Improving the monitoring of corn phenology in large agricultural areas using remote sensing data series

• Main Authors: Ernesto Sifuentes-Ibarra, Waldo Ojeda-Bustamante, Ronald E. Ontiveros-Capurata, Ignacio Sánchez-Cohen
• Format: Article
• Language: English
• Published: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria 2020-12-01
• Series: Spanish Journal of Agricultural Research
• Subjects: zea mays l.; irrigation districts; climate variability; gis; mexico
• Online Access: https://revistas.inia.es/index.php/sjar/article/view/16269

Aim of study: Mexico's large irrigation areas demand non-structural actions to improve the irrigation service, such as monitoring crop phenology; however, its application has been limited by the large volumes of field information generated, diversity of crop management and climatic variability. The objective of this study was to generate and validate a methodology to monitor corn (Zea mays L.) phenology from the historical relationship of the vegetation indexes (VIs), EVI and NDVI, with the phenological development (PD) of corn grown in large irrigation zones. Area of study: Irrigation District (ID) 075 “Valle del Fuerte”, northern Sinaloa, Mexico. Material and methods: We used a database of 20 years of climate, field crop growth and crop phenology data, and Landsat satellite images. A methodology was proposed on a large scale supported with GIS and remote sensing data series. Main results: The methodology was validated in 19 plots with an acceptable correlation between observed PD and estimated PD for the two VIs, with slightly better values for EVI than for NDVI. NDVI and EVI models agreed with experimental PD observations in 92.1% of the farms used to validate the methodology, in 2.5% only the NDVI model coincided with the real, in 3.1% only the EVI model coincided, and in 2.3% both models disagreed with observation, generated a stage out of phase with respect to the real phenological stage. Research highlights: is possible to generalize the methodology applied to large irrigation zones with remote sensing data and GIS.