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Applied Sciences
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New Advances of Remote Sensing in Agriculture
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New Advances of Remote Sensing in Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 2020

Special Issue Editors

Dr. Ya'nan Zhou

E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: remote sensing analysis and application in agriculture
Dr. Nana Yan

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: agriculture and water resources with remote sensing

Special Issue Information

Dear Colleagues,

Remote sensing technology has emerged as a powerful tool for monitoring and managing agricultural production by providing timely and accurate information about crops, soil, and other significant parameters in a non-invasive manner. This Special Issue topic is timely and essential, considering the increasing demand for food production, climate change, and the digitalization of agricultural practices. The Special Issue will present state-of-the-art research on various aspects of remote sensing in agriculture, including crop classification, soil moisture estimation, plant health monitoring, yield prediction and assessment, irrigation management, precision agriculture, sustainable agriculture, and climate change impacts on agriculture. The Special Issue will contribute to advancing the understanding of remote sensing applications in agriculture and has the potential to promote sustainable agricultural practices.

Topics of interest include:

  • Crop classification and mapping;
  • Soil moisture estimation;
  • Plant health monitoring;
  • Yield prediction and assessment;
  • Irrigation management;
  • Precision agriculture;
  • Sustainable agriculture;
  • The impacts of climate change on agriculture.

Dr. Ya'nan Zhou
Dr. Nana Yan
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. Applied Sciences 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 2400 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 monitoring and mapping
  • crop phenology
  • crop yield forecasting
  • crop classification
  • drought stress and irrigation
  • machine learning
  • satellite remote sensing

Published Papers (2 papers)

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Research

15 pages, 7411 KiB  
Article
The Difference between the Responses of Gross Primary Production and Sun-Induced Chlorophyll Fluorescence to the Environment Based on Tower-Based and TROPOMI SIF Data
by Jia Bai, Helin Zhang, Rui Sun, Xinjie Liu and Liangyun Liu
Appl. Sci. 2024, 14(2), 771; https://doi.org/10.3390/app14020771 - 16 Jan 2024
Viewed by 890
Abstract
The strong correlation between gross primary production (GPP) and sun-induced chlorophyll fluorescence (SIF) has been reported in many studies and is the basis of the SIF-based GPP estimation. However, GPP and SIF are not fully synchronous under various environmental conditions, which may destroy [...] Read more.
The strong correlation between gross primary production (GPP) and sun-induced chlorophyll fluorescence (SIF) has been reported in many studies and is the basis of the SIF-based GPP estimation. However, GPP and SIF are not fully synchronous under various environmental conditions, which may destroy a stable GPP–SIF relationship. Therefore, exploring the difference between responses of GPP and SIF to the environment is essential to correctly understand the GPP–SIF relationship. As the common driver of GPP and SIF, the incident radiation could cause GPP and SIF to have similar responses to the environment, which may obscure the discrepancies in the responses of GPP and SIF to the other environmental variables, and further result in the ambiguity of the GPP–SIF relationship and uncertainties in the application of SIF. Therefore, we tried to exclude the dominant role of radiation in the responses of GPP and SIF to the environment based on the binning method, in which continuous tower-based SIF, satellite SIF, and eddy covariance GPP data from two growing seasons were used to investigate the differences in the responses of GPP and SIF to radiation, air temperature (Ta), and evaporation fraction (EF). We found that the following: (1) At both the site and satellite scales, there were divergences in the light response speeds between GPP and SIF which were affected by Ta and EF. (2) SIF and its light response curves were insensitive to EF and Ta compared to GPP, and the consistency in GPP and SIF light responses was gradually improved with the improvement of Ta and EF. (3) The dynamic slope values of the GPP–SIF relationship were mostly caused by the different sensitivities of GPP and SIF to EF and Ta. Our results highlighted that GPP and SIF were not highly consistent, having differences in environmental responses that further confused the GPP–SIF relationship, leading to complex SIF application. Full article
(This article belongs to the Special Issue New Advances of Remote Sensing in Agriculture)
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18 pages, 6285 KiB  
Article
Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
by Xiaoxuan Lyu, Weibing Du, Hebing Zhang, Wen Ge, Zhichao Chen and Shuangting Wang
Appl. Sci. 2024, 14(1), 250; https://doi.org/10.3390/app14010250 - 27 Dec 2023
Cited by 1 | Viewed by 844
Abstract
Crop phenotype observation techniques via UAV (unmanned aerial vehicle) are necessary to identify different winter wheat cultivars to better realize their future smart productions and satisfy the requirement of smart agriculture. This study proposes a UAV-based hyperspectral remote sensing system for the fine [...] Read more.
Crop phenotype observation techniques via UAV (unmanned aerial vehicle) are necessary to identify different winter wheat cultivars to better realize their future smart productions and satisfy the requirement of smart agriculture. This study proposes a UAV-based hyperspectral remote sensing system for the fine classification of different winter wheat cultivars. Firstly, we set 90% heading overlap and 85% side overlap as the optimal flight parameters, which can meet the requirements of following hyperspectral imagery mosaicking and spectral stitching of different winter wheat cultivars areas. Secondly, the mosaicking algorithm of UAV hyperspectral imagery was developed, and the correlation coefficient of stitched spectral curves before and after mosaicking reached 0.97, which induced this study to extract the resultful spectral curves of six different winter wheat cultivars. Finally, the hyperspectral imagery dimension reduction experiments were compared with principal component analysis (PCA), minimum noise fraction rotation (MNF), and independent component analysis (ICA); the winter wheat cultivars classification experiments were compared with support vector machines (SVM), maximum likelihood estimate (MLE), and U-net neural network ENVINet5 model. Different dimension reduction methods and classification methods were compared to get the best combination for classification of different winter wheat cultivars. The results show that the mosaicked hyperspectral imagery effectively retains the original spectral feature information, and type 4 and type 6 winter wheat cultivars have the best classification results with the classification accuracy above 84%. Meanwhile, there is a 30% improvement in classification accuracy after dimension reduction, the MNF dimension reduction combined with ENVINet5 classification result is the best, its overall accuracy and Kappa coefficients are 83% and 0.81, respectively. The results indicate that the UAV-based hyperspectral remote sensing system can potentially be used for classifying different cultivars of winter wheat, and it provides a reference for the classification of crops with weak intra-class differences. Full article
(This article belongs to the Special Issue New Advances of Remote Sensing in Agriculture)
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