Estimación de la productividad del agua a nivel parcelario en maíz forrajero usando sensores remotos y datos climáticos de reanálisis

Crop water productivity (CWP) is an important indicator of agricultural water use. One of the major challenges for the agricultural sector is to produce more food with less water by increasing CWP. CWP values depend on crop yield and evapotranspiration; actual crop evapotranspiration (ETa) varies across agroclimatic zones and is influenced by hybrid type, relative maturity date, agronomic practices, and environmental conditions. This study aimed to estimate actual evapotranspiration (ETa) and water productivity (CWP) in silage maize by integrating remote sensing, climate reanalysis data, and the AquaCrop simulation model. Observed ETa was determined using the AquaCrop model, calibrated with field data and vegetation cover parameters. While the estimated ETa was obtained from the SEBAL model, implemented in Google Earth Engine (GEESEBAL), using Landsat 8 and 9 imagery and climate reanalysis data, the results showed that remote sensing overestimated the observed ETa, with an average overestimation of 13%. On the other hand, the water productivity (WP) estimated with remote sensing was slightly lower (2.79 and 2.62 kg/m³) than the observed WP (3.19 and 2.95 kg/m³), with an average difference of 12%. These results indicate that remote sensing, together with climate reanalysis data, can be adapted as a viable alternative for spatial monitoring of water productivity at the parcel level in large agricultural areas. However, further work is needed to reduce the error, for example, by incorporating spatial yield estimation models or using more than one energy balance model, such as METRIC, SSEBop, and others.

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Estimation of water productivity at the field scale in forage maize using remote sensing and climate reanalysis data

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