Remote sensing has become a cornerstone of modern agricultural monitoring, addressing the dual challenges of increasing production while ensuring environmental sustainability. Based on a conceptual framework developed over the past decade, key application areas include yield estimation, phenology, stress assessment (e.g., drought), crop mapping, and land-use change detection. In Central Europe, regionally specific conditions such as fragmented land ownership, small and irregular plots, and high climate variability shape these applications. Annual field crops, such as cereals, oilseeds, maize, and forage crops dominate production and represent the primary focus of monitoring efforts. Optical data from Sentinel-2 are effective for mapping crop types and analyzing phenology, especially when dense time series are available. However, persistent cloud cover during critical growth phases limits the effectiveness of optical approaches, prompting the integration of radar data from Sentinel-1. Multi-sensor strategies increase the robustness of classification and temporal continuity, supporting monitoring under adverse conditions. Reliable reference data from systems such as the Land Parcel Identification System enables parcel-level validation and facilitates object-oriented analyses in line with management needs. Future developments will increasingly rely on advanced time-series analysis, machine learning, and the integration of agrometeorological and crop model data. As climate change intensifies drought frequency and yield variability, remote sensing will play a pivotal role in enabling near-real-time monitoring and decision support within the evolving landscape of digital agriculture ecosystems. The aim of this review article is to provide an overview of crop monitoring in the Central European region over approximately the past fifteen years, emphasizing trends in subsequent technological and procedural developments.

Remote sensing plays an increasingly important role in agriculture, especially in monitoring the quality of agricultural crops. Optical sensing is often limited in Central Europe due to cloud cover; therefore, synthetic aperture radar data is increasingly being used. However, synthetic aperture radar data is limited by more difficult interpretation mainly due to the influence of speckles. For this reason, its use is often limited to larger territorial units and field blocks. The main aim of this study therefore was to verify the possibility of using satellite synthetic aperture radar images to assess the within-field variability of winter wheat. The lowest radar vegetation index values corresponded to the area of the lowest production potential and the greatest damage to the stand. Also for VH and VV polarizations, the highest values corresponded to the area of the lowest stand quality. Qualitative changes in the stand across the zones defined by frost damage and production potential were assessed with the help of the logistic regression model with resampled data for 10, 50, and 100 m pixel size. The best correlation coefficients were achieved at a spatial resolution of 50 m for both options. The F-score still yielded a promising result ranging from 0.588 to 0.634 for frost damage categories. The regression model of the production potential performed slightly better in terms of the F-score, recall, and precision at higher resolutions. It was proved that modern statistical methods could be used to reduce problems associated with speckles of radar images for practical purposes.

Hops are an essential raw material for beer production and the Czech Republic is not only a traditional hop grower but also participates in the breeding of varieties that are important on a global scale, mainly in terms of quality. The presented study deals with the evaluation of selected varieties grown in conventional and organic management over 4 years (2020–2023). The main contribution of this paper lies in the fact that remote sensing data of hop gardens were obtained by UAV during the growing seasons for four consecutive years and were subsequently used to explain the development of hop stands with respect to their yield and production quality. Hop fields were scanned using a UAV with a multispectral camera and the vegetation indices NDVI, GNDVI, NDRE, CIR and SAVI were derived. These indices were used as indirect indicators for determining vitality, health and structure for predicting yield and quality parameters (alpha acid content in hop cones). Based on statistical analysis it was concluded that in terms of yield assessment, spectral indices can explain hop yields up to 61%, with better results being achieved in the later stages of growth development. However, spectral indices are only able to explain a maximum of 18% of qualitative parameters. On the contrary, the maximum was reached in the early stages of stand development. UAV scanning monitors the canopies of hop stands with high resolution, similar to vineyards. Indices evaluating chlorophyll content appeared to be more suitable for affecting differences between hop stands.

With the changing climate, there is an increasing emphasis on drought-resistant varieties, including the ability to maintain quality production. As there is also interest in ancient wheat varieties, the aim of this study was to evaluate the growth parameters of the ancient Khorasan (Kamut®) and modern Kabot spring wheat varieties using remote sensing data. Images from unmanned aerial vehicles during four growing seasons were processed. Based on vegetation indices, the growth of these varieties and their response to meteorological conditions were evaluated, as well as the ability to resist drought and higher temperatures with respect to specific soil conditions under conventional (CT), minimum (MTC), and minimization (MTD) tillage systems. It was found that Khorasan had the lowest values of the vegetation indices on the CT variant in the dry years 2022 and 2023. On the contrary, in the previous wet years, 2020 and 2021, both varieties showed similar results. Regarding water stress, the CT variant was also the least suitable for ancient Khorasan (average Crop Water Stress Index = CWSI = 0.38). On the contrary, this variant seems to be suitable for the modern Kabot variety (CWSI = 0.29), while no significant difference between tillage variants was found for this variety. In general, water stress was easily detectable from the observed parameters in the growth phase of stem elongation (R2 up to 0.88). Regarding the individual methods of tillage and water stress, the ancient variety Khorasan performed the worst with the CT variant. MTD appeared to be the best tillage method for Khorasan cultivation in terms of water management.

