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Crops and Vegetation Monitoring with Remote/Proximal Sensing II

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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: 30 June 2024 | Viewed by 3482

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Special Issue Editors

Prof. Dr. Kenji Omasa

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Guest Editor

Faculty of Agriculture, Takasaki University of Health and Welfare, 54, Nakaorui-machi 370-0033, Gunma, Japan
Interests: remote sensing; plant phenotyping; agricultural informatics; environmental plant science; global environmental science
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shan Lu

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Guest Editor

School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China
Interests: vegetation remote sensing; biophysical parameter retrieval; multi-angle reflectance; polarized remote sensing; hyperspectral remote sensing
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Prof. Dr. Jie Wang

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Guest Editor

College of Grassland Science and Technology, China Agricultural University, Beijing 100093, China
Interests: land use land cover change; ecological remote sensing; agricultural remote sensing; drylands
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote and proximal sensing are exceedingly powerful techniques for characterizing and monitoring crop or vegetation properties at reasonable temporal and spatial resolutions. Remote sensing uses airborne and spaceborne platforms to collect multi- and hyperspectral imagery and is widely applied for the vegetation monitoring of large-scale interests with respect to the effect of geophysical and climate parameters. In contrast, proximal sensing using various types of sensors mounted on static, mobile, and unmanned aerial vehicle (UAV) platforms can supply functional and structural information for smart agriculture and plant phenotyping, as well as detailed ground information for mechanism analysis in agricultural land, grassland, and forest ecosystems.

The aim of this Special Issue is to develop crop or vegetation monitoring via various remote or proximal sensing techniques ranging from the individual plant to the global level using various types of sensors mounted on static, mobile, UAV, aircraft, and satellite platforms. The used sensors include handheld spectrometers, color cameras, multispectral and hyperspectral imaging systems, thermographic cameras, lidars, and microwave radiometers.

This Special Issue, “Crops and Vegetation Monitoring with Remote/Proximal Sensing”, encourages discussion concerning innovative techniques/approaches based on the various types of remote sensing data, remote or proximal, to monitor crop and vegetation properties, including plant phenotyping, smart agriculture, vegetation mapping, biophysical or biochemical parameter estimation or inversion, health, and productivity in various ecosystems at different spatial and temporal scales.

Prof. Dr. Kenji Omasa
Prof. Dr. Shan Lu
Prof. Dr. Jie Wang
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 monitoring
forest monitoring
smart agriculture
vegetation phenology
chlorophyll fluorescence of vegetation
biophysical parameters retrieval
grassland remote sensing
vegetation remote sensing
observation techniques of in situ measurements, eddy covariance, UAV, and satellites
vegetation health

Published Papers (3 papers)

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Research

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16 pages, 7648 KiB

Open AccessArticle

Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China

by Lei Wang, Xiuzhen Han, Shibo Fang and Fengjin Xiao

Remote Sens. 2024, 16(8), 1363; https://doi.org/10.3390/rs16081363 - 12 Apr 2024

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Abstract

NDVI data are crucial for agricultural and environmental research. The Fengyun-3 (FY-3) series satellites are recognized as primary sources for retrieving NDVI products on a global scale. To apply FY-3 NDVI data for long-term studies, such as climate change, this study conducted a thorough evaluation to detect the potentials of the FY-3B and FY-3D satellites for generating a long time series NDVI dataset. For this purpose, the spatiotemporal consistency between the FY-3B and FY-3D satellites was evaluated, and their performances were compared. Then, a grey relational analysis (GRA) method was applied to detect the factors influencing the consistency among the different satellites, and a gradient boosting regression (GBR) model was constructed to create a long-term FY-3 NDVI product. The results indicate an overall high consistency between the FY-3B and FY-3D NDVIs, suggesting that they could be used as complementary datasets for generating a long-term NDVI dataset. The correlations between the FY-3D NDVI and the MODIS NDVI, as well as the leaf area index (LAI) measurements, were both higher than those of FY-3B, which indicates a better performance of FY-3D in retrieving NDVI data. The grey correlation degrees between the NDVI differences and four parameters, which were land cover (LC), DEM, latitude (LAT) and longitude (LON), were calculated, revealing that the LC was the most related to the NDVI differences. Finally, a GBR model with FY-3B NDVI, LC, DEM, LAT and LON as the input variables and FY-3D NDVI as the target variable was established and achieved a robust performance. The R values between the GBR-estimated NDVI and FY-3D NDVI reached 0.947, 0.867 and 0.829 in the training, testing and validation datasets, respectively, indicating the feasibility of the established model for generating long time series NDVI data by combining data from the FY-3B and FY-3D satellites. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing II)

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Review

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22 pages, 2663 KiB

Open AccessReview

Remote Sensing Application in Chinese Medicinal Plant Identification and Acreage Estimation—A Review

by Jihua Meng, Xinyan You, Xiaobo Zhang, Tingting Shi, Lei Zhang, Xingfeng Chen, Hailan Zhao and Meng Xu

Remote Sens. 2023, 15(23), 5580; https://doi.org/10.3390/rs15235580 - 30 Nov 2023

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Abstract

Chinese Materia Medica Resources (CMMRs) are crucial for developing the tradition and industry of traditional Chinese medicine. Given the increasing demand for CMMRs, an accurate and effective understanding of CMMRs is urgently needed. Chinese medicinal plants (CMPs) are the most important sources of CMMRs. Traditional methods of investigating medicinal plant resources have limitations, including severe subjectivity and poor timeliness, which make it difficult to meet the demand for real-time monitoring of large-scale medicinal plant resources. In recent years, remote sensing technology has become an important means of obtaining information on medicinal plants, and the application of this technology has made up for the shortcomings of traditional methods. This paper first discusses the development of investigation methods of CMMRs; points out the importance of remote sensing technology in the application of spatial distribution and information identification and extraction of Chinese medicinal plant resources (CMPRs); analyzes the characteristics of CMPs in different planting patterns, different habitats, and different regions from the perspective of remote sensing information extraction; and explores the selection of suitable data sources, providing a reference for medicinal plant identification and information extraction. Secondly, according to the existing classification and identification methods, previous studies are summarized from the perspectives of classification scales, classification features, and classification accuracy, and the advantages and disadvantages of the commonly used remote sensing classification methods in the investigation of CMPRs are summarized and compared. Finally, the development trend of remote sensing technology in the identification and information extraction of CMPs is examined, and the key technical problems to be solved in the identification and classification of CMPs and the extraction of area information are summarized so as to provide technical support and experience references for the application of remote sensing in the investigation of CMPRs. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing II)

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31 pages, 11992 KiB

Open AccessReview

A Systematic Review of UAV Applications for Mapping Neglected and Underutilised Crop Species’ Spatial Distribution and Health

by Mishkah Abrahams, Mbulisi Sibanda, Timothy Dube, Vimbayi G. P. Chimonyo and Tafadzwanashe Mabhaudhi

Remote Sens. 2023, 15(19), 4672; https://doi.org/10.3390/rs15194672 - 23 Sep 2023

Cited by 2 | Viewed by 1679

Abstract

Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing II)

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