Precision agriculture is increasingly considered as a powerful solution to mitigate the environmental impact of farming systems. This is because of its ability to use multi-source information in decision support systems to increase the efficiency of farm management. Among the agronomic practices for which precision agriculture concepts were applied in research and operational contexts, variable rate (VR) nitrogen fertilization plays a key role. A promising approach to make quantitative, spatially distributed diagnoses to support VR N fertilization is based on the combined use of remote sensing information and few smart scouting-driven ground estimates to derive maps of nitrogen nutrition index (NNI). In this study, a new smart app for field NNI estimates (PocketNNI) was developed, which can be integrated with remote sensing data. The environmental impact of using PocketNNI and Sentinel 2 products to drive fertilization was evaluated using the Life Cycle Assessment approach and a case study on rice in northern Italy. In particular, the environmental performances of rice fertilized according to VR information derived from the integration of PocketNNI and satellite data was compared with a treatment based on uniform N application. Primary data regarding the cultivation practices and the achieved yields were collected during field tests. Results showed that VR fertilization allowed reducing the environmental impact by 11.0% to 13.6% as compared to uniform N application. For Climate Change, the impact is reduced from 937.3 to 832.7 kg CO eq/t of paddy rice. The highest environmental benefits - mainly due to an improved ratio between grain yield and N fertilizers - were achieved in terms of energy consumption for fertilizer production and of emission of N compounds. Although further validation is needed, these preliminary results are promising and provide a first quantitative indication of the environmental benefits that can be achieved when digital technologies are used to support N fertilization.