Multitemporal Analysis of Observed Flood Recurrence in an Andean–Amazonian Valley in Peru: An Integration of Field Data, SAR Images, and Cloud Computing

Flood monitoring in Andean–Amazonian valleys is constrained by sparse hydrological records and persistent cloud cover. This study mapped observed flood recurrence in the Utcubamba River Valley, Peru, during 2019–2025 using Sentinel-1 imagery processed in Google Earth Engine. PRE–POST backscatter differences were classified using Otsu thresholding and spatial constraints, including a maximum slope of 5° and a 1000 m hydrographic buffer. Eight of 13 cataloged events were retained. VH thresholds had a mean ± standard deviation of −2.83 ± 0.05 dB, compared with −1.73 ± 1.11 dB for VV and −3.33 ± 1.44 dB for (VH+VV)2; the low VH dispersion is a property of the retained subset, which was selected partly on VH threshold behavior. The eight-event map covered 9842.02 ha detected as flooded at least once, with 6.29% classified as high or very high observed recurrence, concentrated near the Utcubamba–Marañón confluence. For E13, assessment against a balanced sample of 1360 reference points yielded a sample-based overall accuracy of 85.15%, flooded-class user’s accuracy of 91.64%, producer’s accuracy of 77.35%, and F1 score of 0.839. These metrics characterize the sampled sectors of E13. Agricultural classes accounted for 73.70% of the area in the 2024 land-cover overlay. Irrigation-related changes could not be fully distinguished from river flooding. The resulting count of detections across cataloged events provides complementary information for land-use planning, with interpretation bounded by temporal coverage, event selection, and reference-data availability.