Mapping of coffee cultivation based on land use/land cover in the tropics of Cajamarca (Peru): An integration of Sentinel data, machine learning, and Google Earth Engine

Abstract

Coffee cultivation is a strategic economic activity in the Andean-Amazonian region of Peru, characterized by its diversity of production systems and complex landscape. In this study, a methodological approach based on multitemporal mapping of land cover and land use (LULC) was developed to identify coffee crops by integrating optical satellite imagery (Sentinel-2) and radar data (Sentinel-1), as well as topographic variables, processed on the Google Earth Engine (GEE) platform, with supervised classification based on the Random Forest (RF) algorithm. The study area covered the provinces of Jaen and San Ignacio (Cajamarca), the main coffee-growing areas in the country's north. A time series of satellite data from 2019 to 2024 was used, considering two climatic seasons (dry and wet). The 69 spectral, textural, and topographic variables were evaluated but subsequently reduced to 22 using the Variance Inflation Factor (VIF). The classification distinguished 11 LULC classes, including shade-grown and non-shade-grown coffee, achieving an overall accuracy of over 84% and a Kappa index of 0.90. The binary coffee maps made it possible to quantify and analyze the spatial dynamics of the crop in both provinces, showing a growing trend toward shade-grown coffee, especially in San Ignacio. The proposed approach proved to be operational for mountainous tropical areas with high cloud cover.