Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variables

Authors

DOI:

https://doi.org/10.4995/raet.2022.15099

Keywords:

agriculture, vegetation indices, crop calendar, multiple regression, Google Earth Engine

Abstract

A precise estimation of agricultural production provides relevant information for upcoming seasons, and helps in the assessment of crop losses before harvest in case of adverse situations. The objective of this work is to explore the development of a model capable of estimating barley production of a small agricultural production (127 ha) in Belchite, Spain. Variables adapted to the crop calendar of the growing barley are used to achieve that purpose. The variables have been created with weather data and remote sensing images. These images are acquired in two ranges of the electromagnetic spectrum, i.e., microwaves and optical spectral range, obtained from Sentinel-1 and Sentinel-2, respectively. Models are defined with a multiple linear regression method using all combinations of the independent  variables correlated with production. The best linear regression model has a prediction error of 57.38 kg/ha (4%). The use of spectral variables, derived from radar vegetation index Cross Ratio (CR) and optical Inverted Red Edge Chlorophyll Index (IRECI), and climatic variables adapted to the crop calendar and climatic conditioning is revealed as an adequate strategy to obtain adjusted models.

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Author Biographies

Cristian Iranzo, Universidad de Zaragoza

Departamento de Geografía y Ordenación del Territorio

Raquel Montorio, Universidad de Zaragoza

Departamento de Geografía y Ordenación del TerritorioGrupo GEOFOREST-IUCA

Alberto García-Martín, Centro Universitario de la Defensa de Zaragoza, Academia General Militar ; Universidad de Zaragoza

Grupo GEOFOREST-IUCA

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Published

2022-01-31

Issue

Section

Research articles