## 1 Introduction  
The influence of water and nitrogen fertilization is crucial for plants due to their condition and the costs of plant production [1; 2]. The use of nitrogen fertilizers in plant production can cause environmental pollution and it can have a negative impact on soil [3; 4]. This is mainly seen in the change in surface and groundwater chemistry and the reduction in soil productivity [5]. On the other hand, nitrogen deficiencies adversely affect plant production and thus reduce profits for farmers [6; 7; 8]. Properly selected doses of fertilizers taking into account the needs of the plant in a given location, will ensure cost optimization and increased plant comfort [9; 10; 11]. In addition, modern fertilization techniques combined with new fertilizer products reduce the amount of harmful compounds introduced into the environment [5]. Gathering information about the condition of plants and deficiencies of basic nutrients is conducted using various methods and techniques. These methods can be divided into invasive--leading to plant destruction [12], or non-invasive, where the sensor does not have direct contact with the analyzed object or does not destroy it [13]. Invasive methods are generally applied under laboratory conditions on a small number of plants. In the case of remote sensing methods, the acquisition of information can in principle take place continuously at different spatial scales. In the case of remote sensing data, this ranges from a single leaf through a selected field to continental and global scales [14]. Conducting research in controlled laboratory conditions allows for the verification of many initial hypotheses, which are then transferred to the field scale [15]. Analyses of the influence of various stress factors on plant growth and development can be conducted using multi- and hyperspectral cameras [16; 17; 18]. One of the most frequently studied stress-inducing factors affecting plant health is water deficiency [19; 20; 21]. Hyperspectral data analysis also enables the detection of stress caused by deficiencies in specific macro-elements in the soil environment, for example, [PERSON] et al. [22], [PERSON] et al. [23] and [PERSON] et al. [24] investigated the possibility of detecting stress associated with fertilizer dosage in barley using visible and near-infrared (VNIR) hyperspectral data. [PERSON] et al. [25] used VNIR hyperspectral data to assess the content of NPK in tomatoes. They achieved the best results (R\({}^{2}\) above 0.9) for nitrogen prediction. [PERSON] et al. [22] used hyperspectral data in the range of 400-2500 nm to determine the possibility of detecting the influence of NPS fertilization on rice. Remote sensing methods for determining the content of individual nutrients in plants are mainly based on: the discrimination of appropriate wavelengths of radiation based on the reduction of input data dimensionality [22; 25; 26; 27; 28], the correlation between vegetation indices and the content of individual macro-elements [22; 29; 30; 31], mathematical modeling based on different input data [32; 33], and data classification to discriminate specific features [34; 35]. Analysis of hyperspectral data provides ample opportunities for discovering relationships between different stress factors for plants in the natural environment [36]. Research conducted under controlled conditions facilitates the elimination of a large group of naturally occurring factors and creates opportunities for selecting only those wavelengths in which the stress-inducing factor has the greatest influence on reflectivity [37]. The aim of this study was to determine the effect of water availability on nitrogen concentrations in sugar beet and celery leaves using hyperspectral data.