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Hyperspectral Remote Sensing of Agriculture and Vegetation

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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: closed (31 July 2020) | Viewed by 90706

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Special Issue Editors

Dr. Simone Pascucci

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Guest Editor

Institute of Methodologies for Environmental Analysis (IMAA)-National Council of Research (CNR), C. da S. Loja, 85050 Tito Scalo, Italy
Interests: multi- and hyperspectral remote sensing for environmental and agricultural applications; imaging spectroscopy; airborne flight campaigns; sensor calibration and validation; ground segment
Special Issues, Collections and Topics in MDPI journals

Dr. Stefano Pignatti

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Guest Editor

CNR IMAA, 85050 Tito Scalo (PZ), Italy
Interests: hyperspectral remote sensing both from airborne (VSWIR and TIR) and satellite platforms; calibration and exploitation of hyperspectral remote sensing in environmental applications, including agricultural issues
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Raffaele Casa

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Department of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University Via San Camillo de Lellis, 01100 Viterbo, Italy
Interests: precision agriculture; remote sensing; agronomic modelling; data assimilation
Special Issues, Collections and Topics in MDPI journals

Dr. Roshanak Darvishzadeh

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Guest Editor

Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
Interests: remote sensing; vegetation physiological properties; imaging spectroscopy; radiative transfer models; smart agriculture; biodiversity
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wenjiang Huang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: vegetation remote sensing; image processing; image classification; parameter mapping; system development; agricultural application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing is providing even more research studies and practical applications for agriculture (soils and crops) and vegetation mapping and monitoring, from regional to within-field scales.

In this Special Issue, we welcome papers from the international research community actively involved in research activities on hyperspectral RS (optical domain) for crop and vegetation. The Special Issue is open to all researchers working in these fields. The choice of papers for publication will rely on quality, soundness, and rigour of research. Specific topics include, but are not limited to, the following:

Hyperspectral studies of agricultural soils, crop, and other vegetation types from ground, drone, air- and space-borne platforms (VIS-NIR, SWIR, and TIR).
Field, and laboratory hyperspectral measurements for monitoring agriculture and vegetation
Retrieval of plant traits at leaf and canopy level from hyperspectral measurements
New methods for hyperspectral data processing and atmospheric compensation techniques
Hyperspectral sensors calibration and products validation for agriculture and vegetation monitoring
Statistical and computational methods for hyperspectral data analysis in agriculture and vegetation applications
Integration or combined use of hyperspectral data from the optical domain with other EO technologies
Modelling of soils, crops, and vegetation using hyperspectral data
Next generation hyperspectral technologies and missions, platforms, and sensors for agriculture and vegetation

Dr. Simone Pascucci
Dr. Stefano Pignatti
Dr. Raffaele Casa
Dr. Roshanak Darvishzadeh
Prof. Dr. Wenjiang Huang
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

Hyperspectral remote sensing for soil and crops in agriculture
Hyperspectral imaging for vegetation
Plant traits
High resolution spectroscopy for agricultural soils and vegetation
VIS-NIR, SWIR, and TIR
Hyperspectral data bases for agricultural soils and vegetation
Hyperspectral data as input for modeling soil, crop, and vegetation
Product validation
New hyperspectral technologies
Future hyperspectral missions

Published Papers (12 papers)

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Editorial

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7 pages, 211 KiB

Open AccessEditorial

Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”

by Simone Pascucci, Stefano Pignatti, Raffaele Casa, Roshanak Darvishzadeh and Wenjiang Huang

Remote Sens. 2020, 12(21), 3665; https://doi.org/10.3390/rs12213665 - 09 Nov 2020

Cited by 20 | Viewed by 5065

Abstract

The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

Research

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21 pages, 2656 KiB

Open AccessArticle

Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with Hyperspectral Data and Partial Least Squares Regression: Replicating Methods and Results across Environments

by K. Colton Flynn, Amy E. Frazier and Sintayehu Admas

Remote Sens. 2020, 12(18), 2867; https://doi.org/10.3390/rs12182867 - 04 Sep 2020

