{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:55:41Z","timestamp":1771653341225,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T00:00:00Z","timestamp":1619654400000},"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>Winter wheat is a main cereal crop grown in the United States of America (USA), and the USA is the third largest wheat exporter globally. Timely and reliable in-season forecast and year-end estimation of winter wheat grain production in the USA are needed for regional and global food security. In this study, we assessed the consistency between the agricultural statistical reports and satellite-based data for winter wheat over the contiguous US (CONUS) at both the county and national scales. First, we compared the planted area estimates from the National Agricultural Statistics Service (NASS) and the Cropland Data Layer (CDL) from 2008\u20132018. Second, we investigated the relationship between gross primary production (GPP) estimated by the vegetation photosynthesis model (VPM) and grain production from the NASS. Lastly, we explored the in-season utility of GPPVPM in monitoring seasonal production. Strong spatiotemporal consistency of planted areas was found between the NASS and CDL datasets. However, in the Southern Great Plains, both the CDL and NASS planted acreage were noticeable larger (&gt;20%) than the NASS harvested area, where some winter wheat fields were used as forage for cattle grazing. County-level GPPVPM was linearly related with grain production of winter wheat, with an R2 value of 0.68 across the CONUS. The relationships between grain production and GPPVPM in those counties without a substantial difference (&lt;20%) between planted and harvested area were much stronger and their harvest index (HIGPP) values ranged from 0.2\u20130.3. GPPVPM in May could explain about 70\u201390% of the variance of winter wheat grain production. Our findings highlight the potential of GPPVPM in winter wheat monitoring, especially for those high harvested\/planted ratio, which could provide useful data to guide planning and marketing for decision makers, stakeholders, and the public.<\/jats:p>","DOI":"10.3390\/rs13091735","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T05:10:55Z","timestamp":1619759455000},"page":"1735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Spatiotemporal Changes of Winter Wheat Planted and Harvested Areas, Photosynthesis and Grain Production in the Contiguous United States from 2008\u20132018"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7447-0257","authenticated-orcid":false,"given":"Xiaocui","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0956-7428","authenticated-orcid":false,"given":"Xiangming","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3779-8717","authenticated-orcid":false,"given":"Jean","family":"Steiner","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6532-2663","authenticated-orcid":false,"given":"Zhengwei","family":"Yang","sequence":"additional","affiliation":[{"name":"National Agricultural Statistics Service, Research and Development Division, United States Department of Agriculture, Washington, DC 20250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanwei","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Grassland Science and Technology, China Agricultural University, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,29]]},"reference":[{"key":"ref_1","unstructured":"Mitchell, D.O., and Mielke, M. (2005). Wheat: The global market, policies, and priorities. Glob. Agric. Trade Dev. Ctries., 195\u2013214."},{"key":"ref_2","unstructured":"USDA (2020). World Agricultural Production."},{"key":"ref_3","unstructured":"USDA NASS (2014). USDA Crop Production 2014 Summary."},{"key":"ref_4","unstructured":"ADB (2015). Results of the Methodological Studies for Agricultural and Rural Statistics, Asian Development Bank."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Carfagna, E., and Carfagna, A. (2010). Alternative Sampling Frames and Administrative Data. What is the Best Data Source for Agricultural Statistics?. Agric. Surv. Methods.","DOI":"10.1002\/9780470665480.ch3"},{"key":"ref_6","unstructured":"USDA (2012). The Yield Forecasting Program of NASS."},{"key":"ref_7","unstructured":"USDA (1999). Understanding USDA Crop Forecasts."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.agsy.2004.06.008","article-title":"The cost of accuracy in crop area estimation","volume":"84","author":"Traore","year":"2005","journal-title":"Agric. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/feart.2017.00017","article-title":"Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping","volume":"5","author":"Shelestov","year":"2017","journal-title":"Front. Earth Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6553","DOI":"10.1080\/01431161.2019.1569791","article-title":"Crop type classification using a combination of optical and radar remote sensing data: A review","volume":"40","author":"Orynbaikyzy","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2017.06.033","article-title":"MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types","volume":"198","author":"Massey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111411","DOI":"10.1016\/j.rse.2019.111411","article-title":"Deep learning based winter wheat mapping using statistical data as ground references in Kansas and