A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI and spectral indices (SIs) from MSI were extracted, respectively. The four indices (CIs in RGB, CIs in MSI, SIs in MSI, and CIs + SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices’ dynamics were analyzed and the indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices’ temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), and Normalized Difference Vegetation Index (NDVI) in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), the Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.

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Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index

Remote Sensing ◽

10.3390/rs11101192 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1192 ◽

Cited By ~ 6

Author(s):

Nianxu Xu ◽

Jia Tian ◽

Qingjiu Tian ◽

Kaijian Xu ◽

Shaofei Tang

Keyword(s):

Forest Canopy ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Multispectral Imaging ◽

Bare Soil ◽

Vegetation Canopy ◽

Vegetation Monitoring ◽

Red Edge ◽

Positive Correlations

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.

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Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽

10.3390/land10050505 ◽

2021 ◽

Vol 10 (5) ◽

pp. 505

Author(s):

Gregoriy Kaplan ◽

Offer Rozenstein

Keyword(s):

Water Vapor ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Near Infrared ◽

Linear Regression Models ◽

Area Index ◽

Estimation Performance ◽

Red Edge ◽

Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

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Developmental and physiological traits associated with high yield and stay-green phenotype in wheat

Australian Journal of Agricultural Research ◽

10.1071/ar07193 ◽

2008 ◽

Vol 59 (4) ◽

pp. 354 ◽

Cited By ~ 106

Author(s):

J. T. Christopher ◽

A. M. Manschadi ◽

G. L. Hammer ◽

A. K. Borrell

Keyword(s):

Soil Moisture ◽

Grain Filling ◽

High Yield ◽

Limiting Factor ◽

Deep Soil ◽

Stay Green ◽

Physiological Basis ◽

Winter Crops ◽

Significant Yield ◽

Grain Filling Period

Water availability is a key limiting factor in wheat production in the northern grain belt of Australia. Varieties with improved adaptation to such conditions are actively sought. The CIMMYT wheat line SeriM82 has shown a significant yield advantage in multi-environment screening trials in this region. The objective of this study was to identify the physiological basis of the adaptive traits underpinning this advantage. Six detailed experiments were conducted to compare the growth, development, and yield of SeriM82 with that of the adapted cultivar, Hartog. The experiments were undertaken in field environments that represented the range of moisture availability conditions commonly encountered by winter crops grown on the deep Vertosol soils of this region. The yield of SeriM82 was 6–28% greater than that of Hartog, and SeriM82 exhibited a stay-green phenotype by maintaining green leaf area longer during the grain-filling period in all environments where yield was significantly greater than Hartog. However, where the availability of deep soil moisture was limited, SeriM82 failed to exhibit significantly greater yield or to express the stay-green phenotype. Thus, the stay-green phenotype was closely associated with the yield advantage of SeriM82. SeriM82 also exhibited higher mean grain mass than Hartog in all environments. It is suggested that small differences in water use before anthesis, or greater water extraction from depth after anthesis, could underlie the stay-green phenotype. The inability of SeriM82 to exhibit stay-green and higher yield where deep soil moisture was depleted indicates that extraction of deep soil moisture is important.

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Using Multispectral Drone Imagery for Spatially Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow

Drones ◽

10.3390/drones4020025 ◽

2020 ◽

Vol 4 (2) ◽

pp. 25

Author(s):

Antoine Mury ◽

Antoine Collin ◽

Thomas Houet ◽

Emilien Alvarez-Vanhard ◽

Dorothée James

Keyword(s):

Salt Marsh ◽

Salt Marshes ◽

Crop Production ◽

Vegetation Index ◽

Near Infrared ◽

Wave Attenuation ◽

Coefficient Of Determination ◽

Coastal Protection ◽

Surface Model ◽

Red Edge

Offering remarkable biodiversity, coastal salt marshes also provide a wide variety of ecosystem services: cultural services (leisure, tourist amenities), supply services (crop production, pastoralism) and regulation services including carbon sequestration and natural protection against coastal erosion and inundation. The consideration of this coastal protection ecosystem service takes part in a renewed vision of coastal risk management and especially marine flooding, with an emerging focus on “nature-based solutions.” Through this work, using remote-sensing methods, we propose a novel drone-based spatial modeling methodology of the salt marsh hydrodynamic attenuation at very high spatial resolution (VHSR). This indirect modeling is based on in situ measurements of significant wave heights (Hm0) that constitute the ground truth, as well as spectral and topographical predictors from VHSR multispectral drone imagery. By using simple and multiple linear regressions, we identify the contribution of predictors, taken individually, and jointly. The best individual drone-based predictor is the green waveband. Dealing with the addition of individual predictors to the red-green-blue (RGB) model, the highest gain is observed with the red edge waveband, followed by the near-infrared, then the digital surface model. The best full combination is the RGB enhanced by the red edge and the normalized difference vegetation index (coefficient of determination (R2): 0.85, root mean square error (RMSE): 0.20%/m).

