Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson’s algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha⁻¹, root mean square error (RMSE) = 0.125 t ha⁻¹) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha⁻¹, RMSE = 0.425 t ha⁻¹) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha⁻¹, RMSE = 0.449 t ha⁻¹). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

Evapotranspiration (ET) is the second largest component of the water cycle in arid and semiarid environments, and, in fact, more than 60% of the precipitation on earth is returned to the atmosphere through it. MOD16 represents an operational source of ET estimates with adequate spatial resolution for several applications, such as water resources planning, at a regional scale. However, the use of these estimates in routine applications will require MOD16 evaluation and validation using accurate ground-based measurements. The main objective of this study was to evaluate the performance of the MOD16A2 product by comparing it with eddy covariance (EC) systems. Additional objectives were the analysis of the limitations, uncertainties, and possible improvements of the MOD16-estimated ET. The EC measurements were acquired for five sites and for a variety of land covers in northwestern Mexico. The indicators used for the comparison were: root mean square error (RMSE), bias (BIAS), concordance index (d), and determination coefficient (R²) of the correlation, comparing measured and modelled ET. The best performance was observed in Rayón (RMSE = 0.77 mm∙day⁻¹, BIAS = −0.46 mm∙day⁻¹, d = 0.88, and R² = 0.86); El Mogor and La Paz showed errors and coefficients of determination comparable to each other (RMSE = 0.39 mm·day⁻¹, BIAS = −0.04 mm∙day⁻¹, R² = 0.46 and RMSE = 0.42 mm·day⁻¹, BIAS = −0.18 mm∙day⁻¹, R² = 0.45, respectively). In most cases, MOD16 underestimated the ET values.

Spatio-temporal variability of droughts in the North Pacific Basin of México (1961-2010)

En México una de las principales fuentes de abastecimiento del riego agrícola es el agua subterránea, irrigando aproximadamente dos millones de hectáreas, aporta 75% del volumen de agua en zonas rurales y urbanas a nivel nacional. Estos recursos hídricos se han visto afectados por contaminantes provenientes de desechos industriales y residuales, así como el lixiviado de sustancias químicas en suelos agrícolas. Con la f inalidad de evaluar las características f isicoquímicas de las aguas subterráneas, se realizó el presente estudio en Zacoalco de Torres y Autlán de Navarro. Se realizaron muestreos en la temporada seca y lluviosa en 2017. Los sitios de muestreo fueron pozos profundos y norias. En total se obtuvieron 48 muestras correspondientes a las dos estaciones; en cada muestra se analizaron pH, aniones y cationes, conductividad eléctrica (CE), carbonato de sodio residual (CSR), relación de adsorción de sodio (RAS), así como la clasif icación hidrogeoquímica. Las aguas subterráneas de Zacoalco son en su mayoría aguas cloradas-magnésicas y en Autlán bicarbonatadas-magnésicas, con concentraciones medias y bajas, por lo que se consideran recomendables para el riego. Con respecto a la salinidad Zacoalco presenta 40% aguas de alta salinidad en secas y 66% aguas de media salinidad en lluvias; Autlán presenta 75% aguas de alta salinidad en secas, y en lluvias 75% aguas de media salinidad, por lo que su uso presenta restricciones moderadas.

El acuífero Cuautitlán-Pachuca ocupa la porción norte de la cuenca del Valle de México, justificando su nombre por la presencia de Cuautitlán en el Estado de México y Pachuca en el estado de Hidalgo. Dicho acuífero en la actualidad esta sobreexplotado. Por tal motivo en este trabajo se estimó la sustentabilidad del acuífero, utilizando el marco para la evaluación de sistemas de manejo de recursos naturales incorporando indicadores de sustentabilidad (MESMIS). Se realizó un estudio longitudinal comparando dos sistemas, el primero se analizó para el periodo 1970-1990 (sistema de referencia) y el segundo para el periodo 1991-2010 (sistema alternativo). Se analizaron 14 indicadores ambientales, económicos y sociales, para los dos periodos de tiempo. Encontrándose que los aspectos ambientales y económicos presentan los mayores rezagos con el paso de los años, en el caso de los indicadores sociales mostraron avances hacia la equidad. También se encontró que 9 de los indicadores presentaron mayor cercanía al valor óptimo propuesto en el sistema de referencia y solo cinco mostraron un acercamiento hacia el óptimo en el sistema alternativo. Los resultados muestran que el acuífero es menos sustentable en la actualidad.