Remote sensing in the agricultural field can be defined as a scientific technology that senses the spectral reflectance and emission characteristics of target objects (such as crops, soil, and water) using physical media like light and heat, interpreting these physical signals into biological phenomena to obtain information. Agricultural remote sensing techniques have become essential tools for non-contact, non-destructive crop growth monitoring, yield prediction, farmland change detection, and efficient resource management, as well as for agricultural policy formulation in the agricultural field. Particularly in light of global food security crises and energy issues related to climate change, the importance of agricultural remote sensing in realizing sustainable agriculture is increasingly emphasized.

South Korea has a relatively low food self-sufficiency rate, making it sensitive to changes in the global market. Therefore, national-level technological development to improve domestic agricultural productivity and resource management is crucial. As of 2022, South Korea’s food self-sufficiency rate stands at only 49.3%, with a grain self-sufficiency rate of just 22.3% (Ministry of Agriculture Food and Rural Affairs, 2023). This leaves the country highly susceptible to the impacts of natural disasters in grain-exporting countries and protectionist trade policies. Consequently, there is a growing need for new technologies to strengthen agricultural production and food security.

Agricultural remote sensing technology primarily developed in the mid-20th century, with advancements centered on countries like the United States and Europe. These countries launched their observation satellites to ensure food security, establishing large-scale agricultural monitoring systems. The U.S. has been a global leader in the use of remote sensing technologies, particularly through its extensive satellite programs (e.g., NASA’s Landsat and MODIS). The integration of remote sensing data into precision agriculture practices, such as yield prediction, soil monitoring, and crop management, is highly advanced. Government initiatives, along with private sector participation, have accelerated technological developments. The European Union (EU), through programs like Copernicus, provides free access to satellite data, which has spurred significant growth in remote sensing applications in agriculture. The region’s focus is on sustainability, reducing environmental impact, and supporting the Common Agricultural Policy goals (Weiss et al., 2020).

In recent years, agricultural remote sensing research has gained attention for its role in crop growth diagnostics and monitoring in response to climate change, especially given the increasing occurrence of abnormal weather conditions due to climate change. According to the World Food Security Summit (Food and Agriculture Organization of the United Nations , 2017), the global population is expected to reach approximately 10 billion by 2050, necessitating a 50% increase in agricultural production compared to 2013 levels. However, this surge in food demand faces environmental challenges, including land degradation, increased greenhouse gas emissions, and the deterioration of water and soil quality, underscoring the need for sustainable agricultural management. In this context, agricultural remote sensing is emerging as an important tool for sustainable management, not only for crop monitoring but also for addressing environmental changes linked to climate change.

In Korea, agricultural remote sensing research began in the 1960s, using aerial photographs for land use surveys in the soil survey project, and began using satellite images to evaluate productivity in major crop production areas. Afterward, full-scale research began in 1995 with the establishment of the Remote Sensing Laboratory under the Soil Management Division of the National Institute of Agricultural Sciences and Technology. In the beginning, research primarily focused on understanding the growth characteristics of crops and the optical response properties of soil through ground measurements (Hong et al., 1997; 1998), and studies estimating rice yield using satellite imagery were conducted (Hong et al., 2000; 2001; Hong and Rim, 2000). These early studies laid the foundation for improving agricultural environmental monitoring and soil management efficiency.

In 2003, the National Institute of Agricultural Science and Technology consolidated the Remote Sensing Research Laboratory into the Soil Information Research Laboratory, with a focus on the integrated use of remote sensing technology and soil information to enhance agricultural productivity. This research led to the development of a nationwide soil database and the creation of a digital soil map system, which later enabled advancements in precision agriculture using satellite data. In 2007, a web system providing soil information in real-time was established, contributing significantly to agricultural activities tailored to local characteristics (Hong et al., 2009). Meanwhile, technologies for estimating the growth of various major crops have been developed through the use of not only optical satellites but also radar scatterometers and radar satellite imagery (Kim et al., 2009; 2011).

The increasing need for agricultural satellites led to the submission of proposals by the Department of Agricultural Environment in 2012 for medium-sized satellite planning. After continued efforts in satellite development, a feasibility study passed in 2018, laying the groundwork for observing agricultural production environments using domestic satellites. As a result of these efforts, the National Agricultural Satellite Center was officially established under the Agricultural Engineering Division of the National Institute of Agricultural Sciences in May 2024. The establishment of the National Agricultural Satellite Center is expected to be a turning point in the implementation of satellite-based smart agriculture in South Korea.

