## 4 Discussion  
The present work explores a multi-sensor and multi-temporal approach to develop a Proof of Concept (POC) for an automatized processing chain for monitoring crop cycles. It can be applied to monitor crops in different seasons, providing a reliable tool for identifying agricultural practices such as crop rotation. The results can support the Common Agricultural Policy (CAP) and the Italian institutional users, which encourage the development of mapping and monitoring through semi-automatic algorithms for identifying crops and agricultural activities throughout the year [7]. The added value of this POC is related to the advantage of retrieving vegetation biomass and a vertical structure, supporting intra-annual crop-rotation mapping, which influences both the sustainability of agricultural practices and the payments related to the European CAP. The added value of a multi-sensor approach has been investigated using TerraSAR-X (TSX) and Sentinel 2 (S2) data in synergy. As demonstrated by several authors, SAR data are widely used for rice and grassland monitoring [11; 14; 21], but the results of this work are related to the advantage of using a multi-sensor approach to retrieve many classes of crops. The increase in the OA of comparing a single-sensor approach (S2) with a multi-sensor approach (S2 + TSX) becomes remarkable considering the producer accuracy of specific crops (e.g., tree crops), with an improvement of 13% due to the use of the coefficient of variation (CoV) from the radar backscatter. The CoV was one of the most relevant parameters in supporting the separation of tree crops from the other classes. A fundamental step was the analysis of the separability of crop classes, analyzing the time trends of the main vegetation and soil descriptors (radiometric indices) and the backscatter signal. If we consider only the S2-based variables, pasture and soybean have similar responses, but by integrating the TSX multi temporal backscatter, it is possible to identify a peak for soybean on three dates (4 August, 26 August, and 6 September). The limited separability of some classes can explain the low values of OA. For example, rice and vetch's multi-temporal patterns are similar for all variables. The limitation related to the separating certain crops that show a similar multi-temporal pattern can be overcome by integrating the processing chain variables derived from SAR data (e.g., the flooded period of rice). Furthermore, the farmers' declaration used to train the processing chain is affected by a bias: it is an annual declaration, so only one crop on each field is reported without considering an intra-annual crop rotation. Thus, the analytical approach developed in this study is independent of the availability of field data since the parameters used (i.e., radiometric indices and backscatter signal) are mostly related to the intrinsic properties of the crops and their phenology (i.e., vegetation biomass and vertical structure). These characteristics remain almost unchanged under similar climatic conditions. This approach has been developed for the Lombardy Region, and it is geographically limited to the specific study area until the agroclimatic conditions do not change enough to determine different patterns in crop phenology. To apply the processing chain to different agroclimatic scenarios, it would require new training datasets regarding the crop typologies and the crop typologies, and this could open new issues related to the overlapping of radiometric signals among crop types. The MESMA decision tree should also be re-adapted in terms of thresholds, considering that this part of the process cannot be completely automatized. The processing chain is ready to be used to trace the patterns of different years, and furthermore, it is not limited to S2 and TSX but is ready to ingest images acquired by other sensors because it is based on algorithms that are well consolidated for different sensors.