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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 December 2021) | Viewed by 36522

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Special Issue Editors

Dr. Thomas Alexandridis

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Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: agronomic applications of earth observation; remote sensing; digital image processing; geoinformatics; spectroscopy; precision agriculture; UAV; crops; irrigation; soil
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Prof. Dr. Dimitrios Moshou

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Head of Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), P.O. 275, 54124 Thessaloniki, Greece
Interests: remote sensing; multiscale fusion robotic agriculture; sensor networks; robotics; development of cognitive abilities; fusion of global and local cognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent technological advances in sensor and platform technology have led towards the penetration of spectroscopy into new fields of application. In agricultural production, spectroscopy is an emerging field that proves novel applications every day. Spectrometers of higher spectral accuracy and light enough to be carried by commercial UAVs are being used to detect subtle changes in reflectance of plant parts or vegetation canopy. Novel data analysis techniques are being introduced to improve the accuracy and efficiency of the collected spectra, moving towards operational real-time applications.

This special issue aims to bring together recent research and developments concerning spectroscopic analysis of plants and vegetation. Submissions on the following topics are invited (but not limited to), as long as they present innovative methods and approaches, or novel applications of existing tools on spectroscopy of plants and vegetation:

Point spectroscopy
Imaging spectroscopy
Satellite hyperspectral imaging
Airborne hyperspectral cameras (UAV)
Spectroscopy of crop health status determination
Spectroscopy for crop phenotyping, germination, emergence and determination of the different growth stages of crops
Spectroscopy for detection of microorganism and pest management
Multisensor systems, sensor fusion
Non-destructive plant and vegetation spectroscopy
Yield estimation and prediction
Detection and identification of crops and weeds
Spectra analysis
Machine learning and emerging algorithms
Cloud computing
Real time processing
On-board processing
Multiple source data fusion
Hyperspectral data cubes
Precision agriculture applications
Monitoring water use and irrigation requirements
Crop damage assessment (frost, droughts, hail)
Site-specific applications and management of agricultural resources
Plant phenotyping

Dr. Thomas Alexandridis
Prof. Dimitrios Moshou
Guest Editor

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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.

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Keywords

Spectroscopic information acquisition
Spectral data processing
Spectroscopy in agriculture
Hyperspectral
Chemometrics
Bioinformatics
Precision agriculture
Phenotyping

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Published Papers (10 papers)

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19 pages, 7197 KiB

Open AccessArticle

Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

by Fumin Wang, Xiaoping Yao, Lili Xie, Jueyi Zheng and Tianyue Xu

Remote Sens. 2021, 13(17), 3390; https://doi.org/10.3390/rs13173390 - 26 Aug 2021

Cited by 25 | Viewed by 4855

Abstract

Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R²) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R² = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method. Full article

(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)

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20 pages, 4282 KiB

Open AccessArticle

Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression

by Luís Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Renato Herrig Furlanetto, Rubson Natal Ribeiro Sibaldelli, Everson Cezar, Liang Sun, José Salvador Simonetto Foloni, Liliane Marcia Mertz-Henning, Alexandre Lima Nepomuceno, Norman Neumaier and José Renato Bouças Farias

Remote Sens. 2021, 13(5), 977; https://doi.org/10.3390/rs13050977 - 4 Mar 2021

Cited by 16 | Viewed by 3066

Abstract

Soybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over technical issues. This paper aimed to predict grain yield in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least square regression (PLSR). Field experiments were undertaken at Embrapa Soja (Brazilian Agricultural Research Corporation) in the 2016/2017, 2017/2018 and 2018/2019 cropping seasons. The data collected were analyzed following a split plot model in a randomized complete block design, with four blocks. The following water conditions were distributed in the field plots: irrigated (IRR), non-irrigated (NIRR) and water deficit induced at the vegetative (WDV) and reproductive stages (WDR) using rainout shelters. Soybean genotypes with different responses to water deficit were distributed in the subplots. Soil moisture and weather data were monitored daily. A total of 7216 leaf reflectance (from 400 to 2500 nm, measured by the FieldSpec 3 Jr spectroradiometer) was collected at 24 days in the three cropping seasons. The PLSR (p ≤ 0.05) was performed to predict soybean grain yield by its leaf-based reflectance spectroscopy. The results demonstrated the highest accuracy in soybean grain yield prediction at the R5 phenological stage, corresponding to the period when grains are being formed (R² ranging from 0.731 to 0.924 and the RMSE from 334 to 403 kg ha⁻¹—7.77 to 11.33%). Analyzing the three cropping seasons into a single PLSR model at R5 stage, R² equal to 0.775, 0.730 and 0.688 were obtained at the calibration, cross-validation and external validation stages, with RMSE lower than 634 kg ha⁻¹ (13.34%). The PLSR demonstrated higher accuracy in plants submitted to water deficit both at the vegetative and reproductive periods in comparison to plants under natural rainfall or irrigation. Full article

(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)

