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

Abstract

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.

VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data

“…Water consumption, biomass, LAI, phenology monitoring [5,31,32] Enhanced vegetation index EVI2 * 2.5…”

“…In agriculture, VIs have been used to indirectly estimate biophysical crop properties such as the crop coefficient, leaf area index, cover fraction, the vigor, and the dynamics of other biophysical crop variables. These VIs have been used in many agricultural applications, for example, to estimate the fraction of vegetation cover, leaf area index (LAI) [2], crop coefficients [3,4] monitoring of phenological stages [5], chlorophyll content, fraction of absorbed photosynthetically active radiation (fAPAR) [6], and biomass [7,8], among other.…”