This paper aims to review the history and current state of agricultural remote sensing research and technology development in South Korea, focusing on the Rural Development Administration (RDA), and to explore future development directions.

2.1. Ground and Proximal Remote Sensing

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Ground-based sensing technology in agriculture plays a crucial role in the real-time monitoring of crop conditions and soil characteristics, providing accurate agricultural management solutions based on this data. Since 1995, the RDA has been exploring various technological advancements using ground sensors, demonstrating the potential of non-destructive, continuous data collection technologies. In particular, significant results have been achieved using optical and radar sensors to monitor crop growth conditions, soil organic matter content, and moisture levels.

Between 1995 and 2000, research was conducted to understand the optical response characteristics of crops and soil by measuring the spectral reflectance properties of major crops such as rice and corn at different growth stages. This included estimating leaf area index, chlorophyll and leaf nitrogen content, yield estimation, and evaluating the physicochemical properties of the soil (Hong et al., 1997; 1998).

In 2007, a non-destructive study estimating rice leaf nitrogen content using ground optical sensors was published (Kim and Hong, 2007), suggesting the possibility of monitoring of crop nutrition status. Additionally, radar scattering measurements of rice commu nities were conducted using ground-based radar scatterometers, highlighting the usefulness of radar technology in monitoring crop conditions regardless of weather (Hong et al., 2007).

In 2008, a study using ground optical sensors to predict the protein content of rice was conducted, identifying the relationship between vegetation indices and rice quality at different growth stages (Kim and Hong, 2008). This presented a new approach for non-destructively predicting the quality of major crops like rice. In 2009, research using multi polarization radar scatterometers was carried out to estimate growth parameters of paddy rice, and methods for accurately monitoring rice growth using radar signals from various frequency bands were developed (Kim et al., 2009). In 2010, a study estimating soil organic matter content using spectrometry succeeded in creating a soil organic matter distribution map based on visible and near-infrared spectrums, providing an important methodology for simultaneous soil management and crop growth monitoring in agricultural fields.

Research since 2011 has focused on the advanced application of ground-based sensing technologies. A 2011 study on soybean growth monitoring using an automatic L, C, and X-band radar scatterometer system proposed a method for real-time crop condition monitoring, independent of weather conditions. Subsequent studies developed techniques to accurately estimate the growth stages of soybeans using radar polarization differences, significantly enhancing agricultural management efficiency. In 2012, a study was conducted to estimate plant moisture content using radar vegetation indices, proposing methods for accurately monitoring crop moisture levels through microwave remote sensing technology. This research opened possibilities for monitoring moisture content, a critical parameter reflecting plant physiological conditions, in real-time, with potential applications in both agricultural and ecosystem management (Kim et al., 2012b; 2012c).

Over the past two decades, ground-based sensing technologies using optical sensors and radar scatterometers have made significant contributions to improving agricultural management efficiency. Optical sensors have been used as tools to monitor crop growth and nutrient content non-destructively, providing useful data for estimating nitrogen and protein content in rice and other crops. It was determined that the radar scatterometer system, unaffected by weather conditions, could contribute to real-time crop management and soil condition assessment.

2.2. Drone-Based Remote Sensing

Research on agricultural remote sensing using drones has surged since the mid-2010s due to the miniaturization of aircraft and the development of commercial drones. Drones provide higher spatial resolution and timely data collection compared to traditional satellite-based crop monitoring. The first study on the use of drones in agriculture in 2015 focused on estimating nitrogen content in crops (Lee et al., 2015). This research demonstrated the feasibility of using drones to efficiently diagnose crop nutritional status, showing that remote sensing technology can offer practical benefits to agriculture (Fig. 1). By monitoring nitrogen content in crops, the study laid the foundation for improving farming efficiency and optimizing fertilizer use.

Figure 1. Example of research: mapping of the amount of nitrogen in hairy vetch on paddy fields using unmanned aerial vehicle imagery (Lee et al., 2015).

