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Special Issue "Crops and Vegetation Monitoring with Remote/Proximal Sensing"

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 April 2023 | Viewed by 2590

Special Issue Editors

Prof. Dr. Kenji Omasa
E-Mail Website
Guest Editor
Faculty of Agriculture, Takasaki University of Health and Welfare, 54, Nakaorui-machi, Gunma 370-0033, 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
E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jie Wang
E-Mail Website
Guest Editor
College of Grassland Science and Technology, China Agricultural University, Beijing 100093, China
Interests: vegetation remote sensing; ecological remote sensing; land-use/land-cover change
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 muti- and hyperspectral imagery, and is widely applied for the vegetation monitoring of large-scale interest 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 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.

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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Article
Quantifying Temperate Forest Diversity by Integrating GEDI LiDAR and Multi-Temporal Sentinel-2 Imagery
Remote Sens. 2023, 15(2), 375; https://doi.org/10.3390/rs15020375 - 07 Jan 2023
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Abstract
Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information [...] Read more.
Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information and multi-temporal phenological characteristics to accurately quantify diversity in large, heterogeneous forest areas are still lacking. In this study, combined with regression models, three different diversity indices, namely Simpson (λ), Shannon (H′), and Pielou (J′), were applied to characterize forest tree species diversity by using GEDI LiDAR data and Sentinel-2 imagery in temperate natural forest, northeast China. We used Mean Decrease Gini (MDG) and Boosted Regression Tree (BRT) to assess the importance of certain variables including monthly spectral bands, vegetation indices, foliage height diversity (FHD), and plant area index (PAI) of growing season and non-growing seasons (68 variables in total). We produced 12 forest diversity maps on three different diversity indices using four regression algorithms: Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Lasso Regression (LR). Our study concluded that the most important variables are FHD, NDVI, NDWI, EVI, short-wave infrared (SWIR) and red-edge (RE) bands, especially in the growing season (May and June). In terms of algorithms, the estimation accuracies of the RF (averaged R2 = 0.79) and SVM (averaged R2 = 0.76) models outperformed the other models (R2 of KNN and LR are 0.68 and 0.57, respectively). The study demonstrates the accuracy of GEDI LiDAR data and multi-temporal Sentinel-2 images in estimating forest diversity over large areas, advancing the capacity to monitor and manage forest ecosystems. Full article
(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)
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Article
Effect of Snow Cover on Detecting Spring Phenology from Satellite-Derived Vegetation Indices in Alpine Grasslands
Remote Sens. 2022, 14(22), 5725; https://doi.org/10.3390/rs14225725 - 12 Nov 2022
Viewed by 477
Abstract
The accurate estimation of phenological metrics from satellite data, especially the start of season (SOS), is of great significance to enhance our understanding of trends in vegetation phenology under climate change at regional or global scales. However, for regions with winter snow cover, [...] Read more.
The accurate estimation of phenological metrics from satellite data, especially the start of season (SOS), is of great significance to enhance our understanding of trends in vegetation phenology under climate change at regional or global scales. However, for regions with winter snow cover, such as the alpine grasslands on the Tibetan Plateau, the presence of snow inevitably contaminates satellite signals and introduces bias into the detection of the SOS. Despite recent progress in eliminating the effect of snow cover on SOS detection, the mechanism of how snow cover affects the satellite-derived vegetation index (VI) and the detected SOS remains unclear. This study investigated the effect of snow cover on both VI and SOS detection by combining simulation experiments and real satellite data. Five different VIs were used and compared in this study, including four structure-based (i.e., NDVI, EVI2, NDPI, NDGI) VIs and one physiological-based (i.e., NIRv) VI. Both simulation experiments and satellite data analysis revealed that the presence of snow can significantly reduce the VI values and increase the local gradient of the growth curve, allowing the SOS to be detected. The bias in the detected SOS caused by snow cover depends on the end of the snow season (ESS), snow duration parameters, and the snow-free SOS. An earlier ESS results in an earlier estimate of the SOS, a later ESS results in a later estimate of the SOS, and an ESS close to the snow-free SOS results in small bias in the detected SOS. The sensitivity of the five VIs to snow cover in SOS detection is NDPI/NDGI < NIRv < EVI2 < NDVI, which has been verified in both simulation experiments and satellite data analysis. These findings will significantly advance our research on the feedback mechanisms between vegetation, snow, and climate change for alpine ecosystems. Full article
(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)
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Review

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Review
Remote Sensing Monitoring of Rice and Wheat Canopy Nitrogen: A Review
Remote Sens. 2022, 14(22), 5712; https://doi.org/10.3390/rs14225712 - 11 Nov 2022
Cited by 2 | Viewed by 921
Abstract
Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for [...] Read more.
Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality. Full article
(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)
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