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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: 15 January 2025 | Viewed by 11744

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Prof. Dr. Kenji Omasa

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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
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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

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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

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Published Papers (8 papers)

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Research

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25 pages, 5178 KiB

Open AccessArticle

Sugarcane Mosaic Virus Detection in Maize Using UAS Multispectral Imagery

by Noah Bevers, Erik W. Ohlson, Kushal KC, Mark W. Jones and Sami Khanal

Remote Sens. 2024, 16(17), 3296; https://doi.org/10.3390/rs16173296 - 5 Sep 2024

Viewed by 866

Abstract

One of the most important and widespread corn/maize virus diseases is maize dwarf mosaic (MDM), which can be induced by sugarcane mosaic virus (SCMV). This study explores a machine learning analysis of five-band multispectral imagery collected via an unmanned aerial system (UAS) during the 2021 and 2022 seasons for SCMV disease detection in corn fields. The three primary objectives are to (i) determine the spectral bands and vegetation indices that are most important or correlated with SCMV infection in corn, (ii) compare spectral signatures of mock-inoculated and SCMV-inoculated plants, and (iii) compare the performance of four machine learning algorithms, including ridge regression, support vector machine (SVM), random forest, and XGBoost, in predicting SCMV during early and late stages in corn. On average, SCMV-inoculated plants had higher reflectance values for blue, green, red, and red-edge bands and lower reflectance for near-infrared as compared to mock-inoculated samples. Across both years, the XGBoost regression model performed best for predicting disease incidence percentage (R² = 0.29, RMSE = 29.26), and SVM classification performed best for the binary prediction of SCMV-inoculated vs. mock-inoculated samples (72.9% accuracy). Generally, model performances appeared to increase as the season progressed into August and September. According to Shapley additive explanations (SHAP analysis) of the top performing models, the simplified canopy chlorophyll content index (SCCCI) and saturation index (SI) were the vegetation indices that consistently had the strongest impacts on model behavior for SCMV disease regression and classification prediction. The findings of this study demonstrate the potential for the development of UAS image-based tools for farmers, aiming to facilitate the precise identification and mapping of SCMV infection in corn. Full article

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

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21 pages, 4390 KiB

Open AccessArticle

Mapping Shrub Biomass at 10 m Resolution by Integrating Field Measurements, Unmanned Aerial Vehicles, and Multi-Source Satellite Observations

by Wenchao Liu, Jie Wang, Yang Hu, Taiyong Ma, Munkhdulam Otgonbayar, Chunbo Li, You Li and Jilin Yang

Remote Sens. 2024, 16(16), 3095; https://doi.org/10.3390/rs16163095 - 22 Aug 2024

Viewed by 1076

Abstract

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R² = 0.62), outperformed models driven solely by spectral bands (R² = 0.33) or vegetation indices (R² = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (<2%) occurring in regions with high shrub coverage (>30%) and biomass production (>300 g/m²). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements. Full article

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

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26 pages, 8634 KiB

Open AccessArticle

New Insights on the Information Content of the Normalized Difference Vegetation Index Sentinel-2 Time Series for Assessing Vegetation Dynamics

by César Sáenz, Víctor Cicuéndez, Gabriel García, Diego Madruga, Laura Recuero, Alfonso Bermejo-Saiz, Javier Litago, Ignacio de la Calle and Alicia Palacios-Orueta

Remote Sens. 2024, 16(16), 2980; https://doi.org/10.3390/rs16162980 - 14 Aug 2024

Viewed by 1644

Abstract

The Sentinel-2 NDVI time series information content from 2017 to 2023 at a 10 m spatial resolution was evaluated based on the NDVI temporal dependency in five scenarios in central Spain. First, time series were interpolated and then filtered using the Savitzky–Golay, Fast Fourier Transform, Whittaker, and Maximum Value filters. Temporal dependency was assessed using the Q-Ljung-Box and Fisher’s Kappa tests, and similarity between raw and filtered time series was assessed using Correlation Coefficient and Root Mean Square Error. An Interpolating Efficiency Indicator (IEI) was proposed to summarize the number and temporal distribution of low-quality observations. Type of climate, atmospheric disturbances, land cover dynamics, and management were the main sources of variability in five scenarios: (1) rainfed wheat and barley presented high short-term variability due to clouds (lower IEI in winter and spring) during the growing cycle and high interannual variability due to precipitation; (2) maize showed stable summer cycles (high IEI) and low interannual variability due to irrigation; (3) irrigated alfalfa was cut five to six times during summer, resulting in specific intra-annual variability; (4) beech forest showed a strong and stable summer cycle, despite the short-term variability due to clouds (low IEI); and (5) evergreen pine forest had a highly variable growing cycle due to fast responses to temperature and precipitation through the year and medium IEI values. Interpolation after removing non-valid observations resulted in an increase in temporal dependency (Q-test), particularly a short term in areas with low IEI values. The information improvement made it possible to identify hidden periodicities and trends using the Fisher’s Kappa test. The SG filter showed high similarity values and weak influence on dynamics, while the MVF showed an overestimation of the NDVI values. Full article

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

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18 pages, 16686 KiB

Open AccessArticle

Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images

by Konstantin Nahrstedt, Tobias Reuter, Dieter Trautz, Björn Waske and Thomas Jarmer

Remote Sens. 2024, 16(14), 2684; https://doi.org/10.3390/rs16142684 - 22 Jul 2024

Cited by 1 | Viewed by 744

Abstract

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures. Full article

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

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19 pages, 1949 KiB

Open AccessArticle

An Angle Effect Correction Method for High-Resolution Satellite Side-View Imaging Data to Improve Crop Monitoring Accuracy

by Jialong Gong, Xing Zhong, Ruifei Zhu, Zhaoxin Xu, Dong Wang and Jian Yin

Remote Sens. 2024, 16(12), 2172; https://doi.org/10.3390/rs16122172 - 15 Jun 2024

Viewed by 1030

Abstract

In recent years, the advancement of CubeSat technology has led to the emergence of high-resolution, flexible imaging satellites as a pivotal source of information for the efficient and precise monitoring of crops. However, the dynamic geometry inherent in flexible side-view imaging poses challenges in acquiring the high-precision reflectance data necessary to accurately retrieve crop parameters. This study aimed to develop an angular correction method designed to generate nadir reflectance from high-resolution satellite side-swing imaging data. The method utilized the Anisotropic Flat Index (AFX) in conjunction with a fixed set of Bidirectional Reflectance Distribution Function (BRDF) parameters to compute the nadir reflectance for the Jilin-1 GP01/02 multispectral imager (PMS). Crop parameter retrieval was executed using regression models based on vegetation indices, the leaf area index (LAI), fractional vegetation cover (FVC), and chlorophyll (T850 nm/T720 nm) values estimated based on angle corrected reflectance compared with field measurements taken in the Inner Mongolia Autonomous Region. The findings demonstrate that the proposed angular correction method significantly enhances the retrieval accuracy of the LAI, FVC, and chlorophyll from Jilin-1 GP01/02 PMS data. Notably, the retrieval accuracy for the LAI and FVC improved by over 25%. We expect that this approach will exhibit considerable potential to improve crop monitoring accuracy from high-resolution satellite side-view imaging data. Full article

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

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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

Viewed by 1133

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

Cited by 1 | Viewed by 1409

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 4 | Viewed by 2645

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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