Cited by 9 | Viewed by 3333

Abstract

Achieving reproducibility and replication (R&R) of scientific results is tantamount for science to progress, and it is also necessary for ensuring the self-correcting mechanism of the scientific method. Topics of R&R have sailed to the forefront of research agenda in many fields recently but have received less attention in remote sensing in general and specifically for studies utilizing hyperspectral data. Given the extremely local environments in which many hyperspectral studies are conducted (e.g., agricultural field plots), purposeful attention to the repeatability of findings across study locales can help ensure methods are generalizable. This study undertakes an investigation of the nutrient content of tef (Eragrostis tef), an understudied plant that is growing in importance due to both food and forage benefits, but does so within the context of the replicability of methods and findings across two study sites situated in different international and environmental contexts. The aims are to (1) determine whether calcium, magnesium, and protein of both the plant and grain can be predicted using hyperspectral data with partial least squares (PLS) regression with waveband selection, and (2) compare the replicability of models across differing environments. Results suggest the method can produce high nutrient prediction accuracy for both the plant and grain in individual environments, but selection of wavebands for nutrient prediction was not comparable across study areas. The findings suggest that the method must be calibrated in each location, thereby reducing the potential to extrapolate methods to different areas. Our findings highlight the need for greater attention to methods and results replication in remote sensing, specifically hyperspectral analyses, in order for scientific findings to be repeatable beyond the plot level. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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20 pages, 3941 KiB

Open AccessArticle

Detection of Canopy Chlorophyll Content of Corn Based on Continuous Wavelet Transform Analysis

by Junyi Zhang, Hong Sun, Dehua Gao, Lang Qiao, Ning Liu, Minzan Li and Yao Zhang

Remote Sens. 2020, 12(17), 2741; https://doi.org/10.3390/rs12172741 - 24 Aug 2020

Cited by 36 | Viewed by 3702

Abstract

The content of chlorophyll, an important substance for photosynthesis in plants, is an important index used to characterize the photosynthetic rate and nutrient grade of plants. The real-time rapid acquisition of crop chlorophyll content is of great significance for guiding fine management and differentiated fertilization in the field. This study used the method of continuous wavelet transform (CWT) to process the collected visible and near-infrared spectra of a corn canopy. This task was conducted to extract the valuable information in the spectral data and improve the sensitivity of chlorophyll content assessment. First, a Savitzky–Golay filter and standard normal variable processing were applied to the spectral data to eliminate the influence of random noise and limit drift on spectral reflectance. Second, CWT was performed on the spectral reflection curve with 10 frequency scales to obtain the wavelet energy coefficient of the spectral data. The characteristic bands related to chlorophyll content in the spectral data and the wavelet energy coefficients were screened using the maximum correlation coefficient and the local correlation coefficient extrema, respectively. A partial least-square regression model was established. Results showed that the characteristic bands selected via local correlation coefficient extrema in a wavelet energy coefficient created a detection model with optimal accuracy. The determination coefficient (R_c²) of the calibration set was 0.7856, and the root-mean-square error (RMSE) of the calibration set (RMSEC) was 3.0408. The determination coefficient (R_v²) of the validation set is was 0.7364, and the RMSE of the validation set (RMSEV) was 3.3032. Continuous wavelet transform is a process of data dimension enhancement which can effectively extract the sensitive variables from spectral datasets and improve the detection accuracy of models. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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21 pages, 4754 KiB

Open AccessArticle

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

by Lucas Prado Osco, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Érika Akemi Saito Moriya, Nilton Nobuhiro Imai, Nayara Estrabis, Felipe Ianczyk, Fábio Fernando de Araújo, Veraldo Liesenberg, Lúcio André de Castro Jorge, Jonathan Li, Lingfei Ma, Wesley Nunes Gonçalves, José Marcato Junior and José Eduardo Creste

Remote Sens. 2020, 12(6), 906; https://doi.org/10.3390/rs12060906 - 12 Mar 2020

Cited by 75 | Viewed by 7193

Abstract

This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec^® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R²), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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21 pages, 5275 KiB

