The excessive use of N in agriculture has created various environmental and economic problems. Remote sensing and unmanned aerial vehicles (UAV) are feasible solutions to infer the status of a crop and enable a better management during the growing season. The objective of this study was to correlate experimental N content and wheat (Triticum aestivum L.) crop aboveground biomass data with vegetation indices estimated using UAV images. In this study, the N nutrition index and N dilution curve were used as indicators of the state of plant N; input variables to estimate these indicators were the N content and aboveground biomass. Four flight campaigns were conducted at different phenological stages of a wheat crop and seven N doses were evaluated. A linear relationship of blue normalized difference vegetation index (BNDVI) and green normalized difference vegetation index (GNDVI) with aboveground biomass and N content was identified. BNDVI and biomass demonstrated high R² during boots swollen and end of anthesis growth stages (0.62 and 0.68, respectively), while GNDVI showed the highest R² during the ear half emerged and beginning of anthesis growth stages (0.84 and 0.79, respectively). For N content estimation, GNDVI showed a higher correlation than BNDVI, and the adjustment curve showed an R² up to 0.81 only for the last flight (end of anthesis), BNDVI showed an R² of 0.78. Remote sensing and vegetation indices estimated from UAV images can be reliably used to estimate N content and wheat biomass, contributing to knowing the crop N status.

Determination of actual crop evapotranspiration (ETc) is a crucial challenge for sustainable irrigation water management. In this sense, robust and accurate estimation models of crop water consumption along with spatial tools and processing platforms in the cloud are necessary to determine the timing and amount of irrigation needed as a first step toward proposing solutions and water use efficiency. The objective of this study was to determine maize crop evapotranspiration using the algorithms of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model in the Google Earth Engine (GEE) platform. The crop was monitored with 14 Landsat images during its growth period. ETc values with METRIC were compared with ETc obtained with the FAO-56 methodology, and the cumulative ETc was compared with ETc derived from a soil moisture sensor. The evaluation between the METRIC model and FAO-56 displayed a determination coefficient (R2) of 0.87, mean squared error (MSE) of 0.8 mm/day, and bias percentage (PBIAS) of -14.5. According to the cumulative ETc, the difference was 16 mm for METRIC and 63 mm for FAO-56, compared with moisture sensor values. METRIC overestimated by 3.0% (PBIAS=-3.0), and FAO-56 underestimated by 11.9% (PBIAS=11.9). The results and the programmed algorithms in this work can be the basis for future calibrations and validations of the evapotranspiration of different crops.

La productividad del agua de los cultivos (PA) es un indicador importante del uso del agua agrícola; uno de los grandes retos del sector agrícola es producir una mayor cantidad de alimentos con la menor cantidad de agua aumentando la PA. Los valores de la PA dependen del rendimiento y la evapotranspiración del cultivo, donde la evapotranspiración actual de los cultivos varía entre las diferentes zonas agroclimáticas y es una función de los tipos de híbridos, la fecha de madurez relativa, las prácticas agronómicas y las condiciones ambientales. El presente estudio tuvo como finalidad estimar la evapotranspiración actual (ETa) y la productividad del agua (PA) en el cultivo de maíz forrajero, mediante la integración de sensores remotos, datos climáticos de reanálisis y el modelo de simulación AquaCrop. La ETa observada se determinó usando el modelo AquaCrop, calibrado con datos de campo y parámetros de la cobertura vegetal; mientras que la ETa estimada se obtuvo a partir del modelo SEBAL, implementado en Google Earth Engine (GEESEBAL), utilizando imágenes Landsat 8 y 9 y datos climáticos de reanálisis. Los resultados mostraron que los sensores remotos sobreestimaron la ETa observada, con una sobreestimación promedio del 13 %. Por otro lado, la productividad del agua (PA) estimada con sensores remotos fue ligeramente menor (2.79 y 2.62 kg/m³) que la observada (3.19 y 2.95 kg/m³), con una diferencia promedio del 12 %. Estos resultados indican que los sensores remotos, junto con datos climáticos de reanálisis, pueden adaptarse como una alternativa viable para el monitoreo espacial de la productividad del agua en zonas agrícolas extensas. No obstante, es necesario trabajar en la reducción del error, por ejemplo, incorporando modelos de estimación espacial del rendimiento o utilizando más de un modelo de balance de energía, como METRIC, SSEBop, entre otros.