Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (Ts), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (Rn) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America.
La determinación de la velocidad de caída de partículas no cohesivas (arena) fue medida experimentalmente mediante el empleo de una técnica de filmación con una cámara fotográfica comercial. El diámetro de los sedimentos varió entre 0,075 mm a 2 mm, los valores experimentales fueron comparados con los valores calculados por ecuaciones empíricas, cuatro ecuaciones consideran solo el diámetro para el cálculo de la velocidad de caída, la quinta ecuación considera el diámetro y el factor de forma (cfs), y la sexta ecuación considera diámetro, factor de forma y la redondez (P). Los resultados mostraron que la técnica de filmación permite medir de forma adecuada la velocidad de caída y que en una faja de diámetro de sedimento entre 0,075 mm a 1,2 mm, la ecuación que incluye los factores de forma y redondez tuvo uno de los mejores desempeños. Palabras clave: Sedimento, factor de forma, redondez, partícula no cohesiva, velocidad de caída.
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