northern Texas, US","volume":"233","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2017.04.026","article-title":"Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model","volume":"195","author":"Skakun","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1093\/ajae\/aaz051","article-title":"Eyes in the Sky, Boots on the Ground: Assessing Satellite- and Ground-Based Approaches to Crop Yield Measurement and Analysis","volume":"102","author":"Lobell","year":"2020","journal-title":"Am. J. Agric. Econ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2017.06.043","article-title":"The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields","volume":"199","author":"Guan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112174","DOI":"10.1016\/j.rse.2020.112174","article-title":"A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt","volume":"253","author":"Deines","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Karthikeyan, L., Chawla, I., and Mishra, A.K. (2020). A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses. J. Hydrol., 586.","DOI":"10.1016\/j.jhydrol.2020.124905"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhuo, W., Huang, J.X., Li, L., Zhang, X.D., Ma, H.Y., Gao, X.R., Huang, H., Xu, B.D., and Xiao, X.M. (2019). Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation. Remote Sens., 11.","DOI":"10.3390\/rs11131618"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.agrformet.2019.03.010","article-title":"Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches","volume":"274","author":"Cai","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Vermote","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Y.L., Xu, X.G., Huang, L.S., Yang, G.J., Fan, L.L., Wei, P.F., and Chen, G. (2019). An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images. Remote Sens., 11.","DOI":"10.3390\/rs11091088"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.agrformet.2018.06.009","article-title":"Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices","volume":"260","author":"Kern","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2015.02.014","article-title":"Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information","volume":"161","author":"Franch","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, M., Kimball, J., Maneta, M.P., Maxwell, B., Moreno, A., Begueria, S., and Wu, X. (2018). Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data. Remote Sens., 10.","DOI":"10.3390\/rs10030372"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1111\/gcb.13136","article-title":"Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence","volume":"22","author":"Guan","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_28","first-page":"1194","article-title":"Net primary production of U.S. midwest croplands from agricultural harvest yield data","volume":"11","author":"Prince","year":"2001","journal-title":"Ecol. Indic."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1046\/j.1365-2486.2002.00503.x","article-title":"Satellite estimates of productivity and light use efficiency in United States agriculture, 1982\u20131998","volume":"8","author":"Lobell","year":"2002","journal-title":"Glob. Chang. Biol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1029\/2018JG004484","article-title":"Spatiotemporal Consistency of Four Gross Primary Production Products and Solar-Induced Chlorophyll Fluorescence in Response to Climate Extremes Across CONUS in 2012","volume":"123","author":"Wu","year":"2018","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2017.165","article-title":"A global moderate resolution dataset of gross primary production of vegetation for 2000\u20132016","volume":"4","author":"Zhang","year":"2017","journal-title":"Sci. Data"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1641\/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2","article-title":"A contiguous satellite-derived measure of global terrestrial primary production","volume":"54","author":"Running","year":"2004","journal-title":"Bioscience"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108240","DOI":"10.1016\/j.agrformet.2020.108240","article-title":"Spatial-temporal dynamics of maize and soybean planted area, harvested area, gross primary production, and grain production in the Contiguous United States during 2008\u20132018","volume":"297","author":"Wu","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_34","unstructured":"Anderson, T. (2015). Southern Plains Assessment of Vulnerability and Preliminary Adaptation and Mitigation Strategies for Farmers, Ranchers and Forest Land Owners."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.2135\/cropsci2011.01.0043","article-title":"Impact of Dual-Purpose Management on Wheat Grain Yield","volume":"51","author":"Edwards","year":"2011","journal-title":"Crop. Sci."