This short communication synthesizes evidence on how the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) altimetry data are used by private sector actors and the implications for economic value creation. Using secondary research that collected and summarized information from existing data from reports, journals, websites, and databases, the work identifies 54 companies across 9 sectors leveraging ICESat-2-derived elevation, canopy height, bathymetry, and surface measurements to inform decision-making, risk assessment, and new business models. The analysis situates ICESat-2 within a broader context where freely available Earth observation data can generate substantial private- and public-sector value, potentially exceeding hundreds of billions in aggregate when scaled across industries such as geospatial services, climate management, real estate, and insurance. The paper uses a four-pillar conceptual model to guide valuation of data-driven impacts: Data Utility (intrinsic information value of altimetry and related metrics), Decision Impact (tangible economic benefits from improved models and operations), Strategic Integration (emergence of new business models and market opportunities), and Data Ecosystem Exclusivity (development of proprietary datasets and workflows that enable competitive differentiation). Empirical findings illustrate how these pillars manifest in practice. The paper seeks to connect private-sector uptake to NASA’s Earth Science to Action framework and related capacity-building efforts, highlighting pathways for broader utilization through training, tutorials, and accessible interfaces. Limitations of the study include partial sector coverage and reliance on publicly reported use cases. Future work should quantify economic returns with standardized metrics and extend the dataset to capture dynamic shifts in data products, governance, and IP development within the evolving data ecosystem.

With the significant increase in the availability of two- to three-daily Harmonized Landsat and Sentinel-2 imagery (HLS), satellite image time series (SITS) can capture temporal and spatial features for accurate cropland mapping. This study evaluated the performance of four SITS deep learning (DL) classification approaches, including convolutional long short-term memory (ConvLSTM), convolutional gated recurrent unit, 3D UNet, and a novel DPC-UNet architecture, for regional cropland mapping in Senegal, using 30-m HLS data. The study focused on the Eastern Casamance (ECAS) and Eastern Transition Zone (ETZ) regions. The study created extensive training data from multi-date 2-m WorldView (WV) imagery covering 15 locations (1500 km(2)) and independently generated >2000 validation points from the WV images. All the SITS DL models generated cropland maps with high overall accuracy >92% in the ECAS and >80% in the ETZ. All models displayed relatively low precision of 76%-80% that was caused by the well-documented overestimation of croplands due to confusion with fallow or abandoned fields and bare sand along drainage lines. The relative performance of the models differed between regions, however, ConvLSTM marginally had the highest F1 score and overall accuracy in both regions. DPC-UNet displayed the most overestimation of croplands in the ECAS, but the least in the ETZ. The cropland maps produced by the DL models otherwise displayed strong agreement with the benchmark Global Land Analysis and Discovery cropland map and may also detect newly expanding fields along the edges of core cropland clusters. Given their performance and rate of progress, it is likely that SITS DL models will become the standard for operational cropland mapping in the near future, especially with the imminent high-frequency 10-m HLS data.

Background/Objectives: Extreme temperatures linked to climate change threaten child health, particularly in Sub-Saharan Africa where malnutrition remains widespread. This study examines how exposure to hot and cold temperature anomalies influences child stunting in Burkina Faso and Kenya and evaluates how household infrastructure and socio-demographic factors interact with climate stressors to shape outcomes. Methods: We combined nationally representative Demographic and Health Surveys (Burkina Faso 2021; Kenya 2022) with daily maximum and minimum temperature data from the Climate Hazards InfraRed Temperature with Stations (CHIRTS). The analytic sample included children aged 24–59 months. Temperature anomalies were calculated as standardized deviations from local historical averages. Multilevel logistic regression models assessed associations between stunting, climate anomalies, and household-level factors, including electricity, water, sanitation, wealth, and rural/urban residence. Results: Heat anomalies were linked to increased stunting risk in Kenya (β = 2.34, p < 0.001), while in Burkina Faso, higher maximum temperatures unexpectedly reduced stunting odds (β = 0.08, p < 0.05). Cold anomalies showed marginal positive associations with stunting in both countries. Infrastructure and socioeconomic factors varied by context: electricity access and urban residence were protective in Burkina Faso, while improved sanitation, household wealth, and child sex differences were significant in Kenya. Conclusions: Climate anomalies and household conditions jointly influence stunting among children aged 24–59 months, with effects varying by country. Cold anomalies were associated with higher odds of stunting in Burkina Faso (BF OR = 2.14) and Kenya (KE OR = 1.20), while heat anomalies reduced stunting in BF (OR = 0.08) but increased it in KE (OR = 2.34). Electricity access was protective in both countries (BF OR = 0.61; KE OR = 0.71), while improved water, sanitation, and wealth were significant only in KE. Older child age consistently reduced stunting risk, and urban residence was protective only in BF. These findings underscore that climate impacts on stunting are context-specific and highlight the need for policies integrating climate adaptation with investments.

