# _remote sensing_
## 5 Conclusions  
Agriculture is one of the major domains in which EO data can be successfully applied in a diversity of agronomical applications, e.g., to optimize the use of chemicals and fertilizer, to foster optimal agronomical practices, and to increase yield and quality production. This domain is a common research issue for both EU and Chinese researchers, since it is formed by an ensemble of operational needs connected to food security and also related to production, prediction and crop diseases, which can affect worldwide food availability. The methodologies, resources and tools applied in the current study, moreover, aim at answering to some of the requirements declared by the European Food Safety Authority [56] concerning the identification of areas of high disease risk where to target the surveys, and the development of detection methodologies and reporting procedures to derive the potential for early detection and monitoring of new outbreaks. This manuscript aims to summarize the effort performed by the SINO-EU research Team, in the framework of the ESA Dragon4 initiative successfully concluded in 2020, for a full exploitation of the Chinese-European EO resources in relation to agricultural applications suitable for both local and regional scales. Most of our effort was addressed to the analysis of the EO data to retrieve, with a suitable accuracy, biophysical and biochemical variables related to crop functioning and photosynthesis. Results show that wheat yield was predicted with a lower RMSE with respect to the standard simulation (e.g., 1.42 t-ha\({}^{-1}\) instead of 4.42 t-ha\({}^{-1}\)) by applying the EnkF assimilation procedure into the SAFY crop model to assimilate LAI values. Regarding the detection of orchards' pathogens, an overall accuracy higher than 95% was achieved by training a NN algorithm. Concerning wheat yellow rust detection, an overall classification accuracy of 89.3% was obtained when applying a WSRF-SVM classification procedure. The research studies performed by the SINO-EU teams still require further steps to minimize the discrepancies between models and data sources when applied at different scales, having also in mind the specificity of the different environments pertaining to EU and China. These studies will constitute a heritage to the ongoing activities in the Dragon-5 program initiative. In this framework, further studies will be addressed to:  
1. Expand the estimation of new variables, by exploring the RTM code PROSPECT-PRO (e.g., for proteins and nitrogen);
2. Exploit the newly operative hyperspectral missions for their application on a regional base to detect pathogens;
3. The implementation of machine/deep learning techniques for the generalization of the pre-operative chains applicable to the local scale for agricultural applications. Further studies will be devoted to exploiting the newly available high spectral resolution satellite data, such as those coming from GF5, PRISMA, EnMAP and, in the future, from SBG and CHIME hyperspectral missions. [PERSON]. ([PERSON]), [PERSON], [PERSON] and [PERSON] designed the conceptual framework of this research; [PERSON] analyzed the data regarding the grain quality; [PERSON] performed the data analysis for the permanent crops' diseases, [PERSON] contributed to the identification of pathogens on cereal; [PERSON]. RTM retrieval and data analysis; [PERSON]. ([PERSON]) acquired and analyzed the field data, contributed to the revision process of the results obtained and reviewed the manuscript. [PERSON] contributed to the data assimilation software and data analysis; [PERSON] was in charge to the SAFY assimilation experiment; [PERSON]. RTM retrieval with hybrid approaches; [PERSON] data analysis; [PERSON] designed the conceptual framework of this research. All authors have read and agreed to the published version of the manuscript. This research was partially funded byDragon4, ESA contract No. 4000121195/17/I-NB. **Institutional Review Board Statement:** Not applicable.