数字农科院2.0

A novel strategy for improving canopy nitrogen density retrieval by deciphering saturation effects and vertical heterogeneity

文献类型： 外文期刊

作者： Hongye Yang; Bo Ming;Chenwei Nie;Dayun Feng; Kexin Gao; Shang Gao; Jun Xue;Guoqiang Zhang;Peng Hou;Ruizhi Xie; Zhenhai Li ;Xiuliang Jin; Keru Wang;Shaokun Li

关键词： Saturation phenomenon; Vegetation indices; Canopy nitrogen density; Vertical heterogeneity; Bell-shape model

期刊名称： COMPUTERS AND ELECTRONICS IN AGRICULTURE

ISSN： 0168-1699

年卷期： 2025 年

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Canopy nitrogen density (CND) is a critical indicator of plant growth, with applications in nutrient diagnosis,disease monitoring, and carbon cycling. However, optical remote sensing of nitrogen is constrained by VIsaturation, particularly in dense canopies. Here, we propose a novel strategy to mitigate saturation by resolvingtwo key issues: (1) the sensing depth of canopy spectra and (2) the quantification of vertical nitrogen heterogeneity. Saturation characteristics of VIs were first analyzed using inflection and saturation points. We found thatCND at the inflection point enhanced the linear correlation with canopy spectra. The 3rd–7th leaf layers contributed most to canopy reflectance, accounting for 67.24%–72.15% of canopy spectra and 62.39% of total CND. Beyond the 7th–8th leaf layers, saturation became prominent. To capture vertical heterogeneity, we employed a bell-shaped model, with the coefficient ω linking the inflection point CND to canopy CND across structural variations. Integrating inflection points with vertical heterogeneity characteristics improved the robustness of VI–CND relationships, reducing RMSE by 10.8%–33.9%. This approach offers an intuitive framework to mitigate VI saturation, enabling more accurate CND estimation under diverse field conditions.

分类号：

  • 相关文献

[1]Deep Neural Network Algorithm For Estimating Maize Biomass Based On Simulated Sentinel 2A Vegetation Indices And Leaf Area Index. Ren, Zhibin,Jin, Xiuliang,Jin, Xiuliang,Li, Shaokun,Feng, Haikuan,Li, Zhenhai. 2020

[2]Comparative Analysis Of Gf-1, Hj-1, A.nd Landsat-8 Data For E stimating The Leaf Area Index Of Winter Wheat. Li, H, Chen, ZX, Jiang, ZW, Wu, WB, Ren, JQ, Liu, B, Hasi, T. 2017

[3]Estimation Of Leaf Water Content U.sing New Vegetation Indices C ombined By Near- And Middle Infrared Spectral Reflectances. Zhang, ZY, Tang, BH, Li, ZL, Tang, RL, Zhong, RF. 2017

[4]Deep Neural Network Algorithm For Estimating Maize Biomass Based On Simulated Sentinel 2A Vegetation Indices And Leaf Area Index. Jin, XL, Li, ZH, Feng, HK, Ren, ZB, Li, SK. 2019

[5]Modeling Winter Wheat Leaf Area I.ndex And Canopy Water C ontent With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data. Pan, HZ, Chen, ZX, Ren, JQ, Li, H, Wu, SR. 2019

[6]Assessment Of Water And Nitrogen Use Efficiencies Through Uav-Based Multispectral Phenotyping In Winter Wheat. Yang, MJ, Hassan, MA, Xu, KJ, Zheng, CY, Rasheed, A, Zhang, Y, Jin, XL, Xia, XC, Xiao, YG, He, ZH. 2020

作者其他论文 更多>>