The spatial–temporal impact of the El Niño–Southern Oscillation (ENSO) phenomenon in Peru is characterised by marked regional variability, affecting the economy and general well-being. This study focuses on the Piura region, which is highly sensitive to ENSO events, with the aim of determining the implications for land management and climate adaptation in the Peruvian coastal region, particularly in the context of ENSO events. The objective of the study is to ascertain the correlation between sea surface temperature (SST) anomalies and the Normalised Difference Vegetation Index (NDVI) in the region. The researchers employed a machine learning approach to model and predict monthly NDVI behaviour, incorporating spatial and seasonal variables from the Moderate Resolution Imaging Spectroradiometer (MODIS) during two periods of ENSO occurrence on the Peruvian coast (2017; 2023) and the one-year post-occurrence periods (2018; 2024). The results demonstrated a correlation between NDVI and SST anomalies in coastal provinces such as Sechura and Morropón, indicating sensitivity to oceanic conditions. In contrast, high Andean provinces such as Ayabaca and Huancabamba exhibited more moderate values, indicating a weaker dependence on SST variability. The study also found that the NDVI exhibited a marked monthly variation associated with altitudinal gradients and climatic conditions. This research demonstrates the potential of remote sensing and GIS technologies in capturing climate-sensitive land-use dynamics and provides a framework for operational monitoring and decision support.
Ramos-Sandoval et al. (2026) studied this question.