In 2016, the scope of drone applications expanded, including research on monitoring the growth of kimchi cabbage in highland areas (Na et al., 2016). This study indicated that drones, by providing high-resolution imagery, could predict the growth status of kimchi cabbage and contribute to agricultural productivity improvements (Figs. 2, 3). Drones, being more economical and accessible than satellite imagery, enabled quicker and more accurate growth monitoring, allowing farmers to manage crops more effectively. Additionally, various image classification methods were researched, establishing a foundation for improving the accuracy of agricultural data and enabling more detailed analysis of growth conditions (Lee et al., 2016).

Figure 2. Location of the study area: (a) Anbandegi, (b) Gwinemi, and (c) Maebongsan (Lee et al., 2017c).

Figure 3. Photos of UAV and flight appearance for a mission (Lee et al., 2017c).

In 2017, a more in-depth analysis of agricultural remote-sensing technology using drones was conducted. Detailed characteristics of crop growth monitoring through drone imagery were analyzed, presenting methods for more effective crop condition tracking (Lee et al., 2017b; 2017d; 2017e; Na et al., 2017a). High-resolution drone imagery became an important tool for real-time crop growth monitoring, demonstrating the clear role drones can play in enhancing agricultural management systems and significantly improving the efficiency of farming operations. Drone-based remote sensing offers methods to optimize crop management by providing real-time crop condition data.

Since 2018, research shifted toward solving more practical problems. The high-resolution imagery provided by drones offered an opportunity to quickly identify issues such as nutrient deficiencies or pest problems, allowing for early detection and response to crop growth issues (Lee et al., 2018).

In 2019, it was discovered that there was a strong correlation between the vegetation index derived from drone imagery during the rice booting stage and rice yield, which led to the creation of a drone-based rice yield distribution map (Fig. 4). This map illustrated the variability in rice yields based on farming practices, demonstrating that drones can serve as a practical tool for improving productivity in agricultural settings (Lee et al., 2019).

Figure 4. Example of research: vegetation index in the booting stage and yield distribution map using UAV (Lee et al., 2019).

Since 2020, drone technology has also been applied to pest and disease management in agriculture. Research was conducted to monitor diseases affecting major crops like rice, such as bacterial blight and blasts. High-resolution imagery collected by drones provided a method for quickly identifying and responding to areas affected by diseases (Lee et al., 2022; 2020c; Ryu et al., 2021). Additionally, extensive research has been conducted on drone-based crop monitoring, accounting for seasonal and diurnal changes. Studies analyzing Normalized Difference Vegetation Index (NDVI) data from drones examined how crop growth varies by season and time of day (Lee et al., 2020a; 2021a; Ahn et al., 2020b; 2022). These studies demonstrated that drone imagery is not only useful for monitoring crop growth but also for optimizing agricultural operations by reflecting temporal and spatial changes.

From 2015 to the present, the research on agricultural remote sensing using drones has continuously expanded its applications and achievements. Initially focused on basic research for monitoring crop growth and nutrient status, the potential of UAVs has since been proven in various fields, such as biomass assessment, pest, and disease management, and seasonal variation analysis (Fig. 5). Remote sensing technology using drones has significantly contributed to improving agricultural management efficiency, and its importance is expected to grow further.

Figure 5. Example of research: a time series of drone-based RGB and NDVI images after the rice blast outbreak (Ryu et al., 2023).

In the future, Looking ahead, research on building information platforms that enhance the usability of drone imagery through standardization and integration with other sensors is considered necessary. Furthermore, it will be essential to study methods of linking diagnostic information with practical farming operations to apply the information in the field. If research progresses on integrating and utilizing information from ground sensors, agricultural machinery, and other platforms in conjunction with drone imagery, it could provide comprehensive support for agricultural decision-making.

2.3. Satellite-Based Remote Sensing

Satellite imagery has long been a critical technological tool in agricultural remote sensing, widely used for monitoring large-scale agricultural land, assessing crop conditions, predicting yields, and analyzing land use changes. In the early stages of the research, Landsat Thematic-Mapper (TM) satellite imagery was used to estimate the rice cultivation area by analyzing the greenness of vegetation and the moisture levels of agricultural land according to the growth stages of rice (Hong et al., 2000; 2001; Hong and Rim, 2000). Additionally, training data for 13 categories, including rice paddies, fields, and greenhouses, were obtained using Differential Global Positioning System (DGPS) data and aerial images to perform land cover classification in the Pyeongtaek area based on Landsat TM satellite imagery, and the accuracy was evaluated (Rim et al., 2001). Field surveys were conducted in line with the acquisition schedule for time-series RADARSAT images to attempt the development of a rice growth estimation model for the first time (Hong et al., 2000).

