@article{fef5d6bb9aff428fb38f94a8068054eb,
title = "Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset",
abstract = "Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018-2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1-4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.",
keywords = "Crop condition, Crop growth stages, Crop progress, Green-up, Land surface phenology, Landsat, Remote sensing phenology, Sentinel-2, Start of the season, Time-series analysis",
author = "Feng Gao and Anderson, {Martha C.} and Johnson, {David M.} and Robert Seffrin and Brian Wardlow and Andy Suyker and Chunyuan Diao and Browning, {Dawn M.}",
note = "Funding Information: Acknowledgments: The Harmonized Landsat and Sentinel-2 (HLS) data were generated by the Acknowledgments: The Harmonized Landsat and Sentinel-2 (HLS) data were generated by the Na-titohnaanlk AJuernocnhaauntgicJsu aanndd SJpefafcMe Aasdemkifnoirstprraotivoindi(nNgAvSeArs)ioGno1d.d4aHrdL SSpdaactae.FTlihgihstr eCseenartechr. wThase aaucothnotrrisbu- thtiaonnkf rJoumncthhaenLgo Jnug a-Tnedr mJefAfMgraoseecko sfyosrt epmroRviedsienagrc vhe(rLsTioAnR 1).4n eHtwLoSr dk.atLaT. ATRhiiss rseusepaprocrht ewdabsy at hcoenUtrnii-ted bSuttaiotens fDroempa rtthme eLnotnogf-ATegrrmicu Altgurroee.cUosSyDstAemis Raneseeqaurachl o (pLpToArRtu) nniteytwpororkv.idLeTrAaRndisesmupplpooyretre.dA bnyy tuhsee of Utnraitdeed, Sfitramte,soDr epproadrtumcetnnta mofe As gisrifcourltduersec.rUipStDivAe pisuarpnoesqeusaol nolpypaonrdtudnoiteys pnrootvimidperlyanenddeomrspelmoyeenrt. by Atnhye Uus.Se .oGfotrvaedren,m fiernmt,. oArn pdryodSuucytk neramaneds Bisr ifaonr dWesacrrdilpotwivea cpkunropwosleeds goenltyheanAdm deoreiFs lnuoxtMimapnalyg eemn-ent dPorrosejemctenfut nbdyi tnhge oUf.Sth. Ge oNveebrrnamskenatc. oArnedsyiteSsuypkroerv iadnedd Bbryiatnh We Uar.dSl.oDwe pacakrtnmowenletdogfeE tnheerAgym{\textquoteright}serOiFffliucxe of Management Project funding of the Nebraska core sites provided by the U.S. Department of Energy{\textquoteright}s Office of Science under Contract No. DE-AC02-05CH11231 and partially supported by the Nebraska Agricultural Experiment Station with funding from the Hatch Act (Accession Number 1020768) through the USDA National Institute of Food and Agriculture. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Funding Information: investigation, all authors; data curation, all authors; writing—original draft preparation, F.G.; writing—review and editing, all authors; visualization, F.G. All authors have read and agreed to the Funding: This work was partially supported by the National Aeronautics and Space Administration Funding Information: This work was partially supported by the National Aeronautics and Space Administration (NASA) Land Cover and Land Use MuSLI program (NNH17ZDA001N-LCLUC). Publisher Copyright: {\textcopyright} 2021 by the author.",
year = "2021",
month = dec,
day = "14",
doi = "10.3390/rs13245074",
language = "English (US)",
volume = "13",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "24",
}