{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T21:51:14Z","timestamp":1772920274438,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T00:00:00Z","timestamp":1599436800000},"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>Shifts in wildflower phenology in response to climate change are well documented in the scientific literature. The majority of studies have revealed phenological shifts using in-situ observations, some aided by citizen science efforts (e.g., National Phenology Network). Such investigations have been instrumental in quantifying phenological shifts but are challenged by the fact that limited resources often make it difficult to gather observations over large spatial scales and long-time frames. However, recent advances in finer scale satellite imagery may provide new opportunities to detect changes in phenology. These approaches have documented plot level changes in vegetation characteristics and leafing phenology, but it remains unclear whether they can also detect flowering in natural environments. Here, we test whether fine-resolution imagery (&lt;10 m) can detect flowering and whether combining multiple sources of imagery improves the detection process. Examining alpine wildflowers at Mt. Rainier National Park (MORA), we found that high-resolution Random Forest (RF) classification from 3-m resolution PlanetScope (from Planet Labs) imagery was able to delineate the flowering season captured by ground-based phenological surveys with an accuracy of 70% (Cohen\u2019s kappa = 0.25). We then combined PlanetScope data with coarser resolution but higher quality imagery from Sentinel and Landsat satellites (10-m Sentinel and 30-m Landsat), resulting in higher accuracy predictions (accuracy = 77%, Cohen\u2019s kappa = 0.39). The model was also able to identify the timing of peak flowering in a particularly warm year (2015), despite being calibrated on normal climate years. Our results suggest PlanetScope imagery holds utility in global change ecology where temporal frequency is important. Additionally, we suggest that combining imagery may provide a new approach to cross-calibrate sensors to account for radiometric irregularity inherent in fine resolution PlanetScope imagery. The development of this approach for wildflower phenology predictions provides new possibilities to monitor climate change effects on flowering communities at broader spatiotemporal scales. In protected and tourist areas where the flowering season draws large numbers of visitors, such as Mt. Rainier National Park, the modeling framework presented here could be a useful tool to manage and prioritize park resources.<\/jats:p>","DOI":"10.3390\/rs12182894","type":"journal-article","created":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T09:18:16Z","timestamp":1599470296000},"page":"2894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Detecting Montane Flowering Phenology with CubeSat Imagery"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4401-1401","authenticated-orcid":false,"given":"Aji","family":"John","sequence":"first","affiliation":[{"name":"Department of Biology, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Justin","family":"Ong","sequence":"additional","affiliation":[{"name":"Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5442-2522","authenticated-orcid":false,"given":"Elli J.","family":"Theobald","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2143-1187","authenticated-orcid":false,"given":"Julian D.","family":"Olden","sequence":"additional","affiliation":[{"name":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amanda","family":"Tan","sequence":"additional","affiliation":[{"name":"eScience Institute, University of Washington, Seattle, WA 98195, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janneke","family":"HilleRisLambers","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Washington, Seattle, WA 98195, USA"},{"name":"Institute of Integrative Biology, ETH Zurich, 8092 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1126\/science.1210288","article-title":"The Pace of Shifting Climate in Marine and Terrestrial Ecosystems","volume":"334","author":"Burrows","year":"2011","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1146\/annurev.ecolsys.37.091305.110100","article-title":"Ecological and Evolutionary Responses to Recent Climate Change","volume":"37","author":"Parmesan","year":"2006","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1093\/aob\/mcv169","article-title":"Plants and climate change: Complexities and surprises","volume":"116","author":"Parmesan","year":"2015","journal-title":"Ann. Bot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2799","DOI":"10.1002\/ecy.1996","article-title":"Climate drives phenological reassembly of a mountain wildflower meadow community","volume":"98","author":"Theobald","year":"2017","journal-title":"Ecology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1111\/ele.12854","article-title":"Interannual bumble bee abundance is driven by indirect climate effects on floral resource phenology","volume":"20","author":"Ogilvie","year":"2017","journal-title":"Ecol. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"eaaq1819","DOI":"10.1126\/sciadv.aaq1819","article-title":"Climate warming drives local extinction: Evidence from observation and experimentation","volume":"4","author":"Panetta","year":"2018","journal-title":"Sci. Adv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.biocon.2014.10.021","article-title":"Global change and local solutions: Tapping the unrealized potential of citizen science for biodiversity research","volume":"181","author":"Theobald","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1890\/110281","article-title":"From Caprio\u2019s lilacs to the USA National Phenology Network","volume":"10","author":"Schwartz","year":"2012","journal-title":"Front. Ecol. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103843","DOI":"10.1016\/j.envexpbot.2019.103843","article-title":"Dynamics of flowering phenology of alpine plant communities in response to temperature and snowmelt time: Analysis of a nine-year phenological record collected by citizen volunteers","volume":"170","author":"Kudo","year":"2020","journal-title":"Environ. Exp. Bot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4916","DOI":"10.1073\/pnas.1323073111","article-title":"Shifts in flowering phenology reshape a subalpine plant community","volume":"111","author":"CaraDonna","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1890\/0012-9615(2003)073[0069:SMFPRT]2.0.CO;2","article-title":"Subalpine meadow flowering phenology responses to climate change: Integrating experimental and gradient methods","volume":"73","author":"Dunne","year":"2003","journal-title":"Ecol. Monogr."