. We considered time-dependent models to enrich the information given by the satellites in a given time period for a seasonal evolution of the vegetation indices for two types of Mediterranean crops (maize and olive tree) of the agricultural producing Jarama region (Madrid, Spain) during the February 2013--December 2017 period (Figure 1).  
Figure 1: Lower Jarama River Basin, maize, olive and weather station location.  
NDVI data were provided from a MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor, produced at 16 day interval composite time series of the mean, which provide consistent spatial and temporal comparisons of vegetation status. The NDVI product chose the best available pixel value from all the acquisitions from the 16 day period, without clouds, low view angle and the highest NDVI value [33]. To eliminate possible errors in the NDVI data, the algorithm computes from surface reflectance corrected for molecular scattering, ozone absorption and aerosols, thus minimizing background variations and maintaining sensitivity in dense vegetation conditions [34].  
The MOD13Q1 V006 NDVI products are available from the EarthExplorer website ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)) (accessed on 20 November 2020)) provided by U.S. Geological Survey (USGS). The downloaded data were in HDF-EOS data format and sinusoidal projection. Therefore, they were reprojected using MODIS Reprojection Tool (MRT). To extract the NDVI data set, we used a vectorial mask for each plot (maize and olive), using a geographic information system (GIS). The GIS provides the ability to manage and analyze spatial and geographic data, thus improving the acquisition of data. Finally, we calculated the average value of the NDVI in each mask of the plots, as this is more representative of the state of the crops.  
For the analysis of the data, four plots of maize and one of olive trees were selected, with areas larger than 7 hectares to ensure the consistency of the spatial resolution of the NDVI, which was 250m, and to eliminate errors due to the edge effect of the crop plots. In the case of maize, four different parcels were considered to carry out the study because in the Jarama region, the seeding and harvesting seasons of the maize crop can vary from one to two months, depending on the variety of maize and according to the use for which it is intended (human food or animal feed); the availability of water for irrigation; or the agroclimatic conditions of each year, such as rainfall and higher or lower temperatures.  
In the database built for this paper, we added data monthly, from February 2013 to December 2017, with a total of 70 records for each plot. We chose the M02 (Arganda del Rey -- UTM30N X457693; Y4462410) SiAR Network weather station for the agroclimatic data, because it is representative of the weather in the Lower Jarama River Basin and is near one of the maize plots [35] (Figure 1). The Agroclimatic Information System for Irrigation (SiAR) is an infrastructure of weather stations that captures, records and disseminates agroclimatic data. This information can be used to calculate the water demand of irrigation areas, which allows useful, rigorous and high-quality information that contributes to better planning, management, handling and control of irrigated farms to be obtained. The M02 station provides data from temperature (\({}^{\circ}\)C), wind speed (km/h), radiation (W/m\({}^{2}\)) and rainfall (mm) mainly.