{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T02:06:12Z","timestamp":1772589972177,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T00:00:00Z","timestamp":1705276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China program","award":["42207098"],"award-info":[{"award-number":["42207098"]}]},{"name":"National Nature Science Foundation of China program","award":["YSS202302"],"award-info":[{"award-number":["YSS202302"]}]},{"name":"Yinshanbeilu Grassland Eco-hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research","award":["42207098"],"award-info":[{"award-number":["42207098"]}]},{"name":"Yinshanbeilu Grassland Eco-hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research","award":["YSS202302"],"award-info":[{"award-number":["YSS202302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Maize gross primary productivity (GPP) contributes the most to the global cropland GPP, making it crucial to accurately estimate maize GPP for the global carbon cycle. Previous research validated machine learning (ML) methods using remote sensing and meteorological data to estimate plant GPP, yet they disregard vegetation physiological dynamics driven by phenology. Leaf nitrogen content per unit leaf area (i.e., specific leaf nitrogen (SLN)) greatly affects photosynthesis. Its maximum allowable value correlates with a phenological factor conceptualized as normalized maize phenology (NMP). This study aims to validate SLN and NMP for maize GPP estimation using four ML methods (random forest (RF), support vector machine (SVM), convolutional neutral network (CNN), and extreme learning machine (ELM)). Inputs consist of vegetation index (NDVI), air temperature, solar radiation (SSR), NMP, and SLN. Data from four American maize flux sites (NE1, NE2, and NE3 sites in Nebraska and RO1 site in Minnesota) were gathered. Using data from three NE sites to validate the effect of SLN and MMP shows that the accuracy of four ML methods notably increased after adding SLN and MMP. Among these methods, RF and SVM achieved the best performance of Nash\u2013Sutcliffe efficiency coefficient (NSE) = 0.9703 and 0.9706, root mean square error (RMSE) = 1.5596 and 1.5509 gC\u00b7m\u22122\u00b7d\u22121, and coefficient of variance (CV) = 0.1508 and 0.1470, respectively. When evaluating the best ML models from three NE sites at the RO1 site, only RF and CNN could effectively incorporate the impact of SLN and NMP. But, in terms of unbiased estimation results, the four ML models were comprehensively enhanced by adding SLN and NMP. Due to their fixed relationship, introducing SLN or NMP alone might be more effective than introducing both simultaneously, considering the data redundancy for methods like CNN and ELM. This study supports the integration of phenology and leaf-level photosynthetic factors in plant GPP estimation via ML methods and provides a reference for similar research.<\/jats:p>","DOI":"10.3390\/rs16020341","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T08:52:03Z","timestamp":1705308723000},"page":"341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Estimation of Daily Maize Gross Primary Productivity by Considering Specific Leaf Nitrogen and Phenology via Machine Learning Methods"],"prefix":"10.3390","volume":"16","author":[{"given":"Cenhanyi","family":"Hu","sequence":"first","affiliation":[{"name":"School of Environmental Studies, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5638-6438","authenticated-orcid":false,"given":"Shun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Environmental Studies, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6659-2702","authenticated-orcid":false,"given":"Linglin","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Keyu","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Environmental Studies, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zilong","family":"Liao","sequence":"additional","affiliation":[{"name":"Institute of Water Resources for Pastoral Area Ministry of Water Resources, Hohhot 010020, China"}]},{"given":"Kuang","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui and Huaihe River Institute of Hydraulic Research, Hefei 230088, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1016\/j.rse.2010.07.012","article-title":"Comparison of Multiple Models for Estimating Gross Primary Production Using MODIS and Eddy Covariance Data in Harvard Forest","volume":"114","author":"Wu","year":"2010","journal-title":"Remote Sens. 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