###### Abstract  
Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R\({}^{2}\) of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C\(\cdot\)m\({}^{-2}\).day\({}^{-1}\) on average, and MAD decreased by 0.87 g C\(\cdot\)m\({}^{-2}\).day\({}^{-1}\) on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow. green chromatic coordinate; gross primary production; remote sensing; carbon flux; Tibetan Plateau; machine learning 2020 IEEE/RSJ Web Conference (); 2020 IEEE/RSJ Web Conference (); 2020 IEEE/RSJ Web Conference (); 2020 IEEE/RSJ Web Conference (); 2020 IEEE/RSJ Web Conference ();dynamics of GPP on a regional and global scale is of great significance for understanding the terrestrial ecosystem carbon cycle, assessing grain production, predicting future climate change scenarios and making climate-policy decisions [6; 7; 8]. Although there are many GPP models have been developed in the past few decades, it is still a challenging work to accurately estimate daily GPP. [PERSON] first proposed the method of estimating vegetation productivity by using photosynthetic effective radiation absorbed by vegetation and established the basis of light use efficiency model [1]. This method is widely used in regional GPP estimation with remote sensing data [9; 10]. However, owing to the influence of atmospheric conditions, cloud and sensor degradation, there were large uncertainties in GPP estimated by remote sensing vegetation index data and the light use efficiency (LUE) model [11], such as the CASA model [12], the global production efficiency model (GLO-PEM) [13], the vegetation photosynthesis model (VPM) [14] or the eddy covariance-light use efficiency (EC-LUE) model [15]. However, it is reported that the satellite remote sensing-based GPP contains many problems caused by snow and cloud contaminants and low temporal resolution. For example, GPP was underestimated at the majority of sites in African ecosystems [16] and has unstable performance at various sites [17; 18]. Phenological cameras can provide almost time-continuous images at a small spatial scale; meanwhile, they are rarely disturbed by clouds [19; 20; 21].