In 1998, an analysis of land cover classification across North Korea using Landsat TM satellite imagery was conducted to understand the status of agricultural land use (Hong et al., 2008). In 2005, an attempt was made to identify suitable cultivation areas by comprehensively considering topography and roads through land cover classification of agricultural land in the Imjin River basin using high-resolution QuickBird imagery. Since 2012, research has focused on various topics such as estimating physiological indices of crops, like the Leaf Area Index (LAI), developing large-scale rice yield prediction models, and estimating paddy field areas in regions like North Korea. This section provides an overall review of the key achievements in satellite imagery applications in agriculture during this period.

Research estimating the LAI of crops using satellite imagery was conducted on corn and soybeans. The LAI is an essential index indicating the crop’s ability to absorb solar energy and produce biomass. A 2012 study compared drone and satellite data for LAI estimation and concluded that satellite data is highly valuable for monitoring large-scale crop conditions(Kim et al., 2012a). This research provided crucial information for analyzing crop biomass production and showed that satellite imagery could be a practical tool for agricultural management.

In 2012, a study was conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and weather data to estimate rice yield in large agricultural areas (Fig. 6). This study presented an efficient method for predicting rice yields, making a significant contribution to monitoring crop growth.

Figure 6. Rice yield prediction schema using MODIS NDVI and climate data (Hong et al., 2012a).

High-resolution satellite imagery has also been used to create agricultural land maps and analyze land use changes in rapidly urbanizing areas, such as Goyang, Namyangju, and Yongin near the Seoul metropolitan area (Lee et al., 2012). This research provided valuable information for monitoring changes in agricultural land and the impact of urban development, highlighting the critical role satellite imagery could play in urban planning and environmental management. Satellite imagery, capable of collecting and analyzing large-scale data in real time, was particularly useful for detecting complex changes in land use.

A study estimating the paddy field area in North Korea using RapidEye imagery demonstrated how satellite imagery could effectively monitor agricultural conditions in regions with limited access (Fig. 7). This research played an important role in real-time assessments of North Korean agriculture and supported agricultural policy decisions through comparative analysis with neighboring countries. Satellite imagery’s ability to monitor hard-to-access areas highlighted its significant contribution to international agricultural management and policy formulation (Hong et al., 2012b).

Figure 7. Example of research: distribution map of paddy fields classified from RapidEye imagery in North Korea (Hong et al., 2012b).

Satellite imagery has proven its practical applicability in agricultural management, enabling the collection of large-scale data and real-time monitoring. Estimating physiological indices such as LAI has contributed significantly to predicting biomass production, and combining satellite imagery with weather data has improved the accuracy of agricultural yield predictions. High-resolution satellite imagery used to create agricultural land maps and analyze land use changes has provided valuable insights into the interaction between urbanization and agriculture.

In 2017, a map distinguishing winter crops was created using multi-temporal satellite imagery (Na et al., 2017b; Fig. 8). Additionally, a study was conducted to estimate the yields of corn and soybeans by combining MODIS NDVI data with weather information, focusing on key regions of the U.S. Corn Belt (Fig. 9), which is a primary source of grain imports for South Korea (Lee et al., 2017a). In 2018, research was carried out on atmospheric correction of multispectral satellite data and the resulting improvements in reflectance and vegetation index accuracy (Ahn et al., 2018a; 2018b). In 2020, studies on cross-validation and data fusion among various satellite platforms were conducted. In particular, the combination of high-resolution data from KOMPSAT-3 and moderate-resolution data from Landsat-8 enhanced the consistency of time-series analysis (Ahn et al., 2020a). Furthermore, research on monitoring rice paddy cultivation using Sentinel-1 SAR data proposed a method for estimating rice cultivation areas regardless of weather conditions (Lee et al., 2020b). Since radar data is not affected by clouds or precipitation, it plays a crucial role in reliably assessing the area of rice paddies on a national scale, thereby supporting rice supply and demand policies.

