## 1 Introduction
The use of traditional laboratory-based methods for measuring both plant (such as the Kjeldahl digestion method) and soil nitrogen concentrations (both soil inorganic nitrogen content and soil fertility tests) are often complex, expensive and impractical over large swaths [10; 28; 35; 36]. Nevertheless, the use of remotely sensed data provides a more feasible solution in characterising the nitrogen saturation status of tropical grasslands [7]. The ability of remote sensing technology to assess foliar biochemical concentrations, such as nitrogen and chlorophyll, have been widely researched within the remote sensing academic community [10; 28; 37; 38; 39; 40; 41]. Hyperspectral remote sensing, which utilizes many narrow contiguous spectral bands, has been widely utilized to assess the nutritional status of vegetation [7; 39; 42; 43; 44]. However, its practical application has often been restricted by its excessive costs, high degree of multicollinearity and related processing, and its unavailability within developing regions [45; 46]. Nonetheless, the current generation of multispectral sensors, such as Worldwide-3, which have improved spectral and spatial configurations, could provide a more practical alternative [45; 46; 47]. As such, research documenting the ability of multispectral sensors in assessing vegetative nutrient condition has grown in recent years [44; 47; 48; 49]. For instance, [PERSON] et al. [50] used airborne multispectral data (457.8-778.8 nm) to effectively quantify Leaf Area Index (R\({}^{2}\) = 0.77) and nitrogen concentrations (R\({}^{2}\) = 0.78) within an agricultural area in Denmark, whilst in a later study, [PERSON] et al. [51] utilized the RapidEye sensor (440-850 nm) to map foliar (R\({}^{2}\) = 0.48) and canopy (R\({}^{2}\) = 0.64) nitrogen at a regional scale within a savanna rangeland. In a following study, [PERSON] et al. [47] applied Worldview-2 imagery (400-1040 nm) and Random Forest (RF) techniques to successfully map leaf nitrogen (R\({}^{2}\) = 0.81) and aboveground biomass (R\({}^{2}\) = 0.90) within a South African rangeland. To improve nutrient model estimates and to handle the complex relationships between response and predictor variables, [PERSON] et al. [47] and other studies often employed machine learning models, such as RF [52; 53]. For example, both [PERSON] et al. [54] and [PERSON] et al. [55] used RF and Worldview-2 data to successfully estimate grassland nutrients within the Drakensburg mountain range of South Africa, whilst in a more recent study, [PERSON] et al. [49] applied RapidEye imagery (440-850 nm) and RF techniques to map key grassland nutrients (i.e., neutral detergent fibre, acid detergent fibre, and lignin) within KwaZulu-Natal, South Africa. Meanwhile, in the Tibetan region of China, [PERSON] et al. [56] used a multi-factor machine learning algorithm to model Alpine grassland forage quality (R\({}^{2}\) = 0.67). Thereafter, in a related study, [PERSON] et al. [57] used Sentinel-2 imagery and an RF algorithm to map forage nitrogen and phosphorus ratios within the same grassland ecosystem. Despite such contributions towards this research domain, to our understanding, no study has attempted to characterize nutrient saturation levels within tropical grasslands and its impact on plant growth using remotely sensed data. In consideration of this, this study aims to characterize tropical grassland plots under the initial stages of nitrogen saturation using high resolution multispectral imagery and decision tree techniques. It is hypothesized that the use of high-resolution multispectral imagery and decision tree techniques can accurately detect nitrogen saturation in tropical grasslands plots subjected to diverse fertilization treatments. To accomplish this objective, the study firstly differentiated nitrogen-saturated plots using specific physiological criteria identified by [PERSON] et al. [16].