## 3 Results and Discussion
### Methodology
#### 3.3.3 PRISMA Dataset Descriptions  
Figure 9 depicts the location of the study in India (11 deg38' N, 78 deg10' E) with a land coverage of 900 km\({}^{2}\). The study area is in the Tirunelveli District, Tamil Nadu, India and the most comprehensive soil mapping provides less than 0.4 percent cloud coverage. From 402 to 2500 nm, a total of 143 bands are present. As illustrated in Figure 9, the SAM classification identifies the amount of soil that matches the original spectra. The 4 th, 17 th, 18 th, and 63 rd Digital Numbers (DNs) of 65 soil samples have the best mapping with more AOC content. The fourth reflectance soil sample had 662,569 points and a success rate of 47.24 percent [35; 36]. Table 6 shows the number of points for the remaining soil samples.  
Four lab-derived models were used to map the soil nutrient levels (AOC, AN, AP, and AK) for the study area at the regional scale using the ideal spectral characteristics from the PRISMA image. By resampling the PRISMA spectral data to predict soil nutrient concentrations, the original ideal spectral variables for AOC, AN, AP, and AK were obtained. Figure 10 shows that the PLSR indicated soil nutrient concentration is the best match spectra based on laboratory hyperspectral data. Using linear spectral unmixing analysis, the ideal spectral variables for soil components were generated from the bands. When applied to PLSR, lab-derived models with optimal spectral variables produced better results, but PLSR produced similar regional distributions of soil nutrient concentrations.  
Figure 9: Study area for soil mappingin Tirunelveli district, Tamilnadu, India.  
The PRISMA HSI data are also used to map soil nutrient levels in the study area in order to test the PLRS prediction models' regional scale applicability. Figure 11 shows that the AOC content was more reliable than the AN, AP, and AK contents, indicating that the SAM could map soil AOC content at the regional scale using PRISMA HSI images which is shown in Figure 11. SAM models are used to map soil AOC content. Soil nutrient content prediction accuracy using the PRISMA HSI image was only moderately better than measured hyperspectral data due to differences in spectral band ranges between measured hyperspectral data and hyperspectral images. These variables will be taken into account in future analyses. PRISMA and ground truth data were used for the spectral mapping. SAM performs comparably with other per-pixel-based classification systems in terms of classification results [37; 38; 39]. The SAM approach analyses images and soil spectra, and manipulates the angular distance between the image and soil spectra. In total, 173 bands are present from 402 to 2500 nm [40; 41]. The SAM classification identifies the amount of soil that matches with the original spectra, as shown in Figure 10. Out of 65 soil samples, the 4 th, 17 th, 18 th, and 63 rd Digital Numbers (DN) are the samples that have the best mapping with more AOC content [15; 31; 34]. The fourth reflectance soil sample had 662,569 points at 47.24%. The numbers of points for the remaining soil samples are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **DN** \\ **Reflectance Soil Sample** \\ \end{tabular} & \begin{tabular}{c} **Number of Points** \\ \end{tabular} & \
\begin{tabular}{c} **Actual** \\ \end{tabular} &
\begin{tabular}{c} **Percentage Accuracy** \\ \end{tabular} \\ \hline RP16A & 662,577 & 662,577 & 47.2648 \\ \hline RP17A & 589,432 & 1,252,009 & 89.3118 \\ \hline RP23A & 149,615 & 1,401,624 & 99.9846 \\ \hline RP36B & 5 & 1,401,629 & 99.9849 \\ \hline RP8A & 207 & 1,401,836 & 99.9997 \\ \hline RP98 & 4 & 1,401,840 & 100.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Mapping of raw spectral reflectance with PRISMA hyperspectral data.  
Figure 10: Spectral mapping with PRISMA and ground truth data.