NDVI Performance for Monitoring Agricultural Energy Inputs Using Landsat Imagery: A Study in the Ecuadorian Andes (2012–2023)

Pedro Zea, Cristina Pascual (), Luis G. García-Montero and Hugo Cedillo
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Pedro Zea: Centro para la Conservación de la Biodiversidad y el Desarrollo Sostenible (CBDS), E.T.S.I. Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
Cristina Pascual: Centro para la Conservación de la Biodiversidad y el Desarrollo Sostenible (CBDS), E.T.S.I. Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
Luis G. García-Montero: Centro para la Conservación de la Biodiversidad y el Desarrollo Sostenible (CBDS), E.T.S.I. Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
Hugo Cedillo: Centro para la Conservación de la Biodiversidad y el Desarrollo Sostenible (CBDS), E.T.S.I. Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain

Sustainability, 2025, vol. 17, issue 8, 1-23

Abstract: The NDVI is typically associated with medium-resolution images, e.g., Landsat imagery, and has often been linked to various agricultural parameters, except agricultural energy inputs. Thus, our objective was to analyze the performance of the NDVI associated with Landsat images to monitor both the evolution and impact of energy inputs on the spectral activity in some rural mountain crops. To do so, we studied energy inputs in three scenarios in the Ecuadorian Andes: high-mountain agroforestry systems (HAFSs), short-cycle production systems (SHCs), and low-mountain agroforestry systems (LAFSs). In 2022, information on energy inputs was collected for 415 systems (through field surveys). Using Google Earth Engine, we analyzed NDVI data associated with Landsat images between 2012 and 2023. Statistical analysis demonstrated significant positive correlations between energy inputs and the NDVI. As a novelty, this result means that energy inputs influence crops’ spectral activity. Furthermore, we demonstrated a historical enhancement of energy inputs across the inputs at the Landsat image scale. Therefore, further studies are needed to improve the resolution of this approach, for example, by integrating higher-resolution images to assess a more accurate NDVI response.

Keywords: normalized difference vegetation index; Landsat imagery; Google Earth Engine; energy inputs; crop temporal analysis; agro-productive systems; Ecuadorian Andes; cocoa; deciduous fruit trees; short-cycle crops (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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