{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T19:01:07Z","timestamp":1775934067288,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T00:00:00Z","timestamp":1684972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Systematic measurement of pasture biomass (kg DM\/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM\/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM\/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM\/ha for kikuyu and 406 kg DM\/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM\/ha for kikuyu and 402 kg DM\/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species.<\/jats:p>","DOI":"10.3390\/rs15112752","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T02:00:19Z","timestamp":1685066419000},"page":"2752","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2418-0398","authenticated-orcid":false,"given":"Juan I.","family":"Gargiulo","sequence":"first","affiliation":[{"name":"NSW Department of Primary Industries, Menangle, NSW 2568, Australia"},{"name":"Dairy Science Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2567, Australia"}]},{"given":"Nicolas A.","family":"Lyons","sequence":"additional","affiliation":[{"name":"NSW Department of Primary Industries, Menangle, NSW 2568, Australia"}]},{"given":"Fernando","family":"Masia","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias Agropecuarias, Universidad Nacional de C\u00f3rdoba, Cordoba 5000, Argentina"},{"name":"Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (CONICET), Buenos Aires C1425FQB, Argentina"}]},{"given":"Peter","family":"Beale","sequence":"additional","affiliation":[{"name":"Local Land Services Hunter, Taree, NSW 2430, Australia"}]},{"given":"Juan R.","family":"Insua","sequence":"additional","affiliation":[{"name":"Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (CONICET), Buenos Aires C1425FQB, Argentina"},{"name":"Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Balcarce 7620, Argentina"}]},{"given":"Martin","family":"Correa-Luna","sequence":"additional","affiliation":[{"name":"Dairy Science Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2567, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2742-0262","authenticated-orcid":false,"given":"Sergio C.","family":"Garcia","sequence":"additional","affiliation":[{"name":"Dairy Science Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2567, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,25]]},"reference":[{"key":"ref_1","first-page":"16","article-title":"Key Determinants of Profit for Pasture-based Dairy Farms","volume":"23","author":"Beca","year":"2020","journal-title":"Australas. 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