Phosphorus is a major nutrient for plants and the determination of available phosphorus in soil is necessary for agricultural practices. Phosphorus balance in soil was assessed in the long-term stationary experiment at Hněvčeves from 1980 to 2015. The results indicate that the phosphorus balance was significantly influenced by the doses of phosphorus in mineral fertilization and farmyard manure. The most well balanced balance of phosphorus in soil was achieved at the plots with low doses of phosphorus applied in mineral fertilization and farmyard manure. Negative phosphorus balance was found out in the combination without the dose of phosphorus in both fertilizers. On the contrary, phosphorus surplus was indicate in the combination with very high level of phosphorus dose in mineral fertilization and medium level of phosphorus dose in farmyard manure or in the combination with medium level of phosphorus in mineral fertilization and very high level of phosphorus in farmyard manure. Very high positive correlation of phosphorus contents were found in plants to yield. Data about phosphorus balance can help to modify phosphorus fertilization doses for subsequent crops and optimize production inputs.

Cereals in Europe are mainly grown with intensive management. This often leads to the deterioration of the physical properties of the soil, especially increasing bulk density due to heavy machinery traffic, which causes excessive soil compaction. Controlled traffic farming (CTF) technology has the potential to address these issues, as it should be advantageous technology for growing cereals during climate change. The aim of this study was to compare the yield potential of CTF and standardly used random traffic farming (RTF) technology using yield maps obtained from combine harvester and satellite imagery as a remote sensing method. The experiment was performed on a 16-hectare experimental field with a CTF system established in 2009 (with conversion from a conventional (ploughing) to conservation tillage system). Yield was compared in years when small cereals were grown, a total of 7 years within a 13-year period (2009–2021). The results show that CTF technology was advantageous in dry years. Cereals grown in the years 2016, 2017 and 2019 had significantly higher yields under CTF technology. On the contrary, in years with higher precipitation, RTF technology had slightly better results—up to 4%. This confirms higher productivity when using CTF technology in times of climate change.

Khorasan wheat (Triticum turgidum ssp. turanicum (Jakubz.)) is an ancient tetraploid spring wheat variety originating from northeast parts of Central Asia. This variety can serve as a full-fledged alternative to modern wheat but has a lower yield than modern varieties. It is commonly known that wheat growth is influenced by soil tillage technology (among other things). However, it is not known how soil tillage technology affects ancient varieties. Therefore, the main objective of this study was to evaluate the influence of different soil tillage technologies on the growth of the ancient Khorasan wheat variety in comparison to the modern Kabot spring wheat (Triticum aestivum) variety. The trial was arranged in six small plots, one half of which was sown by the Khorasan wheat variety and the other half of which was sown by the Kabot wheat variety. Three soil tillage methods were used for each cultivar: conventional tillage (CT) (20–25 cm), minimum tillage (MTC) with a coulter cultivator (15 cm), and minimization tillage (MTD) with a disc cultivator (12 cm). The soil surface of all of the variants were leveled after tillage (harrows & levelling bars). An unmanned aerial vehicle with multispectral and thermal cameras was used to monitor growth during the vegetation season. The flight missions were supplemented by measurements using the GreenSeeker hand-held sensor and plant and soil analysis. The results showed that the Khorasan ancient wheat was better suited the conditions of conventional tillage, with low values of bulk density and highvalues of total soil porosity, which generally increased the nutritional value of the yield in this experimental plot. At the same time, it was found that this ancient wheat does not deplete the soil. The results also showed that the trend of developmental growing curves derived from different sensors was very similar regardless of measurement method. The sensors used in this study can be good indicators of micronutrient content in the plant as well as in the grains. A low-cost RGB camera can provide relevant results, especially in cases where equipment that is more accurate is not available.

