The carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.

Potato is one of the most significant food crops globally due to its essential role in the human diet. The growing demand for potato, coupled with severe environmental losses caused by extensive farming activities, implies the need for better crop protection and management practices. Precision agriculture is being well recognized as the solution as it deals with the management of spatial and temporal variability to improve agricultural returns and reduce environmental impact. As the initial step in precision agriculture, the traditional methods of crop and field characterization require a large input in labor, time, and cost. Recent developments in remote sensing technologies have facilitated the process of monitoring crops and quantifying field variations. Successful applications have been witnessed in the area of precision potato farming. Thus, this review reports the current knowledge on the applications of remote sensing technologies in precision potato trait characterization. We reviewed the commonly used imaging sensors and remote sensing platforms with the comparisons of their strengths and limitations and summarized the main applications of the remote sensing technologies in potato. As a result, this review could update potato agronomists and farmers with the latest approaches and research outcomes, as well as provide a selective list for those who have the intentions to apply remote sensing technologies to characterize potato traits for precision agriculture.

Phenology has been useful to better understand the climate-vegetation relationship, and it is considered an indicator of climate change impact. Phenological data have been generated through multiple remote sensing techniques and ground-based observations through professional or citizen science. The combination of both techniques is known as cross-scale phenological monitoring. However, no comparative analysis has been carried out to assess the advantages and disadvantages of each of these techniques to characterize the phenological cycle of forest ecosystem species. This work is a content-analysis-based review of scientific literature published between 2000 and 2018 related to cross-scale monitoring methods, to estimate the phenological variation in different forest ecosystems worldwide. For this study, 97 publications related to cross-scale phenological monitoring were selected. We found that 71% of the articles aimed to corroborate the data generated through satellite imagery using surface data from either phenocams, flux towers, or from citizen science networks. More publications were published by authors in the USA (30%), Canada (8%), and China (7%). The most commonly used vegetation index was the normalized difference vegetation index (65%). Some deficiencies in the evaluation of the phenological phases of autumn when compared with surface observations were found. Flux towers and phenocams were included as alternatives for ground-based monitoring. Cross-scale phenological monitoring has the potential to characterize the phenological response of vegetation accurately due to data combinations at two different observation scales. This work contributes to specifying the methodologies used in gathering phenological parameters of the world\'s forest ecosystems.

In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than 10 years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs.
We performed a systematic scoping review in Scopus of specific gridded population datasets and \"population\" or \"household\" \"survey\" reports, and solicited additional published and unpublished sources from colleagues.
We identified 43 national and sub-national gridded population-based household surveys implemented across 29 LMICs. Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, some survey teams interviewed all households in each cluster or segment, and others sampled households from larger clusters. Tools to select gridded population survey clusters include the GridSample R package, Geo-sampling tool, and GridSample.org. In the field, gridded population surveys generally relied on geographically accurate maps based on satellite imagery or OpenStreetMap, and a tablet or GPS technology for navigation.
For gridded population survey sampling to be adopted more widely, several strategic questions need answering regarding cell-level accuracy and uncertainty of gridded population estimates, the methods used to group/split cells into sample frame units, design effects of new sample designs, and feasibility of tools and methods to implement surveys across diverse settings.

Rapid development and applications of unmanned aerial vehicles (UAVs) provide promising solutions to and new opportunities for environmental monitoring. Owing to their flexibility in flight scheduling, high spatial resolution, and costs-effectiveness, UAVs have become a popular tool for monitoring dynamic environmental processes, such as emergence and outbreak of harmful algae blooms (HABs). The HABs outbreak, often linked with anthropogenic eutrophication, has become a serious environmental health problem that threats our communities. Existing studies show that UAV-based HABs monitoring is a cost-effective means of assisting environmental managers in developing precautionary warning system and coping strategies. This article summarized the state-of-the-art of using UAVs and lightweight onboard multispectral sensors for HABs monitoring from the perspective of quantitative remote sensing. It culminates in a discussion of challenges and opportunities for future research and applications on drone-based HABs monitoring.

