## 5 Conclusions  
In this study, we proposed a method to determine the optimal field-scale yield prediction period by Sentinel-2 VI measurements. To illustrate its application, this method was applied to a wheat field in the Po River Valley (Emilia Romagna, Italy) using the NDVI index. The method was specifically developed to operate at the single-field level in order to minimize the influence of external factors such as soil type, topography, microclimate variations, and agricultural practices, which can significantly affect yield predictions. These factors are particularly relevant in areas with highly fragmented agricultural landscapes, such as Italy.  
As a preliminary step, Sentinel-2's 5-day temporal resolution was used to generate mosaics free from clouds or humidity. A self-developed \"NDVI-based Clear Pixel Procedure\" (NDVI-CPP) was applied to optimize cloud detection by adjusting the cloud probability (CP) threshold, which improved yield prediction accuracy compared to scenes without this adjustment. For the case study, the optimal CP threshold was set at 66%. However, this step can be omitted in areas with less persistent fog or cloud cover.  
The core of the proposed method is its ability to classify variable-length periods using historical VI compositions from Sentinel-2, identifying the optimal time window for yield prediction for specific crops and fields. In the example provided, which focuses on a wheat field in Northern Italy, 15 NDVI periods of varying lengths were composed on a monthly basis, with February showing the strongest correlation with yield prediction. Upon analyzing air temperature data, it was hypothesized that this stronger correlation was due to stem elongation occurring earlier (in February rather than March) because of unusually high temperatures. More generally, the satellite-detected vegetation cover during the January-March period was more representative than that observed in April-May, even though the latter is closer to the harvest.  
Beyond the results obtained on this specific application, which are not directly transferable due to the variability between fields, the broader objective of this work is to present an exportable method, particularly applicable in areas with highly fragmented landscapes that often lack sufficient homogeneous data to test and validate ML models. The method allows users to determine, for any specific application, the following:  
1. The optimal cloud probability threshold;
2. The optimal period for yield prediction. And the following can also be applied:
1. Different space-time intervals;
2. Other types of crops;
3. Other variable-threshold cloud mask collections;
4. A different MVC time (15 days, 3 weeks, 6 weeks and so on);
5. No (or any other) time-based composite technique (like MVC).  
Once the optimal CP threshold (which requires NDVI and ground vegetation cover to be applied) has been set, the optimal period for yield prediction can be determined (in the same space-time interval):  
6. For any Vegetation Index (VI).  
The generalized process for \"optimal yield forecast period determination using the NDVI-CPP\" is outlined in Figure 13.  
**Author Contributions:** Conceptualization, R.C.; methodology, R.C. and V.T.; software, R.C.; validation, R.C., E.C., and N.G.; formal analysis, R.C. and C.F. ([PERSON]); investigation, R.C.; resources, R.C., C.F. ([PERSON]) and V.T.; data curation, R.C.; writing--original draft preparation, R.C.; writing--review and editing, V.T., P.D., and C.F. ([PERSON]); visualization, R.C. and C.F. ([PERSON]); supervision, V.T., P.D.; project administration, V.T. and N.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** This study is a re-analysis of existing data, which are openly available at locations cited in the reference section. Publicly available datasets were analyzed in this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.