# _remote sensing_
###### Abstract
The first step of this joint research was to retrieve crop biophysical variables, such as Leaf Area Index (LAI), leaf pigments both at the leaf and the canopy scales and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), to understand their impact on a variety of agronomical applications including precision agriculture, crop traits monitoring and yield prediction as well as grain quality. To these aims, the team has co-authored a study [11] that evaluates the performance of the most recent retrieval algorithms, including hybrid methods as well as machine learning regression (MLR) algorithms exploring active learning strategies. The performance of different retrieval algorithms using Sentinel-2 data were tested on two test areas: Maccarese (Italy) and Sunshy (China). Results of study [11] showed that it is difficult to identify a single algorithm as the best for the retrieval of the biophysical crop variables considered in all cases. Nevertheless, the study showed that the estimation of the different variables was consistent for both the Italian and Chinese datasets. The second step regards the use of the retrieved biophysical variables within crop growth models. Crop biophysical variables and indices derived from EO optical imagery have been used as a proxy for other crop characteristics that cannot be directly accessed from EO sensors, such as crop yield. However, as highlighted in the recent review by [PERSON] et al. [10], the link between optical EO and crop yield is rather complex and nonlinear, due to the interactions of many agro-environmental factors such as weather, plant growth and development dynamics, water and nutrient balance and crop management. Empirical methods that link, through parametric or nonparametric regression, crop optical properties to crop yield show poor generalization capability and require additional field work for calibration and validation, especially when applied on a new location or crop variety. Coupling crop models, which describe processes and interactions within the agricultural systems using mathematical equations, with EO data, which provide in-season updates of the canopy state, has been considered a winning strategy to obtain more accurate yield predictions [12]. Data assimilation, for example, is a procedure that updates a model state variable, such as for example simulated LAI, each time a new in-season observation, i.e., EO-derived LAI, is acquired, correcting the model simulation trajectories. The first assimilation tests, as part of the results obtained in the framework of the ESA-MOST Dragon 3 cooperation programme ([[https://dragon3.esa.int/web/dragon-3/home](https://dragon3.esa.int/web/dragon-3/home)]([https://dragon3.esa.int/web/dragon-3/home](https://dragon3.esa.int/web/dragon-3/home)), accessed on 13 April 2021), were focused on the estimation of wheat yield, both at the field and the regional scales. The study of [11] assimilated LAI and canopy cover (CC) into the SAFY model [13] for the Yangling area (Central China) using both the Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) imagery. In this first experiment, the retrieved LAI values were assimilated into the SAFY models by applying an Ensemble Kalman filter (EnKF), while the obtained CC values were used in the Particle Swarm Optimization (PSO) procedures in the Aquacrop model. The results of this study [14] showed that this assimilation approach could lead to an estimation error of about 20% for the yield. These results are also extremely dependent on models' assimilation procedures (EnKF for SAFY and PSO for Aquacrop) and previous model calibration. In more recent tests, with different assimilation procedures, EO data proved to be useful in combination with the Aquacrop-OS crop model, allowing for more precise estimations of crop yield as a result of the sensitivity analysis and calibration of Aquacrop using EO-retrieved canopy cover [15; 16].