{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T13:28:29Z","timestamp":1774618109264,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NOAA JPSS Program","award":["NA20NES4320003"],"award-info":[{"award-number":["NA20NES4320003"]}]},{"name":"University of Houston GEAR Program","award":["NA20NES4320003"],"award-info":[{"award-number":["NA20NES4320003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Floods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms of implementation and scalability due to computational burdens and data availability issues. Current forecasting services in the U.S. largely rely on hydrodynamic modeling, limited to river reaches near in situ gauges and requiring extensive data for model setup and calibration. Here, we have successfully adapted the Forecasting Inundation Extents using REOF (FIER) analysis framework to produce forecasted water fraction maps in two U.S. flood-prone regions, specifically the Red River of the North Basin and the Upper Mississippi Alluvial Plain, utilizing Visible Infrared Imaging Radiometer Suite (VIIRS) optical imagery and the National Water Model. Comparing against historical VIIRS imagery for the same dates, FIER 1- to 8-day medium-range pseudo-forecasts show that about 70\u201380% of pixels exhibit absolute errors of less than 30%. Although originally developed utilizing Synthetic Aperture Radar (SAR) images, this study demonstrated FIER\u2019s versatility and effectiveness in flood forecasting by demonstrating its successful adaptation with optical VIIRS imagery which provides daily water fraction product, offering more historical observations to be used as inputs for FIER during peak flood times, particularly in regions where flooding commonly happens in a short period rather than following a broad seasonal pattern.<\/jats:p>","DOI":"10.3390\/rs16234357","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T04:21:36Z","timestamp":1732249296000},"page":"4357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Forecasting Flood Inundation in U.S. Flood-Prone Regions Through a Data-Driven Approach (FIER): Using VIIRS Water Fractions and the National Water Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2899-4249","authenticated-orcid":false,"given":"Amirhossein","family":"Rostami","sequence":"first","affiliation":[{"name":"Department of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-Hung","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6478-7533","authenticated-orcid":false,"given":"Hyongki","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hung-Hsien","family":"Wan","sequence":"additional","affiliation":[{"name":"Department of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1745-5621","authenticated-orcid":false,"given":"Tien Le Thuy","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Civil & Environmental Engineering, University of Houston, 5000 Gulf Fwy, Bldg. 4, Rm#216, Houston, TX 77204, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kel N.","family":"Markert","sequence":"additional","affiliation":[{"name":"Department of Civil and Construction Engineering, Brigham Young University, Engineering Building 430, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2781-0738","authenticated-orcid":false,"given":"Gustavious P.","family":"Williams","sequence":"additional","affiliation":[{"name":"Department of Civil and Construction Engineering, Brigham Young University, Engineering Building 430, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. James","family":"Nelson","sequence":"additional","affiliation":[{"name":"Department of Civil and Construction Engineering, Brigham Young University, Engineering Building 430, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanmei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"Straka III","sequence":"additional","affiliation":[{"name":"Space Science and Engineering Center, University of Wisconsin\u2014Madison, 1225 W. Dayton St., Madison, WI 53706, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean","family":"Helfrich","sequence":"additional","affiliation":[{"name":"National Environmental Satellite Data and Information Service, National Oceanic and Atmospheric Administration, 1335 East-West Highway, SSMC1 8th Floor Suite 8300, Silver Spring, MD 20910, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angelica L.","family":"Gutierrez","sequence":"additional","affiliation":[{"name":"Office of Water Prediction, National Weather Service, National Oceanic and Atmospheric Administration, 1325 East-West Highway, Silver Spring, MD 20910, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"ref_1","unstructured":"National Centers for Environmental Information (2020). U.S. Billion-Dollar Weather and Climate Disasters, 1980\u2014Present, National Centers for Environmental Information."