## 1 Introduction  
Soil qualities vary throughout time and space, and must be evaluated and monitored in real time, and frequent monitoring is also needed. Assessment of soil nutrients is important for agricultural development and natural resource management. Estimation of soil characteristics in the laboratory using wet chemistry methods requires extensive sample preparation and analysis. The physical soil quality analysis requires more time to assess soil's chemical properties. In this context, remote sensing techniques become a viable option for quick soil assessment [1]. A priori, spectral reflectance, and the relationship between spectral characteristics and soil properties are well known. In particular, hyperspectral remote sensing provides data over the entire spectrum in a narrow range of 1 nm and improves the accuracy of soil chemical component quantification [2]. Laboratory studies have revealed that the differential reflectance spectroscopy (DRS) technique can be used to estimate soil parameters such as texture and organic carbon (OC), and nutrient content such as nitrogen (N), phosphorus (P), and potassium (K) [3]. These methods can reliably estimate soil nutrient levels, but spatially detailed projections for the entire research area require more time and money [4]. In China, for example, each soil sample costs approximately USD 1650 to analyze for actual nitrogen, phosphate, and potassium. Using standard methods, it would cost USD 29,650 to generate spatially explicit estimations of soil parameters over a 50 \(\times\) 50 area. This price excludes travel expenses and time spent collecting soil samples. It is possible to quickly assess soil nutrient levels using remote sensing and observe their variations at regional orderscales [5]. Extensive research have been conducted in this field; ref. [6] quantified 15 key soil elements (e.g., nitrogen, potassium, etc.) of tilled farm regions (2011). The authors of [7] forecasted soil parameters in northeast China using a high signal-to-noise ratio measurement technique. They investigated the relationships between pan-sharpened-multispectral indices and soil true nitrogen levels (i.e., available nitrogen (AN)). They used a random forest approach to build a soil AN prediction model in Bijapur District, Karnataka State, South India. Several researchers have measured soil nutrient levels using hyperspectral techniques. Currently, hyperspectral soil nutrient assessment methods are classified as linear or nonlinear forecasts. To forecast soil nutrient content, image-based linear mathematical correlations are used. The most commonly used methods for determining soil parameters are multiple linear regression (MLR) and partial least square regression (PLSR) [8; 9]. Many scientists have used nonlinear approaches to evaluate soil nutrient levels. Machine learning is used in nonlinear methods to build nonlinear correlations between spectral characteristics and soil nutrient levels, and nonlinear models such as SVM, RF, and BPNNs have been widely used to predict soil AN, AP, AK, organic matter, and other parameters [8]. These models have flaws; SVM is difficult to apply to large-scale training samples because quadratic programming procedures are complex and require a lot of memory and calculation time [10; 11; 12]. Also, RF models are trained on training data, they pick up noise and other artefacts, making them less accurate when applied to a new data. Understanding this, this study employs a partial least square regression (PLSR) technique over PRISMA hyperspectral data on part of the Radhapuram area, Tirunleveli District, India.