The use of machine learning methods to estimate aboveground biomass of grasslands: a review
; Teixeira, R.
Figueiredo, M. A. T.
; Domingos, T.
Ecological Indicators Vol. 130, Nº 0, pp. 108081 - 108081, November, 2021.
ISSN (print): 1470-160X
Scimago Journal Ranking: 1,28 (in 2021)
Digital Object Identifier: 10.1016/j.ecolind.2021.108081
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The study of grasslands using machine learning (ML) methods combined with proximal/remote sensing data (RS) has been steadily increasing in the last decades. Available algorithms range from a primarily academic use to more widespread practical applications intended at helping farm management. Here, we review the use of ML methods applied to aboveground biomass (AGB) estimation in grassland systems. Based on 26 recent papers, we perform a literature review of the topic to identify common practices, namely the relation between estimation performance and the ML method used, data sources, and scale (local/regional). In order to identify the relation between the characteristics of the studies and the estimation accuracy, we use descriptive and correlation analysis. In spite of a surge in the number of papers and application examples, there is no evidence that the estimation performance of the algorithms has been improving over time. In all approaches used by the authors of the papers herein considered, the number of field samples, RS data source, and species composition of the grassland systems are the most relevant variables to explain the estimation accuracy. This accuracy increases with the number of field samples until it plateaus, hinting at the existence of an optimum level for monitoring efforts. Accuracy also increases with the proximity of the sensor to the field, i.e., on average accuracy is higher using field spectroscopy than using satellite data. There is no evidence that any particular ML method is more suited to this problem. The literature also displays significant limitations in terms of its applications of the ML algorithms. For example, a limited number of papers validated the models, casting doubt on the potential of the models for generalized application. Despite those limitations, and considering the advancements verified, we expect that, in the near future, ML methods combined with RS/proximal data will continue to improve and be helpful for farm management.