Tag Archives: spectral indices

554-570 S.A.S. Silva, G.A.S. Ferraz, V.C. Figueiredo, M.M.L. Volpato, M.L. Machado, V.A. Silva, C.S.M. Matos, L. Conti and G. Bambi
Spatial variability of chlorophyll and NDVI obtained by different sensors in an experimental coffee field
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Spatial variability of chlorophyll and NDVI obtained by different sensors in an experimental coffee field

S.A.S. Silva¹, G.A.S. Ferraz¹*, V.C. Figueiredo², M.M.L. Volpato², M.L. Machado², V.A. Silva², C.S.M. Matos², L. Conti³ and G. Bambi³

¹Federal University of Lavras, School of Engineering, Department of Agricultural Engineering, Rotary Clover Professor Edmir Sá Santos, BR37200-900 Lavras, Brazil
²Agricultural Research Company of Minas Gerais, Av. José Cândido da Silveira 1647, Bairro União Belo Horizonte, BR31170-495 Belo Horizonte, Brazil
³University of Florence – UniFI, Department of Agriculture, Food, Environment and Forestry (DAGRI), Via San Bonaventura, 13, IT50145 Florence, Italy
* Correspondence: gabriel.ferraz@ufla.br

Abstract:

The objective of this research was to study the spatial variability of NDVI and chlorophyll sampled by different sensors, as well as to evaluate the correlation between them in a coffee field. The study was carried out on a coffee farm located in the municipality of Três Pontas, Minas Gerais. A sampling grid containing 30 points was created for the study area. Each sampling point was represented by one plant, which was georeferenced by a GNSS RTK. For each sample plant, NDVI and chlorophyll were obtained by the optical and active sensors GreenSeeker and ClorofiLOG, respectively. In addition, it was carried out a flight with an RPA equipped with a passive and multispectral sensor. Using the data obtained by active sensors, a geostatistical analysis was carried out to evaluate the spatial variability of NDVI and chlorophyll. The geostatistical analysis verified the existence of spatial dependence for the two attributes, and thus it was possible to generate spatialization maps through kriging. The images obtained by the passive sensor resulted in five multispectral orthomosaics, making it possible to calculate the NDVI, thus generating a spatialization map of this index. It was possible to observe in the generated maps, points that presented a certain similarity and for this purpose a correlation analysis was carried out for the values of each attribute, sampled directly in the maps, and in different sampling grids (30, 60, 90 and 120 points). By analyzing the Pearson coefficient (R) it was possible to quantify the level of correlation between the data obtained by the different sensors and through the t test it was possible to find significant correlations between them.

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447–455 Z. Jelínek, K. Starý and J. Kumhálová
Assessment of production zones modelling accuracy based on satellite imaging and yield measurement of selected agriculture plot
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Assessment of production zones modelling accuracy based on satellite imaging and yield measurement of selected agriculture plot

Z. Jelínek*, K. Starý and J. Kumhálová

Czech University of Life Sciences Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ165 00 Prague, Czech Republic
*Correspondence: jelinekzdenek@tf.czu.cz

Abstract:

Currently, remote sensing or yield monitor equipment offer possibilities how to estimate productivity of the agriculture field. That is why the main aim of this study is to assess how the latest satellite images from vegetation season and final yield data from combine harvester can be used to predict yield and to assess site-specific zones productivity. The study is focused on the accuracy of these systems for the field productivity estimation. The 24.7 ha experimental field is located near to Vendoli village (the Czech Republic) and it is cultivated by conventional agricultural practices with emphasis on typical agricultural crops growing in the Czech Republic (winter wheat, spring barley and winter rape). The results showed that both methods of estimation can be used for yield prediction. Nevertheless, each of them need specific processing and has typical limitations.

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055–068 J.A. Domínguez, J. Kumhálová and P. Novák
Assessment of the relationship between spectral indices from satellite remote sensing and winter oilseed rape yield
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Assessment of the relationship between spectral indices from satellite remote sensing and winter oilseed rape yield

J.A. Domínguez¹, J. Kumhálová²* and P. Novák³

¹UNED Department of Mathematical and Fluid Physics, Science Faculty,
C/Senda del Rey, nº9, ES280 40 Madrid, Spain
²Czech University of Life Sciences Prague, Faculty of Engineering, Department of
Machinery Utilization, Kamýcká 129, CZ165 21 Prague, Czech Republic
³Czech University of Life Sciences in Prague, Faculty of Engineering, Department of
Agricultural Machines, Kamýcká 129, CZ165 21 Prague, Czech Republic
*Correspondence: kumhalova@seznam.cz

Abstract:

Winter oilseed rape (Brassica napus L.) belongs among the most common and strategic
crops in the Czech Republic. Growth and vitality status, yield potential and yield prediction of
oilseed rape on plots of different sizes can be effectively examined using remote sensing. That is
why the main aim of this study was to discuss a possibility of deriving spectral indices for an
assessment which spectral index is more adequate to forecast oilseed winter rape development
and consequent yield in the Czech Republic. Information about the winter oilseed rape growth
and yield was collected in three years – 2004, 2008, 2012. A relationship between grown crops
and selected vegetation indices was evaluated. The Landsat 7 satellite images were selected as a
source for deriving spectral indices. The relationship between each spectral index and yield was
analysed in 2012 only. Five images on different dates during the whole life of winter oilseed rape
were found during this year. The images from the years 2004 and 2008 were cloudier. The spectral
indices showing the best relationship with yield from 2012 were then analysed in the images from
2004 and 2008. The results showed that Enhanced Moisture Stress Index is the most acceptable
index from the selected indices used in this study. From an agronomical point of view no available
index was found to be suitable for the winter rape growth evaluation due to dependence on
precipitation conditions. For monitoring of the yield components in winter oilseed rape in
conditions of the Czech Republic, it seems necessary to develop a new vegetation index which
will reliably describe the winter oilseed rape growth stages during the whole vegetation season.

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