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Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery

Geospatial health ◽

10.4081/gh.2020.851 ◽

2020 ◽

Vol 15 (1) ◽

Author(s):

Tasya Vadya Sarira ◽

Kenneth Clarke ◽

Philip Weinstein ◽

Lian Pin Koh ◽

Megan Lewis

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Multispectral Imaging ◽

Imaging System ◽

South Australia ◽

Rapid Identification ◽

Breeding Habitat ◽

Mapping Accuracy ◽

Normalised Difference Vegetation Index ◽

Mosquito Breeding

Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen’s Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.

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Dissecting Bread Wheat Heterosis through the Integration of Agronomic and Physiological Traits

Biology ◽

10.3390/biology10090907 ◽

2021 ◽

Vol 10 (9) ◽

pp. 907

Author(s):

Kevin Gimenez ◽

Pierre Blanc ◽

Odile Argillier ◽

Jean-Baptiste Pierre ◽

Jacques Le Le Gouis ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agronomic Traits ◽

Wheat Yield ◽

Grain Filling ◽

Kernel Weight ◽

Hybrid Vigor ◽

Delayed Senescence ◽

Yield Increase ◽

Wheat Hybrids

To meet the challenge of feeding almost 10 billion people by 2050, wheat yield has to double by 2050. However, over the past 20 years, yield increase has slowed down and even stagnated in the main producing countries. Following the example of maize, hybrids have been suggested as a solution to overcome yield stagnation in wheat. However, wheat heterosis is still limited and poorly understood. Gaining a better understanding of hybrid vigor holds the key to breed for better varieties. To this aim, we have developed and phenotyped for physiological and agronomic traits an incomplete factorial design consisting of 91 hybrids and their nineteen female and sixteen male parents. Monitoring the plant development with normalized difference vegetation index revealed that 89% of the hybrids including the five higher yielding hybrids had a longer grain filling phase with a delayed senescence that results in larger grain size. This average increase of 7.7% in thousand kernel weight translated to a positive mid-parent heterosis for grain yield for 86% of hybrids. In addition, hybrids displayed a positive grain protein deviation leading to a +4.7% heterosis in protein yield. These results shed light on the physiological bases underlying yield heterosis in wheat, paving new ways to breed for better wheat hybrids.

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Crop Type Classification Using Vegetation Indices of RapidEye Imagery

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-195-2014 ◽

2014 ◽

Vol XL-7 ◽

pp. 195-198 ◽

Cited By ~ 23

Author(s):

M. Ustuner ◽

F. B. Sanli ◽

S. Abdikan ◽

M. T. Esetlili ◽

Y. Kurucu

Keyword(s):

Remote Sensing ◽

Classification Accuracy ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Support Vector ◽

Red Edge ◽

Crop Type ◽

Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

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Hyperspectral Characteristics and Scale Effects of Leaf and Canopy of Summer Maize under Continuous Water Stresses

Agriculture ◽

10.3390/agriculture11121180 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1180

Author(s):

Meng Li ◽

Ronghao Chu ◽

Xiuzhu Sha ◽

Feng Ni ◽

Pengfei Xie ◽

...

Keyword(s):

Scale Effect ◽

Spectral Reflectance ◽

Vegetation Index ◽

Near Infrared ◽

Scale Effects ◽

Spectral Characteristics ◽

Summer Maize ◽

Infrared Band ◽

Area Index ◽

Closed Canopy

The scale effect problem is one of the most challenging issues in remote sensing studies. However, the research on the methodology and theory of the scale effect is scarcely applied in practice. To this end, in this study, 3 years of field experimental data of continuous water stresses on summer maize were used for this purpose. Furthermore, the Prospect and Sail models were employed to investigate the scale effects of reflectance characteristics and vegetation indexes. The results indicated that the spectral characteristics of canopy and leaf of summer maize were similar under continuous water stresses at various stages. The reflectance at the canopy level was distinct from that at the leaf level, considering the soil background differences. From leaf to canopy scales, with the increase in the leaf area index (LAI), the spectral reflectance of all treatments in the visible band decreased, but increased in the near-infrared band, and the reflectance was saturated when LAI increased to 5. The reflectance difference caused by LAI variation was enlarged as the drought stress intensified in the short-wave infrared band. The spectral reflectance in the near-infrared band was susceptible to leaf inclination angle (LIA) variation and changed significantly, especially in the closed canopy. With the increase in LAI, the difference vegetation index (DVI) and normalized difference vegetation index (NDVI) values under each treatment showed a gradually increasing trend. With the increase in LIA, the DVI value decreased gradually, and the DVI value under the saturated canopy was significantly higher than that under the unclosed canopy. However, the NDVI values of all treatments did not change with LIA, mostly under the closed canopy. Overall, the results demonstrated that LAI had a more significant influence on canopy reflectance than LIA. In addition, NDVI was not able to capture the LAI and LIA information when the canopy was closed, but DVI performed better.

