Hyperspectral field reflectance measurements to estimate wheat grain yield and plant height

Hyperspectral crop reflectance data are useful for several remote sensing applications in agriculture, but there is still a need for studies to define optimal wavebands to estimate crop biophysical parameters. The objective of this work is to analyze the use of narrow and broad band vegetation indices (VI) derived from hyperspectral field reflectance measurements to estimate wheat (Triticum aestivum L.) grain yield and plant height. A field study was conducted during the winter growing season of 2003 in Campinas, São Paulo State, Brazil. Field canopy reflectance measurements were acquired at six wheat growth stages over 80 plots with four wheat cultivars (IAC-362, IAC-364, IAC-370, and IAC-373), five levels of nitrogen fertilizer (0, 30, 60, 90, and 120 kg of N ha-1) and four replicates. The following VI were analyzed: a) hyperspectral or narrow-band VI (1. optimum multiple narrow-band reflectance, OMNBR; 2. narrow-band normalized difference vegetation index, NB_NDVI; 3. first- and second-order derivative of reflectance; and 4. four derivative green vegetation index); and b) broad band VI (simple ratio, SR; normalized difference vegetation index, NDVI; and soil-adjusted vegetation index, SAVI). Hyperspectral indices provided an overall better estimate of biophysical variables when compared to broad band VI. The OMNBR with four bands presented the highest R² values to estimate both grain yield (R² = 0.74; Booting and Heading stages) and plant height (R² = 0.68; Heading stage). Best results to estimate biophysical variables were observed for spectral measurements acquired between Tillering II and Heading stages.

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Yield prediction and nitrogen recommendation in maize using normalized difference vegetation index

Agronomy Journal of Nepal ◽

10.3126/ajn.v3i0.9009 ◽

2013 ◽

Vol 3 ◽

pp. 82-88 ◽

Cited By ~ 1

Author(s):

TB Karki

Keyword(s):

Grain Yield ◽

Growth Stage ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Correlation Coefficients ◽

Growth Stages ◽

Strong Positive Correlation ◽

Positive Correlation ◽

Nutrient Levels ◽

Seed Sowing

A study was carried out using three maize genotypes with three levels of nitrogen (30 kg, 60 kg and 120 kg per hectare) during the summer season of 2010 and 2011with the aim of predicting maize (Zea mays L.) yield through the Normalized difference vegetation index (NDVI). The NDVI was recorded at different times throughout the growing season using a Greenseeker™ handheld sensor. Significant effect of genotypes and nutrient levels on the NDVI was observed at different growth stages of maize. There was positive correlation between the NDVI and grain yield. In the first season, the correlation coefficients were 0.90, 0.92, 0.76 and 0.73, respectively at 15, 45, 75 and 110 days after seeding. In the second season, the correlation coefficients were 0.80, 0.92, 0.77 and 0.75 respectively at 15, 45, 75 and 110 days after seeding. The NDVI based N calculator showed that irrespective of genotypes, yield potentials under farmers' levels of nutrient management were almost half of the recommended doses of nitrogen. The amount of N to be top dressed decreased with increased crop duration. Grain yield varied significantly due to season, genotypes and nutrient levels. NDVI was affected due to season, stages of the crop (DAS), genotypes and nutrient levels. Interaction effects were significant for season x genotype, growth stage x genotype, growth stage x nutrient levels, genotype x nutrient levels and genotype x growth stage x nutrient levels. There was a strong positive correlation between NDVI and grain yields of hybrid maize at 15 and 45 DAS, but this correlation declined thereafter. This means that N top-dressed at or after 75 days of seed sowing will not increase grain yield as significantly as N applied earlier in the season. In contrast, topdressed N was producing significant effects on the open pollinated Rampur Composite even after 75 days of seed sowing. Further confirmation of the finding could be useful for top dressing N in the maize crop. Agronomy Journal of Nepal (Agron JN) Vol. 3. 2013, Page 82-88 DOI: http://dx.doi.org/10.3126/ajn.v3i0.9009

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Spectral-temporal characterization of wheat cultivars through NDVI obtained by terrestrial sensors

