Today, researchers are focused on studying and identifying new products that can be beneficial for both food and pharmaceutical purposes. In this context, sprouts and microgreens are widely recognized as functional foods due to their high nutritional value, and in particular, their high bioactive compound content, which is mainly composed of phytochemicals. However, despite extensive research on the germination of species cultivated under optimal conditions, knowledge gaps remain regarding various aspects of sprout and microgreen production. To address this research issue, interdisciplinary collaboration is crucial. This involves the identification of novel species for edible sprout cultivation, the exploration of innovative strategies to stimulate bioactive compound production, and the development of new “nutraceutical” foods. Moreover, it is imperative to elucidate the mechanisms underlying the beneficial effects of the consumption of microgreens. This reprint presents cutting-edge research on sprouts and microgreens, providing valuable insights for the academic community.

Listed by Unglue.it.

Plant stress could be defined as any unfavorable condition or substance that can affect or block the metabolism, growth or development of a plant. The response of the plant may vary depending on the frequency and intensity of the stressor, as well as the developmental stage of the plant. Plants, throughout their life cycle, are exposed to a large number of conditions or stressors. Abiotic stress is stress caused by non-living agents. Depending on the nature of the causal agent, it can be divided into physical and chemical. Physical (actually, physical-chemical) stresses include water deficits, salinity (in its osmotic component), temperature extremes (heat, cold, freezing), excessive or insufficient irradiation, anaerobiosis caused by waterlogging or flooding, mechanical stress caused by wind or excessive soil compaction and stress induced by wounds or injuries. Chemical stress is caused by salinity (in its ionic or toxic component), by the lack of mineral elements and by environmental pollutants such as sulphur dioxide (SO2), nitrogen oxides (NOx), chlorofluorocarbon compounds (CFCs), ozone (O3) and metals. The abiotic stresses that most negatively affect growth and production are probably drought, salt stress and temperature stress (high and low temperatures), all of which are associated with climate change.

Listed by Unglue.it.

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.

Listed by Unglue.it.

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research.

Listed by Unglue.it.

Remote sensing is a powerful technique for characterizing and monitoring crop or vegetation properties at reasonable temporal and spatial resolutions. Remote sensing uses airborne and spaceborne platforms to collect various imageries and is widely applied for the vegetation monitoring of local- or large-scale interest concerning the effect of geophysical and climate parameters. The Special Issue highlights vegetation monitoring using remote sensing data acquired from satellite or unmanned aerial vehicle platforms. In addition to the optical data, thermal data is utilized to estimate crop yield or production, orchard water status, chlorophyll content, forest diversity mapping, or vegetation phenology.

Listed by Unglue.it.

In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Listed by Unglue.it.

Image processing in agriculture and forestry represents a challenge towards the automation of tasks for better performances. Agronomists, computer and robotics engineers, and agricultural machinery industry manufacturers now have at their disposal a book containing a collection of methods, procedures, designs, and descriptions at the technological forefront, which serves as an important support and aid for the implementation and development of their own ideas.The book describes: (1) Applications (canopy on trees, aboveground biomass, phenotyping, chlorophyll, leaf area index, water and nutrient content, land cover change, soil properties, and secure autonomous navigation); (2) Imaging devices onboard robots, unmanned aerial vehicles (UAVs), and satellites operating at different spectral ranges (visible, infrared, hyper-multispectral bands, and radar), as well as guidelines for selecting machine vision systems in outdoor environments; and (3) (Specific computer vision methods (generic and convolutional neural networks, machine learning, specific segmentation approaches, vegetation indices, and three-dimensional (3D) reconstruction).

Listed by Unglue.it.

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools.

Listed by Unglue.it.

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices.

Listed by Unglue.it.

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry.

Listed by Unglue.it.

Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling,

Listed by Unglue.it.

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment.

Listed by Unglue.it.

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the world

Listed by Unglue.it.

Mangrove forests are in constant flux due to both natural and anthropogenic forces. The changing mangroves will have significant consequences to coastal communities. Observation and monitoring of the distribution and dynamics of mangroves is central to a wide range of scientific investigations conducted in both terrestrial and marine ecosystems. Recent advancements in remote sensing data availability, image-processing methodologies, computing and information technology, and human resource development have provided an opportunity to observe and monitor mangroves from local to global scales on a regular basis. The spectral, spatial, and temporal resolution of remote sensing data and their availability have improved, making it possible to observe and monitor mangroves with unprecedented spatial thematic and temporal details. This journal Remote Sensing Special Issue reprint dedicated to the observation and monitoring of mangroves using remote sensing from local to global scales. The Issue broadly covers the application of remote sensing using optical (multi-spectral and hyperspectral), radar, and Lidar data obtained from multiple platforms including ground, air, and space. The research papers published use the latest techniques to acquire, manage, exploit, process, and analyze a wide variety of remote sensing data for mangrove forest applications. Both research papers and innovative review papers are included.

Listed by Unglue.it.

Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

Listed by Unglue.it.

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome.

Listed by Unglue.it.

This book compiles a set of original and innovative papers included in the Special Issue on UAVs for vegetation monitoring, which proves the wide scope of UAVs in very diverse vegetation applications, both in agricultural and forestry scenarios, ranging from the characterization of relevant vegetation features to the detection of plant or crop stressors. New methods and techniques are developed and applied to diverse vegetation scenarios to meet the main challenge of sustainability.

Listed by Unglue.it.