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Assessing drought from satellite data to support agricultural production

Science & Technology Development Journal - Science of The Earth & Environment ◽

10.32508/stdjsee.v4i1.510 ◽

2020 ◽

Vol 4 (1) ◽

pp. First

Author(s):

Van Tran Thi ◽

Toi Nguyen Duong Lam ◽

Huynh Phan Thi Diem ◽

Ha Nguyen Ngan ◽

Bao Ha Duong Xuan

Keyword(s):

Surface Temperature ◽

Social Life ◽

Vegetation Index ◽

Near Infrared ◽

Global Climate ◽

Landsat 8 ◽

Agricultural Crop ◽

Infrared Band ◽

Drought Assessment ◽

Optical Satellite Images

Drought is one of the disasters causing the problems to the economy and social life of people, especially where agriculture is the main source of income. The paper presents the results of studying the application of optical satellite images to investigate the drought situation for the southern part of Binh Phuoc province for perennial cropland, the main agricultural crop of the province. The image used is Landsat 8 of the dry season month 2015. The method of drought assessment is based on the relationship of surface temperature, and the Normalization Difference Vegetation Index (NDVI) integrated into the Temperature-Vegetation Dryness Index TVDI. In particular, the NDVI index is determined from the red and near-infrared bands, and the surface temperature is determined from the thermal infrared band of Landsat 8 images. The results show that the whole area of southern Binh Phuoc has drought area accounting for 54.9% of the total area, of which the majority is mild drought level 38.3%, high and serious level is 16.7%. About the area of perennial land has drought area accounted for 33.76% of the total area, of which Dong Xoai town has the highest percentage of drought-affected areas compare to other districts. The results of the study aimed to identify drought areas with different levels so that managers can promptly take measures to protect agricultural crops and to ensure people's livelihoods in the global climate change trend seriously affecting the localities today.

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Analyzing Rainfall and Greenness Vegetation Level on Forest/Land Fire Area in Jambi and Central Kalimantan Provinces using Remote Sensing Data

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/893/1/012067 ◽

2021 ◽

Vol 893 (1) ◽

pp. 012067

Author(s):

Khalifah Insan Nur Rahmi ◽

Indah Prasasti ◽

Jalu Tejo Nugroho ◽

M. Rokhis Khomaruddin

Keyword(s):

Confidence Level ◽

Vegetation Index ◽

Near Infrared ◽

Remote Sensing Data ◽

Forest Land ◽

Infrared Band ◽

Central Kalimantan ◽

Previous Period ◽

Vegetation Greenness ◽

Dry Conditions

Abstract El-Nino, which occurred in 2019 in Indonesia, caused longer dry conditions than usual. Low rainfall and vegetation drought cause widespread forest/land fires. This study aims to know the relationship between drought conditions and forest/land fires from the parameters of rainfall and vegetation greenness level. The study located in Jambi and Central Kalimantan Provinces during the peak months of fires which is September 2019. To see fluctuations in the peak of fires, eight daily data were taken for this period. Extraction of rainfall information is derived from the Himawari-8 infrared band L1 image into L2 rainfall rate data. Vegetation greenness level information is derived from Terra/Aqua MODIS red and near-infrared band images into L2 Enhance Vegetation Index (EVI) data. Hotspot data comes from the images of Terra, Aqua MODIS, SNPP VIIRS, and NOAA20. Fire data was extracted from hotspot data and delineation of MODIS RGB image smoke. Rainfall fluctuation affects the number of forest/land fire hotspots. The decrease in rainfall was followed by an increase of hotspot numbers and vice versa. In Jambi Province, rainfall decreased in first to second period i.e. 40 to 0 mm was followed by an increase of hotspot number which dominated by high confidence level. In Central Kalimantan rainfall increased from third to fourth period i.e. 0 to 100-400 mm followed by the decreasing of hotspot number which dominated by medium confidence level. Meanwhile, the TKV variable had little effect on the number of hotspots but related with rainfall data. In Central Kalimantan Province, the driest TKV (0.1) on September 14-21, 2019, was influenced by low rainfall in the previous period which also has highest number of fire hotspots. In Jambi Province, the driest TKV happened on third period which also the result of lowest rainfall and highest number of fire hotspot in the previous period.

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