Open AccessArticle

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

by Anita Sabat-Tomala, Edwin Raczko and Bogdan Zagajewski

Remote Sens. 2020, 12(3), 516; https://doi.org/10.3390/rs12030516 - 05 Feb 2020

Cited by 79 | Viewed by 9514

Abstract

Invasive and expansive plant species are considered a threat to natural biodiversity because of their high adaptability and low habitat requirements. Species investigated in this research, including Solidago spp., Calamagrostis epigejos, and Rubus spp., are successfully displacing native vegetation and claiming new areas, which in turn severely decreases natural ecosystem richness, as they rapidly encroach on protected areas (e.g., Natura 2000 habitats). Because of the damage caused, the European Union (EU) has committed all its member countries to monitor biodiversity. In this paper we compared two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to identify Solidago spp., Calamagrostis epigejos, and Rubus spp. on HySpex hyperspectral aerial images. SVM and RF are reliable and well-known classifiers that achieve satisfactory results in the literature. Data sets containing 30, 50, 100, 200, and 300 pixels per class in the training data set were used to train SVM and RF classifiers. The classifications were performed on 430-spectral bands and on the most informative 30 bands extracted using the Minimum Noise Fraction (MNF) transformation. As a result, maps of the spatial distribution of analyzed species were achieved; high accuracies were observed for all data sets and classifiers (an average F1 score above 0.78). The highest accuracies were obtained using 30 MNF bands and 300 sample pixels per class in the training data set (average F1 score > 0.9). Lower training data set sample sizes resulted in decreased average F1 scores, up to 13 percentage points in the case of 30-pixel samples per class. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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18 pages, 6908 KiB

Open AccessArticle

Application of UAV-Based Multi-angle Hyperspectral Remote Sensing in Fine Vegetation Classification

by Yanan Yan, Lei Deng, XianLin Liu and Lin Zhu

Remote Sens. 2019, 11(23), 2753; https://doi.org/10.3390/rs11232753 - 22 Nov 2019

Cited by 40 | Viewed by 5366

Abstract

To obtain a high-accuracy vegetation classification of high-resolution UAV images, in this paper, a multi-angle hyperspectral remote sensing system was built using a six-rotor UAV and a Cubert S185 frame hyperspectral sensor. The application of UAV-based multi-angle remote sensing in fine vegetation classification was studied by combining a bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods. This method can not only effectively reduce the classification phenomena that influence different objects with similar spectra, but also benefit the construction of a canopy-level BRDF. Then, the importance of the BRDF characteristic parameters are discussed in detail. The results show that the overall classification accuracy (OA) of the vertical observation reflectance based on BRDF extrapolation (BRDF_0°) (63.9%) was approximately 24% higher than that based on digital orthophoto maps (DOM) (39.8%), and kappa using BRDF_0° was 0.573, which was higher than that using DOM (0.301); a combination of the hot spot and dark spot features, as well as model features, improved the OA and kappa to around 77% and 0.720, respectively. The reflectance features near hot spots were more conducive to distinguishing maize, soybean, and weeds than features near dark spots; the classification results obtained by combining the observation principal plane (BRDF_PP) and on the cross-principal plane (BRDF_CP) features were best (OA = 89.2%, kappa = 0.870), and especially, this combination could improve the distinction among different leaf-shaped trees. BRDF_PP features performed better than BRDF_CP features. The observation angles in the backward reflection direction of the principal plane performed better than those in the forward direction. The observation angles associated with the zenith angles between −10° and −20° were most favorable for vegetation classification (solar position: zenith angle 28.86°, azimuth 169.07°) (OA was around 75%–80%, kappa was around 0.700–0.790); additionally, the most frequently selected bands in the classification included the blue band (466 nm–492 nm), green band (494 nm–570 nm), red band (642 nm–690 nm), red edge band (694 nm–774 nm), and the near-infrared band (810 nm–882 nm). Overall, the research results promote the application of multi-angle remote sensing technology in vegetation information extraction and provide important theoretical significance and application value for regional and global vegetation and ecological monitoring. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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21 pages, 5303 KiB

Open AccessArticle

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

by Mohsen Mirzaei, Jochem Verrelst, Safar Marofi, Mozhgan Abbasi and Hossein Azadi