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations\' Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.
The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.
We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs\' Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.
A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.
Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Water is essential for life as it provides drinking water and food for humans and animals. Additionally, the water environment provides habitats for numerous species and plays an important role in hydrological, nutrient, and carbon cycles. Among the existing natural resources on Earth\'s surface, water is the most extensive as it covers more than 70% of the Earth. To gather a comprehensive understanding of the focus of past, present, and future directions of remote sensing water research, we provide an alternative perspective on water research using moderate resolution imaging spectroradiometer (MODIS) imagery by conducting a comparative quantitative and qualitative analysis of research development, current hotspots, and future directions using a bibliometric analysis. Our study suggests that there has been a rapid growth in the scientific outputs of water research using MODIS imagery over the past 15 years compared to other popular satellites around the world. The analysis indicated that Remote Sensing of Environment was the most active journal, and \"remote sensing,\" \"imaging science photographic technology,\" \"environmental sciences ecology,\" \"meteorology atmospheric sciences,\" and \"geology\" are the top 5 most popular subject categories. The Chinese Academy of Sciences was the most productive institution with a total of 477 papers, and Hu CM (Chinese) was the most productive author with 76 papers. A keyword analysis indicated that \"vegetation index,\" \"evapotranspiration,\" and \"phytoplankton\" were the most active research topics throughout the study period. In addition, it is predicted that more attention will be paid to research on climate change and phenology in the future. Based on the keyword analysis and in consideration of current environmental problems, more studies should focus on the following three aspects: (1) develop methods suitable for data assimilation to fully explain climate or phenological phenomena at continental or global scales rather than at local scales; (2) accurately predict the effect of global change and human activities on evapotranspiration and the water cycle; and (3) determine the evolutionary process of the water environment (i.e., water quality, macrophytes, cyanobacteria, etc.), ascertaining its dominant factors and driving mechanisms. By focusing on these three aspects, researchers will be able to provide timely monitoring and evaluation of water quality and its response to global change and human activities.

Spatial technologies, i.e. geographic information systems, remote sensing, and global positioning systems, offer an opportunity for rapid assessment of malaria endemic areas. These technologies coupled with prevalence/incidence data can provide reliable estimates of population at risk, predict disease distributions in areas that lack baseline data and provide guidance for intervention strategies, so that scarce resources can be allocated in a cost-effective manner. This review focuses on the spatial technology applications that have been used in epidemiology and control of malaria in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geospatial technology OR Geographic Information Systems OR Remote Sensing OR Earth Observation OR Global Positioning Systems OR geospatial modelling OR malaria incidence OR malaria prevalence OR malaria risk prediction OR malaria mapping AND malaria AND Africa were used. These included mapping malaria incidence and prevalence, assessing the relationship between malaria and environmental variables as well as applications for malaria early warning systems. The potential of new spatial technology applications utilising emerging satellite information, as they hold promise to further enhance infectious risk mapping and disease prediction, are outlined. We stress current research needs to overcome some of the remaining challenges of spatial technology applications for malaria so that further and sustainable progress can be made to control and eliminate this disease.

Schistosomiasis continues to impact socio-economic development negatively in sub-Saharan Africa. The advent of spatial technologies, including geographic information systems (GIS), Earth observation (EO) and global positioning systems (GPS) assist modelling efforts. However, there is increasing concern regarding the accuracy and precision of the current spatial models. This paper reviews the literature regarding the progress and challenges in the development and utilization of spatial technology with special reference to predictive models for schistosomiasis in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geo-spatial analysis OR remote sensing OR modelling OR earth observation OR geographic information systems OR prediction OR mapping AND schistosomiasis AND Africa were used. Statistical uncertainty, low spatial and temporal resolution satellite data and poor validation were identified as some of the factors that compromise the precision and accuracy of the existing predictive models. The need for high spatial resolution of remote sensing data in conjunction with ancillary data viz. ground-measured climatic and environmental information, local presence/absence intermediate host snail surveys as well as prevalence and intensity of human infection for model calibration and validation are discussed. The importance of a multidisciplinary approach in developing robust, spatial data capturing, modelling techniques and products applicable in epidemiology is highlighted.

To accurately evaluate the responses of spatial and temporal variation of ecosystem functioning (evapotranspiration and photosynthesis) and services (regulating and cultural services) to the rapid changes caused by global warming, we depend on long-term, continuous, near-surface, and satellite remote sensing of phenology over wide areas. Here, we review such phenological studies in Japan and discuss our current knowledge, problems, and future developments. In contrast with North America and Europe, Japan has been able to evaluate plant phenology along vertical and horizontal gradients within a narrow area because of the country\'s high topographic relief. Phenological observation networks that support scientific studies and outreach activities have used near-surface tools such as digital cameras and spectral radiometers. Differences in phenology among ecosystems and tree species have been detected by analyzing the seasonal variation of red, green, and blue digital numbers (RGB values) extracted from phenological images, as well as spectral reflectance and vegetation indices. The relationships between seasonal variations in RGB-derived indices or spectral characteristics and the ecological and CO2 flux measurement data have been well validated. In contrast, insufficient satellite remote-sensing observations have been conducted because of the coarse spatial resolution of previous datasets, which could not detect the heterogeneous plant phenology that results from Japan\'s complex topography and vegetation. To improve Japanese phenological observations, multidisciplinary analysis and evaluation will be needed to link traditional phenological observations with \"index trees,\" near-surface and satellite remote-sensing observations, \"citizen science\" (observations by citizens), and results published on the Internet.