Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one. This radical change has shaken existing farming techniques and produced the latest prospects in a series of challenges. This comprehensive review article enlightens the potential of the IoT in the advancement of agriculture and the challenges faced when combining these advanced technologies with conventional agricultural systems. A brief analysis of these advanced technologies with sensors is presented in advanced agricultural applications. Numerous sensors that can be implemented for specific agricultural practices require best management practices (e.g., land preparation, irrigation systems, insect, and disease management). This review includes the integration of all suitable techniques, from sowing to harvesting, packaging, transportation, and advanced technologies available for farmers throughout the cropping system. Besides, this review article highlights the utilization of other tools such as unmanned aerial vehicles (UAVs) for crop monitoring and other beneficiary measures, such as optimizing crop yields. In addition, advanced programs based on the IoT are also discussed. Finally, based on our comprehensive review, we identified advanced prospects regarding the IoT, which are essential tools for sustainable agriculture.

The hydrologic sciences and water resources management have long depended on a combination of in situ measurements and remotely sensed data for research and regulatory purposes. In situ measurements are constrained in their spatial distribution by both logistics and costs. Satellite‐ and manned aircraft‐based remote sensing provide spatially broad data, but often of a coarse resolution. Furthermore, all these data collection methods are often limited in their ability to respond to specific short‐term events when the data and imagery could inform real‐time assessment and decision‐making, especially during and immediately after natural disasters. Researchers and regulators are increasingly using unmanned aerial vehicles (UAVs) to fill these gaps in both spatial and temporal data resolution. UAVs are often more cost‐effective than traditional remote sensing or in situ data collection methodologies and the availability of new advancements in UAV design, power supply, payload capacity, and sensors has been driving rapid innovations in their use in the hydrological sciences. This in‐depth review explores these UAV technologies and how they are being employed for on‐demand and cost‐effective characterization, monitoring, assessment, and modeling of soil and water resources. Our discussion addresses both key opportunities and challenges in surface water and groundwater studies, land‐atmosphere interaction studies, water management in agriculture and forestry, ecosystem monitoring, and hydrological modeling.

The coronavirus 2019 (COVID 19, or SARS-CoV-2) pandemic that started in December 2019 has caused an unprecedented impact in most countries globally and continues to threaten human lives worldwide. The COVID-19 and strict lockdown measures have had adverse effects on human health and national economies. These lockdown measures have played a critical role in improving air quality, water quality, and the ozone layer and reducing greenhouse gas emissions. Using Soil Moisture Active Passive (SMAP) Level 4 carbon (SMAP LC4) satellite products, this study investigated the impacts of COVID-19 lockdown measures on annual carbon emissions globally, focusing on 47 greatly affected countries and their 105 cities by December 2020. It is shown that while the lockdown measures significantly reduced carbon emissions globally, several countries and cities observed this reduction as temporary because strict lockdown measures were not imposed for extended periods in 2020. Overall, the total carbon emissions of select 184 countries reduced by 438 Mt in 2020 than in 2019. Since the global economic activities are slowly expected to return to the non-COVID-19 state, the reduction in carbon emissions during the pandemic will not be sustainable in the long run. For sustainability, concerned authorities have to put significant efforts to change transportation, climate, and environmental policies globally that fuel carbon emissions. Overall, the presented results provide directions to the stakeholders and policymakers to develop and implement measures to control carbon emissions for a sustainable environment.

Advanced technologies and innovation are essential for promoting sustainable food systems (SFSs) because these technologies can be used to answer some of the critical questions needed to transform SFSs and help us better understand global food security and nutrition. The main objective of this study is to address the question of whether technological innovations have an impact on the transformation of SFSs. There are certain innovations including agricultural land utilization, food processing, production systems, improvement in diets according to people’s needs, and management of waste products. This study provides an overview of new technologies and innovations being used with potential to transform SFSs. Applications of emerging technologies in digital agriculture, including the Internet of Things (IoT), artificial intelligence and machine learning, drones, use of new physical systems (e.g., advanced robotics, autonomous vehicles, advanced materials), and gene technology (e.g., biofortified crops, genome-wide selection, genome editing), are discussed in this study. Additionally, we suggest eight action initiatives, which are transforming mindsets, enabling social licensing, changing policies and regulations, designing market incentives, safeguarding against undesirable effects, ensuring stable finance, building trust, and developing transition pathways that can hasten the transition to more SFSs. We conclude that appropriate incentives, regulations, and social permits play a critical role in enhancing the adoption of modern technologies to promote SFSs.