Quantifying aboveground grass biomass using space-borne sensors : a meta-analysis and systematic review

dc.contributor.authorMaake, Reneilwe
dc.contributor.authorMutanga, Onisimo
dc.contributor.authorChirima, Johannes George
dc.contributor.authorSibanda, Mbulisi
dc.date.accessioned2024-07-24T07:29:33Z
dc.date.available2024-07-24T07:29:33Z
dc.date.issued2023-12
dc.descriptionDATA AVAILABILITY STATEMENT : The secondary data used in this study is available in the following databases: WoS, IEEE Explorer, Scopus, and Google scholar.en_US
dc.descriptionSUPPLEMENTARY MATERIAL : TABLE S1: The 108 articles extracted from WoS, IEEE, Scopus and Google scholar.en_US
dc.description.abstractRecently, the move from cost-tied to open-access data has led to the mushrooming of research in pursuit of algorithms for estimating the aboveground grass biomass (AGGB). Nevertheless, a comprehensive synthesis or direction on the milestones achieved or an overview of how these models perform is lacking. This study synthesises the research from decades of experiments in order to point researchers in the direction of what was achieved, the challenges faced, as well as how the models perform. A pool of findings from 108 remote sensing-based AGGB studies published from 1972 to 2020 show that about 19% of the remote sensing-based algorithms were tested in the savannah grasslands. An uneven annual publication yield was observed with approximately 36% of the research output from Asia, whereas countries in the global south yielded few publications (<10%). Optical sensors, particularly MODIS, remain a major source of satellite data for AGGB studies, whilst studies in the global south rarely use active sensors such as Sentinel-1. Optical data tend to produce low regression accuracies that are highly inconsistent across the studies compared to radar. The vegetation indices, particularly the Normalised Difference Vegetation Index (NDVI), remain as the most frequently used predictor variable. The predictor variables such as the sward height, red edge position and backscatter coefficients produced consistent accuracies. Deciding on the optimal algorithm for estimating the AGGB is daunting due to the lack of overlap in the grassland type, location, sensor types, and predictor variables, signalling the need for standardised remote sensing techniques, including data collection methods to ensure the transferability of remote sensing-based AGGB models across multiple locations.en_US
dc.description.departmentGeography, Geoinformatics and Meteorologyen_US
dc.description.librarianhj2024en_US
dc.description.sdgSDG-15:Life on landen_US
dc.description.sponsorshipThe Agricultural Research Council (ARC) and the National Research Foundation (NRF) Research Chair in Land Use Planning and Management.en_US
dc.description.urihttps://doaj.org/toc/2673-7418en_US
dc.identifier.citationMaake, R.; Mutanga, O.; Chirima, G.; Sibanda, M. Quantifying Aboveground Grass Biomass Using Space-Borne Sensors: A Meta-Analysis and Systematic Review. Geomatics 2023, 3, 478–500. https://doi.org/10.3390/geomatics3040026.en_US
dc.identifier.issn2673-7418 (online)
dc.identifier.other10.3390/geomatics3040026
dc.identifier.urihttp://hdl.handle.net/2263/97199
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.rights© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en_US
dc.subjectAboveground grass biomass (AGGB)en_US
dc.subjectMeta-analysisen_US
dc.subjectGrass biomassen_US
dc.subjectSavannah ecosystemsen_US
dc.subjectRemote sensingen_US
dc.subjectSDG-15: Life on landen_US
dc.titleQuantifying aboveground grass biomass using space-borne sensors : a meta-analysis and systematic reviewen_US
dc.typeArticleen_US

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