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v21n11p769-773 ◽

2017 ◽

Vol 21 (11) ◽

pp. 769-773 ◽

Cited By ~ 6

Author(s):

Carlos E. V. Cattani ◽

Murilo R. Garcia ◽

Erivelto Mercante ◽

Jerry A. Johann ◽

Marcus M. Correa ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Triticum Aestivum L ◽

Growth Stages ◽

Wheat Cultivars ◽

Spectral Signatures ◽

Spectral Bands ◽

Sensing Applications ◽

Spectral Patterns ◽

Temporal Profiles

ABSTRACT Remote sensing applications in agriculture are presented as a very promising reality, but research is still needed for the correct use of spectral data. The objective of this study was to evaluate the spectral-temporal patterns of eleven wheat cultivars (Triticum aestivum L.). The experiment was conducted in Cascavel, PR, in the year 2014. With the help of the GreenSeeker and FieldSpec 4 terrestrial sensors, spectral signatures were determined and the temporal profiles of the Normalized Difference Vegetation Index (NDVI) were created. Statistical differences between wheat cultivars were analysed using analysis of variance (ANOVA) and Scott-Knott test. Grain yields obtained with INSEY (In-Season Estimate of Yield) factors were correlated. NDVI normalized by degree-days accumulated from the Feekes growth stages 2 and 8 showed to be more consistent in the estimation of grain yield, explaining approximately 70% of the variation. At the Feekes stage 10.1, wheat cultivars presented different spectral patterns in the near and medium infrared bands. This suggests that these spectral bands can be used to differentiate wheat cultivars.

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Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

Agronomy ◽

10.3390/agronomy10111842 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1842

Author(s):

Ewa Panek ◽

Dariusz Gozdowski ◽

Michał Stępień ◽

Stanisław Samborski ◽

Dominik Ruciński ◽

...

Keyword(s):

Winter Wheat ◽

Grain Yield ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Correlation Coefficients ◽

Soil Nutrient ◽

Growth Stages ◽

Plant Growth Stages

The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

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Normalized Difference Vegetation Index and Chlorophyll Content for Precision Nitrogen Management in Durum Wheat Cultivars under Semi-Arid Conditions

Sustainability ◽

10.3390/su13073725 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3725

Author(s):

Ferhat Kizilgeci ◽

Mehmet Yildirim ◽

Mohammad Sohidul Islam ◽

Disna Ratnasekera ◽

Muhammad Aamir Iqbal ◽

...

Keyword(s):

Grain Yield ◽

Durum Wheat ◽

Chlorophyll Content ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Screening Tools ◽

Growth Stages ◽

Quality Traits ◽

Wheat Genotypes ◽

N Deficiency

To impart sustainability to modern intensive farming systems, environmental pollution caused by nitrogenous fertilizers in needs to be reduced by optimizing their doses. To estimate the grain yield and nutrtional quallity of wheat, the normalized difference vegetation index (NDVI) and chlorophyll content (SPAD) are potential screening tools to identify the N deficiency and screen out the promising cultivars. The two-year field study was comprised with five levels of nitrogen (N) (control, 50, 100, 150 and 200 kg N ha−1) and two durum wheat genotypes (Sena and Svevo). The experimental design was split-plot, in which N levels were placed in the main plots, while wheat genotypes were arranged in sub-plots. To predict the yield and quality traits, the NDVI and SPAD values recorded at heading, anthesis and milky growth stages were taken as response variables. The results revealed that N fertilization significantly influenced the SPAD and NDVI attributed traits of durum wheat, except NDVI at milky stage (NDVI-M) during the first year. The maximum value of NDVI was recorded by 150 kg N ha−1, while control treatment gave the minimum value. The grain yield was increased with the increasing dose of the N up to 100 kg N ha−1 (4121 kg ha−1), and thereafter, it was declined with further increased of N levels. However, the variation between the genotypes was not significant, except NDVI and SPAD values at the milky stage. The genotype Svevo had the highest NDVI values at all growth stages, while the genotype Sena recorded the maximum SPAD values during both years. Similarly, the N levels significantly influenced the quality traits (protein, wet gluten, starch test weight and Zeleny sedimentation) of both genotypes. The highly significant relationship of SPAD and NDVI with the grain yield and yield attributes showed their reliability as indicators for determining the N deficiency and selection of superior wheat genotypes for ensuring food security under climate change scenario.

