# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 2 Materials and Methods
### Methods
#### 2.3.1 Downscaling of FPAR and LAI  
The Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) ([PERSON] et al., 2017) was adopted to downscale 1 km MODIS LAI/GLASS LAI to 30 m LAI. The STEM-LAI is a downscaling method based on STARFM and regression tree to generate time series high resolution LAI from MODIS data and Landsat images. As STARFM is extendable to some other biophysical properties ([PERSON] et al., 2016; [PERSON] et al., 2017), such as evaporation ([PERSON] et al., 2017) and land surface temperature ([PERSON] et al., 2017), high resolution FPAR data were also derived from this method. The specific steps of LAI and FPAR downscaling as follows:  
Firstly, high-quality MODIS/GLASS LAI samples retrieved from the main algorithm were selected based on the product quality flags. The MODIS/GLASS LAI product provide quality control flags for  
Figure 2: Location of field sampling sites. Twenty-one field observation sites distributed in the 30 km \(\times\) 30 km region, among which 17 field observation sites are in the core 5.5 km \(\times\) 5.5 km region.  
each pixel. The quality control flags embedded in the MODIS and GLASS product reflect the retrieval quality reasonably well. To ensure that only the best quality of data are being used in the regression tree training.  
Then the homogeneous MODIS/GLASS pixels were selected by using the coefficients of variation of the Landsat pixels inside each MODIS/GLASS 1 km pixel. The coefficient of variation was defined as the ratio of the standard deviation to the mean value [37]. The coefficient of variation (CV) was computed and averaged among all Landsat spectral bands from Landsat surface reflectance inside each MODIS/GLASS pixel cell [37]. The CV described the relative variation of a MODIS/GLASS pixel in a Landsat resolution (30 m). The smaller the CV was, the purer the MODIS/GLASS pixel was. If the band averaged CV of a MODIS/GLASS pixel was less than a threshold, the MODIS/GLASS pixel was considered to be a homogeneous sample.  
A regression tree approach [37] was then established between a reference dataset (MODIS LAI/GLASS LAI) and pixel-aggregated Landsat surface reflectance, and then applied to Landsat imagery to estimate LAI at 30 m resolution during the Landsat acquisition dates. Specifically, high quality MODIS LAI/GLASS LAI retrievals from homogeneous pixels were used as reference samples to build rule-based Cubist regression tree [48] that relate low resolution LAI samples to aggregated Landsat surface reflectance via multivariate linear regression, then the high resolution LAI (30 m) on the Landsat acquisition data could be obtained from the regression tree. The cubist regression tree method was a data mining approach that builds rule-based predictive multivariate linear regression models based on available samples.  
At last, STARFM was then applied to time series MODIS LAI/GLASS LAI (1 km) and Landsat derived high resolution LAI (30 m) data streams to interpolate high spatial resolution LAI. As STARFM is extendable to some other biophysical properties [1; 25] so long as the multi-scale input data streams are comparable and spatially and temporally consistent, high resolution FPAR data were also derived from this method.  
Figure 3: Flowchart of gross primary production (GPP) and net primary production (NPP) estimates from downscaling methods. High resolution (30 m) fraction photosynthetically active radiation (FPAR) and leaf area index (LAI) were firstly obtained by using a downscaling method, then time series high resolution (30 m) GPP and NPP were estimated by using a Light Use Efficiency (LUE) model.