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/s11069-020-04470-2","article-title":"Flood Exposure and Social Vulnerability in the United States","volume":"106","author":"Tate","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s41558-021-01265-6","article-title":"Inequitable Patterns of US Flood Risk in the Anthropocene","volume":"12","author":"Wing","year":"2022","journal-title":"Nat. Clim. Chang."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"034023","DOI":"10.1088\/1748-9326\/aaac65","article-title":"Estimates of Present and Future Flood Risk in the Conterminous United States","volume":"13","author":"Wing","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2020EF001778","DOI":"10.1029\/2020EF001778","article-title":"Increased Flood Exposure Due to Climate Change and Population Growth in the United States","volume":"8","author":"Swain","year":"2020","journal-title":"Earths Future"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e1517","DOI":"10.1002\/wat2.1517","article-title":"Operational and Emerging Capabilities for Surface Water Flood Forecasting","volume":"8","author":"Speight","year":"2021","journal-title":"Wiley Interdiscip. Rev. Water"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.envsci.2015.04.016","article-title":"The Monetary Benefit of Early Flood Warnings in Europe","volume":"51","author":"Pappenberger","year":"2015","journal-title":"Environ. Sci. Policy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.envsoft.2017.01.006","article-title":"Flood Inundation Modelling: A Review of Methods, Recent Advances and Uncertainty Analysis","volume":"90","author":"Teng","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1146\/annurev-fluid-030121-113138","article-title":"Flood Inundation Prediction","volume":"54","author":"Bates","year":"2021","journal-title":"Annu. Rev. Fluid. Mech."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e2022WR033836","DOI":"10.1029\/2022WR033836","article-title":"Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations","volume":"59","author":"Fraehr","year":"2023","journal-title":"Water Resour. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2022WR032248","DOI":"10.1029\/2022WR032248","article-title":"Upskilling Low-Fidelity Hydrodynamic Models of Flood Inundation Through Spatial Analysis and Gaussian Process Learning","volume":"58","author":"Fraehr","year":"2022","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1175\/BAMS-D-15-00243.1","article-title":"Real-Time Flood Forecasting and Information System for the State of Iowa","volume":"98","author":"Krajewski","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bates, P.D., Pappenberger, F., and Romanowicz, R.J. (2014). Uncertainty in Flood Inundation Modelling. Applied Uncertainty Analysis for Flood Risk Management, World Scientific.","DOI":"10.1142\/9781848162716_0010"},{"key":"ref_14","unstructured":"Davidian, J. (1984). Computation of Water-Surface Profiles in Open Channels, USGS. Techniques of Water-Resources Investigations."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Adams, T.E. (2016). Flood Forecasting in the United States NOAA\/National Weather Service. Flood Forecasting: A Global Perspective, Academic Press.","DOI":"10.1016\/B978-0-12-801884-2.09999-0"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1038\/s44221-023-00132-2","article-title":"Supercharging Hydrodynamic Inundation Models for Instant Flood Insight","volume":"1","author":"Fraehr","year":"2023","journal-title":"Nat. Water"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2021GL093585","DOI":"10.1029\/2021GL093585","article-title":"Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting","volume":"48","author":"Ivanov","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.1016\/j.rse.2008.03.018","article-title":"HAND, a New Terrain Descriptor Using SRTM-DEM: Mapping Terra-Firme Rainforest Environments in Amazonia","volume":"112","author":"Nobre","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jhydrol.2011.03.051","article-title":"Height Above the Nearest Drainage\u2014A Hydrologically Relevant New Terrain Model","volume":"404","author":"Nobre","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1002\/hyp.10581","article-title":"HAND Contour: A New Proxy Predictor of Inundation Extent","volume":"30","author":"Nobre","year":"2016","journal-title":"Hydrol. Process"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10013","DOI":"10.1029\/2018WR023457","article-title":"GeoFlood: Large-Scale Flood Inundation Mapping Based on High-Resolution Terrain Analysis","volume":"54","author":"Zheng","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e2022WR032039","DOI":"10.1029\/2022WR032039","article-title":"Extending Height Above Nearest Drainage to Model Multiple Fluvial Sources in Flood Inundation Mapping Applications for the U.S. National Water Model","volume":"59","author":"Aristizabal","year":"2023","journal-title":"Water Resour. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.5194\/nhess-19-2405-2019","article-title":"An Integrated Evaluation of the National Water Model (NWM)-Height above Nearest Drainage (HAND) Flood Mapping Methodology","volume":"19","author":"Munasinghe","year":"2019","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1002\/(SICI)1099-1085(199708)11:10<1427::AID-HYP473>3.0.CO;2-S","article-title":"Satellite Remote Sensing of River Inundation Area, Stage, and Discharge: A Review","volume":"11","author":"Smith","year":"1997","journal-title":"Ltd. Hydrol. Process"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111732","DOI":"10.1016\/j.rse.2020.111732","article-title":"Hindcast and Forecast of Daily Inundation Extents Using Satellite SAR and Altimetry Data with Rotated Empirical Orthogonal Function Analysis: Case Study in Tonle Sap Lake Floodplain","volume":"241","author":"Chang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105643","DOI":"10.1016\/j.envsoft.2023.105643","article-title":"Operational Forecasting Inundation Extents Using REOF Analysis (FIER) over Lower Mekong and Its Potential Economic Impact on Agriculture","volume":"162","author":"Chang","year":"2023","journal-title":"Environ. Model. Softw."},{"key":"ref_27","unstructured":"(2024, September 05). Red River Valley Farmers Look for Solutions to Three Generations of Cropland Flooding\u2014Agweek|#1 Source for Agriculture News, Farming, Markets. Available online: https:\/\/www.agweek.com\/business\/red-river-valley-farmers-look-for-solutions-to-three-generations-of-cropland-flooding."},{"key":"ref_28","unstructured":"(2024, September 05). Heavy Rains Add to Farm Flooding in New Madrid, MO. Available online: https:\/\/www.kfvs12.com\/2019\/05\/31\/heavy-rains-add-farm-flooding-new-madrid-mo\/."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2022.08.025","article-title":"A Downscaling Model for Derivation of 3-D Flood Products from VIIRS Imagery and SRTM\/DEM","volume":"192","author":"Li","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.rse.2017.09.032","article-title":"Automatic near Real-Time Flood Detection Using Suomi-NPP\/VIIRS Data","volume":"204","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1002\/agj2.20093","article-title":"Impacts and Management Strategies for Crop Production in Waterlogged or Flooded Soils: A Review","volume":"112","author":"Kaur","year":"2020","journal-title":"Agron. J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Atashi, V., Rosati, M., Lim, Y.H., and Taufique, M. (2022, January 5\u20138). Characteristics of Seasonality on 3D Velocity and Bathymetry Profiles in Red River of the North. Proceedings of the World Environmental and Water Resources Congress 2022: Adaptive Planning and Design in an Age of Risk and Uncertainty\u2014Selected Papers from the World Environmental and Water Resources Congress 2022, Atlanta, Georgia.","DOI":"10.1061\/9780784484258.024"},{"key":"ref_33","unstructured":"(2024, September 05). What Makes the Red River of the North So Vulernable to Flooding?. Available online: https:\/\/www.ndsu.edu\/fargo_geology\/whyflood.htm."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Roy, D., Jia, X., Steele, D.D., Chu, X., and Lin, Z. (2020). Infiltration into Frozen Silty Clay Loam Soil with Different Soil Water Contents in the Red River of the North Basin in the USA. Water, 12.","DOI":"10.3390\/w12020321"},{"key":"ref_35","unstructured":"(2024, September 05). Rising Waters Along the Red River, Available online: https:\/\/earthobservatory.nasa.gov\/images\/50170\/rising-waters-along-the-red-river."},{"key":"ref_36","unstructured":"(2024, September 05). Another Flood on the Red River, Available online: https:\/\/earthobservatory.nasa.gov\/images\/146616\/another-flood-on-the-red-river."},{"key":"ref_37","unstructured":"(2024, September 05). Red River Flooding Is Worst in a Decade, Available online: https:\/\/earthobservatory.nasa.gov\/images\/149822\/red-river-flooding-is-worst-in-a-decade."