Remote Sens. 2019, 11(23), 2731; https://doi.org/10.3390/rs11232731 - 20 Nov 2019

Cited by 16 | Viewed by 4084

Abstract

Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants’ physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350–2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R² rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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17 pages, 4543 KiB

Open AccessArticle

Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces

by Mengmeng Xie, Zhongqiang Wang, Alfredo Huete, Luke A. Brown, Heyu Wang, Qiaoyun Xie, Xinpeng Xu and Yanling Ding

Remote Sens. 2019, 11(18), 2148; https://doi.org/10.3390/rs11182148 - 15 Sep 2019

Cited by 12 | Viewed by 3202

Abstract

Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC measurements, this study proposed a dorsiventral leaf adjusted ratio index (DLARI) to adjust dorsiventral leaf structure and improve LCC retrieval accuracy. Moreover, the modified Datt (MDATT) index, which was insensitive to leaves structure, was optimized for peanut plants. All possible wavelength combinations for the DLARI and MDATT formulae were evaluated. When reflectance from both sides were considered, the optimal combination for the MDATT formula was

(R_{723} - R_{738}) / (R_{723} - R_{722})

with a cross-validation R²_cv of 0.91 and RMSE_cv of 3.53 μg/cm². The DLARI formula provided the best performing indices, which were

(R_{735} - R_{753}) / (R_{715} - R_{819})

for estimating LCC from the adaxial surface (R²_cv = 0.96, RMSE_cv = 2.37 μg/cm²) and

(R_{732} - R_{754}) / (R_{724} - R_{773})

for estimating LCC from reflectance of both sides (R²_cv = 0.94, RMSE_cv = 2.81 μg/cm²). A comparison with published vegetation indices demonstrated that the published indices yielded reliable estimates of LCC from the adaxial surface but performed worse than DLARIs when both leaf sides were considered. This paper concludes that the DLARI is the most promising approach to estimate peanut LCC. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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14 pages, 4501 KiB

Open AccessArticle

Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction

by Changhui Jiang, Yuwei Chen, Haohao Wu, Wei Li, Hui Zhou, Yuming Bo, Hui Shao, Shaojing Song, Eetu Puttonen and Juha Hyyppä

Remote Sens. 2019, 11(17), 2007; https://doi.org/10.3390/rs11172007 - 26 Aug 2019

Cited by 26 | Viewed by 3982

Abstract

Non-contact and active vegetation or plant parameters extraction using hyperspectral information is a prospective research direction among the remote sensing community. Hyperspectral LiDAR (HSL) is an instrument capable of acquiring spectral and spatial information actively, which could mitigate the environmental illumination influence on the spectral information collection. However, HSL usually has limited spectral resolution and coverage, which is vital for vegetation parameter extraction. In this paper, to broaden the HSL spectral range and increase the spectral resolution, an Acousto-optical Tunable Filter based Hyperspectral LiDAR (AOTF-HSL) with 10 nm spectral resolution, consecutively covering from 500–1000 nm, was designed. The AOTF-HSL was employed and evaluated for vegetation parameters extraction. “Red Edge” parameters of four different plants with green and yellow leaves were extracted in the lab experiments for evaluating the HSL vegetation parameter extraction capacity. The experiments were composed of two parts. Firstly, the first-order derivative of the spectral reflectance was employed to extract the “Red Edge” position (REP), “Red Edge” slope (RES) and “Red Edge” area (REA) of these green and yellow leaves. The results were compared with the referenced value from a standard SVC^© HR-1024 spectrometer for validation. Green leaf parameter differences between HSL and SVC results were minor, which supported that notion the HSL was practical for extracting the employed parameter as an active method. Secondly, another two different REP extraction methods, Linear Four-point Interpolation technology (LFPIT) and Linear Extrapolation technology (LET), were utilized for further evaluation of using the AOTF-HSL spectral profile to determine the REP value. The differences between the plant green leaves’ REP results extracted using the three methods were all below 10%, and the some of them were below 1%, which further demonstrated that the spectral data collected from HSL with this spectral range and resolution settings was applicable for “Red Edge” parameters extraction. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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Review