},{"key":"ref_36","first-page":"224","article-title":"Measuring land-use and land-cover change using the U.S. department of agriculture\u2019s cropland data layer: Cautions and recommendations","volume":"62","author":"Lark","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-18045-z","article-title":"Cropland expansion in the United States produces marginal yields at high costs to wildlife","volume":"11","author":"Lark","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4134","DOI":"10.1073\/pnas.1215404110","article-title":"Recent land use change in the Western Corn Belt threatens grasslands and wetlands","volume":"110","author":"Wright","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"085198","DOI":"10.1117\/1.JRS.8.085198","article-title":"Assessing bioenergy-driven agricultural land use change and biomass quantities in the U.S. Midwest with MODIS time series","volume":"8","author":"Wang","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.compag.2012.03.005","article-title":"CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support","volume":"84","author":"Han","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2003.11.008","article-title":"Satellite-based modeling of gross primary production in an evergreen needleleaf forest","volume":"89","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.rse.2004.03.010","article-title":"Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data","volume":"91","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.agee.2008.10.017","article-title":"Modeling gross primary productivity for winter wheat\u2013maize double cropping system using MODIS time series and CO2 eddy flux tower data","volume":"129","author":"Yan","year":"2009","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.agwat.2018.04.001","article-title":"Responses of gross primary production of grasslands and croplands under drought, pluvial, and irrigation conditions during 2010\u20132016, Oklahoma, USA","volume":"204","author":"Doughty","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2015.02.022","article-title":"Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought","volume":"162","author":"Dong","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.agrformet.2015.07.003","article-title":"Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model","volume":"213","author":"Jin","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2016.11.025","article-title":"Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images","volume":"190","author":"Xin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1016\/j.agrformet.2011.06.007","article-title":"Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data","volume":"151","author":"Kalfas","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Xin, F.F., Xiao, X.M., Cabral, O.M.R., White, P.M., Guo, H.Q., Ma, J., Li, B., and Zhao, B. (2020). Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models. Remote Sens., 12.","DOI":"10.3390\/rs12142186"},{"key":"ref_50","unstructured":"Bond, J.K. (2020). Wheat Outlook: May 2020, Wheat Sector at a Glance."},{"key":"ref_51","unstructured":"Widmar, D. (2019). Disappointing Wheat Prices Headed into 2020. Agric. Econ. Insights, Available online: https:\/\/aei.ag\/2019\/10\/07\/disappoint-wheat-prices-headed-into-2020\/."},{"key":"ref_52","unstructured":"USDA (2017). USDA Crop Production 2017 Summary."},{"key":"ref_53","unstructured":"Hossain, I., Epplin, F.M., Horn, G.W., and Krenzer, E.G. (2004). Wheat Production and Management Practices Used by Oklahoma Grain and Livestock Producers, Bulletin 818 Oklahoma State University Cooporation Extension Service."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10584-017-1965-5","article-title":"Vulnerability of Southern Plains agriculture to climate change","volume":"146","author":"Steiner","year":"2018","journal-title":"Clim. Chang."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1111\/j.1744-7348.1995.tb05015.x","article-title":"Harvest Index\u2014A Review of Its Use in Plant-Breeding and Crop Physiology","volume":"126","author":"Hay","year":"1995","journal-title":"Ann. Appl. Biol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Monfreda, C., Ramankutty, N., and Foley, J.A. (2008). Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles, 22.","DOI":"10.1029\/2007GB002947"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.isprsjprs.2019.12.005","article-title":"Estimating winter wheat yield based on a light use efficiency model and wheat variety data","volume":"160","author":"Dong","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","unstructured":"Vocke, G., and Ali, M.U.S. (2013). Wheat Production Practices, Costs, and Yields: Variations Across Regions."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.ecolind.2015.08.013","article-title":"Estimating crop yield using a satellite-based light use efficiency model","volume":"60","author":"Yuan","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2018.08.001","article-title":"Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems","volume":"217","author":"Marshall","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2018.02.045","article-title":"A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach","volume":"210","author":"Cai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.isprsjprs.2016.09.016","article-title":"Winter wheat mapping combining variations before and after estimated heading dates","volume":"123","author":"Qiu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1735\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:55:47Z","timestamp":1760162147000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1735"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,29]]},"references-count":62,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091735"],"URL":"https:\/\/doi.org\/10.3390\/rs13091735","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,29]]}}}