{
  "abstract": "Accurate estimation of leaf nitrogen content (LNC) and grain protein content (GPC) in winter wheat is crucial for making fertilization recommendations and assessing grain quality. Previous methods primarily relied on chlorophyll (Chl)-sensitive vegetation indices from the visible and near-infrared domain, while shortwave-infrared spectral features linked to dry matter and nitrogen remain underutilized due to strong leaf water masking. Although existing water removal methods can mitigate the absorption effects of leaf water, the reliability of the resulting simulated dry-canopy reflectance spectra (SDR) is limited by inaccurate leaf mass per area (LMA) retrieval. This study aimed to develop a semi-empirical water removal approach by integrating PRior knowledge and INitialization of leaf water conTent and LMA (PRINT) to simulate SDR, with the objective of improving the estimation performance of LNC and GPC. To achieve this goal, a Random Forest-based hybrid retrieval model was developed to increase LMA retrieval accuracy. Meanwhile, a visible and shortwave-based nitrogen index (VSNI) by combining the visible (proxy of Chl) and shortwave-infrared features was proposed for improving estimation performance of LNC and GPC. Moreover, experimental datasets covering multiple growth stages and geographic sites were collected to improve sample representativeness. These datasets were utilized to improve the retrieval of LMA and evaluate the performance of LNC and GPC estimations. The results demonstrated that the PRINT significantly improved the estimation accuracy of LMA over two standard physically-based inversion models (RMSE: 6.23 g/m 2 vs 8.25 g/m 2 and 45.73 g/m 2 ). Compared with existing VI-based models, the VSNI-based models yielded substantial improvements in LNC (R 2 : 0.62 ∼ 0.65, RMSE = 0.42 ∼ 0.54%) across sites, and enhanced GPC estimation accuracy up to R 2 = 0.66 (RMSE = 0.70%) at the peak filling stage across two years at one site. The proposed methodology advances the spectroscopic understanding of shortwave-infrared responses to nitrogen-related agronomic biochemicals, and holds significant potential for future applications in large-scale growth monitoring of wheat systems using satellite imagery.",
  "abstract_shown": true,
  "affiliations": [
    "National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Smart Agriculture, Nanjing Agricultural University, 666 Binjiang Avenue, Nanjing 211899, Jiangsu, PR China",
    "Hefei Meteorological Bureau, 16 Shihe, Hefei 230061, Anhui, PR China",
    "Precision Agriculture Lab, School of Life Sciences, Technical University of Munich, Freising, 85354, Germany",
    "State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agricultural University, 62 Nongye Road, Zhengzhou 450046, Henan, PR China",
    "National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Smart Agriculture, Nanjing Agricultural University, 666 Binjiang Avenue, Nanjing 211899, Jiangsu, PR China; Corresponding author."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Peng Fang"
    },
    {
      "affs": [
        1
      ],
      "name": "Yan Yan"
    },
    {
      "affs": [
        2
      ],
      "name": "Dong Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yapeng Wu"
    },
    {
      "affs": [
        3
      ],
      "name": "Wei Feng"
    },
    {
      "affs": [
        0
      ],
      "name": "Caili Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Xia Yao"
    },
    {
      "affs": [
        0
      ],
      "name": "Yan Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "Weixing Cao"
    },
    {
      "affs": [
        4
      ],
      "name": "Tao Cheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/488pez2z0waah.bib",
  "cited_by": 0,
  "doi": "10.1016/j.jag.2026.105651",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S1569843226005674",
  "id": "488pez2z0waah",
  "issns": [
    "1569-8432",
    "1872-826X"
  ],
  "landing_url": "https://doi.org/10.1016/j.jag.2026.105651",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "GB3-5030"
    ],
    [
      "lcc",
      "GE1-350"
    ],
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
  ],
  "openalex": "W7223551140",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S1569843226005674/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/488pez2z0waah.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:47:23Z",
      "native_id": "e4d96b914fb54c6aaf21679dce6c4f04",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.jag.2026.105651"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "PRINT: A semi-empirical canopy water removal approach for improving estimates of leaf nitrogen and grain protein contents in winter wheat",
  "title_text": "PRINT: A semi-empirical canopy water removal approach for improving estimates of leaf nitrogen and grain protein contents in winter wheat",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:23Z",
  "url": "https://research.draconitic.com/paper/488pez2z0waah",
  "venue": "International Journal of Applied Earth Observations and Geoinformation",
  "versions": []
}