# _remote sensing_
###### Abstract
A further step of this research regarded the prediction of both yield and grain quality in protein concentration before harvest by using hyperspectral and atmospheric data in a hierarchical linear modelling (HLM) [17]. The authors obtained a good correlation with yield data by configuring the HLM with a specific spectral index and using local climatological data. Even though a variability has been observed at the interannual scale, results showed the best accuracy by using the sLAIDI spectral index (RMSE of about 1.10 t/ha) and a grain protein content (GPC) prediction with an RMSE of 1.37%. The third step involves the detection, recognition, and mapping of crop diseases. Both European and Chinese researchers have exploited the potential of recently available hyperspectral remote sensing data for crop stress and diseases early detection. The authors of [18] investigated the potential of hyperspectral data, when combined with leaf textures, to recognize yellow rust (_Puccinia striiformis_) infection on winter wheat and _Fusarium spp_. of wheat ears [19; 20]. The Chinese team has discovered that the use of six optimal spectral bands, in the Visible and Near Infrared (VNIR) spectral regions, identified by applying a continuous wavelet analysis, and 24 texture features extracted from a principal component analysis (PCA) provides a very good sensitivity to the yellow rust disease [19]. Moreover, to test their applicability to a hyperspectral data set, when available at the ESA or Chinese level, support vector machine (SVM) models were created. Concerning the _Fusarium_ head blight in wheat, the authors have identified, using a Continuous Wavelet Analysis (CWA), six wavelet features with a good sensitivity to these diseases, five in the VNIR and one in the SWIR spectral range. As for the yellow rust, a detection model based on Fisher linear discriminant analysis was developed and preliminarily tested on unmanned aerial vehicle (UAV) imagery, with an overall accuracy of about 87%, in view of a possible application and upscaling to satellite data [20]. It is important to consider the increasing availability of hyperspectral satellite data: (a) Italy launched the PRecursore IperSpettrale della Missione Applicativa (PRISMA) Earth Observation satellite in 2019 [21]; (b) China launched the GaoFen-5 (GF-5) in 2018 [22]; (c) Japan launched the Hyperspectral Imager Suite (HISUI) hyperspectral satellite sensor in 2019 [23]; (d) India launched the ISRO's Hyperspectral Imaging Satellite (HYSIS) hyperspectral satellite in 2018 [24]; and (e) Germany launched the DLR Earth Sensing Imaging Spectrometer (DESIS) hyperspectral instrument in 2018 on board the International Space Station (ISS) and is planning to launch the Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite in 2022 [25; 26]. Moreover, NASA and ESA are cooperating for two new hyperspectral missions, i.e., SBG-NASA and CHIME-ESA [27]. In summary, this study intends to describe the latest results of the different research activities conducted within the SINO-EU programme performed between 2016 and 2020 by the Italian and Chinese researchers on different test areas. The manuscript is structured so that each section deals with the retrieval of biophysical and agronomic variables and the agricultural pests and diseases' monitoring and forecasting. Different test cases were presented, trying to provide answers to the variety of emerging user needs in the agricultural domain and each topic can report different test cases, which, for their peculiarity, can highlight the innovative research results obtained.