Assessing the societal impact of satellite remote sensing datasets is essential to understanding how these data influence decision-making and to identifying opportunities for further engagement. However, measuring such impacts remains challenging for missions serving diverse stakeholder communities. In this study, we evaluate the broader impact of NASA's Ice, Cloud, and land Elevation Satellite (ICESat) and its successor mission, ICESat-2 by adapting the scientific impact framework (SIF), originally developed to assess public health research, into an Earth sciencespecific version (e-SIF). This framework captures data dissemination, community awareness, data-driven actions, measurable changes, and future influence, moving beyond traditional academic metrics to assess the missions' reach and effectiveness. Our findings reveal extensive global usage of ICESat and ICESat-2 data, with applications including, but not limited to, shallow water bathymetry, climate mitigation strategies, and forest management. By comparing the prevalence of topical areas in academic literature to real-world applications, we found that althoughthe cryosphere is the most frequently studied domain, differences between research focus and practical use highlight potential areas where further research could better support stakeholders. We found that ICESat and ICESat-2 data are widely employed by national and international governmental and non-governmental organizations but found only limited use by private sector and local governments. We recommend that the ICESat-2 Applications Team expand outreach efforts to these sectors to enhance dissemination of mission data. Furthermore, numerous ICESat2 applications benefit from long-term data continuity, reinforcing the need for a successor mission. This study demonstrates the feasibility to use e-SIF to evaluate the impact of Earth science missions.

Human-induced land cover land use (LCLU) changes such as agricultural extensification and forest degradation and loss have extensive negative impacts including biodiversity loss, land degradation, and a disruption to ecological services. In Senegal, where people are heavily reliant on dryland agricultural production, climate change and land degradation pose particularly significant threats especially as rapid population growth continues to fuel frequent LCLU change. Considering these challenges, approaches that facilitate increased insight into the spatial and temporal dynamics of land use are needed to implement sustainable land management practices and mitigation strategies. However, difficulties associated with Senegal’s highly variable phenology, sparse woody cover and small, irregular fields necessitate the use of Very High Resolution (VHR; < 3 m spatial resolution) data and modern techniques for modeling land use at sufficient scales.We take advantage of VHR data’s spatial resolution and Sentinel-1’s high temporal resolution by implementing an object-based data fusion strategy to model land use. By generating high resolution vector objects from single-date WorldView imagery and using the corresponding Synthetic Aperture Radar (SAR) time series to train a One-Dimensional Convolutional Neural Network (1D CNN), we can effectively leverage deep learning techniques to extract land use signals from multi-resolution and multi-temporal data in a near-autonomous manner.

Agricultural Greenhouses (AGs) are increasingly crucial in global food security, particularly in arid and semi-arid regions like the Maghreb, where they offer sustainable solutions to water scarcity and climate change challenges. Despite their growing importance, the precise extent and distribution of AGs in these regions remain poorly mapped, largely due to the limitations of traditional survey methods and the diverse nature of AG infrastructures. This study introduces a novel, deep learning-based approach to map AGs in the Maghreb, focusing on a 20-year case study in Biskra, Algeria (2000-2024). Employing remote sensing with Landsat imagery and the U-Net deep learning architecture, this research aims to provide a detailed, temporal mapping of AGs, offering insights into their dynamics and informing sustainable agricultural policies in the region.

This study aims to provide practical insights for developing trauma-informed interaction strategies between service providers and participants in homeless service organizations (HSOs). Twelve providers and 17 participants were interviewed for a qualitative study on trauma-informed care (TIC) within an HSO. Thematic analysis revealed six themes regarding provider approaches to TIC interactions with participants: provide a sounding board, promote safety, foster understanding and respect, build relationships and trust, facilitate connection to services, and ensure flexibility in service provision. Participants noted three themes regarding their views of TIC interactions with providers: possess education and experience, build relationships and trust, and demonstrate supportive interpersonal styles. This study describes the application of TIC within an HSO, emphasizing the importance of supportive, positive interactions that promote understanding, respect, and trust. It highlights key factors in service provision. The findings expand our understanding of TIC implementation in HSOs and suggest areas for improvement.

