Tag Archives: Remote sensing

xxx H. Taia, A.S. Bernoussi, E. Wozniak, M. Amharref and S. El Azizi
Using hyperspectral reflectance to evaluate the impact of irrigation and fertilization on mint
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Using hyperspectral reflectance to evaluate the impact of irrigation and fertilization on mint

H. Taia¹*, A.S. Bernoussi¹, E. Wozniak², M. Amharref¹ and S. El Azizi¹

¹University Abdelmalek Essaadi of Tangier, Faculty of Sciences and Techniques, Laboratory CBM-VR, BP. 416, MA90000 Tangier, Morocco
²Space Research Centre of the Polish, Academy of Sciences, Bartycka 18A, PL00-716 Warszawa, Poland
*Correspondence: halima.taia@gmail.com

Abstract:

In agriculture, water and fertilizer are two limiting elements of plant growth. Indeed, the lack or the excess of one of them disturbs the yields in terms of quality and quantity. Optimal irrigation/fertilization and precisely dosed nutrient supply allow fast-growing plants to reach their full potential, offering much larger and better quality yields. To monitor agricultural crop characteristics, Hyperspectral remote sensing provides an opportunity for an efficient nondestructive method. In this paper, we present a method for smart management of water irrigation and fertilizer using remote sensing. For this purpose, a protocol has been developed to detect the effects of nitrogen nutriments and water supply on potted mint by using UV-PIR field spectroscopy. Results suggest hyperspectral remote sensing has great promise to perfect smart agriculture. In fact, with this method, the effect of nutriments and water supply have been clearly detected.

Key words:

agriculture, field spectroscopy, mint, nitrogen, nutriments, Remote sensing, water

1929-1937 P. Šařec, J. Korba, V. Novák and K. Křížová
Digestate application with regard to greenhouse gases and physical soil properties
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Digestate application with regard to greenhouse gases and physical soil properties

P. Šařec¹, J. Korba¹*, V. Novák¹ and K. Křížová¹²

¹Czech University of Life Sciences Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ165 00 Prague 6, Czech Republic
²Crop Research Institute, Division of Crop Protection and Plant Health, Drnovská 507/73, CZ161 06 Prague, Czech Republic
*Correspondence: korba@tf.czu.cz

Abstract:

The article deals with the method of application of digestate with regard to the environment, soil properties and utilization of nutrients by plants. The aim is to monitor the dependence of the emission gas leakage and the dose of applied fertilizer. With the current expansion of biogas plants, a large amount of waste product, especially digestate, is being generated. This product is most often used as a liquid organic fertilizer because it contains substances important for plant growth. The disadvantage of this fertilizer is the release of greenhouse gases into the air. The digestate contains mainly ammonia, nitrogen in the residual organic matter and is a fertilizer with rapidly releasing nitrogen. The ammonium nitrogen contained in the digestate is easily subject to air losses. Therefore, a method of application for a certain crop is sought, where the smallest leaks of gases into the air occur. Different amounts of doses for the same route of administration are compared. To measure the amount of emission gases, a wind tunnel was placed on each variant of the application, taking air above the soil surface, which is discharged to the gas analyser. The monitored greenhouse gases are CH₄, NH₃ and CO₂. Furthermore, physical properties of soil were monitored in order to verify the conditions of the experiment. One of the parameters measured was the soil bulk density of the soil by taking intact soil samples. The penetration resistance of the soil was also determined, which indicates the degree of compaction. The use of nutrients was assessed through the condition of the stand on each variant by monitoring vegetation indices using remote sensing of the earth.

Key words:

ammonia, digestate, greenhouse gases, physical soil properties, Remote sensing

2665-2676 W.R. Żelazny
Application of feature selection for predicting leaf chlorophyll content in oats (Avena sativa L.) from hyperspectral imagery
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Application of feature selection for predicting leaf chlorophyll content in oats (Avena sativa L.) from hyperspectral imagery

W.R. Żelazny¹²*

¹Crop Research Institute, Division of Crop Management Systems, Drnovská 507/73, CZ161 06 Praha 6 Ruzyně, Czech Republic
²Czech University of Life Sciences Prague, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ165 00 Praha 6 Suchdol, Czech Republic
*Correspondence: wzelazny@vurv.cz

Abstract:

Feature selection can improve predictions generated by partial least squares models. In the context of hyperspectral imaging, it can also enable the development of affordable devices with specialized applications. The feasibility of feature selection for oat leaf chlorophyll estimation from hyperspectral imagery was assessed using a public domain dataset. A wrapper approach resulted in a simplistic model with poor predictive performance. The number of model inputs decreased from 94 to 3 bands when a filter approach based on the minimum redundancy, maximum relevance criterion was attempted. The filtering led to improved prediction quality, with the root mean square error decreasing from 0.17 to 0.16 g m^-2 and R² increasing from 0.57 to 0.62. Accurate predictions were obtained especially for low chlorophyll levels. The obtained model estimated leaf chlorophyll concentration from near infra-red reflectance, canopy darkness, and its blueness. The prediction robustness needs to be investigated, which can be done by employing an ensemble methodology and testing the model on a new dataset with improved ground-truth measurements and additional crop species.

Key words:

imaging spectroscopy, partial least squares, Remote sensing, reproducibility, unmanned aerial vehicles

1636–1645 K. Křížová and J. Kumhálová
Comparison of selected remote sensing sensors for crop yield variability estimation
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Comparison of selected remote sensing sensors for crop yield variability estimation

K. Křížová¹* and J. Kumhálová²

¹Czech University of Life Sciences in Prague, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ165 21 Prague, Czech Republic
²Czech University of Life Sciences in Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ165 21 Prague, Czech Republic
*Correspondence: krizovak@tf.czu.cz

Abstract:

Currently, spectral indices are very common tool how to describe various characteristics of vegetation. In fact, these are mathematical operations which are calculated using specific bands of electromagnetic spectrum. Nevertheless, remote sensing sensors can differ due to the variations in bandwidth of the particular spectral channels. Therefore, the main aim of this study is to compare selected sensors in terms of their capability to predict crop yield by NDVI utilization. The experiment was performed at two locations (Prague-Ruzyně and Vendolí) in the year 2015 for both locations and in 2007 for Prague-Ruzyně only, when winter barley or spring barley grew on the plots. The cloud-free satellite images were chosen and normalised difference vegetation indices (NDVI) were calculated for each image. Landsat satellite images with moderate spatial resolution (30 m per pixel) were chosen during the crop growth for selected years. The other data sources were commercial satellite images with very high spatial resolution – QuickBird (QB) (0.6 m per pixel) in 2007 and WorldView-2 (WV-2) (2 m per pixel) in 2015 for Prague-Ruzyně location; and SPOT-7 (6 m per pixel) satellite image in 2015 for Vendolí location. GreenSeeker handheld crop sensor (GS) was used for collecting NDVI data for both locations in 2015 only. NDVI calculated at each of images was compared with the yield data. The data sources were compared with each other at the same term of crop growth stage. The results showed that correlation between GS and yield was relatively weak at Ruzyně. Conversely, significant relation was found at Vendolí location. The satellite images showed stronger relation with yield than GS. Landsat satellite images had higher values of correlation coefficient (in 30 m spatial resolution) at Ruzyně in both selected years. However, at Vendolí location, SPOT-7 satellite image has significantly better results compared to Landsat image. It is necessary to do more research to define which sensor measurements are most useful for selected applications in agriculture management.

Key words:

crop yield, Greenseeker, NDVI, Remote sensing, satellite images

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.

Key words:

Landsat 7 images, Remote sensing, spectral indices, winter oilseed rape, yield rating