One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.

In this research, species distribution prediction models (i.e., MaxEnt) were applied to analyze the suitability of the ecological environment among the clades of the genus Gynoxys in Peru. Bioclimatic, edaphic, and topographic variables were integrated to predict the areas with the most significant potential for optimal development of this genus. These data were combined to generate potential distribution maps, taking into account the most relevant variables for each clade. The validation of the MaxEnt model showed an outstanding performance, reaching AUC indices above 0.9, reflecting the high accuracy of the predictions. The results reveal that the key variables influencing the selection of the clade occurrence areas are: mintempwarmest (47.70% contribution) in the Discoide clade, topowet (33.20%) in the Gynoxys clade, and monthcountbytemp10 (33.30%) in the Praegynoxys clade. The potential distribution areas of these clades were 132,594 km² for Discoide, 168,574 km² for Gynoxys, and 37,392 km² for Praegynoxys. The areas with the highest probability of presence of the genus were found in the Andean regions of northern and central Peru. However, a significant proportion of these areas were threatened by habitat fragmentation and land degradation. In terms of conservation, it was found that 32.05, 35.46, and 61.02% of the potential distribution areas of the discoid, Gynoxys, and Praegynoxys clades, respectively, are conserved, which could be a relevant factor for the preservation of this genus. These findings underscore the relevance of safeguarding key areas for conserving Gynoxys and montane ecosystems in Peru, emphasizing the need for protection strategies that guarantee the long-term sustainability of these species and their associated habitats.