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Understanding how such local climatic conditions vary is challenging to measure at adequate spatio-temporal resolution. Microclimate models provide the means to address this limitation, but require as inputs, measurements, or estimations of multiple environmental variables that describe vegetation and terrain variation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>To describe the key components of microclimate models and their associated environmental parameters. To explore the potential of drones to provide scale relevant data to measure such environmental parameters.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We explain how drone-mounted sensors can provide relevant data in the context of alternative remote sensing products. We provide examples of how direct micro-meteorological measurements can be made with drones. We show how drone-derived data can be incorporated into 3-dimensional radiative transfer models, by providing a realistic representation of the landscape with which to model the interaction of solar energy with vegetation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We found that for some environmental parameters (i.e. topography and canopy height), data capture and processing techniques are already established, enabling the production of suitable data for microclimate models. For other parameters such as leaf size, techniques are still novel but show promise. For most parameters, combining spatial landscape characterization from drone data and ancillary data from lab and field studies will be a productive way to create inputs at relevant spatio-temporal scales.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Drones provide an exciting opportunity to quantify landscape structure and heterogeneity at fine resolution which are in turn scale-appropriate to deliver new microclimate insights.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s10980-020-01180-9","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T07:02:56Z","timestamp":1611212576000},"page":"685-702","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Drones provide spatial and volumetric data to deliver new insights into microclimate modelling"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7971-3924","authenticated-orcid":false,"given":"James P.","family":"Duffy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karen","family":"Anderson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dominic","family":"Fawcett","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robin J.","family":"Curtis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilya M. 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