Remote Sensing for Precision Nitrogen Management

Edited by
November 2022
602 pages
  • ISBN978-3-0365-5709-0 (Hardback)
  • ISBN978-3-0365-5710-6 (PDF)

This book is a reprint of the Special Issue Remote Sensing for Precision Nitrogen Management that was published in

Environmental & Earth Sciences

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment.

  • Hardback
© by the authors
UAS; multiple sensors; vegetation index; leaf nitrogen accumulation; plant nitrogen accumulation; pasture quality; airborne hyperspectral imaging; random forest regression; sun-induced chlorophyll fluorescence (SIF); SIF yield indices; upward; downward; leaf nitrogen concentration (LNC); wheat (Triticum aestivum L.); laser-induced fluorescence; leaf nitrogen concentration; back-propagation neural network; principal component analysis; fluorescence characteristics; leaf nitrogen concentration; canopy nitrogen density; radiative transfer model; hyperspectral; winter wheat; flooded rice; pig slurry; aerial remote sensing; vegetation indices; N recommendation approach; Mediterranean conditions; nitrogen; vertical distribution; plant geometry; remote sensing; maize; UAV; multispectral imagery; LNC; vegetation index; non-parametric regression; radiative transfer model; red-edge; NDRE; dynamic change model; sigmoid curve; grain yield prediction; leaf chlorophyll content; red-edge reflectance; spectral index; winter wheat; precision N fertilization; chlorophyll meter; NDVI; NDRE; NNI; canopy reflectance sensing; N mineralization; farmyard manures; Triticum aestivum; leaf nitrogen concentration; discrete wavelet transform; partial least squares; hyper-spectra; rice; nitrogen management; remote sensing; multispectral imagery; reflectance index; multiple variable linear regression; Lasso model; Multiplex®3 sensor; nitrogen balance index; nitrogen nutrition index; nitrogen status diagnosis; precision nitrogen management; terrestrial laser scanning; spectrometer; plant height; vegetation indices; biomass; nitrogen concentration; precision agriculture; leaf nitrogen concentration; leaf nitrogen accumulation; unmanned aerial vehicle (UAV); digital camera; vegetation indices; leaf chlorophyll concentration; portable chlorophyll meter; crop; PROSPECT-D; sensitivity analysis; remote sensing; radiative transfer model; UAV multispectral imagery; spectral vegetation indices; machine learning; plant nutrition; canopy spectrum; non-destructive nitrogen status diagnosis; drone; multispectral camera; SPAD; smartphone photography; fixed-wing UAV remote sensing; nitrogen status diagnosis; random forest; precision nitrogen management; machine learning; canopy reflectance; crop N status; Capsicum annuum; proximal optical sensors; nitrogen status diagnosis; Dualex sensor; precision nitrogen management; leaf position; proximal sensing; nitrogen balance index; cross-validation; feature selection; hyperparameter tuning; image processing; image segmentation; nitrogen fertilizer recommendation; supervised regression; RapidSCAN sensor; nitrogen recommendation algorithm; in-season nitrogen management; nitrogen use efficiency; yield potential; yield responsiveness; standard normal variate (SNV); continuous wavelet transform (CWT); wavelet features optimization; competitive adaptive reweighted sampling (CARS); partial least square (PLS); UAV; random forest; nitrogen; maize; grapevine; hyperparameter optimization; machine learning; multispectral imaging; nitrogen; precision viticulture; UAV; RGB; multispectral; coverage adjusted spectral index; vegetation index; vegetation coverage; random frog algorithm; precision nitrogen management; active canopy sensing; integrated sensing system; machine learning; nitrogen nutrition index; discrete NIR spectral band data; soil total nitrogen concentration; moisture absorption correction index; particle size correction index; coupled elimination