# Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems
## 4 Data Ingestion
### LNC Mapping, Applications, and Yield Mapping  
Leaf nitrogen concentration (LNC) is an important index of photosynthetic capacity of crops [30]. LNC is necessary to farmers for making their decisions on fertilization, especially the topdressing ones [31]. Remote sensing has been widely used in LNC estimations in crops on a large geographic scale [7; 8; 9], as rapid determination of LNC could provide valuable information for monitoring crop physiology to the farmers [5]. In this direction, precision agriculture applications can improve use efficiency of nitrogen fertilizers [6]. The common practice is to establish linear or nonlinear relationships between leaf N content and spectral features derived from leaf reflectance spectra. Figure 4: Indicative subset of the inundation map produced from Sentinel-1 with computer vision (5 August 2019, central Scheme 56. ha; background: RapidEye composite image of 2015). In this research, LNC was estimated from Sentinel-2 images acquired on a variety of dates over the 4-year study period: 06 July 2016, 21 July 2016, 16 August 2016, 18 June 2017, 28 June 2017, 28 July 2017, 27 August 2017, 8 June 2018, 13 July 2018, 18 July 2018, 28 July 2018, 22 August 2018, 13 June 2019, 28 June 2019, 18 July 2019, 28 July 2019, and 1 September 2019. However, only a part of the LNC maps were used by the grower to support his decisions on N-fertilization; those acquired close before the planned topdressing applications (i.e., 06 July 2016, 28 June 2017, 13 July 2018, and 28 June 2019). Sentinel-2 is free and ready-to-use, high-resolution optical imagery by the European Space Agency (ESA). Its MultiSpectral Instrument (MSI) carries 13 spectral bands ranging from the visible and near infrared to the short-wave infrared. The spatial resolution varies from 10 to 60 m, depending on the spectral band, with a 290 km field of view. This unique combination of high spatial resolution, wide field of view and broad spectral coverage, together with free availability of ready-to-use products in an atmospherically corrected reflectance mode, has opened a new window in operational land use mapping [32]. Leaf Nitrogen Concentration (\(LNC\)) was estimated and mapped using equations established originally from hyperspectral data by [PERSON] et al., (2011) [33], here applied with Sentinel-2 imagery as follows:  
\[LNC=4.060\cdot NDRE+0.43 \tag{1}\]  
where \(NDRE\): the Normalized Difference RedEdge index;  
\[NDRE=(R790-R720)/(R790+R720) \tag{2}\]  
where \(R720\) : reflectance at 720 nm or Band-5 for Sentinel-2; \(R790\) : reflectance at 790 nm or Band-9 for Sentinel-2. Using an available multi-temporal hyperspectral dataset from experimental rice plots, the \(LNC\) values were calibrated according to the exact wavelengths of Equation (2) vs. Sentinel-2 band wavelengths and for the exact seeding date of each field; relevant work on the specific hyperspectral dataset can be found in [PERSON] (2016) [34]. The Leaf Nitrogen Content (\(LNC\)) was estimated using the latest cloud-free Sentinel-2 images available before the nitrogen topdressing applications (Figure 5). Based on the mapping outputs, a concrete N-dose was recommended to the grower per management zone. Figure 5: Indicative LNC map from the central sector of the studied rice farm (56 ha; 28 June 2017). The fertilizers were applied with a Variable Rate Technology (VRT) system, namely, a Kverneland Geospread model with a Tellus Pro terminal. The GEOSPREAD system on the Kverneland disc spreaders enables farmers to reduce the spreading pattern in sections of 1 m with the highest accuracy. The Tellus PRO terminal is the center for connecting all ISOBUS machines and a platform for running precision farming applications. The terminal was supplied with the digital maps containing the fertilization recommendations (one polygon per management zone) in the appropriate format (i.e., after their conversion from shapefiles to XML files). Yield values per zone were derived from annual yield maps (except cultivation year 2016).