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1038\/s41597-021-01048-w","article-title":"The Changing Face of Floodplains in the Mississippi River Basin Detected by a 60-Year Land Use Change Dataset","volume":"8","author":"Rajib","year":"2021","journal-title":"Sci. Data"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"235","DOI":"10.2983\/035.039.0205","article-title":"Mass Mortality of the Eastern Oyster Crassostrea Virginica in the Western Mississippi Sound Following Unprecedented Mississippi River Flooding in 2019","volume":"39","author":"Gledhill","year":"2020","journal-title":"J. Shellfish. Res."},{"key":"ref_40","unstructured":"(2024, September 05). 2011 Mississippi River Flood Report Now Available > Mississippi Valley Division > News Releases. Available online: https:\/\/www.mvd.usace.army.mil\/Media\/News-Releases\/Article\/473851\/2011-mississippi-river-flood-report-now-available\/."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e2019GL086933","DOI":"10.1029\/2019GL086933","article-title":"The 2019 Mississippi and Missouri River Flooding and Its Impact on Atmospheric Boundary Layer Dynamics","volume":"47","author":"Pal","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-Resolution Mapping of Global Surface Water and Its Long-Term Changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_43","unstructured":"(2024, September 05). Section 4: Red River Valley|4th Grade North Dakota Studies, Available online: https:\/\/www.ndstudies.gov\/gr4\/geology-geography-and-climate\/part-2-geography\/section-4-red-river-valley."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2181143","DOI":"10.1080\/15481603.2023.2181143","article-title":"Thematic Accuracy Assessment of the NLCD 2019 Land Cover for the Conterminous United States","volume":"60","author":"Wickham","year":"2023","journal-title":"GIsci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Goldberg, M.D., Li, S., Goodman, S., Lindsey, D., Sjoberg, B., and Sun, D. (2018). Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey. Remote Sens., 10.","DOI":"10.3390\/rs10081256"},{"key":"ref_46","unstructured":"(2024, September 05). About National Water Model, Available online: https:\/\/water.noaa.gov\/about\/nwm."},{"key":"ref_47","unstructured":"(2024, September 05). United States Production, Available online: https:\/\/ipad.fas.usda.gov\/countrysummary\/Default.aspx?id=US."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/BF02289233","article-title":"The Varimax Criterion for Analytic Rotation in Factor Analysis","volume":"23","author":"Kaiser","year":"1958","journal-title":"Psychometrika"},{"key":"ref_49","unstructured":"Hannachi, A. (2004). A Primer for EOF Analysis of Climate Data, Department of Meteorology, University of Reading."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s13762-013-0287-z","article-title":"Caspian Sea Level Prediction Using Satellite Altimetry by Artificial Neural Networks","volume":"11","author":"Imani","year":"2014","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"126423","DOI":"10.1016\/j.jhydrol.2021.126423","article-title":"Can Artificial Intelligence and Data-Driven Machine Learning Models Match or Even Replace Process-Driven Hydrologic Models for Streamflow Simulation?: A Case Study of Four Watersheds with Different Hydro-Climatic Regions across the CONUS","volume":"598","author":"Kim","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1175\/JCLI-D-12-00821.1","article-title":"Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue","volume":"26","author":"Maraun","year":"2013","journal-title":"J. Clim."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"401","DOI":"10.2166\/wcc.2020.261","article-title":"Bias Correction Capabilities of Quantile Mapping Methods for Rainfall and Temperature Variables","volume":"12","author":"Enayati","year":"2021","journal-title":"J. Water Clim. Chang."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Markert, K.N., Markert, A.M., Mayer, T., Nauman, C., Haag, A., Poortinga, A., Bhandari, B., Thwal, N.S., Kunlamai, T., and Chishtie, F. (2020). Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12152469"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2990","DOI":"10.1080\/01431161.2016.1192304","article-title":"Sentinel-1-Based Flood Mapping: A Fully Automated Processing Chain","volume":"37","author":"Twele","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Vollrath, A., Mullissa, A., and Reiche, J. (2020). Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12111867"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5844","DOI":"10.1002\/2017GL072874","article-title":"A High-Accuracy Map of Global Terrain Elevations","volume":"44","author":"Yamazaki","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1109\/36.62623","article-title":"Adaptive Speckle Filters and Scene Heterogeneity","volume":"28","author":"Lopes","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lozano, J.S., Bustamante, G.R., Hales, R.C., Nelson, E.J., Williams, G.P., Ames, D.P., Jones, N.L., Lozano, S., Romero Bustamante, J., and Hales, G. (2021). A Streamflow Bias Correction and Performance Evaluation Web Application for GEOGloWS ECMWF Streamflow Services. Hydrology, 8.","DOI":"10.3390\/hydrology8020071"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hales, R.C., Sowby, R.B., Williams, G.P., Nelson, E.J., Ames, D.P., Dundas, J.B., and Ogden, J. (2022). SABER: A Model-Agnostic Postprocessor for Bias Correcting Discharge from Large Hydrologic Models. Hydrology, 9.","DOI":"10.3390\/hydrology9070113"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"130279","DOI":"10.1016\/j.jhydrol.2023.130279","article-title":"Bias Correcting Discharge Simulations from the GEOGloWS Global Hydrologic Model","volume":"626","author":"Hales","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4236\/jbise.2024.171001","article-title":"Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images","volume":"17","author":"Matsuyama","year":"2024","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1002\/met.296","article-title":"A Long-Term Assessment of Precipitation Forecast Skill Using the Fractions Skill Score","volume":"20","author":"Mittermaier","year":"2013","journal-title":"Meteorol. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"116087","DOI":"10.1016\/j.eswa.2021.116087","article-title":"Structural Similarity Index (SSIM) Revisited: A Data-Driven Approach","volume":"189","author":"Bakurov","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1146\/annurev.environ.032108.105046","article-title":"Nitrogen in Agriculture: Balancing the Cost of an Essential Resource","volume":"34","author":"Robertson","year":"2009","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s11738-004-0037-4","article-title":"The Role of Nitric Oxide in Plant Growth Regulation and Responses to Abiotic Stresses","volume":"26","author":"Kopyra","year":"2004","journal-title":"Acta Physiol. Plant"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"809","DOI":"10.2136\/sssaj2012.0231","article-title":"Biochemical Processes Controlling Soil Nitrogen Mineralization under Waterlogged Conditions","volume":"77","author":"Haddad","year":"2013","journal-title":"Soil. Sci. Soc. Am. J."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.2134\/agronj2015.0547","article-title":"Root and Shoot Responses of Summer Maize to Waterlogging at Different Stages","volume":"108","author":"Ren","year":"2016","journal-title":"Agron. J."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1094\/CM-2008-0730-02-RV","article-title":"Environmental Impacts of Enhanced-Efficiency Nitrogen Fertilizers","volume":"7","author":"Motavalli","year":"2008","journal-title":"Crop Manag."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"607","DOI":"10.2134\/agronj2008.0067x","article-title":"Corn Response to Conventional and Slow-Release Nitrogen Fertilizers across a Claypan Landscape","volume":"101","author":"Noellsch","year":"2009","journal-title":"Agron. J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1002\/jpln.201300424","article-title":"Oxygen Enrichment with Magnesium Peroxide for Minimizing Hypoxic Stress of Flooded Corn","volume":"177","author":"Liu","year":"2014","journal-title":"J. Plant Nutr. Soil. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Bera, T., Inglett, K.S., and Liu, G.D. (2020). Effects of Solid Oxygen Fertilizers and Biochars on Nitrous Oxide Production from Agricultural Soils in Florida. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-78198-1"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.rse.2013.03.015","article-title":"Derivation of 30-m-Resolution Water Maps from TERRA\/MODIS and SRTM","volume":"134","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1501005","DOI":"10.1109\/LGRS.2020.3031190","article-title":"Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for Rapid and Large Scale Flood Analysis","volume":"19","author":"Peter","year":"2022","journal-title":"IEEE Geosci. Remote Sens. 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