{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T19:01:05Z","timestamp":1775934065790,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T00:00:00Z","timestamp":1612742400000},"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>Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of \u22480.6, a root-mean-square error (RMSE) of \u2248356 kg dry matter (DM)\/ha and a mean absolute error (MAE) of 262 kg DM\/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm\u2019s in situ measurement, pasture management and grazing practices have on the model\u2019s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.<\/jats:p>","DOI":"10.3390\/rs13040603","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:33:46Z","timestamp":1612931626000},"page":"603","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":100,"title":["Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Yun","family":"Chen","sequence":"first","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation (CSIRO) Land &amp; Water, Canberra, ACT 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7464-6304","authenticated-orcid":false,"given":"Juan","family":"Guerschman","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation (CSIRO) Land &amp; Water, Canberra, ACT 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1657-1361","authenticated-orcid":false,"given":"Yuri","family":"Shendryk","sequence":"additional","affiliation":[{"name":"CSIRO Agriculture &amp; Food, Malborne, VIC 3030, Australia"}]},{"given":"Dave","family":"Henry","sequence":"additional","affiliation":[{"name":"CSIRO Agriculture &amp; Food, Malborne, VIC 3030, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7425-452X","authenticated-orcid":false,"given":"Matthew Tom","family":"Harrison","sequence":"additional","affiliation":[{"name":"Tasmanian Institute of Agriculture, University of Tasmania, Hobart, TAS 7320, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1071\/CP16394","article-title":"The impact of extreme climatic events on pasture-based dairy systems: A review","volume":"68","author":"Harrison","year":"2017","journal-title":"Crop Pasture Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/nature13281","article-title":"Seasonal not annual rainfall determines grassland biomass response to carbon dioxide","volume":"511","author":"Hovenden","year":"2014","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"i","DOI":"10.1071\/CPv66n4_FO","article-title":"Dual-purpose cropping\u2014Capitalising on potential grain crop grazing to enhance mixed-farming profitability","volume":"66","author":"Bell","year":"2015","journal-title":"Crop Pasture Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1071\/CP11235","article-title":"Recovery dynamics of rainfed winter wheat after livestock grazing 2. 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