Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.2
Journals
Remote Sensing
Special Issues
Crop Biophysical Parameters Retrieval Using Remote Sensing Data (Second Edition)
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Crop Biophysical Parameters Retrieval Using Remote Sensing Data (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 1266

Special Issue Editors

Dr. Jiangang Liu

E-Mail Website
Guest Editor
Department of Potato, Institute of Vegetables and Flowers Chinese Academy of Agricultural Sciences (IVF-CAAS), Beijing 100081 China
Interests: crop model; plant phenotyping; UAV; proximal remote sensing; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Li

E-Mail Website
Guest Editor
Seeds Research, Syngenta, Jealott’s Hill, Warfield, Bracknell RG42 6EY, UK
Interests: computer vision; image analysis; plant phenotyping; remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhenjiang Zhou

E-Mail Website
Guest Editor
Department of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058 China
Interests: precision irrigation and fertilization; remote sensing; agronomy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Crop biophysical parameters are essential for exploring crop growth dynamics and determining optimized strategies for crop management. High-throughput estimation of spatial–temporal biophysical parameters can accelerate crop research or breeding efficiency and prompt intelligent agriculture development. Remote sensing can provide timely, rapid, noninvasive, and efficient access to crop biophysical parameters. Crop biophysical traits including morphological parameters, spectrum and texture characteristics, physiological traits, and responses to abiotic/biotic stress under different environments have been retrieved by various remote sensing platforms and sensors (e.g., digital camera, multispectral camera, hyperspectral imager, thermal imager, and light detection and ranging (LiDAR) systems). All these technologies have been achieved with the development of engineering, computer science, plant physiology, molecular research, and bioinformatics. Considering the complex environment that crops grow with, the precise and robust retrieval of biophysical information still faces challenges. At the same time, various novel methods and technologies are being developed by scientists in varying fields, especially beyond the agricultural field. Research outputs from different fields need to be gathered to gain more quantitative knowledge of the structure and function of plants.

This Special Issue invites studies covering crop biophysical parameter retrieval through different remote sensing platforms and sensors coupled with diversified inversion methods. Original research articles and reviews are welcome, especially in crop biophysical parameter retrieval with multisource data integration and multiscale approaches. Research areas may include (but are not limited to) the following:

  • Crop biophysical parameter retrieval;
  • High-throughput crop phenotyping;
  • Crop growth monitoring;
  • Crop morphological parameters;
  • Crop physiological traits;

We look forward to receiving insightful contributions.

Dr. Jiangang Liu
Dr. Bo Li
Prof. Dr. Zhenjiang Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crop biophysical parameters
  • optical remote sensing
  • active remote sensing
  • multisource data integration
  • radiative transfer model
  • deep learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 3704 KiB  
Article
Hyperspectral Estimation of Leaf Nitrogen Content in White Radish Based on Feature Selection and Integrated Learning
by Yafeng Li, Xingang Xu, Wenbiao Wu, Yaohui Zhu, Guijun Yang, Lutao Gao, Yang Meng, Xiangtai Jiang and Hanyu Xue
Remote Sens. 2024, 16(23), 4479; https://doi.org/10.3390/rs16234479 - 29 Nov 2024
Cited by 1 | Viewed by 1018
Abstract
Nitrogen is the main nutrient element in the growth process of white radish, and accurate monitoring of radish leaf nitrogen content (LNC) is an important guide for precise fertilization decisions for radish in the field. Using white radish LNC monitoring as an object, [...] Read more.
Nitrogen is the main nutrient element in the growth process of white radish, and accurate monitoring of radish leaf nitrogen content (LNC) is an important guide for precise fertilization decisions for radish in the field. Using white radish LNC monitoring as an object, research on radish nitrogen hyperspectral estimation methods was carried out based on leaf hyperspectral and field sample nitrogen data at multiple growth stages using feature selection and integrated learning algorithm models. First, the Vegetation Index (VI) was constructed from hyperspectral data. We extracted sensitive features of hyperspectral data and VI response to radish LNC based on Pearson’s feature-selection approach. Second, a stacking-integrated learning approach is proposed using machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Ridge and K-Nearest Neighbor (KNN) as the base model in the first layer of the architecture, and the Lasso algorithm as the meta-model in the second layer of the architecture, to realize the hyperspectral estimation of radish LNC. The analysis results show the following: (1) The sensitive bands of the radish LNC are mainly centered around 600–700 nm and 1950 nm, and the constructed sensitive VIs are also concentrated in this band range. (2) The Stacking model with spectral features as inputs achieved good prediction accuracy at the radish spectral leaf, with R2 = 0.7, MAE = 0.16, MSE = 0.05 estimated over the whole growth stage of radish. (3) The Lasso algorithm with variable filtering function was chosen as the meta-model, which has a redundant model-selection effect on the base model and helps to improve the quality of the integrated learning framework. This study demonstrates the potential of the stacking-integrated learning method based on hyperspectral data for spectral estimation of nitrogen content in radish at multiple growth stages. Full article
(This article belongs to the Special Issue Crop Biophysical Parameters Retrieval Using Remote Sensing Data (Second Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop