# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## Materials and Methods
### Datasets
#### 2.2.2 MODIS  
We obtained MODIS satellite-data-tile subsets from 2000 to 2016 from the Oak Ridge National Laboratory Distributed Active Archive Center (DACC; [[http://www.modis.ornl.gov/modis/index.cfm](http://www.modis.ornl.gov/modis/index.cfm)]([http://www.modis.ornl.gov/modis/index.cfm](http://www.modis.ornl.gov/modis/index.cfm))), alongside the EVI from the standard VI product (MOD13A1, 8-day, 500 m), the LSWI from the surface reflectance product (MOD09A1, 8-day, 500 m) and the LST from the land-surface temperature product (MOD11A2, 8-day, 1 km). The soil moisture and water-vapor pressure deficits are two main factors that directly determine the water contents of vegetation leaves, and the canopy ultimately affects vegetation photosynthesis. The LSWIs were derived from the NIR and SWIR bands, which are sensitive to vegetation's water content and soil moisture [36; 37; 38], as shown in Equation (2):  
\[\text{LSWI}=\frac{\rho_{NIR}-\rho_{SWIR}}{\rho_{NIR}+\rho_{SWIR}}. \tag{2}\]  
where LSWI is the land-surface water index and \(\rho_{NIR}\) and \(\rho_{SWIR}\) are the reflectance values of the NIR and SWIR bands, respectively. The _NIR_ and _SWIR_ bands were derived from the MODIS 8-day surface reflectance product (MOD09A1), which included seven spectral bands. Band 2 (841-875 nm) and Band 6 (1628-1652 nm) are the NIR and SWIR bands, respectively. MOD17 is used to monitor global vegetation productivity and provides 8-day composites of the GPP, annual GPP and NPP at a 1-km resolution. Previous studies confirmed that no consistent overestimation or underestimation exists in MOD17 compared to the productivity from field-observed and flux-tower measurements. A direct validation comparison work on GPP (MOD17A3) that used 38 years of site-observation data showed a high correlation and low bias (r = 0.859 \(\pm\) 0.173; relative error = 24%) [39]. However, MOD17 GPP and NPP still exhibit inaccuracies because of land cover, the FPAR, the LAI and daily meteorological values; these inputs contain substantial inaccuracies [39].  
The preprocessing steps for the MODIS data in this study included constructing subset layers, transforming projections, creating subsets of the study area and creating continuous time series. The monthly data composite used the maximum value composite (MVC) criterion via the 8-day MODIS product. The annual dataset was synthesized by using the mean value during the growing season. A recent study highlighted the importance of the temporal dimension; even the spatial heterogeneity is sacrificed to some degree in low-resolution imagery [40]. This characteristic explains our choice of MODIS imagery, which satisfies the spatial resolution requirement with high temporal resolution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Acquisition Date** & **Years Since the Fire** \\ \hline
1 & 3 August 1999 & Pre-fire \\
2 & 14 September 2000 & Fire year \\
3 & 23 July 2001 & 1 \\
4 & 15 July 2004 & 4 \\
5 & 6 August 2006 & 6 \\
6 & 2 August 2014 & 14 \\
7 & 22 August 2016 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Landsat TM and ETM+ scenes that were used in this analysis. Time-series Landsat images were used to assess the post-fire recovery forests in Northeast China. All images were acquired from the same path (column 122 and row 23).