# _remote sensing_
## 2 Materials and Methods
### Retrieval of Biophysical and Agoronomical Variables
#### 2.1.2 Cropping Seasons and Input Variables  
The crop selected for this study was durum wheat, and two cropping seasons were considered for modeling purposes (2017-2018 and 2018-2019). The farm fields that were cultivated with durum wheat during these two seasons are shown in Figure 1. This period was selected since durum wheat yield was recorded by the farm staff using a combine harvester yield mapping system.  
Further information on crop growth was collected in the form of LAI as retrieved by Sentinel 2 satellite data. Sentinel 2 L2A images with a cloud cover < 20%, between November and June of each growing season, were downloaded and processed using the Sentinel Application Platform (SNAP, [[https://earth.esa.int/eogateway/tools/snap](https://earth.esa.int/eogateway/tools/snap)]([https://earth.esa.int/eogateway/tools/snap](https://earth.esa.int/eogateway/tools/snap)), accessed on 12 April 2021) of the ESA. Fifteen Sentinel-2 images were used for each of the cropping seasons, since they satisfied the above-mentioned criteria, and passed visual inspection performed to avoid irregular estimations over the selected fields (e.g., due to the shadows of scattered clouds). In 2017-2018, most of the useful images fell into the period of April and May, due to frequent cloud cover in winter. In 2018-2019, useful images were more evenly distributed. To simplify the subsequent modeling processes and due to the lack of spatialized information on soil properties, both LAI and yield field averages were considered for simulations. Raw LAI values obtained from satellite imagery were further processed to obtain realistic LAI curves that better supported crop modeling processing. The steps undertaken in this process are like those reviewed by [28]. The steps were as follows:  
1. A spline interpolation was used to fill the missing dates with LAI values to ensure continuous data throughout the entire growing season.
2. A 4253H twice filter was used as a form of moving average to smooth LAI values.
3. A double logistic curve was fit to provide a more realistic representation of the field-wise LAI curves. LAI fit following [29; 30; 31] were visually inspected to select the best fitting method that gave the most realistic LAI curves given the satellite data available. The equation reported by [31] was finally selected.  
The soil parameters requested by the model (soil water limits, as reported in the following section) were extracted from the maps of topsoil physical properties of ESDAC-JRC  
Figure 1: Maccarese farm area and durum wheat fields’ locations. Durum wheat fields of the 2017–2018 growing season were used for calibration, whereas durum wheat fields of the 2018–2019 growing season for data assimilation. RGB Sentinel-2 images ([[https://scihub.opernicus.eu/](https://scihub.opernicus.eu/)]([https://scihub.opernicus.eu/](https://scihub.opernicus.eu/)) accessed on 13 April 2020) of 6 April 2018 and 22 March 2019 were used as a base layer for the field.  
(European Soil Data Center-Joint Research Center, [[https://esdac.jrc.ece.europa.eu/](https://esdac.jrc.ece.europa.eu/)]([https://esdac.jrc.ece.europa.eu/](https://esdac.jrc.ece.europa.eu/)) accessed on 13 April 2021) based on the LUCAS dataset [32]. The choice was made to assume the absence of a priori knowledge of soil properties in the farming site. The topsoil properties provided by these maps were: clay, silt and sand content, bulk density and coarse fragment. The soil water limits were estimated from these properties using a pedotransfer function [33].