Figure 8. Example of research: winter crop map using hybrid classification in Gimje-si (Na et al., 2017b).

Figure 9. Example of research: soybean yield map of Illinois and Iowa, USA (Lee et al., 2017a).

In 2021, a study was conducted that combined Sentinel-1 SAR data with unmanned aerial vehicle (UAV) imagery. This research particularly focused on the early estimation of rice cultivation areas in the Gimje region of South Korea (Lee et al., 2021b). By integrating the two data sources, researchers were able to detect rice cultivation areas early and create distribution maps. Early and accurate estimation of crop areas supports efficient resource allocation and enables agricultural policies to respond more quickly to changing conditions.

Future research will focus on enhancing the precision of the previously developed technologies and developing methodologies applicable to various crops and environments. Specifically, while existing studies primarily used multiple regression models or machine learning techniques, it is anticipated that future research will need to incorporate the latest technologies such as process-oriented biophysical parameter estimation models based on leaf and canopy spectroscopy (e.g., PROSAIL) and deep learning. Additionally, while satellite imagery demonstrates excellent performance in diagnosing current conditions, it will be necessary to explore ways to integrate satellite imagery with crop models to enhance future yield predictions based on weather data. Furthermore, with the upcoming launch of the National Agricultural Satellite Center, research on establishing a systematic framework for timely information provision to support the dissemination of research outcomes will also be essential.

This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.

The National Agricultural Satellite Center was established in May 2024 within the RDA research complex in Jeonju, Jeollabuk-do, and aims to conduct satellite-based agricultural observation and research (Fig. 10). The center’s primary purpose is to process satellite data related to agricultural production and environmental observation and use this information to support agricultural policy formulation and on-site decision-making. In collaboration with the next-generation medium-sized satellite development project, the National Agricultural Satellite Center focuses on analyzing satellite data and applying it to agriculture.

Figure 10. Photo of the National Agricultural Satellite Center.

The ongoing Phase 2 of the next-generation medium-sized satellite project targets the launch of an agricultural satellite in 2025. This satellite is equipped with a wide-area electro-optical camera that can collect spectral data from five channels—Blue, Green, Red, Red Edge, and Near Infrared (NIR)—covering an area of over 120 km with a spatial resolution of 5 meters (Fig. 11, Table 1). This capability will allow the rapid and accurate collection of data on changes in agricultural production areas, crop growth status, and responses to disasters and emergencies.

Figure 11. Appearance and key components of the medium-sized satellite for agriculture and forestry (CAS500-4).

Table 1 . Specifications of the medium-sized satellite for agriculture and forestry (CAS500-4).

Feature	Specification
Chanel (Band)	Visible to Near-IR (R, G, B, RE, NIR)
Swath	≥ 120 km
Revisit time	1 day
Spatial resolution	≥ 5 m
Payload weight	≤ 150 kg
Payload volume	X: 1,600 mm, Y: 1,000 mm, Z: 1,400 mm
Operational altitude	≒ 888 km
Design lifespan	≥ 5 years

Before the 2025 satellite launch, the National Agricultural Satellite Center will serve as a platform for processing and utilizing the data received from satellites. This project aims to establish a system for collecting, processing, managing, and distributing satellite images, providing real-time agricultural environmental information. Once the satellite information utilization system is established, it will be used in various fields, such as national crop acreage statistics, crop growth monitoring, and agricultural disaster forecasting, contributing to the digital transformation of agriculture.

The core initiatives of the National Agricultural Satellite Center can be divided into four main areas (Fig. 12). The first initiative is the establishment of a foundation for the development and operation of agricultural satellites. This includes the development of satellite payloads, the establishment of a precise calibration and correction system for satellite imagery, and the creation of a system that can effectively collect and manage satellite information. The agricultural satellite, equipped with high-resolution devices like electro-optical cameras, enables precise observations and real-time monitoring of changes in large agricultural areas and environmental conditions. Establishing a solid operational foundation for the satellite is essential for securing accurate data and providing information that can be immediately utilized in agricultural settings. Additionally, this technological foundation will play a crucial role in the development and launch of next-generation satellites in the future.

Figure 12. Vision, implementation plans, and strategies of the National Agricultural Satellite Center.

