Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data

Panek, Ewa; Gozdowski, Dariusz

doi:10.1016/j.rsase.2019.100286

Cited by 49 publications

(45 citation statements)

References 35 publications

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“…Such a relationship was proved for about half of the countries, mostly located in central and eastern Europe. It confirms our previous research conducted at the regional level (based on the data for provinces-NUTS 2) for four countries: Poland, Germany, Czechia and Slovakia [13]. The crop status of the winter cereals, which dominate in the studied countries in early spring, depends on their conditions in late autumn [33,34].…”

Section: Discussionsupporting

confidence: 86%

“…Based on the analyses for individual countries, the slope of regression (coefficient of regression) is usually lower and varies from 0.5 to 1.4 t/ha. These values are slightly smaller than those in the previous study conducted at regional level for central Europe [13]. The different period for which the NDVI was taken for the analyses may be one of the reasons for that.…”

Section: Discussioncontrasting

confidence: 63%

“…The most common vegetation index used for such purpose is normalized difference vegetation index (NDVI) which is based on reflectance in red and near-infrared light spectrum [12]. In the simplest approach, the mid-season NDVI is treated as a cereal grain yield predictor where simple linear regression is applied for yield forecasts [13,14]. Thanks to the use of such methods, it was possible, in some studies, to forecast grain yield 2-3 months before the harvest.…”

Section: Introductionmentioning

confidence: 99%

“…Other vegetation indices such as SR (Simple Ratio Index), RVI (Ratio Vegetation Index), EVI (Enhanced Vegetation Index), NDRE (Normalized Difference Red Edge Index), FAPAR (Fraction of Absorbed Photosynthetically Active Radiation), SAVI (Soiladjusted vegetation index, GNDVI (Green Normalized Difference Vegetation Index) are also used [23][24][25][26][27][28]. Yield forecasting at regional level, using satellite remote sensing, can be performed by using mean values of the vegetation indices for the total area or only for the total cropland or for the crop masks for individual crops [13,18,29,30].…”

Section: Introductionmentioning

confidence: 99%

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

Panek

Gozdowski

2021

Agronomy

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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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Section: Discussionsupporting

confidence: 86%

Section: Discussioncontrasting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Panek

Gozdowski

2021

Agronomy

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“…NDVI and UHI sizes in the atmosphere was conducted by determining direction of the relationship of data from correlation coefficient (r) at a significance level of 0.01, according to Equation 4. This was consistent with the trend line of linear regression equation that led to the acquisition of decision coefficient (r 2 ) (Oyler et al, 2016;Liu et al, 2019;Panek, & Gozdowski, 2019)…”

Section: Creating Buffer Zonessupporting

confidence: 85%

The Relationship Between Normalized Difference Vegetation Index and Canopy Temperature That Affects the Urban Heat Island Phenomenon

Prohmdirek¹,

Chunpang²,

Laosuwan³

2020

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The objective of this research is to analyze the relationship between Normalized Difference Vegetation Index (NDVI) and canopy temperature that affects the Urban Heat Island (UHI) phenomenon in Mueang Maha Sarakham Municipality. The operational methods were divided into three main steps: 1) Analyzing NDVI by using Landsat 8 OLI satellite data 2) Monitoring canopy temperature with mobile traverses installed with digital thermometer and auto-record GPS receiver 3) Analyzing the relationship between NDVI and canopy temperature that passed through the spatial interpolation process using the kriging method. The results of the operational methods indicated that the trend of average atmospheric temperature in the urban area was higher than the outer area. Mueang Maha Sarakham Municipality had the highest UHI in the atmosphere at 4.65 o C. And from the buffer zone with a distance of 800 m around the centroid point, the highest UHI in the atmosphere was 5.08 o C. From the regression analysis between NDVI and the atmospheric temperature, it could be found a negative relationship between the data with a significance level of 0.01 and the decision coefficient (R 2) of 0.9392. This can be explained that if the area from the buffer zone decreased towards the centroid point, the value of NDVI decreased as well. In addition, when the equation set from the regression analysis was brought to predict the difference in atmospheric temperature in the area of Mueang Maha Sarakham Municipality and the surrounding area, the UHI sizes at all periods (16.00-20.00 hrs.) increased in accordance with the decreases of NDVI values. Meanwhile, considering the tendency between the changes in UHI size in the atmosphere in each period and NDVI values with the polynomial regression analysis (degree 4), it was found that the maximum formation of UHI was during 18.00-19.00 hrs.'. The decreases in NDVI values resulted in a higher UHI size in each period. In addition, when the NDVI values in Mueang Maha Sarakham Municipality were less than 0.302, the largest size of UHI in the atmosphere will change at the time approaching 19.00 hrs.

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Role of the Genomics–Phenomics–Agronomy Paradigm in Plant Breeding

Chen

Rutkoski

Schnable

et al. 2022

Plant Breeding Reviews

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Highlights• Phenomics allows genetic mapping of traits throughout plant growth and a better understanding of both pleiotropy and endophenotypes. • Many GWAS identified loci are not validated and remain unused.• Genomics provides a path to validate phenomics traits, especially the ones without direct links to agronomics traits. • Multiple trait GBLUP remains the most sophisticated method to integrate agronomics and phenomics traits but does not demonstrate consistent advantage across traits and environments. • Bayesian methods have been extended to incorporate multiple traits, multiple environments, and prior biological knowledge. • Prediction using explainable machine learning incorporates domain knowledge with complex modules allowing to learn from data but guided by confirmed domain knowledge rather than less reliable human expert experiences.

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Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data

Cited by 49 publications

References 35 publications

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

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

The Relationship Between Normalized Difference Vegetation Index and Canopy Temperature That Affects the Urban Heat Island Phenomenon

Role of the Genomics–Phenomics–Agronomy Paradigm in Plant Breeding

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