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Optical variables, such as vegetation indices (VI), have been extensively employed in monitoring AGB. However, the limited robustness of inversion models remains a significant impediment to the widespread application of UAV-based multispectral remote sensing in AGB inversion. In this study, a novel stem\u2013leaf separation strategy for AGB estimation is delineated. Convolutional neural network (CNN) and transfer learning (TL) methodologies are integrated to estimate leaf biomass (LGB) across multiple growth stages, followed by the development of an allometric growth model for estimating stem biomass (SGB). To enhance the precision of LGB inversion, the large-scale remote sensing data and image simulation framework over heterogeneous scenes (LESS) model, which is a three-dimensional (3D) radiative transfer model (RTM), was utilized to simulate a more extensive canopy spectral dataset, characterized by a broad distribution of canopy spectra. The CNN model was pre-trained in order to gain prior knowledge, and this knowledge was transferred to a re-trained model with a subset of field-observed samples. Finally, the allometric growth model was utilized to estimate SGB across various growth stages. To further validate the generalizability, transferability, and predictive capability of the proposed method, field samples from 2022 and 2023 were employed as target tasks. The results demonstrated that the 3D RTM + CNN + TL method outperformed best in LGB estimation, achieving an R\u00b2 of 0.73 and an RMSE of 72.5 g\/m\u00b2 for the 2022 dataset, and an R\u00b2 of 0.84 and an RMSE of 56.4 g\/m\u00b2 for the 2023 dataset. In contrast, the PROSAIL method yielded an R\u00b2 of 0.45 and an RMSE of 134.55 g\/m\u00b2 for the 2022 dataset, and an R\u00b2 of 0.74 and an RMSE of 61.84 g\/m\u00b2 for the 2023 dataset. The accuracy of LGB inversion was poor when using only field-measured samples to train a CNN model without simulated data, with R\u00b2 values of 0.30 and 0.74. Overall, learning prior knowledge from the simulated dataset and transferring it to a new model significantly enhanced LGB estimation accuracy and model generalization. Additionally, the allometric growth model\u2019s estimation of SGB resulted in an accuracy of 0.87 and 120.87 g\/m\u00b2 for the 2022 dataset, and 0.74 and 86.87 g\/m\u00b2 for the 2023 dataset, exhibiting satisfactory results. Separate estimation of both LGB and SGB based on stem and leaf separation strategies yielded promising results. This method can be extended to the monitor and inversion of other critical variables.<\/jats:p>","DOI":"10.3390\/rs16163000","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T04:29:57Z","timestamp":1723782597000},"page":"3000","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Estimation of Maize Biomass at Multi-Growing Stage Using Stem and Leaf Separation Strategies with 3D Radiative Transfer Model and CNN Transfer Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Dan","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8506-7295","authenticated-orcid":false,"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fenghua","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengjian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aohua","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7788-7015","authenticated-orcid":false,"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Surveying and Mapping Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongyu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Long, N.V., Assefa, Y., Schwalbert, R., and Ciampitti, I.A. 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