## 3 Methods
### Effects of Changes in Weather and LULC on NPP  
NPP variations are driven by many impact factors. As illustrated in CASA model, two main input factors are weather variables and land cover data. CASA is a process-based model and appropriate to estimate NPP on a global or regional scale [13]. In the CASA model, NPP is the product of modulated absorbed photosynthetically-active radiation (APAR) and a light use efficiency factor, namely [54]: \begin{table}
\begin{tabular}{l l l} \hline \hline
**LULC Types** & \
\begin{tabular}{l} **Canopy** \\ **Cover (\%)** \\ \end{tabular} &
\begin{tabular}{l} **Canopy Woody Communities** \\ \end{tabular} \\ \hline Evergreen Needleaf Forests (* ENF) & \(>\)60 & Green Needleaf forests species. \\ Evergreen Broadleaf Forests (* EBF) & \(>\)60 & Green Broadleaf forests species. \\ Mixed Forests (* MF) & \(>\)60 & Mosaic of multiple forest species, but no single canopy greater than 60\%. \\ (Open) Shrublands (* SL) & 10–60 & Woody vegetation less than 2 meters tall and mainly shrub. \\ Woody Savannas (* WS) & 30–60 & Herbaceous and other understory systems. \\ Grasslands (* GL) & 10–60 & Lands with herbaceous types of cover. Tree and shrub cover is less than 10\%. \\ Croplands (* CL) & 10–30 & Temporary crops followed by harvest and bare soil. \\ Cropland/Natural Vegetation & & A mosaic of croplands, forests, shrubland grasslands in which no one \\ Mosaics (* CL\&NVM) & 10–60 & component comprises more than 60\% of the landscape. \\ Barren (* BR) & 0–10 & Exposed soil, sand, rocks or snow that never has more than 10\% \\ Water bodies (* WB) & NA & Oceans, seas, lakes, reservoirs and rivers. \\ Urban and Built-up Lands (*U\&BL) & 10–30 & Buildings and other man-made structures. \\ \hline \end{tabular} Note: * is the abbreviation of the classes.  
\end{table} 
Table 1: International Geosphere-Biosphere Program (IGBP) criteria used for the land use/land cover classes. \[NPP(x,t)=APAR(x,t)\times\varepsilon(x,t) \tag{2}\]  
where, \(NPP(x,t)\) represents NPP in the geographic coordinate of a given location \(x\) and time \(t\). \(APAR(x,t)\) (MJ\(\cdot\)m\({}^{-2}\cdot\)mon\({}^{-1}\)) is the APAR absorbed by the vegetation. \(\varepsilon(x,t)\) is the actual light use efficiency (g\(\cdot\)C\(\cdot\)MJ\({}^{-1}\)) of the vegetation. The algorithm of light use efficiency can be expressed as Equation (3):  
\[\varepsilon(x,t)=T_{\varepsilon 1(x,t)}\times T_{\varepsilon 2(x,t)}\times W_{ \varepsilon(x,t)}\times\varepsilon_{\max} \tag{3}\]  
where \(T_{\varepsilon 1(x,t)}\) and \(T_{\varepsilon 2(x,t)}\) are temperature stress coefficients; \(W_{\varepsilon(x,t)}\) is a moisture stress coefficient and \(\varepsilon_{\max}\) is a biome-specific light use efficiency factor that is estimated from daily meteorological conditions. \(\varepsilon_{\max}\) is the maximal light use efficiency of the specific biome under ideal conditions (minimum temperature and vapour pressure deficit) and is related to vegetation fraction and types. The CASA ecosystem is robust in describing NPP spatial and temporal patterns [54], and can easily obtain NPP with the help of remote sensing data, but which does not take into account the physics processes of photosynthetic assimilation, respiration, and assimilate allocation [13]. On the basis of light use efficiency (LUE) and weather variables of temperature and moisture [55], inputting with the time-series NDVI data for the specific covers, and thus, the NPP variation process, can be estimated for each pattern. So, the derived LUE and NDVI data present significant differences among various vegetation types. Remote sensing data combined with basic field survey work is beneficial to obtain the accurate value for long-term monitoring of a wide area [56]. As the above stated, the effects of LULC change on NPP can be regarded as a disturbance caused by human activities in urban expansions, and the effects of climate change indicates the natural change of the ecological system. However, most studies only account for one of the two aspects [57].