ArticleRemote sensing of environment2020
Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions.
Article in Remote sensing of environment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
57 citing papers in PubMed.
- Growth-stage deep learning for UAV hyperspectral estimation of leaf nitrogen content in winter wheat.Plant phenomics (Washington, D.C.) · 2026Article
- Automated Individual-Level ROI-to-Spectrum Extraction for Hyperspectral Analysis in Forensic Entomology.Insects · 2026Article
- Leaf to Root: Harnessing leaf spectral signatures for non-destructive monitoring of soybean nodule traits.Plant phenomics (Washington, D.C.) · 2026Article
- RGB image-based drought stress classification of garden plants using SVM model.Scientific reports · 2026Article
- Underestimation of Future Agricultural Soil NGlobal change biology · 2026Article
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- Estimation of Nitrogen Status inPlants (Basel, Switzerland) · 2026Article
- Leveraging UAV hyperspectral imaging for crop physiology and biochemistry: A comprehensive review of feature extraction and selection methods.Plant phenomics (Washington, D.C.) · 2026Review
- Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision.Journal of imaging · 2026Review
- Review
- The performance of directional area scattering factor proxies for correcting canopy structural and soil effects on leaf chlorophyll content estimation varies with canopy conditions in agricultural systems.Frontiers in plant science · 2026Article
- Deep Architectures Fail to Generalize: A Lightweight Alternative for Agricultural Domain Transfer in Hyperspectral Images.Sensors (Basel, Switzerland) · 2025Article
- DeepSpecN: A new hybrid method combining PROSPECT-PRO and Conv-Transformer to estimate leaf nitrogen content from leaf reflectance.Plant phenomics (Washington, D.C.) · 2025Article
- Linking grassland yield and nitrogen nutrition status to management practices: Towards large-scale grassland use intensity assessment.Remote sensing applications : society and environment · 2025Article
- Multi-Sensor NDVI Time Series for Crop and Fallow Land Classification in Khabarovsk Krai, Russia.Sensors (Basel, Switzerland) · 2025Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- A Handheld Multispectral Device for Assessing Leaf Nitrogen Concentrations in Maize.Sensors (Basel, Switzerland) · 2025Article
- Enhancing NUE in Corn Through Optimized Sensor-Based Prescription Maps.Sensors (Basel, Switzerland) · 2025Article
- Retrieving the chlorophyll content of individual apple trees by reducing canopy shadow impact via a 3D radiative transfer model and UAV multispectral imagery.Plant phenomics (Washington, D.C.) · 2025Article
- Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale.BMC plant biology · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Nitrogen (N) is considered as one of the most important plant macronutrients and proper management of N therefore is a pre-requisite for modern agriculture. Continuous satellite-based monitoring of this key plant trait would help to understand individual crop N use efficiency and thus would enable site-specific N management. Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents, they have a theoretical advantage over multi-spectral sensing for the detection of crop N. The current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data in the agricultural sector and in the context of future satellite imaging spectroscopy missions. Over 400 studies were reviewed for this purpose, identifying those estimating mass-based N (N concentration, N%) and area-based N (N content, N
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.