RIVERA SALAZAR, DIEGO ANDRÉS
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RIVERA SALAZAR, DIEGO ANDRÉS
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diegorivera@udd.cl
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25822747600

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Publication

A satellite-based ex post analysis of water management in a blueberry orchard

2020 , Eduardo Holzapfel , Mario Lillo-Saavedra , RIVERA SALAZAR, DIEGO ANDRÉS , Viviana Gavilán , Angel García-Pedrero , Consuelo Gonzalo-Martín

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Early Estimation of Tomato Yield by Decision Tree Ensembles

2022 , Mario Lillo-Saavedra , Alberto Espinoza-Salgado , Angel García-Pedrero , Camilo Souto , Eduardo Holzapfel , Consuelo Gonzalo-Martín , Marcelo Somos-Valenzuela , RIVERA SALAZAR, DIEGO ANDRÉS

Crop yield forecasting allows farmers to make decisions in advance to improve farm management and logistics during and after harvest. In this sense, crop yield potential maps are an asset for farmers making decisions about farm management and planning. Although scientific efforts have been made to determine crop yields from in situ information and through remote sensing, most studies are limited to evaluating data from a single date just before harvest. This has a direct negative impact on the quality and predictability of these estimates, especially for logistics. This study proposes a methodology for the early prediction of tomato yield using decision tree ensembles, vegetation spectral indices, and shape factors from images captured by multispectral sensors on board an unmanned aerial vehicle (UAV) during different phenological stages of crop development. With the predictive model developed and based on the collection of training characteristics for 6 weeks before harvest, the tomato yield was estimated for a 0.4 ha plot, obtaining an error rate of 9.28%.

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Ex Post Analysis of Water Supply Demand in an Agricultural Basin by Multi-Source Data Integration

2021 , Mario Lillo-Saavedra , Viviana Gavilán , Angel García-Pedrero , Consuelo Gonzalo-Martín , Felipe de la Hoz , Marcelo Somos-Valenzuela , RIVERA SALAZAR, DIEGO ANDRÉS

In this work, we present a new methodology integrating data from multiple sources, such as observations from the Landsat-8 (L8) and Sentinel-2 (S2) satellites, with information gathered in field campaigns and information derived from different public databases, in order to characterize the water demand of crops (potential and estimated) in a spatially and temporally distributed manner. This methodology is applied to a case study corresponding to the basin of the Longaví River, located in south-central Chile. Potential and estimated demands, aggregated at different spatio-temporal scales, are compared to the streamflow of the Longaví River, as well as extractions from the groundwater system. The results obtained allow us to conclude that the availability of spatio-temporal information on the water availability and demand pairing allows us to close the water gap—i.e., the difference between supply and demand—allowing for better management of water resources in a watershed.

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