# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 1 Introduction  
As vegetation productivity is one of the most variable components of the terrestrial carbon cycle, accurately estimating this component is important in research on terrestrial ecosystems, carbon cycles and climate change. The technology of remote sensing is developing rapidly, but it is still difficult to precise monitor dynamic changes in vegetation productivity in both high spatial and temporal resolution simultaneously. The 16-day revisit cycle and cloud contamination of Landsat images have long limited their use in studying regional and global biophysical processes, which evolve rapidly during the growing season [1]. In contrast, the coarse resolution of sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have a short revisit period, but the lower spatial resolution images introduce some difficulty in fine scale environmental applications [1; 2]. Although several vegetation gross primary production (GPP) and net primary production (NPP) products from MODIS and Landsat have been generated, and some GPP and NPP algorithms have been developed, the contradiction between spatial and temporal resolution of GPP and NPP productsfrom single sensor limits their application in vegetation dynamics research and ecosystem services [2]. In this condition, advances and new methods are needed to exploit multi-scale data streams for producing time series high accuracy GPP and NPP in both a higher spatial and temporal resolution, which is also of great significance in studying vegetation productivity dynamics and carbon budgets. To produce high accuracy GPP and NPP datasets in both high spatial and temporal resolution, high quality input data with high resolution are needed. MODIS GPP and NPP products (MOD 17) were generated from a Light Use Efficiency (LUE) model from high quality MODIS leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products (R\({}^{2}\) = 0.6705 and RMSE = 1.1173 for the LAI product, R\({}^{2}\) = 0.8048 and RMSE = 0.1276 for the FPAR product against the Earth Observing System sites) [3; 4]. The estimated Global LAnd Surface Satellite (GLASS) GPP and NPP products [5] were derived from high quality GLASS LAI and FPAR products (RMSE = 0.7848 and R\({}^{2}\) = 0.8095 for the LAI product, and RMSE = 0.1276 and R\({}^{2}\) = 0.8048 for the FPAR product against the Validation of Land European Remote sensing Instrument) [6; 7]. But the contradiction between spatial and temporal resolution still exists in the inputs data for estimating high resolution GPP and NPP. Downscaling methods are good ways for blending the multi-scale remote sensing data and create predictions at a finer resolution than the inputs [8], therefore, have great potential in generating high resolution input data for vegetation productivity estimation. Traditional downscaling methods include image fusion methods, such as intensity-hue-saturation (IIHS) [9], principal component substitution (PCS) [10], wavelet decomposition [11] and wavelet transforms [12] that focus on producing new multispectral images that combine high-resolution panchromatic data with low resolution multispectral observations [13]. Regression approaches focus on downscaling images using relations between fine resolution data and coarse resolution images in other wavebands [14; 15]. Machine learning techniques, such as support vector machine (SVM) [16; 17] and artificial neural network (ANN) [18; 19] are also used for downscaling in remote sensing. The spatial and temporal adaptive reflectance fusion model (STARFM) [1] was initially developed for blending Landsat and MODIS surface reflectance, and has demonstrated utility in generating maps of surface reflectance that preserve the high spatial resolution of Landsat and the high frequency of MODIS.