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Inouye, D.W., Saavedra, F., and Lee-Yang, W. (2003). Environmental influences on the phenology and abundance of flowering by Androsace septentrionalis (Primulaceae). Am. J. Bot., 90.","DOI":"10.3732\/ajb.90.6.905"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/nature11014","article-title":"Warming experiments underpredict plant phenological responses to climate change","volume":"485","author":"Wolkovich","year":"2012","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1080\/01431161.2019.1580819","article-title":"Mapping a keystone shrub species, huckleberry (Vaccinium membranaceum), using seasonal colour change in the Rocky Mountains","volume":"40","author":"Shores","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2019.08.006","article-title":"An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations","volume":"156","author":"Chen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fang, S., Tang, W., Peng, Y., Gong, Y., Dai, C., Chai, R., and Liu, K. (2016). Remote estimation of vegetation fraction and flower fraction in oilseed rape with unmanned aerial vehicle data. Remote Sens., 8.","DOI":"10.3390\/rs8050416"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Horton, R., Cano, E., Bulanon, D., and Fallahi, E. (2017). Peach Flower Monitoring Using Aerial Multispectral Imaging. J. Imaging, 3.","DOI":"10.3390\/jimaging3010002"},{"key":"ref_18","unstructured":"Planet (2018). Planet Application Program Interface: In Space for Life on Earth, Planet."},{"key":"ref_19","first-page":"110","article-title":"Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud","volume":"81","author":"Oliphant","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Houborg, R., and McCabe, M. (2018). Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data. Remote Sens., 10.","DOI":"10.3390\/rs10060890"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2013.01.007","article-title":"Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics","volume":"173","author":"Bolton","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"99","DOI":"10.5589\/m09-003","article-title":"Yellow flowers can decrease NDVI and EVI values: Evidence from a field experiment in an alpine meadow","volume":"35","author":"Shen","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_23","first-page":"79","article-title":"Use landsat image to evaluate vegetation stage in sunflower crops","volume":"4","author":"Herbei","year":"2015","journal-title":"AgroLife Sci. J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"John, A., Ausmees, K., Muenzen, K., Kuhn, C., and Tan, A. (2019). SWEEP: Accelerating Scientific Research Through Scalable Serverless Workflows. UCC \u201919 Companion: Proceedings of the 12th IEEE\/ACM International Conference on Utility and Cloud Computing Companion, Auckland, New Zeland, 2\u20135 December 2019, ACM Press.","DOI":"10.1145\/3368235.3368839"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huete, A.R. (2004). Remote sensing for environmental monitoring. Environmental Monitoring and Characterization, Elsevier.","DOI":"10.1016\/B978-012064477-3\/50013-8"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Phil. Trans. R. Soc. A"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random Forests for Classification in Ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"UAV remote sensing for urban vegetation mapping using random forest and texture analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111660","DOI":"10.1016\/j.rse.2020.111660","article-title":"Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series","volume":"239","author":"Taymans","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s11284-009-0679-1","article-title":"The effect of pollination on resource allocation among sexual reproduction, clonal reproduction, and vegetative growth in Sagittaria potamogetifolia (Alismataceae)","volume":"25","author":"Liu","year":"2010","journal-title":"Ecol. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"S124","DOI":"10.5589\/m07-062","article-title":"Crop fraction estimation from casi hyperspectral data using linear spectral unmixing and vegetation indices","volume":"34","author":"Liu","year":"2008","journal-title":"Can. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.rse.2012.08.019","article-title":"How deep does a remote sensor sense? Expression of chlorophyll content in a maize canopy","volume":"126","author":"Ciganda","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pasqualotto, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M., and Moreno, J. (2019). Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI). Sensors, 19.","DOI":"10.3390\/s19040904"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.isprsjprs.2014.08.014","article-title":"Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series","volume":"102","author":"Zhu","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2307\/1942049","article-title":"Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types","volume":"5","author":"Gamon","year":"1995","journal-title":"Ecol. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tian, H., Huang, N., Niu, Z., Qin, Y., Pei, J., and Wang, J. (2019). Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11070820"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1093\/forestry\/cpv054","article-title":"Elevational shifts in thermal suitability for mountain pine beetle population growth in a changing climate","volume":"89","author":"Bentz","year":"2016","journal-title":"Forestry"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/S0034-4257(01)00207-3","article-title":"Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments","volume":"77","author":"Okin","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bourgoin, C., Blanc, L., Bailly, J.-S., Cornu, G., Berenguer, E., Oszwald, J., Tritsch, I., Laurent, F., Hasan, A., and Sist, P. (2018). The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest. Forests, 9.","DOI":"10.3390\/f9060303"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"104893","DOI":"10.1016\/j.compag.2019.104893","article-title":"Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies","volume":"164","author":"Leach","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5739","DOI":"10.1080\/01431161.2018.1506951","article-title":"Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment","volume":"39","author":"Wicaksono","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1080\/01431161.2019.1697004","article-title":"Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data","volume":"41","author":"Kuplich","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3756","DOI":"10.1038\/s41598-018-21963-0","article-title":"Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging","volume":"8","author":"Li","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"20160429","DOI":"10.1098\/rspb.2016.0429","article-title":"How to colour a flower: On the optical principles of flower coloration","volume":"283","author":"Elzenga","year":"2016","journal-title":"Proc. R. Soc. B"},{"key":"ref_48","first-page":"1025","article-title":"Image-based atmospheric corrections-revisited and improved","volume":"62","author":"Chavez","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/0034-4257(88)90019-3","article-title":"An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data","volume":"24","author":"Chavez","year":"1988","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2894\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:07:31Z","timestamp":1760177251000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,7]]},"references-count":49,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182894"],"URL":"https:\/\/doi.org\/10.3390\/rs12182894","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,7]]}}}