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Special Issue "Remote Sensing in Ecophysiological and Agricultural Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 20 August 2023 | Viewed by 1285

Special Issue Editors

Deimos Space UK Ltd., Building R103, Fermi Avenue, Harwell, Oxford OX11 0QR, UK
Interests: neural networks; image processing; remote sensing; modelling; Imaging spectroscopy; hydrology; water management; image fusion; drought monitoring; PCNN; anthropogenic activities; long-term change detection; wetland identification
Special Issues, Collections and Topics in MDPI journals
School of Information Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
Interests: plant diseases and pests; remote sensing; hyperspectral analysis; smart agriculture; monitoring model
Special Issues, Collections and Topics in MDPI journals
Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: leaf biochemical and biophysical parameters; hypersepctral remote sensing; vertical distribution; multi-angle remote sensing; crop; precision agriculture
National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
Interests: precision agriculture; UAV; hyperspectral remote sensing; data mining and analysis; image processing; deep learning

Special Issue Information

Dear Colleagues,

With a growing global population, sustainable agriculture becomes more and more important. While dealing with climate variability and change, how to produce more food of higher quality with limited resources is a great challenge. Therefore, information on the ecological and physiological (Ecophysiological) status of crops would be essential for crop growth diagnostics and yield prediction.

In this context, to fulfill these demands, there have been significant improvements in assessing plant Ecophysiology technologies. The developments in remote sensing, such as using ground-based, airborne, and satellite platforms, are providing new insight and capabilities for understanding vegetation and ecosystem properties, dynamics, and functional processes. Meanwhile, global observations span multiple spectral ranges (visible, near-infrared, thermal, microwave, etc.), enabling more precise documentation and a new understanding of vegetation changes and their environmental controls.

In this Special Issue on “Remote Sensing in Ecophysiological and Agricultural Applications”, we welcome topics that include, but are not limited to, the following:

  • Precision agriculture
  • UAV spectral sampling
  • Vegetation phenology and stress
  • Satellite environmental data
  • Plant–climate interactions
  • Plant Ecophysiology
  • Crop growth monitoring
  • Crop production and environmental quality
  • Vegetation indices and other spectral transformations

Dr. Alireza Taravat
Dr. Lin Yuan
Dr. Weiping Kong
Prof. Dr. Dongyan Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 2500 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.


  • plant ecophysiology
  • precision agriculture
  • unmanned aerial vehicles (UAVs)
  • satellite remote sensing
  • image data fusion
  • climate change
  • crop production

Published Papers (1 paper)

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Planting Age Identification and Yield Prediction of Apple Orchard Using Time-Series Spectral Endmember and Logistic Growth Model
Remote Sens. 2023, 15(3), 642; - 21 Jan 2023
Viewed by 774
In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise [...] Read more.
In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future. Full article
(This article belongs to the Special Issue Remote Sensing in Ecophysiological and Agricultural Applications)
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