A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments

Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland: An Analysis of Worldwide Research

The total area of plastic-covered crops of 3019 million hectares has been increasing steadily around the world, particularly in the form of crops maintained under plastic-covered greenhouses to control their environmental conditions and their growth, thereby increasing production. This work analyzes the worldwide research dynamics on remote sensing-based mapping of agricultural greenhouses and plastic-mulched crops throughout the 21st century. In this way, a bibliometric analysis was carried out on a total of 107 publications based on the Scopus database. Different aspects of these publications were studied, such as type of publication, characteristics, categories and journal/conference name, countries, authors, and keywords. The results showed that “articles” were the type of document mostly found, while the number of published documents has exponentially increased over the last four years, growing from only one document published in 2001 to 22 in 2019. The main Scopus categories relating to the topic analyzed were Earth and Planetary Sciences (53%), Computer Science (30%), and Agricultural and Biological Sciences (28%). The most productive journal in this field was “Remote Sensing”, with 22 documents published, while China, Italy, Spain, USA, and Turkey were the five countries with the most publications. Among the main research institutions belonging to these five most productive countries, there were eight institutions from China, four from Italy, one from Spain, two from Turkey, and one from the USA. In conclusion, the evolution of the number of publications on Remote Sensing of Agricultural Greenhouses and Plastic-Mulched Farmland found throughout the period 2000–2019 allows us to classify the subject studied as an emerging research topic that is attracting an increasing level of interest worldwide, although its relative significance is still very limited within the remote sensing discipline. However, the growing demand for information on the arrangement and spatio-temporal dynamics of this increasingly important model of intensive agriculture is likely to drive this line of research in the coming years

Implementing a GIS-Based Digital Atlas of Agricultural Plastics to Reduce Their Environmental Footprint; Part I: A Deductive Approach

The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open‐source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc

Implementing a GIS-based Digital Atlas of Agricultural Plastics to Reduce Their Environmental Footprint. Part I: A Deductive Approach.

The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open‐source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc

METODOLOGÍA PARA EL DESARROLLO DE APLICACIONES DE MONITORIZACIÓN REMOTA DE VARIABLES CON IoT (METHODOLOGY FOR THE DEVELOPMENT OF REMOTE MONITORING APPLICATIONS OF VARIABLES WITH IoT)

ResumenSe presenta un método para el diseño de aplicaciones de monitorización remota de variables de procesos usando Internet de las Cosas y las tecnologías que pueden usarse para obtener la mejor solución. El objetivo del trabajo es proponer la metodología que permita determinar los componentes necesarios para el diseño e implantación de la solución adecuada tomando en cuenta las necesidades de la aplicación y tecnologías disponibles. El documento presenta la problemática en la mayoría de ambientes donde es necesaria una solución de este tipo. A continuación, se indica el método para el diseño de la misma y posteriormente se propone la metodología. En la metodología se analizan las principales opciones tecnológicas de hardware y software que pueden usarse tomando en cuenta rendimiento, crecimiento, costo y seguridad. Finalmente se exponen recomendaciones derivadas de la experiencia obtenida en pruebas y resultados de trabajos previamente realizados.Palabras Claves: Hardware, IoT, monitorización, software, variables. AbstractThis paper presents a method for the design of remote monitoring applications of process variables using the Internet of Things and the technologies that can be used to obtain the best solution. The objective of the work is to propose the methodology that allows determining the necessary components for the design and implementation of the appropriate solution taking into account the needs of the application and available technologies. The document presents the problem in most environments where such a solution is necessary. Next, the method for its design is indicated and then the methodology is proposed. The methodology analyzes the main technological options of hardware and software that can be used taking into account performance, growth, cost and security. Finally, recommendations derived from the experience obtained in tests and results of previously performed work are presented.Keywords: Hardware, IoT, monitoring, software, variables

Variable Fall Climate Conditions on Carbon Assimilation and Spring Phenology of Young Peach Trees

Variable fall temperature and moisture conditions may alter leaf senescence of deciduous fruit trees, influencing carbon assimilation before dormancy and phenology the following spring. This study explored gas exchange of young peach trees (Prunus persica (L.) Batsch) when senescence proceeded normally or was delayed during the fall under two soil moisture treatments: Well-irrigated trees or water deficit. Results showed leaf carbon assimilation was similar between the senescence treatments, but whole tree assimilation was estimated to be greater in delayed senescence trees compared to normal senescence trees based on timing of defoliation and total leaf area. The effect of soil moisture on carbon assimilation was not consistent between years. Delayed sap flow and bloom time resulted as a consequence of delayed senescence the previous fall, but soil moisture did not affect spring phenology

Methodology for the Automatic Inventory of Olive Groves at the Plot and Polygon Level

The aim of this study was to develop and validate a methodology to carry out olive grove inventories based on open data sources and automatic photogrammetric and satellite image analysis techniques. To do so, tools and protocols have been developed that have made it possible to automate the capture of images of different characteristics and origins, enable the use of open data sources, as well as integrating and metadating them. They can then be used for the development and validation of algorithms that allow for improving the characterization of olive grove surfaces at the plot and cadastral polygon scales. With the proposed system, an inventory of the Andalusian olive grove has been automatically carried out at the level of cadastral polygons and provinces, which has accounted for a total of 1,519,438 hectares and 171,980,593 olive trees. These data have been contrasted with various official statistical sources, thus ensuring their reliability and even identifying some inconsistencies or errors of some sources. Likewise, the capacity of the Sentinel 2 satellite images to estimate the FCC at the cadastral polygon, parcel and 10 × 10 m pixel level has been demonstrated and quantified, as well as the opportunity to carry out inventories with temporal resolutions of approximately up to 5 days

Parameterization and performance analysis of a scalable, near real-time packet capturing platform

The rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of connectivity has also opened new avenues for cyber threats, emphasizing the critical need for Intrusion Detection Systems (IDSs) to adapt and operate efficiently in this evolving landscape. In response, companies are increasingly seeking IDSs characterized by horizontal, modular, and elastic attributes, capable of dynamically scaling with the fluctuating volume of network data flows deemed essential for effective monitoring and threat detection. Yet, the task extends beyond mere data capture and storage; robust IDSs must integrate sophisticated components for data analysis and anomaly detection, ideally functioning in real-time or near real-time. While Machine Learning (ML) techniques present promising avenues for detecting and mitigating malicious activities, their efficacy hinges on the availability of high-quality training datasets, which in turn poses a significant challenge. This paper proposes a comprehensive solution in the form of an architecture and reference implementation for (near) real-time capture, storage, and analysis of network data within a 1 Gbps network environment. Performance benchmarks provided offer valuable insights for prototype optimization, demonstrating the capability of the proposed IDS architecture to meet objectives even under realistic operational scenarios.This work was partially supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within project “CybersSeCIP” (NORTE-01-0145-FEDER- 000044). This work was also supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDP/ 05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020).info:eu-repo/semantics/publishedVersio

Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships