Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model
Nitrogen is one of the most important macronutrients for plant growth and timely estimation of canopy nitrogen content (CNC) is crucial for agricultural applications. Remote sensing has emerged as an important tool to quantify CNC using either empirically or physically based methods. Most empirical...
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Elsevier
2024-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224006368 |
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| author | Dong Li Yapeng Wu Katja Berger Qianliang Kuang Wei Feng Jing M. Chen Wenhui Wang Hengbiao Zheng Xia Yao Yan Zhu Weixing Cao Tao Cheng |
| author_facet | Dong Li Yapeng Wu Katja Berger Qianliang Kuang Wei Feng Jing M. Chen Wenhui Wang Hengbiao Zheng Xia Yao Yan Zhu Weixing Cao Tao Cheng |
| author_sort | Dong Li |
| collection | DOAJ |
| description | Nitrogen is one of the most important macronutrients for plant growth and timely estimation of canopy nitrogen content (CNC) is crucial for agricultural applications. Remote sensing has emerged as an important tool to quantify CNC using either empirically or physically based methods. Most empirical methods use chlorophyll related spectral indices and are dependent on the relationship between nitrogen and chlorophyll, which varies with vegetation types and growth stages. In contrast, physically based methods use the full-range reflectance data and retrieve CNC from coupled leaf and canopy radiative transfer models (such as PROSPECT-PRO + 4SAIL, PROSAIL-PRO). However, the subtle absorption features of nitrogen and protein in fresh leaves hinder the accurate estimation of CNC. Therefore, this study proposed an efficient and mechanistic framework to estimate CNC (PROSAIL-NAM) by coupling PROSAIL-PRO with a nitrogen allocation model, which divided the total nitrogen into non-photosynthetic nitrogen (NPN) and photosynthetic nitrogen (PN). At the canopy level, PN and NPN are assumed to be proportional to canopy chlorophyll content (CCC) and canopy dry matter content (CDM), respectively. The PROSAIL-PRO model was first used to estimate CCC and CDM, and then the resulting CCC and CDM were fed to the nitrogen allocation model to estimate CNC. The estimation accuracy of CNC was assessed with six diverse datasets: four from field crop experiments across geographic sites, one from multiple ecosystems, and one from a satellite-ground joint experiment. Our results showed that satisfactory estimations of CNC were obtained when CCC and CDM were estimated using a model inversion method (RMSE = 0.54–1.56 g/m2) and a hybrid retrieval method (RMSE = 0.49–2.25 g/m2). The model inversion method was comparable with the hybrid retrieval method for ground platforms, but performed better for airborne and satellite platforms. In addition, the traditional protein-nitrogen conversion model obtained CNC from the canopy protein content and led to clear overestimations of CNC with RMSE > 1.95 g/m2. This study represents a first attempt to develop a robust approach by coupling PROSAIL-PRO with a nitrogen allocation model for accurate estimation of CNC across geographic sites, ecosystems, and platforms. These finding will advance the monitoring of CNC from regional to global scales. |
| format | Article |
| id | doaj-art-8bb511361654436b938cf33425ede5ea |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-8bb511361654436b938cf33425ede5ea2025-08-20T02:34:59ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-12-0113510428010.1016/j.jag.2024.104280Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation modelDong Li0Yapeng Wu1Katja Berger2Qianliang Kuang3Wei Feng4Jing M. Chen5Wenhui Wang6Hengbiao Zheng7Xia Yao8Yan Zhu9Weixing Cao10Tao Cheng11National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, ChinaImage Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain; Mantle Labs GmbH, Grünentorgasse 19/4, 1090 Vienna, AustriaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, ChinaCollege of Agronomy/State Key Laboratory of Wheat and Maize Crop Science, CIMMYT-China Joint Center of Wheat and Maize Improvement, Henan Technology Innovation Centre of Wheat, Henan Agricultural University, #15 Longzihu College District, Zhengzhou, Henan 450046, ChinaDepartment of Geography and Planning, University of Toronto, Toronto, Ontario, CanadaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, China; Langfang Normal University, 100 Aimin West Road, Langfang, Hebei 065000, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu 210095, China; Corresponding author.Nitrogen is one of the most important macronutrients for plant growth and timely estimation of canopy nitrogen content (CNC) is crucial for agricultural applications. Remote sensing has emerged as an important tool to quantify CNC using either empirically or physically based methods. Most empirical methods use chlorophyll related spectral indices and are dependent on the relationship between nitrogen and chlorophyll, which varies with vegetation types and growth stages. In contrast, physically based methods use the full-range reflectance data and retrieve CNC from coupled leaf and canopy radiative transfer models (such as PROSPECT-PRO + 4SAIL, PROSAIL-PRO). However, the subtle absorption features of nitrogen and protein in fresh leaves hinder the accurate estimation of CNC. Therefore, this study proposed an efficient and mechanistic framework to estimate CNC (PROSAIL-NAM) by coupling PROSAIL-PRO with a nitrogen allocation model, which divided the total nitrogen into non-photosynthetic nitrogen (NPN) and photosynthetic nitrogen (PN). At the canopy level, PN and NPN are assumed to be proportional to canopy chlorophyll content (CCC) and canopy dry matter content (CDM), respectively. The PROSAIL-PRO model was first used to estimate CCC and CDM, and then the resulting CCC and CDM were fed to the nitrogen allocation model to estimate CNC. The estimation accuracy of CNC was assessed with six diverse datasets: four from field crop experiments across geographic sites, one from multiple ecosystems, and one from a satellite-ground joint experiment. Our results showed that satisfactory estimations of CNC were obtained when CCC and CDM were estimated using a model inversion method (RMSE = 0.54–1.56 g/m2) and a hybrid retrieval method (RMSE = 0.49–2.25 g/m2). The model inversion method was comparable with the hybrid retrieval method for ground platforms, but performed better for airborne and satellite platforms. In addition, the traditional protein-nitrogen conversion model obtained CNC from the canopy protein content and led to clear overestimations of CNC with RMSE > 1.95 g/m2. This study represents a first attempt to develop a robust approach by coupling PROSAIL-PRO with a nitrogen allocation model for accurate estimation of CNC across geographic sites, ecosystems, and platforms. These finding will advance the monitoring of CNC from regional to global scales.http://www.sciencedirect.com/science/article/pii/S1569843224006368Canopy nitrogen contentNitrogen allocation modelPROSPECT-PROPROSAIL-PROModel inversionHybrid retrieval |
| spellingShingle | Dong Li Yapeng Wu Katja Berger Qianliang Kuang Wei Feng Jing M. Chen Wenhui Wang Hengbiao Zheng Xia Yao Yan Zhu Weixing Cao Tao Cheng Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model International Journal of Applied Earth Observations and Geoinformation Canopy nitrogen content Nitrogen allocation model PROSPECT-PRO PROSAIL-PRO Model inversion Hybrid retrieval |
| title | Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model |
| title_full | Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model |
| title_fullStr | Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model |
| title_full_unstemmed | Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model |
| title_short | Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model |
| title_sort | estimating canopy nitrogen content by coupling prosail pro with a nitrogen allocation model |
| topic | Canopy nitrogen content Nitrogen allocation model PROSPECT-PRO PROSAIL-PRO Model inversion Hybrid retrieval |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224006368 |
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