{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:37:37Z","timestamp":1768678657407,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program in Guangxi","award":["AB19245039"],"award-info":[{"award-number":["AB19245039"]}]},{"name":"Key Research and Development Program in Guangxi","award":["2019AB20009"],"award-info":[{"award-number":["2019AB20009"]}]},{"name":"Key Research and Development Program in Guangxi","award":["162301202679"],"award-info":[{"award-number":["162301202679"]}]},{"name":"Key Research and Development Program in Guangxi","award":["GXHRI-WEMS-2020-06"],"award-info":[{"award-number":["GXHRI-WEMS-2020-06"]}]},{"name":"Key Research and Development Program in Guangxi","award":["AB19245039"],"award-info":[{"award-number":["AB19245039"]}]},{"name":"Key Research and Development Program in Guangxi","award":["2019AB20009"],"award-info":[{"award-number":["2019AB20009"]}]},{"name":"Key Research and Development Program in Guangxi","award":["162301202679"],"award-info":[{"award-number":["162301202679"]}]},{"name":"Key Research and Development Program in Guangxi","award":["GXHRI-WEMS-2020-06"],"award-info":[{"award-number":["GXHRI-WEMS-2020-06"]}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["AB19245039"],"award-info":[{"award-number":["AB19245039"]}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["2019AB20009"],"award-info":[{"award-number":["2019AB20009"]}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["162301202679"],"award-info":[{"award-number":["162301202679"]}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["GXHRI-WEMS-2020-06"],"award-info":[{"award-number":["GXHRI-WEMS-2020-06"]}]},{"name":"Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures","award":["AB19245039"],"award-info":[{"award-number":["AB19245039"]}]},{"name":"Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures","award":["2019AB20009"],"award-info":[{"award-number":["2019AB20009"]}]},{"name":"Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures","award":["162301202679"],"award-info":[{"award-number":["162301202679"]}]},{"name":"Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures","award":["GXHRI-WEMS-2020-06"],"award-info":[{"award-number":["GXHRI-WEMS-2020-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sugarcane is an important sugar source in America and is mainly planted in the states of Florida and Louisiana. The purpose of this study was to predict the sugarcane yield in these two states from 2008 to 2016. Three statistical sugarcane yield models (i.e., the CNDVI, K\u2013M, and SiPAR models) were applied to predict yield in America, using remote sensing and weather data. To verify the robustness of models, model parameters obtained in places (i.e., Reunion Island and Southwestern China) far away from America were used. The results showed that the SiPAR model outperformed the CDNVI and K\u2013M models for yield prediction. Solar radiation was an important constraint factor to ensure the statistical model\u2019s robustness under different conditions. The CNDVI model had the lowest robustness because of the absence of solar radiation, although it could reflect the yield trend to some extent. The K\u2013M model failed to predict the low sugarcane yield, owing to the lack of consideration of temperature and soil water stresses. Florida had a low sugarcane yield in the west and southwest; however, Louisiana had high sugarcane yield in the same directions. This study demonstrated the robustness of the SiPAR model and investigated the sugarcane yield status in Florida and Louisiana. It can be a reference for similar studies in the future.<\/jats:p>","DOI":"10.3390\/rs14163870","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T04:20:32Z","timestamp":1660105232000},"page":"3870","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Regional Yield Estimation for Sugarcane Using MODIS and Weather Data: A Case Study in Florida and Louisiana, United States of America"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5638-6438","authenticated-orcid":false,"given":"Shun","family":"Hu","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Yangtze River Basin Environmental Aquatic Science, School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0446-0488","authenticated-orcid":false,"given":"Liangsheng","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4323-0730","authenticated-orcid":false,"given":"Yuanyuan","family":"Zha","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, Wuhan 430072, China"}]},{"given":"Linglin","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Sugarcane sucrose Metabolism: Scope for molecular manipulation","volume":"28","author":"Grof","year":"2001","journal-title":"Aust. J. Plant Physiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12355-014-0342-1","article-title":"Sugarcane Agriculture and Sugar Industry in China","volume":"17","author":"Li","year":"2014","journal-title":"Sugar Tech"},{"key":"ref_3","unstructured":"Doyle, E., Friedman, Z., and Johnson, M. (2018). Sustainability Measurement of the American Sugarcane Industry, School of Environment and Sustainability, University of Michigan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1080\/02571862.1991.10634587","article-title":"A growth model for sugarcane based on a simple carbon balance and the CERES-Maize water balance","volume":"8","year":"1991","journal-title":"S. Afr. J. Plant Soil"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.fcr.2005.01.011","article-title":"Sugarcane Physiology: Integrating from cell to crop to advance sugarcane production","volume":"92","author":"Bonnett","year":"2005","journal-title":"Field Crops Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S0378-4290(02)00118-1","article-title":"A new method of simulating dry matter partitioning in the Canegro sugarcane model","volume":"78","author":"Singels","year":"2002","journal-title":"Field Crops Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/S0378-4290(98)00167-1","article-title":"Modelling sugarcane production systems. I. Development and performance of the sugarcane module","volume":"63","author":"Keating","year":"1999","journal-title":"Field Crops Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0304-3800(01)00372-6","article-title":"Simulation of biomass and sugar accumulation in sugarcane using a process-based model","volume":"144","author":"Liu","year":"2001","journal-title":"Ecol. Model."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.agrformet.2007.05.004","article-title":"Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts","volume":"146","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0308-521X(00)00025-1","article-title":"Scaling-up crop models for climate variability applications","volume":"65","author":"Hansen","year":"2000","journal-title":"Agric. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1016\/j.jhydrol.2017.10.061","article-title":"Simultaneous state-parameter estimation supports the evaluation of data assimilation performance and measurement design for soil-water-atmosphere-plant system","volume":"555","author":"Hu","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.fcr.2018.12.009","article-title":"Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation","volume":"232","author":"Hu","year":"2019","journal-title":"Field Crops Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.rse.2013.07.018","article-title":"Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction","volume":"138","author":"Ines","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pauwels, V.R.N., Verhoest, N.E.C., De Lannoy, G.J.M., Guissard, V., Lucau, C., and Defourny, P. (2007). Optimization of a coupled hydrology-crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter. Water Resour. Res., 43.","DOI":"10.1029\/2006WR004942"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1007\/s12524-018-0839-2","article-title":"Remote Sensing-Based Yield Forecasting for Sugarcane (Saccharum officinarum L.) Crop in India","volume":"46","author":"Dubey","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"93","DOI":"10.4236\/ars.2016.52008","article-title":"A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region","volume":"05","author":"Rahman","year":"2016","journal-title":"Adv. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0034-4257(90)90029-L","article-title":"Yield estimation of sugarcane based on agrometeorological-spectral models","volume":"33","author":"Rudorff","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_20","first-page":"1","article-title":"Relationships among leaf area index, visual growth rating, and sugarcane yield","volume":"32","author":"Sandhu","year":"2012","journal-title":"J. Am. Soc. Sugar Cane Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1002\/jsfa.1937","article-title":"Spectral variables, growth analysis and yield of sugarcane","volume":"62","author":"Rocha","year":"2005","journal-title":"Sci. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1590\/S0103-90162005000100005","article-title":"Growth indices and productivity in sugarcane","volume":"62","author":"Rocha","year":"2005","journal-title":"Sci. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1080\/01431160701874603","article-title":"The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: A review of the literature","volume":"29","author":"Ahmed","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","unstructured":"Basso, B., Cammarano, D., and Carfagna, E. (2013). Review of Crop Yield Forecasting Methods and Early Warning Systems, Food and Agriculture Organization."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"744","DOI":"10.2307\/2401901","article-title":"Solar radiation and productivity in tropical ecosystems","volume":"9","author":"Monteith","year":"1972","journal-title":"J. Appl. Ecol."},{"key":"ref_26","unstructured":"Kumar, M., and Monteith, J.L. (1981). Remote Sensing of Crop Growth, Academic Press."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6620","DOI":"10.3390\/rs6076620","article-title":"Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island","volume":"6","author":"Morel","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","first-page":"62","article-title":"A model for evaluating climatic productivity and water balance of irrigated rice and its application to Southeast Asia","volume":"25","author":"Horie","year":"1987","journal-title":"Southeast Asian Stud."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"90","DOI":"10.2135\/cropsci1989.0011183X002900010023x","article-title":"Leaf nitrogen, photosynthesis and crop radiation use efficiency: A review","volume":"29","author":"Sinclair","year":"1989","journal-title":"Crop Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1002\/agj2.20949","article-title":"A new sugarcane yield model using the SiPAR model","volume":"114","author":"Hu","year":"2021","journal-title":"Agron. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/0034-4257(85)90095-1","article-title":"Estimation of total above-ground phytomass production using remotely sensing data","volume":"17","author":"Asrar","year":"1985","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/j.rse.2010.01.004","article-title":"Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model","volume":"114","author":"Liu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1590\/S0103-90162013000600011","article-title":"Climatic effects on sugarcane ripening under the influence of cultivars and crop age","volume":"70","author":"Cardozo","year":"2013","journal-title":"Sci. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1590\/S0103-90162004000500004","article-title":"Sugarcane maturity estimation through edaphic-climatic parameters","volume":"61","author":"Scarpari","year":"2004","journal-title":"Sci. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1590\/S0103-90162009000500006","article-title":"Physiological model to estimate the maturity of sugarcane","volume":"66","author":"Scarpari","year":"2009","journal-title":"Sci. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Baez-Gonzalez, A., Kiniry, J., Meki, M., Williams, J., Alvarez-Cilva, M., Ramos-Gonzalez, J., Magallanes-Estala, A., and Zapata-Buenfil, G. (2017). Crop Parameters for Modeling Sugarcane under Rainfed Conditions in Mexico. Sustainability, 9.","DOI":"10.3390\/su9081337"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/0378-4290(94)90051-5","article-title":"Temperature and seasonal effects on canopy development and light interception of sugarcane","volume":"36","year":"1994","journal-title":"Field Crops Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3870\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:06:31Z","timestamp":1760141191000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,10]]},"references-count":37,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14163870"],"URL":"https:\/\/doi.org\/10.3390\/rs14163870","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,10]]}}}