## Materials and Methods
### Net Primary Productivity Assessment and Validation  
Net primary productivity (NPP) describes the degree to which plants within an ecological unit yield net valuable biochemical energy. It is equivalent to the variance concerning the amount at which plants produce beneficial biochemical energy in an ecosystem and the degree to which they consume roughly of that energy on behalf of respiration to produce all tissue that make up their biomass [20], thus NPP is closely related to biomass. The NPP datasets that were retrieved from the MODIS satellite of the Land Processes Distributed Active Archive Center (LP DAAC), available at the link ([[https://lpdaac.usgs.gov/products/mod17a3](https://lpdaac.usgs.gov/products/mod17a3) hgfv006/]([https://lpdaac.usgs.gov/products/mod17a3](https://lpdaac.usgs.gov/products/mod17a3) hgfv006/), accessed on 17 August 2020), were downloaded and processed in ArcMap 10.3.1. The MODI7A3 HGF version six (MOD17A3 HGFv006) product offers data of NPP at 500-m pixel resolution, and the annual NPP is derived from the sum of all 8-day Net Photosynthesis product (MOD17A2H of a given year, which is expressed in KgC/m\({}^{2}\)/year [21]. This product has passed the three-stage validation of accuracy using small independent measurements, wide distributed location sets, and systematic as well as statistically strong means that represent worldwide \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Algorithms** \\ \hline Overall Accuracy & (Total number of correctly classified pixels (diagonal))/(Total number of reference pixels) \(\times\) 100 \\ \hline Users Accuracy & (Number of correctly classified pixels in each category)/(Total number of classified pixels in that category (the row total)) \(\times\) 100 \\ \hline Producer Accuracy & (Number of correctly classified pixels in each category)/(Total number of reference pixels in that category (the column total)) \(\times\) 100 \\ \hline Kappa Coefficient (T) & ((TS \(\times\) TCS)-\(\sum\) (column total \(\times\) row total))/(TS\({}^{2}\)-\(\sum\) (column total x row Total)) \(\times\) 100 \\  & TS (Total Sample) \\  & TCS (Total Corrected Sample) = Total Number of Corrected Classified Pixels \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithms for land cover accuracy assessment, Songkhla Province. conditions. However, in the study, these retrieved MODIS NPP values were compared to an operational science data network called FLUXNET to validate the medium resolution estimations. FLUXNET is a worldwide network of micrometeorological tower sites that use eddy covariance procedures to quantify the exchanges of carbon dioxide, water vapor, and energy amongst terrestrial ecosystems and the atmosphere. The FLUXNET NPP data were downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) available at the link ([[https://modis.ornl.gov/data.html](https://modis.ornl.gov/data.html)]([https://modis.ornl.gov/data.html](https://modis.ornl.gov/data.html)), accessed on 31 December 2019), these are pre-processed subsets for a wide field and flux tower sites for validation of models, also remote sensing products [22]. This work leveraged the NPP data from the FLUXNETS profiled and described in Table 2 to validate MODIS NPP and previous field surveys data coupled with tree AGB-related characteristics (more details Table 5) as ground reference data for AGB evaluation. While conducting the validation process, the root mean square error (RMSE) was used as a conventional standard statistical approach of quantifying the error of a model in predicting quantitative data to correlate the MODIS-NPP, and FLUXNET-NPP, NDVI, and EVI. This analysis aims to quantify and infer how accurately the MODIS NPP values match vegetation parameters across the Songkhla province. The formula for the (RMSE) is given as:  
\[RMSE=\sqrt{\left[\Sigma\frac{(Pi\_{-}Qi)^{2}}{n}\right]} \tag{1}\]  
where \(\Sigma\) is the summation symbol. \(Pi\) refers to the projected value meant for the \(i\)th observed dataset.