Monitoring Irrigated Agriculture Using Remote Sensing and Census Data: A Case Study from Guanajuato, Mexico

Irrigated agriculture faces significant challenges due to increasing water scarcity and the effects of climate change, particularly in arid and semi-arid regions like Mexico. This study aims to analyze agricultural dynamics in central Mexico, with a specific focus on the municipality of Pénjamo, Guanajuato. This research aimed to assess the consistency and effectiveness of official census data and remote sensing data to monitor crop patterns. The analysis utilized official census data alongside remote sensing data from MODIS, Landsat, and Sentinel-2 satellites to monitor crop areas during autumn/winter and spring/summer agricultural cycles from 2003 to 2023. The study employed Normalized Difference Vegetation Index (NDVI) values to assess crop coverage and compared coarse-resolution MODIS data with higher-resolution Sentinel-2 data for enhanced monitoring of crop dynamics. A strong correlation ( $$R^2=0.82$$ ) was identified between NDVI values derived from MODIS images and irrigated crop areas, demonstrating the effectiveness of coarse-resolution remote sensing for large-scale monitoring. Sentinel-2 data provided refined insights, particularly for short-cycle crops, by offering higher temporal and spatial resolution. The findings highlight the utility of integrating census data with remote sensing technologies for monitoring irrigated agriculture. This approach is particularly valuable in addressing the challenges posed by water scarcity and climate variability. The study underscores the potential of remote sensing tools in supporting sustainable agricultural practices in regions experiencing significant environmental pressures.