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Normalized Difference Vegetation Index as a Tool for Wheat Yield Estimation: A Case Study from Faisalabad, Pakistan

The Scientific World JOURNAL ◽

10.1155/2014/725326 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 17

Author(s):

Syeda Refat Sultana ◽

Amjed Ali ◽

Ashfaq Ahmad ◽

Muhammad Mubeen ◽

M. Zia-Ul-Haq ◽

...

Keyword(s):

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Field Experiments ◽

Wheat Yield ◽

Grain Filling ◽

Research Area ◽

Wheat Crop ◽

Growth Stages ◽

Wheat Cultivars

For estimation of grain yield in wheat, Normalized Difference Vegetation Index (NDVI) is considered as a potential screening tool. Field experiments were conducted to scrutinize the response of NDVI to yield behavior of different wheat cultivars and nitrogen fertilization at agronomic research area, University of Agriculture Faisalabad (UAF) during the two years 2008-09 and 2009-10. For recording the value of NDVI, Green seeker (Handheld-505) was used. Split plot design was used as experimental model in, keeping four nitrogen rates (N1= 0 kg ha−1,N2= 55 kg ha−1,N3=110 kg ha−1, andN4= 220 kg ha−1) in main plots and ten wheat cultivars (Bakkhar-2001, Chakwal-50, Chakwal-97, Faisalabad-2008, GA-2002, Inqlab-91, Lasani-2008, Miraj-2008, Sahar-2006, and Shafaq-2006) in subplots with four replications. Impact of nitrogen and difference between cultivars were forecasted through NDVI. The results suggested that nitrogen treatment N4(220 kg ha−1) and cultivar Faisalabad-2008 gave maximum NDVI value (0.85) at grain filling stage among all treatments. The correlation among NDVI at booting, grain filling, and maturity stages with grain yield was positive (R2 = 0.90;R2 = 0.90;R2 = 0.95), respectively. So, booting, grain filling, and maturity can be good depictive stages during mid and later growth stages of wheat crop under agroclimatic conditions of Faisalabad and under similar other wheat growing environments in the country.

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Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

Remote Sensing ◽

10.3390/rs12020220 ◽

2020 ◽

Vol 12 (2) ◽

pp. 220 ◽

Cited By ~ 2

Author(s):

Han Xiao ◽

Fenzhen Su ◽

Dongjie Fu ◽

Qi Wang ◽

Chong Huang

Keyword(s):

Spatial Distribution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Mangrove Ecosystem ◽

Early Growth ◽

Growth Stages ◽

Human Disturbances ◽

Vegetation Growth ◽

Two Parameters ◽

The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

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Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Agronomy ◽

10.3390/agronomy11020340 ◽

2021 ◽

Vol 11 (2) ◽

pp. 340

Author(s):

Ewa Panek ◽

Dariusz Gozdowski

Keyword(s):

Linear Regression ◽

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Arable Land ◽

Early Spring ◽

European Countries ◽

Yield Prediction ◽

Country Level ◽

Using Data

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

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Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress

Plant Breeding ◽

10.1111/pbr.12003 ◽

2012 ◽

Vol 131 (6) ◽

pp. 716-721 ◽

Cited By ~ 9

Author(s):

Shahnoza Hazratkulova ◽

Ram C. Sharma ◽

Safar Alikulov ◽

Sarvar Islomov ◽

Tulkin Yuldashev ◽

...

Keyword(s):

Heat Stress ◽

Winter Wheat ◽

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Genotypic Variation ◽

Terminal Heat Stress

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Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Agriculture ◽

10.3390/agriculture8070116 ◽

2018 ◽

Vol 8 (7) ◽

pp. 116 ◽

Cited By ~ 30

Author(s):

Alessandro Matese ◽

Salvatore Di Gennaro

Keyword(s):

Water Stress ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Stress Index ◽

Crop Water Stress Index ◽

Practical Applications ◽

Water Stress Index ◽

Sensing Applications ◽

Wide Range ◽

Precision Viticulture

High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression (R2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard.

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