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21 pages, 42789 KiB

Open AccessArticle

Hyperspectral Monitoring of Non-Native Tropical Grasses over Phenological Seasons

by Kirrilly Pfitzner, Renee Bartolo, Tim Whiteside, David Loewensteiner and Andrew Esparon

Remote Sens. 2021, 13(4), 738; https://doi.org/10.3390/rs13040738 - 17 Feb 2021

Cited by 7 | Viewed by 2531

Abstract

The miniaturisation of hyperspectral sensors for use on drones has provided an opportunity to obtain hyper temporal data that may be used to identify and monitor non-native grass species. However, a good understanding of variation in spectra for species over time is required to target such data collections. Five taxological and morphologically similar non-native grass species were hyper spectrally characterised from multitemporal spectra (17 samples over 14 months) over phenological seasons to determine their temporal spectral response. The grasses were sampled from maintained plots of homogenous non-native grass cover. A robust in situ standardised sampling method using a non-imaging field spectrometer measuring reflectance across the 350–2500 nm wavelength range was used to obtain reliable spectral replicates both within and between plots. The visible-near infrared (VNIR) to shortwave infrared (SWIR) and continuum removed spectra were utilised. The spectra were then resampled to the VNIR only range to simulate the spectral response from more affordable VNIR only hyperspectral scanners suitable to be mounted on drones. We found that species were separable compared to similar but different species. The spectral patterns were similar over time, but the spectral shape and absorption features differed between species, indicating these subtle characteristics could be used to distinguish between species. It was the late dry season and the end of the wet season that provided maximum separability of the non-native grass species sampled. Overall the VNIR-SWIR results highlighted more dissimilarity for unlike species when compared to the VNIR results alone. The SWIR is useful for discriminating species, particularly around water absorption. Full article

(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)

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24 pages, 7451 KiB

Open AccessArticle

Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance

by Luis Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Renato Herrig Furlanetto, Rubson Natal Ribeiro Sibaldelli, Everson Cezar, Liang Sun, José Salvador Simonetto Foloni, Liliane Marcia Mertz-Henning, Alexandre Lima Nepomuceno, Norman Neumaier and José Renato Bouças Farias

Remote Sens. 2021, 13(2), 172; https://doi.org/10.3390/rs13020172 - 6 Jan 2021

Cited by 17 | Viewed by 3855

Abstract

Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted. Full article

(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)

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27 pages, 9897 KiB

Open AccessArticle

Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability

by Wiktor R. Żelazny and Jan Lukáš

Remote Sens. 2020, 12(20), 3462; https://doi.org/10.3390/rs12203462 - 21 Oct 2020

Cited by 10 | Viewed by 3525

Abstract

Hyperspectral imaging (HSI) has been gaining recognition as a promising proximal and remote sensing technique for crop drought stress detection. A modelling approach accounting for the treatment effects on the stress indicators’ standard deviations was applied to proximal images of oilseed rape—a crop subjected to various HSI studies, with the exception of drought. The aim of the present study was to determine the spectral responses of two cultivars, ‘Cadeli’ and ‘Viking’, representing distinctive water management strategies, to three types of watering regimes. Hyperspectral data cubes were acquired at the leaf level using a 2D frame camera. The influence of the experimental factors on the extent of leaf discolorations, vegetation index values, and principal component scores was investigated using Bayesian linear models. Clear treatment effects were obtained primarily for the vegetation indexes with respect to the watering regimes. The mean values of RGI, MTCI, RNDVI, and GI responded to the difference between the well-watered and water-deprived plants. The RGI index excelled among them in terms of effect strengths, which amounted to

- 0.96 [- 2.21, 0.21]

and

- 0.71 [- 1.97, 0.49]

units for each cultivar. A consistent increase in the multiple index standard deviations, especially RGI, PSRI, TCARI, and TCARI/OSAVI, was associated with worsening of the hydric regime. These increases were captured not only for the dry treatment but also for the plants subjected to regeneration after a drought episode, particularly by PSRI (a multiplicative effect of

0.33 [0.16, 0.68]

for ‘Cadeli’). This result suggests a higher sensitivity of the vegetation index variability measures relative to the means in the context of the oilseed rape drought stress diagnosis and justifies the application of HSI to capture these effects. RGI is an index deserving additional scrutiny in future studies, as both its mean and standard deviation were affected by the watering regimes. Full article

(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)

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14 pages, 2013 KiB

Open AccessArticle

An Experimental Study on Field Spectral Measurements to Determine Appropriate Daily Time for Distinguishing Fractional Vegetation Cover

by Du Lyu, Baoyuan Liu, Xiaoping Zhang, Xihua Yang, Liang He, Jie He, Jinwei Guo, Jufeng Wang and Qi Cao

Remote Sens. 2020, 12(18), 2942; https://doi.org/10.3390/rs12182942 - 10 Sep 2020

Cited by 5 | Viewed by 2463

Abstract

Remote sensing technology has been widely used to estimate fractional vegetation cover (FVC) at global and regional scales. Accurate and consistent field spectral measurements are required to develop and validate spectral indices for FVC estimation. However, there are rarely any experimental studies to determine the appropriate times for field spectral measurements, and the existing guidelines or references are rather general or inconsistent, it is still not agreed upon and detailed experiments are missing for a local research. In this experiment, five groundcover objects were measured continuously from 07:30 a.m. to 17:30 p.m. local time in three consecutive sunny days using a portable spectrometer. The coefficients of variation (CV) were applied to investigate the reflectance variation at wavelengths corresponding to MODIS satellite channels and the derived spectral indices used to estimate FVC, including photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV). The results reveal little variation in the reflectance measured between 10:00 a.m. and 16:00 p.m., with CV values generally less than 10%. The CV values of FVC spectral indices for estimating PV, NPV and bare soil (BS) are generally less than 3%. While more experiments are yet to be carried out at different locations and in different seasons, the findings so far imply that the in situ spectrum measured between 9:00 a.m. and 17:00 p.m. local time would be useful to discriminate FVC objects and validate satellite estimates-based indices using visible, near-infrared and shortwave infrared channels. Full article

(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)

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19 pages, 10366 KiB

Open AccessArticle

Toward Mapping Dietary Fibers in Northern Ecosystems Using Hyperspectral and Multispectral Data

by Jyoti S. Jennewein, Jan U.H. Eitel, Jeremiah R. Pinto and Lee A. Vierling

Remote Sens. 2020, 12(16), 2579; https://doi.org/10.3390/rs12162579 - 11 Aug 2020

Cited by 2 | Viewed by 2700

Abstract

Shrub proliferation across the Arctic from climate warming is expanding herbivore habitat but may also alter forage quality. Dietary fibers—an important component of forage quality—influence shrub palatability, and changes in dietary fiber concentrations may have broad ecological implications. While airborne hyperspectral instruments may effectively estimate dietary fibers, such data captures a limited portion of landscapes. Satellite data such as the multispectral WorldView-3 (WV-3) instrument may enable dietary fiber estimation to be extrapolated across larger areas. We assessed how variation in dietary fibers of Salix alaxensis (Andersson), a palatable northern shrub, could be estimated using hyperspectral and multispectral WV-3 spectral vegetation indices (SVIs) in a greenhouse setting, and whether including structural information (i.e., leaf area) would improve predictions. We collected canopy-level hyperspectral reflectance readings, which we convolved to the band equivalent reflectance of WV-3. We calculated every possible SVI combination using hyperspectral and convolved WV-3 bands. We identified the best performing SVIs for both sensors using the coefficient of determination (adjusted R²) and the root mean square error (RMSE) using simple linear regression. Next, we assessed the importance of plant structure by adding shade leaf area, sun leaf area, and total leaf area to models individually. We evaluated model fits using Akaike’s information criterion for small sample sizes and conducted leave-one-out cross validation. We compared cross validation slopes and predictive power (Spearman rank coefficients

ρ

) between models. Hyperspectral SVIs (R² = 0.48–0.68; RMSE = 0.04–0.91%) outperformed WV-3 SVIs (R² = 0.13–0.35; RMSE = 0.05–1.18%) for estimating dietary fibers, suggesting hyperspectral remote sensing is best suited for estimating dietary fibers in a palatable northern shrub. Three dietary fibers showed improved predictive power when leaf area metrics were included (cross validation

ρ

= +2–8%), suggesting plant structure and the light environment may augment our ability to estimate some dietary fibers in northern landscapes. Monitoring dietary fibers in northern ecosystems may benefit from upcoming hyperspectral satellites such as the environmental mapping and analysis program (EnMAP). Full article

(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)

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15 pages, 5227 KiB

Open AccessArticle

Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves

by Hubert Skoneczny, Katarzyna Kubiak, Marcin Spiralski, Jan Kotlarz, Artur Mikiciński and Joanna Puławska

Remote Sens. 2020, 12(13), 2101; https://doi.org/10.3390/rs12132101 - 30 Jun 2020

Cited by 32 | Viewed by 5008

Abstract

The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight. Full article

(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)

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22 pages, 3215 KiB

Open AccessArticle

Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy

by Antonios Morellos, Georgios Tziotzios, Chrysoula Orfanidou, Xanthoula Eirini Pantazi, Christos Sarantaris, Varvara Maliogka, Thomas K. Alexandridis and Dimitrios Moshou

Remote Sens. 2020, 12(12), 1920; https://doi.org/10.3390/rs12121920 - 13 Jun 2020

Cited by 26 | Viewed by 4247

Abstract

Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%. Full article

(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)

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2 pages, 155 KiB

Open AccessErratum

Erratum: Skoneczny, H., et al. Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves. Remote Sensing 2020, 12(13), 2101

by Hubert Skoneczny, Katarzyna Kubiak, Marcin Spiralski, Jan Kotlarz, Artur Mikiciński and Joanna Puławska

Remote Sens. 2020, 12(15), 2485; https://doi.org/10.3390/rs12152485 - 3 Aug 2020

Viewed by 2561

Abstract

The authors wish to make the following corrections to this paper [...] Full article

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