# _remote sensing_
###### Abstract  
Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t-ha\({}^{-1}\) vs 4.42 t-ha\({}^{-1}\) RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the _Pseudomonas syringae_ pathogen on kiwi orchards with an overall accuracy higher than 91%. multispectral data analysis; satellite data assimilation; crop variables estimation; modeling; crop pest and disease 2021 132889. 132889.  
pests and disease in fragmented agricultural systems such as those of Southern Europe and China [7; 8; 9]. The European ESA Sentinels, the ESA Third Party Mission (TPM) and the Chinese satellites constellation, when used in synergy, provide a unique satellite data set that needs to be fully exploited for the benefit of the agricultural domain [10]. It is of great interest to the SINO-EU community to test new procedures and algorithms, as well as to improve the existing ones, to exploit the potential agronomic information that can be retrieved when assimilating the ensemble of the SINO-EU multi-temporal and multi-sensor satellite data sets. Meanwhile, a full EO synergy between China and the EU will be envisaged for the community, and joint research in the framework of the ESA and Ministry of Science and Technology (MOST) collaborative program ([[http://dragon4.esa.int/](http://dragon4.esa.int/)]([http://dragon4.esa.int/](http://dragon4.esa.int/)), accessed on 13 April 2021) are planned to exploit and demonstrate the contribution of SINO-UE components to agricultural systems. The research included activities for: (a) methodological issues concerning the optimal algorithms to be applied for the quantitative assessment of crop vegetation variables and their errors and uncertainties related to the remote sensing observations; and (b) the methodological aspects related to the assimilation of EO-based vegetation quantitative variables within crop models as well as crop models' formulation errors and parameter uncertainties. This work aims at providing examples of the added value that a SINO-EU EO satellite data synergy could provide for specific agricultural variables' retrieval and prediction. In this study, activities focused on common cropping systems in Italy and China for: (1) the estimation of variables of agronomic interest linked to the water and nitrogen cycling in the agro-ecosystems; (2) the improvement of the prediction of wheat yield and quality, both at the regional and the farm scale; and (3) the detection, recognition and mapping of the status related to biotic stresses for cereal crops (yellow rust and _Fusarium_) and orchards (attacked by _Xylella fustidiosa_ or other diseases). To fulfill these general objectives, a research roadmap has been drawn to identify the steps required to exploit the collected SINO-EU data sets.