## 4 Discussion  
Land cover result trends show an increase of vegetation land cover thickness from the northern peninsula to the southern part of the province. Natural forest, plantation, water body, and agriculture all have user accuracy values greater than 70%, while built up field, miscellaneous land, and bare land all have user accuracy values less than 70%, indicating that time pixels have been substituted for adjacent land cover groups. This may be attributed to poor image visibility caused by cloud interference in the study area. However, the trend is consistent with the findings of [PERSON] et al. [40], who reported an increase in plantation land use land cover within the area between 2010 and 2017. The existence of a wide expanse of paddy fields in the north relative to the scanty ones in the south supports this pattern. Land suitability considerations might be responsible for this trend, and this may be attributed to land availability occasioned by seasonal flooding of the peninsula resulting in plains suitable for rice in the north. Whereas the forested vegetation scenario in the south where afforestation and urbanization being likely the major driving forces for land-use change in the area. The distribution trends of vegetation indices conformed to the spatial patterns in the study area. However, several works of literature [7; 17; 41] indicate a robust relationship between vegetation indices and biomass, thus suggests that vegetation indices can serve as a phantom guide for presumably quantifying plant community composition and biophysical properties. The distribution of the NPP pattern is characteristic of tropical environments in Southeast Asia, South America, and Africa, with sufficient solar radiation, warmth, and precipitation. A popular explanation of NPP is that it is the difference between the gross productivity and the rate of energy lost due to metabolism and maintenance, hence refer to as the rate of plants' energy storage as biomass. Normally, NPP distribution varies with the ecosystem, warmer and wetter climates encourage and increase the proportion of gross productivity being exhausted in respiration. These observations are consistent with existing studies on NPP, and the findings agree well with the results reported by [PERSON] et al., [41], who used the Carnegie-Ames-Stanford Approach (CASA) model to predict the mean annual NPP from for Southeast Asian countries. They reported Thailand's NPP by evergreen broadleaf forest, evergreen needle leaf forest, mixed forest, and open wood savanna land cover classes as 862 gC/m\({}^{2}\)/year, 704 gC/m\({}^{2}\)/year, 647 gC/m\({}^{2}\)/year, and 718 gC/m\({}^{2}\)/year respectively. They further affirmed that particular forest regions in these countries having the highest assessed NPP at more than 1100 gC/m\({}^{2}\)/year included southern Thailand. The consistent trend of NPP estimates in Songkhla province could be a function of the abundance of highly productive tropical forests in the southernmost part and saline marshes in the northern districts, in addition to the prevailing warm and humid climate coupled with nutrient availability induced by agriculture among other factors across the province. MODIS has become one reliable tool for tracking modifications in the productivity of the terrestrial ecosystem, it has sensors capable of collecting surface spectral signatures to measure the fluxes in terrestrial GPP/NPP on larger scales. The sophistication of this technology does not authenticate it, considering its tendencies to errors from interactions with atmospheric components [42], hence the need to validate remotely sensed NPP against ground measurements. By way of validation, the MODIS NPP outputs were compared to eddy covariance flux towers to assess the consistency and coherence in MODIS product estimations. This work demonstrates a good agreement between MODIS NPP and the flux tower NPP.