Wheat and rapeseed are significant crops in Czech agriculture and remote sensing has huge potential for their management, given Sentinel-1 can overcome issues of cloudiness and monitor vegetation development via radar backscatter. This study compares radar and optical data characterizing the development of wheat and rapeseed in an agricultural cooperative in the Czech Republic. Radar Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) time-series of the main vegetation seasons between 2015 and 2018 are processed, analysed, and compared with each other. In 2018, the comparison of data with ground measurement by camera was also used. The temporal development of RVI is affected by noise, which is caused by the composition of imagery from different Relative orbits. The separation of imagery according to the Relative orbit used seemed to provide results more comparable to the phenological curve. Simple linear regression between NDVI and RVI illustrated that considering Relative orbit can slightly increase the Coefficient of determination. By selecting a suitable Relative orbit, the coefficient of determination between NDVI and RVI increased from 0.281 to 0.387 in the case of wheat and from 0.233 to 0.316 in the case of rape monitoring. The RVI for rapeseed and the height of canopy correlation was 0.392. The results for RVI presented in this article demonstrated that monitoring wheat and rapeseed development by Sentinel-1 has potential, however more research needs to be conducted in the areas of spatial and temporal noise removal.

The influence of climate and topography on crop condition and yield estimates is most effectively monitored by non-invasive satellite imagery. This paper evaluates the efficiency of free-access Sentinel 2 and Landsat 5, 7 and 8 satellite images scanned by different sensors on wheat growth and yield prediction. Five winter and spring wheat cultivars were grown between 2005 and 2017 in a relatively small 11.5 ha field with a 6% slope. The normalized difference vegetation index was derived from the satellite images acquired for later growth phases of the wheat crops (Biologische Bundesanstalt, Bundessorenamt and Chemical industry 55 - 70) and then compared with the topography wetness index, crop yields and yield frequency maps. The results showed a better correlation of data obtained over one day (R-2 = 0.876) than data with a one-day delay (R-2 = 0.689) using the Sentinel 2 B8 band instead of the B8A band for the near-infrared part of electromagnetic spectrum in the normalized difference vegetation index calculation.

The construction of logistics centres and the selection of their localities affects not only the activities of urban goods movement, but also the urban environment. The phenomenon called as logistics sprawl, i.e. the relocation of logistics facilities away from inner urban areas to suburban areas has received an increasing level of attention from scientific community and public as well. The paper results are focused on the square increase of logistics centres in Prague 's suburb with the use of satellite images. These images were compared with the cadastre of the Czech Republic. Build up squares of logistics centres enlarged about 400.10(4) (m(2)) in suburb from the year 2013 to 2018 at the expense of agricultural land. This fact can exacerbate the quality of life in the Prague's suburban areas.

Water, wind, or tillage-induced soil erosion can significantly degrade soil quality and decrease crop yield from farm fields. Traditionally, research in soil erosion is mostly focused on water or wind erosion. Recent studies over the past two decades, however, point to the importance of tillage operations as a source of soil translocation on undulating agricultural land. Tillage disturbs the soil not only vertically but also horizontally by throwing soil in the direction of tillage. These operations influence physical, chemical, and biological soil properties, which also affect soil quality and consequently plant growth. Moreover, the translocation of soil particles in topsoil by tillage practice has been recognized as an important factor of redistribution of soil over time and in the development of morphological changes within agricultural fields. Therefore, understanding the issue of topsoil displacement by tillage translocation is an important step towards developing tillage practices that do not degrade soil resources. This study was designed to assess the soil translocation effect in topsoil before and after 5 tillage sequences by using three different practices, namely mouldboard plough (I.), chisel plough (II.), and disc harrow (III.) in the Chernozems region in Sardice village (South Moravia, Czech Republic). The influence of different tillage practices on the changes in depth of topsoil was assessed through description of 40 shallow pits -10 pits were dug out before the tillage operations and then 30 more after five tillage operations. The results of the soil survey are based on the evaluation of the stratigraphy of the soil profile where the potential loss of topsoil was determined by a change in transition between the dark Ac horizon and yellow loess Ck horizon and by the type of transition. Shift of topsoil after five performed operations is in the range of 9-15 cm at the top position of convex-concave slope and 4-10 cm at the top of convex-linear slope. Significant shifts across each tillage practice (from III. to I.) are also apparent from the results of the experiment, which can be caused not only by the used technology but as well as by the shape of the slope.

Abstract Remote sensing is a methodology using different tools to monitor and predict yields. Spatial variability of crops can be monitored through sampling of vegetation indices derived from the entire crop growth; spatial variability can be used to plan further agronomic management. This paper evaluates the suitability of vegetation indices derived from satellite Landsat and EO-1 data that compare yield, topography wetness index, solar radiation, and meteorological data over a relatively small field (11.5 ha). Time series images were selected from 2006, 2010, and 2014, when oat was grown, and from 2005, 2011 and 2013, when winter wheat was grown. The images were selected from the entire growing season of the crops. An advantage of this method is the availability of these images and their easy application in deriving vegetation indices. It was confirmed that Landsat and EO-1 images in combination with meteorological data are useful for yield component prediction. Spatial resolution of 30 m was sufficient to evaluate a field of 11.5 ha.

The influence of mineral fertilisers, liming, farmyard manure and sowing rate on the winter wheat grain yields was studied in a long-term field experiment at 4 sites under different soil and climatic conditions in the Czech Republic.A total of 135 partial fraction-factorial experiments were performed between 1980 and 2013 and evaluated using a statistical model with linear and quadratic terms for each factor.Yield trends demonstrated remarkable influence of fertilisation at two sites of lower starting productivity.Here, grain yields increased by 50% and 25% since the trial commencement, while the rate of yield increase was low at more productive sites.Yields were the most frequently influenced by nitrogen (N) fertilisation, uniformly at all sites.N response curves were strongly curvilinear, but these differed between sites and were affected by preceding crops.The relative frequency of statistically significant influences decreased in the following order: N (significant at α < 0.05 in 89% of all partial trials) > sowing rate (29%) > phosphorus (22%) > farmyard manure (15%) > potassium (12%) > liming (8%).This order and the frequencies of these influences are discussed with regard to relevant site and soil conditions.

High proportion of arable land is typically for agriculture in Czech Republic. Nowadays there is a problem with decrease of livestock production and increase of biofuel production. This problems causes decrease in the level of soil carbon in the soil. Decrease in levels of organic carbon also leads to easier soil degradation by other negative factors (soil erosion, compaction). Organic matter application into soils is the only corrective action. Decomposition of applied organic matter is a problem in the decarburized soils. Organic matter can be supplemented by biological transformation’s activators. The objective of this paper is to demonstrate the efficacy activators of organic matter to improve the soil environment. Field trial has been established in this purpose at locality Sloveč in the Central Bohemia Region. Very heavy soil is located on the experimental field. Results of the six variants with application of manure are presented in this paper. PRP Sol (PRP Technologies) was used like soil activator. PRP Fix (PRP Technologies) was used like activator of the biological transformation of manure. Favorable effect on crop state of cereals was observed. This was confirmed by using vegetation indices (using satellite images). They suggest a beneficial effect of application of bio-activators.

Vegetation indices, as a non-destructive and low-cost method, represent an effective approach to vegetation cover evaluation. The simple mathematical formulas used describe wide spectra of conditions (water and nutrition saturation, influence of topography). Remote sensing data are used to derive vegetation indices. Calculations are performed using specialized software when operating with particular bands of electromagnetic spectra. The results contribute to adjustment of current agricultural management that becomes more economically efficient while having lower negative impact on the environment. The paper deals with selected vegetation indices and examines their suitability for yield prediction. Study area was 11.5 ha agricultural plot in Praha-Ruzyně. The experiment was conducted on wheat (2005, 2011) and oat (2006, 2010) having spatially related yield data. The selected indices were derived from LANDSAT 5 imagery with 30 m spatial resolution using SW ENVI. Specific values were obtained using SW ArcGIS. Correlation analysis was conducted to examine the relation between particular VI and the yield data or the Topography Wetness Index. The results indicated relation between vegetation indices and yield in all cases. The Moisture Stress Index reached -0.835 in 2011 as the maximal value of the correlation coefficient, while the minimum was performed by value 0.495 of the Chlorophyll Vegetation Index in 2005. The analysis also indicated relation of the yield to the topographic conditions of the agricultural plot. The Simple Ratio Vegetation Index in 2010 had the strongest correlation with the Topography Wetness Index, the correlation coefficient reached 0,6. Conversely, the minimal value was observed by the Chlorophyll Vegetation Index in 2005, namely 0.19. The Normalized Difference Vegetation Index, as the last of the selected indices, showed average results. Nevertheless, the data were evaluated in terms of the weather conditions as well. The influence of temperature and precipitation in particular growth stages was discussed. At the end, the conclusion was drawn that selected vegetation indices are suitable to describe the yield despite the fact they were derived from 30 m spatial resolution imagery.