The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach.

The importance of studying coastal areas is justified by their resources, ecosystem services, and key role played in socio-economic development. Coastal landscapes are subject to increasing demands and pressures, requiring in-depth analyses for finding appropriate tools or policies for a sustainable landscape management. The present study addresses this issue globally, based on case studies from three continents: Romania (Europe), Algeria (Africa), and Vietnam (Asia), focusing on the anthropogenic pressure resulting from land use/land cover change or urban sprawl, taking into account the role of socioeconomic and political factors. The methodology consisted of producing maps and computing and analyzing indicators, correlating geospatial and socio-economic data in a synergistic manner to explore the changes of landscapes, and identify the specific driving forces. The findings show that the pressure of urbanization and tourism on coastal areas increased, while the drivers and impacts vary. Urbanization is due to derogatory planning in Romania and Algeria, and different national and local goals in Vietnam. The two drivers determine local exemptions from the national regulations, made for profit. In addition to the need for developing and enforcing policies for stopping the degradation and restoring the ecosystems, the findings underline the importance of international cooperation in policy development.

The results of absolute satellite-derived bathymetry (SDB) are presented in the current study. A comparative analysis was conducted on empirical methods in order to explore the potential of SDB in shallow water on the coast of Misano, Italy. Operations were carried out by relying on limited in situ water depth data to extract and calibrate bathymetry from a QuickBird satellite image acquired on a highly dynamic coastal environment. The image was processed using the log-band ratio and optimal band ratio analysis (OBRA) methods. Preprocessing steps included the conversion of the raw satellite image into top of atmosphere reflectance, spatial filtering, land and water classification, the determination of the optimal OBRA spectral band pairs, and the estimation of relative SDB. Furthermore, calibration and vertical referencing were performed via in situ bathymetry acquired in November 2007. The relative bathymetry obtained from different band ratios were vertically referenced to the local datum using in situ water depth in order to obtain absolute SDB. The coefficient of determination (R2) and vertical root mean square error (RMSE) were computed for each method. A strong correlation with in situ field bathymetry was observed for both methods, with R2 = 0.8682 and RMSE = 0.518 m for the log-band ratio method and R2 = 0.8927–0.9108 and RMSE = 0.35 m for the OBRA method. This indicated a high degree of confidence of the SDB results obtained for the study area, with a high performance of the OBRA method for SDB mapping in turbid water.

Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops in Tarom region (Iran). For this purpose, Sentinel 1 and Sentinel 2 images were used to create a map in the study area. The Sentinel 1 data came from Google Earth Engine’s (GEE) Level-1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. The Sen2Cor method was used to mask for clouds and cloud shadows, and the Sentinel 2 Level-1C data was sourced from the Copernicus Open Access Hub. To estimate the purpose of classification, stochastic forest classification method was used to predict classification accuracy. Using seven types of crops, the classification map of the 2020 growth season in Tarom was prepared using 10-day Sentinel 2 smooth mosaic NDVI and 12-day Sentinel 1 back mosaic. Kappa coefficient of 0.75 and a maximum accuracy of 85% were reported in this study. To achieve maximum classification accuracy, it is recommended to use a combination of radar and optical data, as this combination increases the chances of examining the details compared to the single-sensor classification method and achieves more reliable information.