Monitoring of Agricultural Crops by Remote Sensing in Central Europe: A Comprehensive Review.
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| Title: | Monitoring of Agricultural Crops by Remote Sensing in Central Europe: A Comprehensive Review. |
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| Authors: | Kumhálová, Jitka1 (AUTHOR) kumhalova@tf.czu.cz, Sedlák, Jiří2 (AUTHOR), Marčan, Jiří3 (AUTHOR), Vandírková, Věra1 (AUTHOR), Novotný, Petr1,2 (AUTHOR), Kohútek, Matěj3 (AUTHOR), Kumhála, František3 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 7, p1075. 38p. |
| Subjects: | Remote sensing, Precision farming, Climate change, Plant species, Machine learning, Artificial satellites |
| Geographic Terms: | Central Europe |
| Abstract: | Highlights: What are the main findings? Sentinel-1 and Sentinel-2 time series have become the backbone of crop monitoring in Central Europe, enabling parcel-scale mapping, phenology analysis, and stress detection despite frequent cloud cover. The integration of LPIS/IACS parcel databases, machine learning methods, and multi-sensor remote sensing indicators improves crop classification accuracy and supports climate-related yield variability assessment. What are the implications of the main findings? Multi-sensor remote sensing frameworks combining optical, SAR, and climate data enable scalable monitoring of agricultural systems across heterogeneous Central European landscapes. These integrated monitoring approaches enable operational applications, including agricultural policy oversight, verification of Common Agricultural Policy (CAP) compliance, and evidence-based farm management. 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Sentinel-1 and Sentinel-2 time series have become the backbone of crop monitoring in Central Europe, enabling parcel-scale mapping, phenology analysis, and stress detection despite frequent cloud cover. The integration of LPIS/IACS parcel databases, machine learning methods, and multi-sensor remote sensing indicators improves crop classification accuracy and supports climate-related yield variability assessment. What are the implications of the main findings? Multi-sensor remote sensing frameworks combining optical, SAR, and climate data enable scalable monitoring of agricultural systems across heterogeneous Central European landscapes. These integrated monitoring approaches enable operational applications, including agricultural policy oversight, verification of Common Agricultural Policy (CAP) compliance, and evidence-based farm management. 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18071075 |
