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Remote Sensing
Special Issues
Quantitative Remote Sensing and Its Applications in Agriculture and Vegetation
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Quantitative Remote Sensing and Its Applications in Agriculture and Vegetation

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 September 2025 | Viewed by 2480

Special Issue Editors

Dr. Zhijun Zhen

E-Mail Website
Guest Editor
CESBIO, CNES-CNRS-IRD-UPS, University of Toulouse, CEDEX 09, 31401 Toulouse, France
Interests: three-dimensional radiative transfer modelling; remote sensing scene modelling; temperature and emissivity separation; vegetation index; crop mapping
Prof. Dr. Shengbo Chen

E-Mail Website
Guest Editor
College of Geo-Exploration Science and Technology, Jilin University, No. 938 Ximinzhu Street, Chaoyang Distract, Changchun 130026, China
Interests: bidirectional reflectance distribution function; precision agriculture; geological remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Tiangang Yin

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, The HongKong Polytechnic University, Hong Kong, China
Interests: radiative transfer modelling; lidar; forestry; voxelization; ray tracing; biophysical information retrieval
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jean-Philippe Gastellu-Etchegorry

E-Mail Website
Guest Editor
CESBIO, Toulouse University, CNES, CNRS, IRD, UT3, 31055 Toulouse, France
Interests: three-dimensional radiative transfer modelling; vegetation; hyperspectral; LiDAR; fluorescence; radiative budget
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Quantitative remote sensing has revolutionized the way we monitor and manage agricultural landscapes and vegetation. With advancements in sensor technology, data processing, and modelling techniques, remote sensing offers unprecedented opportunities to gather detailed and accurate information about crops and vegetation. These developments are crucial for enhancing our understanding of agricultural systems and vegetation dynamics, enabling better decision-making, and improving sustainability.

This Special Issue aims to explore the latest advancements in quantitative remote sensing and its wide range of applications in agriculture and vegetation studies. We invite submissions that delve into innovative methodologies, sensor technologies, and modelling approaches that contribute to the quantitative assessment and monitoring of agricultural and vegetative systems. Articles may address, but are not limited, to the following topics:

  • Sensor Calibration and Design
  • Radiation Directionality Observing and Modelling
  • Kernel-driven Models
  • Geometric Optics Models
  • Three-dimensional Remote Sensing Scene Modelling
  • Radiative Transfer
  • Radiation Budget and Balance
  • Authenticity Validation and Spatial Scale Conversion
  • Photosynthetically Active Radiation (PAR)
  • Leaf Area Index (LAI) and Clumping Index
  • Chlorophyll Content and Fluorescence
  • Vegetation Index
  • Artificial Intelligence in Remote Sensing
  • Disaster Monitoring and Crop Yield Estimation

Dr. Zhijun Zhen
Prof. Dr. Shengbo Chen
Dr. Tiangang Yin
Prof. Dr. Jean-Philippe Gastellu-Etchegorry
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

  • quantitative remote sensing
  • radiation directionality observing and modelling
  • radiative transfer and radiation budget
  • bidirectional reflectance distribution function (BRDF)
  • directional anisotropy
  • angular anisotropy
  • retrieval of plant biochemical parameters
  • precision agriculture

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

Published Papers (3 papers)

