New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain.

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Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing

Agricultural and Food Science ◽

10.23986/afsci.72741 ◽

1996 ◽

Vol 5 (3) ◽

pp. 367-376 ◽

Cited By ~ 1

Author(s):

Tsuyoshi Akiyama ◽

Y. Inoue ◽

M. Shibayama ◽

Y. Awaya ◽

N. Tanaka

Keyword(s):

Remote Sensing ◽

Near Infrared ◽

Vegetation Indices ◽

Nutrient Deficiency ◽

The Arctic ◽

Ground Vegetation ◽

Spectral Vegetation Indices ◽

Project Monitoring ◽

Crop Types ◽

Crop Discrimination

LANDSAT/TM data, which are characterized by high spectral/spatial resolutions, are able to contribute to practical agricultural management. In the first part of the paper, the authors review some recent applications of satellite remote sensing in agriculture. Techniques for crop discrimination and mapping have made such rapid progress that we can classify crop types with more than 80% accuracy. The estimation of crop biomass using satellite data, including leaf area, dry and fresh weights, and the prediction of grain yield, has been attempted using various spectral vegetation indices. Plant stresses caused by nutrient deficiency and water deficit have also been analysed successfully. Such information may be useful for farm management. In the latter half of the paper, we introduce the Arctic Science Project, which was carried out under the Science and Technology Agency of Japan collaborating with Finnish scientists. In this project, monitoring of the boreal forest was carried out using LANDSAT data. Changes in the phenology of subarctic ground vegetation, based on spectral properties, were measured by a boom-mounted, four-band spectroradiometer. The turning point dates of the seasonal near-infrared (NIR) and red (R) reflectance factors might indicate the end of growth and the beginning of autumnal tints, respectively.

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Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation

Drones ◽

10.3390/drones3010025 ◽

2019 ◽

Vol 3 (1) ◽

pp. 25 ◽

Cited By ~ 3

Author(s):

René Heim ◽

Ian Wright ◽

Peter Scarth ◽

Angus Carnegie ◽

Dominique Taylor ◽

...

Keyword(s):

Near Infrared ◽

Multispectral Imaging ◽

Spectral Band ◽

Management Strategies ◽

Vegetation Indices ◽

Unmanned Aerial Systems ◽

Disease Detection ◽

Myrtle Rust ◽

Spectral Vegetation Indices ◽

Austropuccinia Psidii

Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (Backhousia citriodora) plantation. Multispectral aerial imagery was collected from fungicide treated and untreated tree canopies, the fungicide being used to control myrtle rust. Spectral vegetation indices and single spectral bands were used to train a random forest classifier. Treated and untreated trees could be classified with high accuracy (95%). Important predictors for the classifier were the near-infrared (NIR) and red edge (RE) spectral band. Taking some limitations into account, that are discussedherein, our work suggests potential for mapping myrtle rust-related symptoms from aerial multispectral images. Similar studies could focus on pinpointing disease hotspots to adjust management strategies and to feed epidemiological models.

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Assessing the Maize (Zea mays L.) Crop Performance Using Spectral Indices under Different Sowing Dates and Irrigations Schedules

International Journal of Bio-resource and Stress Management ◽

10.23910/1.2021.2411a ◽

2021 ◽

Vol 12 (5) ◽

pp. 319-331

Author(s):

Ch. Pallavi ◽

◽

G. Sreenivas ◽

M. Yakadri ◽

Anima Biswal ◽

...

Keyword(s):

Grain Yield ◽

Near Infrared ◽

Vegetation Indices ◽

Visible Region ◽

Stressed Condition ◽

Spectral Indices ◽

Sowing Dates ◽

Spectral Vegetation Indices ◽

Scheduling Irrigation ◽

Plot Design

Field experiment was conducted at ARI, PJTSAU, Hyderabad with four dates of sowing (18th June, 04th July, 19th July and 03rd August in Kharif, 2016 and 01st November, 18th November, 01st December and 17th December in rabi, 2016–17) as main plots and four irrigations regimes (Control, 0.4 IW/CPE, 0.6 IW/CPE and 0.8 IW/CPE in kharif and 0.4 IW/CPE, 0.6 IW/CPE, 0.8 IW/CPE and 1.0 IW/CPE in rabi) as sub-plots in split plot design replicated thrice. Scheduling irrigation at unstressed conditions, 0.8 IW/CPE (I3) and 1.0 IW/CPE (I4) of maize resulted in low reflectance in visible region (400 to 700 nm) and mid infrared (MIR) region (1350–2500 nm) and high in near infrared (NIR) region (700 to 1350 nm) during kharif and rabi respectively. While, under stressed condition, the reflectance was high in visible and MIR region and low in NIR region in rainfed and 0.4 IW/CPE (I0) respectively in kharif and rabi. Significantly higher drymatter, LAI and grain yield was observed in 04th July (D2) and 01st November (D1) sown crop in kharif and rabi respectively. However, spectral indices (SR, PRI, NDWI at 1240, 1640 and 2130 nm, NDII, NMDI, WBI and SWRI) was attained by 18th June (D1) and 01st November (D1) during kharif and rabi respectively. Higher drymatter, LAI, grain yield and spectral indices was recorded with I3 (0.8 IW/CPE) and I4 (1.0 IW/CPE) in kharif and rabi respectively. All the spectral vegetation indices correlated positively with LAI, drymatter and grain yield.