The second initiative involves the development of multi-fusion technologies, which aim to achieve precise monitoring in agricultural fields through the integration of the latest technologies such as drones and artificial intelligence (AI) alongside satellites. By combining drone and satellite imagery, data related to crop growth status, cultivation areas, and pest occurrences can be collected and analyzed in real-time. For instance, drones can conduct detailed observations of specific areas in fields through low-altitude flights, while satellites are advantageous for detecting overall changes in large regions through macro observations. This fusion technology can make decision-making in agricultural fields more scientific and efficient. The National Agricultural Satellite Center is working to strengthen the foundation of smart agriculture through such multi-fusion technologies and improve the accuracy of crop management and harvest predictions.

The third initiative focuses on providing user-centered, purpose-driven information services. The goal is to deliver agricultural information produced from satellite data in a tailored manner to policymakers, agricultural managers, and field farmers. While agricultural satellites collect vast amounts of data, the value is determined by how this data is processed to be easily understood and applied by users. To this end, the National Agricultural Satellite Center provides policymakers with information necessary for formulating agricultural strategies in response to climate change and delivers timely data in real-time to farmers regarding decisions on irrigation management, fertilizer application, and pest control related to crop cultivation. Such user-centric services play a crucial role in increasing agricultural productivity and minimizing losses due to disasters.

Finally, the establishment of a public-private partnership system has become an important initiative. The National Agricultural Satellite Center is collaborating with various government departments related to the development of next-generation medium-sized satellites while also strengthening partnerships with private companies. This is an essential process to promote satellite development, data utilization, and the commercialization of related technologies. Collaborating with various departments enhances the applicability of satellite data not only in agriculture but also in fields such as forestry, water resource management, and urban planning, while partnerships with private companies accelerate the pace of technological development and expand opportunities for the commercial use of satellite data. Through this, the National Agricultural Satellite Center is expected to play a leading role in agricultural satellite technology both domestically and internationally.

The National Agricultural Satellite Center will contribute to solving global agricultural challenges, such as predicting changes in the global grain supply chain and monitoring agricultural productivity in regions like North Korea, through cooperation with international organizations and data sharing. Furthermore, the integration of domestic and international satellite data will enhance the accuracy of agricultural information.

In this paper, we reviewed the history and current status of agricultural remote sensing in South Korea, focusing on research conducted by the RDA, summarized the technological achievements, and examined future development directions. Remote sensing in agriculture has evolved from being a mere tool for monitoring agricultural environments to becoming an essential technology for addressing climate change and food security challenges. It has played a significant role in real-time monitoring of crop growth, yield prediction, and optimizing resource management, contributing to the improvement of agricultural productivity and policy formulation.

Remote sensing technologies, through the use of satellites, drones, and ground sensors, have provided a comprehensive framework for analyzing crops and agricultural environments. The application of optical and radar sensors has been especially critical in real-time crop condition assessments and yield prediction. Moreover, high-resolution imagery from drones has opened up new possibilities for real-time monitoring of crop growth and pest issues, maximizing the efficiency of agricultural management.

Looking ahead, the advancement of remote sensing technologies is expected to become more sophisticated through the integration of AI and big data analysis. This will enable more precise monitoring of crop conditions and early prediction and response to disasters caused by climate change. Such technological integration will significantly contribute to improving agricultural productivity and strengthening food security in the future.

The establishment of the National Agricultural Satellite Center marks a turning point in the development of agricultural remote sensing in Korea. The center, which was founded in 2024, plays a central role in collecting, processing, and analyzing satellite-based agricultural data for agricultural production and environmental monitoring. With the launch and operation of next-generation medium-sized satellites, the center will be able to monitor large-scale agricultural regions and environmental conditions in real time, contributing to the formulation of agricultural policies and supporting decision-making in the field.

The center also focuses on providing accurate and timely agricultural information to users by combining satellite data with drone and AI technologies, laying the groundwork for precision agriculture. By offering customized agricultural information services to policymakers and farmers, the center will play a significant role in increasing agricultural productivity and minimizing losses caused by disasters. Additionally, the National Agricultural Satellite Center will collaborate with various domestic and international organizations to share satellite data, contributing to the resolution of global agricultural issues.

This research was funded by the Rural Development Administration (grant number: PJ01676801).

No potential conflict of interest relevant to this article was reported.