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Research

16 pages, 3461 KiB  
Article
Investigating the Influence of the Weed Layer on Crop Canopy Reflectance and LAI Inversion Using Simulations and Measurements in a Sugarcane Field
by Longxia Qiu, Xiangqi Ke, Xiyue Sun, Yanzi Lu, Shengwei Shi and Weiwei Liu
Remote Sens. 2025, 17(12), 2014; https://doi.org/10.3390/rs17122014 - 11 Jun 2025
Abstract
Recent research in agricultural remote sensing mainly focuses on how soil background affects canopy reflectance and the inversion of LAI, while often overlooking the influence of the weed layer. The coexistence of crop and weed layers forms two-layered vegetation canopies in tall crops [...] Read more.
Recent research in agricultural remote sensing mainly focuses on how soil background affects canopy reflectance and the inversion of LAI, while often overlooking the influence of the weed layer. The coexistence of crop and weed layers forms two-layered vegetation canopies in tall crops such as sugarcane and maize. Although radiative transfer models can simulate the weed layer’s influence on canopy reflectance and LAI inversion, few experimental investigations use in situ measurement data to verify these effects. Here, we propose a practical background modification scheme in which black material with near-zero reflectance covers the weed layer and alters the background spectrum of crop canopies. We conduct an experimental investigation in a sugarcane field with different background properties (i.e., bare soil and a weed layer). Tower-based and UAV-based hyperspectral measurements examine the spectral differences in sugarcane canopies with and without the black covering. We then use LAI measurements to evaluate the weed layer’s impact on LAI inversion from UAV-based hyperspectral data through a hybrid inversion method. We find that the weed layer significantly affects the canopy reflectance spectrum, changing it by 13.58% and 42.53% in the near-infrared region for tower-based and UAV-based measurements, respectively. Furthermore, the weed layer substantially interferes with LAI inversion of sugarcane canopies, causing significant overestimation. Estimated LAIs of sugarcane canopies with a soil background generally align well with measured values (root mean square error (RMSE) = 0.69 m2/m2), whereas those with a weed background are considerably overestimated (RMSE = 2.07 m2/m2). We suggest that this practical background modification scheme quantifies the weed layer’s influence on crop canopy reflectance from a measurement perspective and that the weed layer should be considered during the inversion of crop LAI. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing and Its Applications in Agriculture and Vegetation)
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24 pages, 13146 KiB  
Article
Identifying the Peak Flowering Dates of Winter Rapeseed with a NBYVI Index Using Sentinel-1/2
by Fazhe Wu, Peng Lu, Shengbo Chen, Yucheng Xu, Zibo Wang, Rui Dai and Shuya Zhang
Remote Sens. 2025, 17(6), 1051; https://doi.org/10.3390/rs17061051 - 17 Mar 2025
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Abstract
Determining the peak flowering dates of winter rapeseed is crucial for both increasing yields and developing tourism resources. Currently, the Normalized Difference Yellow Index (NDYI), widely used for monitoring these dates, faces stability and accuracy issues due to atmospheric interference and limited optical [...] Read more.
Determining the peak flowering dates of winter rapeseed is crucial for both increasing yields and developing tourism resources. Currently, the Normalized Difference Yellow Index (NDYI), widely used for monitoring these dates, faces stability and accuracy issues due to atmospheric interference and limited optical data during the flowering period. This research examines changes in remote-sensing parameters caused by canopy variations during winter rapeseed’s flowering period from crop canopy morphological characteristics and canopy optical properties. By integrating Sentinel-1 and Sentinel-2 data, a new spectral index, the Normalized Backscatter Yellow Vegetation Index (NBYVI), is introduced. The study uses phenological characteristics and the random forest classification algorithm to create a map of winter rapeseed in parts of the middle and lower reaches of the Yangtze River Basin, achieving a Kappa coefficient of 90.57%. It evaluates the effectiveness of crop morphological indices in monitoring growth stages and explores the impacts of elevation and latitude on the peak flowering dates of winter rapeseed. The error ranges for predicting the peak flowering dates with the NDYI (traditional optical index) and the VV (crop morphological index) are generally 2–7 days and 2–6 days, respectively, while the error range for the NBYVI index is generally 0–4 days, demonstrating superior stability and accuracy compared to the NDYI and VV indices. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing and Its Applications in Agriculture and Vegetation)
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19 pages, 7901 KiB  
Article
Impact of Standing Water Level and Observation Time on Remote-Sensed Canopy Indices for Rice Nitrogen Status Monitoring
by Gonzalo Carracelas, John Hornbuckle and Carlos Ballester
Remote Sens. 2025, 17(6), 1045; https://doi.org/10.3390/rs17061045 - 16 Mar 2025
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Abstract
The observation time and water background can affect the remote sensing estimates of the nitrogen (N) content in rice crops. This makes the use of vegetation indices (VIs) for N status monitoring and topdressing recommendations challenging, as the timing of panicle initiation and [...] Read more.
The observation time and water background can affect the remote sensing estimates of the nitrogen (N) content in rice crops. This makes the use of vegetation indices (VIs) for N status monitoring and topdressing recommendations challenging, as the timing of panicle initiation and the water level in bays usually differ between farms even when managed using the same irrigation technique. This study aimed to investigate the influence of standing water levels (from 0 to 20 cm) and the time of image acquisition on a set of N-sensitive VIs to identify those less affected by these factors. The experiment was conducted using a split-plot experimental design with two side-by-side bays (main plots) where rice was grown ponded for most of the growing season and aerobically (not permanently ponded), each with four fertilization N rates. The SCCCI and SCCCI2 were the only indices that did not vary depending on the time of the day when the multispectral images were collected. These indices showed the lowest variation among water layer treatments (5%), while the Clg index showed the highest (20%). All VIs were significantly correlated with N uptake (average R2 = 0.73). However, the SCCCI2 was the index that showed the lowest variation in N-uptake estimates resulting in equal N-fertilizer recommendations across water level treatments. The consistent performance of SCCCI2 across different water levels makes this index of interest for different irrigation strategies, including aerobic management, which is gaining increasing attention to improve the sustainability of the rice industry. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing and Its Applications in Agriculture and Vegetation)
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