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Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Remote Sensing ◽

10.3390/rs13030536 ◽

2021 ◽

Vol 13 (3) ◽

pp. 536

Author(s):

Eve Laroche-Pinel ◽

Mohanad Albughdadi ◽

Sylvie Duthoit ◽

Véronique Chéret ◽

Jacques Rousseau ◽

...

Keyword(s):

Near Infrared ◽

Water Status ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Short Wave ◽

Spatial And Temporal Scales ◽

Spectral Bands ◽

Main Challenge ◽

Spectral Scale ◽

Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

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Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Remote Sensing ◽

10.3390/rs13020233 ◽

2021 ◽

Vol 13 (2) ◽

pp. 233

Author(s):

Ilja Vuorinne ◽

Janne Heiskanen ◽

Petri K. E. Pellikka

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Fibre Production ◽

Additive Models ◽

Leaf Biomass ◽

Ground Vegetation ◽

Perennial Crop ◽

Red Edge ◽

Agave Sisalana ◽

Sentinel 2

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.

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Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Plants ◽

10.3390/plants10020341 ◽

2021 ◽

Vol 10 (2) ◽

pp. 341

Author(s):

Pauliina Salmi ◽

Matti A. Eskelinen ◽

Matti T. Leppänen ◽

Ilkka Pölönen

Keyword(s):

Chlorophyll Fluorescence ◽

Chlorophyll A ◽

Near Infrared ◽

Vegetation Indices ◽

Hyperspectral Data ◽

Strong Correlations ◽

Algae Biomass ◽

Chlorophyll A Concentration ◽

Cost Efficient ◽

Hyperspectral Camera

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

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Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Remote Sensing ◽

10.3390/rs13112060 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2060

Author(s):

Trylee Nyasha Matongera ◽

Onisimo Mutanga ◽

Mbulisi Sibanda ◽

John Odindi

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Critical Role ◽

Spectral Characteristics ◽

Vegetation Indices ◽

Machine Learning Algorithms ◽

Special Focus ◽

Land Surface Phenology ◽

Spectral Vegetation Indices ◽

Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

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Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

Scientific Reports ◽

10.1038/s41598-020-79439-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Guillaume Lassalle ◽

Sophie Fabre ◽

Anthony Credoz ◽

Rémy Hédacq ◽

Dominique Dubucq ◽

...

Keyword(s):

Metal Uptake ◽

Near Infrared ◽

Vegetation Indices ◽

Good Alternative ◽

Entire Study ◽

Metal Contents ◽

Very High Spatial Resolution ◽

Airborne Imaging ◽

Very High ◽

Plant Metal Uptake

AbstractMonitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on previous work, this study proposes a methodology for mapping the content of several metals in leaves (Cr, Cu, Ni and Zn) under realistic field conditions and from airborne imaging. For this purpose, the reflectance of Rubus fruticosus L., a pioneer species of industrial brownfields, was linked to leaf metal contents using optimized normalized vegetation indices. High correlations were found between the vegetation indices exploiting pigment-related wavelengths and leaf metal contents (r ≤ − 0.76 for Cr, Cu and Ni, and r ≥ 0.87 for Zn). This allowed predicting the metal contents with good accuracy in the field and on the image, especially Cu and Zn (r ≥ 0.84 and RPD ≥ 2.06). The same indices were applied over the entire study site to map the metal contents at very high spatial resolution. This study demonstrates the potential of remote sensing for assessing metal uptake by plants, opening perspectives of application in risk assessment and phytoextraction monitoring in the context of trace metal pollution.

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Satellite derived maps of pasture growth status: association of classification with botanical composition

Australian Journal of Experimental Agriculture ◽

10.1071/ea97014 ◽

1997 ◽

Vol 37 (5) ◽

pp. 547 ◽

Cited By ~ 17

Author(s):

P. J. Vickery ◽

M. J. Hill ◽

G. E. Donald

Keyword(s):

Near Infrared ◽

Perennial Grass ◽

Vegetation Indices ◽

Fast Growth ◽

Perennial Grasses ◽

Bayesian Probability ◽

Cumulative Probability ◽

Botanical Composition ◽

South Wales ◽

Native Pastures

Summary. Spectral data from the green, red and near-infrared bands of Landsat MSS and Landsat TM satellite imagery acquired in mid-spring were classified into 3 and 6 pasture growth classes respectively. The classifications were compared with a site database of botanical composition for the Northern Tablelands of New South Wales to examine the association between spectral growth class and pasture composition. Pastures ranged in composition from unimproved native perennial grasses through semi-improved mixtures of native and naturalised grasses and legumes to highly improved temperate perennial grasses and legumes. For 3 years of MSS data, the fast growth class had a mean botanical composition of about 80% improved perennial grass and 0% native; medium growth class averaged 46% improved perennial grass and 14% native; while the slow growth class had about 60% native and 1% improved perennial grass when averaged over 3 years of MSS data. For the 6 class TM data from a single year, a predictive logistic regression of cumulative probability was developed for percentage of ‘very fast’ growth pixels and ordered 10 percentile categories of improved perennial grass or native grass. Differences in patch characteristics between classes with MSS disappeared with TM reclassified to the same 3 class level. Most probable pasture type was inferred from 3 class MSS and TM data using Bayesian probability analysis. The resulting maps were similar in general appearance but detail was better with the TM data. The pasture growth classification identified highly improved perennial grass pastures and native pastures but sensitivity to intermediate pasture types was poor. Future improvement will come from direct measurement of biophysical characteristics using vegetation indices or inversion of reflectance models.

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