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40 pages, 3131 KiB

Open AccessEditor’s ChoiceReview

Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

by Bing Lu, Phuong D. Dao, Jiangui Liu, Yuhong He and Jiali Shang

Remote Sens. 2020, 12(16), 2659; https://doi.org/10.3390/rs12162659 - 18 Aug 2020

Cited by 388 | Viewed by 29976

Abstract

Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. In recent years, different mini-sized and low-cost airborne hyperspectral sensors (e.g., Headwall Micro-Hyperspec, Cubert UHD 185-Firefly) have been developed, and advanced spaceborne hyperspectral sensors have also been or will be launched (e.g., PRISMA, DESIS, EnMAP, HyspIRI). Hyperspectral imaging is becoming more widely available to agricultural applications. Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis). It is hence beneficial to conduct a thorough and in-depth review of the hyperspectral imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing hyperspectral information, and recent advances of hyperspectral imaging in agricultural applications. Publications over the past 30 years in hyperspectral imaging technology and applications in agriculture were thus reviewed. The imaging platforms and sensors, together with analytic methods used in the literature, were discussed. Performances of hyperspectral imaging for different applications (e.g., crop biophysical and biochemical properties’ mapping, soil characteristics, and crop classification) were also evaluated. This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology. Recommendations for future hyperspectral imaging research for precision agriculture are also presented. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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27 pages, 4122 KiB

Open AccessReview

Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability

by Andrew Hennessy, Kenneth Clarke and Megan Lewis

Remote Sens. 2020, 12(1), 113; https://doi.org/10.3390/rs12010113 - 01 Jan 2020

Cited by 120 | Viewed by 8695

Abstract

Hyperspectral sensing, measuring reflectance over visible to shortwave infrared wavelengths, has enabled the classification and mapping of vegetation at a range of taxonomic scales, often down to the species level. Classification with hyperspectral measurements, acquired by narrow band spectroradiometers or imaging sensors, has generally required some form of spectral feature selection to reduce the dimensionality of the data to a level suitable for the construction of a classification model. Despite the large number of hyperspectral plant classification studies, an in-depth review of feature selection methods and resultant waveband selections has not yet been performed. Here, we present a review of the last 22 years of hyperspectral vegetation classification literature that evaluates the overall waveband selection frequency, waveband selection frequency variation by taxonomic, structural, or functional group, and the influence of feature selection choice by comparing such methods as stepwise discriminant analysis (SDA), support vector machines (SVM), and random forests (RF). This review determined that all characteristics of hyperspectral plant studies influence the wavebands selected for classification. This includes the taxonomic, structural, and functional groups of the target samples, the methods, and scale at which hyperspectral measurements are recorded, as well as the feature selection method used. Furthermore, these influences do not appear to be consistent. Moreover, the considerable variability in waveband selection caused by the feature selectors effectively masks the analysis of any variability between studies related to plant groupings. Additionally, questions are raised about the suitability of SDA as a feature selection method, with it producing waveband selections at odds with the other feature selectors. Caution is recommended when choosing a feature selector for hyperspectral plant classification: We recommend multiple methods being performed. The resultant sets of selected spectral features can either be evaluated individually by multiple classification models or combined as an ensemble for evaluation by a single classifier. Additionally, we suggest caution when relying upon waveband recommendations from the literature to guide waveband selections or classifications for new plant discrimination applications, as such recommendations appear to be weakly generalizable between studies. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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15 pages, 2822 KiB

Open AccessLetter

Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

by Lucas Prado Osco, Ana Paula Marques Ramos, Érika Akemi Saito Moriya, Lorrayne Guimarães Bavaresco, Bruna Coelho de Lima, Nayara Estrabis, Danilo Roberto Pereira, José Eduardo Creste, José Marcato Júnior, Wesley Nunes Gonçalves, Nilton Nobuhiro Imai, Jonathan Li, Veraldo Liesenberg and Fábio Fernando de Araújo

Remote Sens. 2019, 11(23), 2797; https://doi.org/10.3390/rs11232797 - 26 Nov 2019

Cited by 31 | Viewed by 4298

Abstract

Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325–1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)

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