Altimeter data from the first- and second-generation NASA Ice, Cloud and land Elevation (ICESat) satellite missions has the potential to transform a variety of key applications through improved decision making such as ocean navigation, surface water monitoring, and wildland fuels assessments. Here we review the literature using ICESat and ICESat-2 data to identify applications-relevant literature, defined as research that can lead to the use of data products in decision-making, policy making or risk reduction activity for societal benefit. We used the scholarly database ProQuest to identify bibliographic citations which were likely to lead to applications and user communities of data from the ICESat and ICESat-2 satellite lidar missions. We use the network visualization software tool Quid to create visual representation of clusters according to the textual similarity of their abstracts. We then text mine the NASA Applied Sciences Project Portfolio website to identify applications-relevant keywords and use these in combination with Quid-generated terms to create subsets of the literature according to how likely it was to lead to an application. Visualizations generated using the Quid software are presented separately for both ICESat and ICESat-2. We found an overall increase in the number of applications-relevant publications for ICESat-2 when comparing publications in the first four years after launch for both missions. This increase may be attributable to sustained efforts by the mission to reach out to the scientific and stakeholder communities during the pre-launch phase and to significant efforts by the mission to facilitate access to the data. However, our discovery of the literature also revealed a markedly low number of publications demonstrating actual applications of both ICESat and ICESat-2 data. We conclude that there is a need to cultivate a culture in academia that values linking science with use. We also identify multiple applications-relevant research where future applications efforts could focus.

Agricultural production and household food security are hypothesized to play a critical role connecting climate change to downstream effects on women's health, especially in communities dependent on rainfed agriculture. Seasonal variability in agriculture strains food and income resources and makes it a challenging time for households to manage a pregnancy or afford a new child. Yet, there are few direct assessments of the role locally varying agricultural quality plays on women's health, especially reproductive health. In this paper we build on and integrate ideas from past studies focused on climate change and growing season quality in low-income countries with those on reproductive health to examine how variation in local seasonal agricultural quality relates to childbearing goals and family planning use in three countries in sub-Saharan Africa: Burkina Faso, Kenya, and Uganda. We use rich, spatially referenced data from the Performance Monitoring for Action (PMA) individual surveys with detailed information on childbearing preferences and family planning decisions. Building on recent advances in remote monitoring of seasonal agriculture, we construct multiple vegetation measures capturing different dimensions of growing season conditions across varying time frames. Results for the Kenya sample indicate that if the recent growing season is better a woman is more likely to want a child in the future. In Uganda, when the growing season conditions are better, women prefer to shorten the time until their next birth and are also more likely to discontinue using family planning. Additional analyses reveal the importance of education and birth spacing in moderating these findings. Overall, our findings suggest that, in some settings, women strategically respond to growing season conditions by adjusting fertility aspirations or family planning use. This study also highlights the importance of operationalizing agriculture in nuanced ways that align with women's lives to better understand how women are impacted by and respond to seasonal climate conditions.

lthough more than half of the world’s population lives in urban areas, there is a dire lack of data on trade, cost, and origin of the food that the urban dwellers rely on. Understanding the impact of escalating water-food systems variability on urban quality of life is critical for designing data systems needed to implement appropriate policies and state-supported interventions in urban areas.

Soil moisture reflects the amount of water available to crops in the top layer of soil. As such, considering soil moisture provides important insight into water availability and ultimately crop yields in agricultural settings. In studies of climate change, food security, and health, however, soil moisture is rarely empirically considered despite its connection to crop health and yields. In this project, we aim to advance understanding of climate impacts on food security by incorporating soil moisture into quantitative models of child health. Combining spatially referenced health survey data from the Demographic and Health Surveys for 2005 and 2010 in Senegal and 2007, 2011, and 2014 in Bangladesh, with soil moisture data from the Famine Early Warning System Network Land Data Assimilation System, we explore the linkages between sub-annual and sub-seasonal climate conditions and child malnutrition in two rainfed agriculture dependent countries—Bangladesh and Senegal. Results suggest that soil moisture, measured on very short time scales, may be associated with reductions in anthropometric weight-for-height z -scores, but the relationship is highly dependent upon geographic context.

Achieving sustainable development requires understanding how human behavior and the environment interact across spatial scales. In particular, knowing how to manage tradeoffs between the environment and the economy, or between one spatial scale and another, necessitates a modeling approach that allows these different components to interact. Existing integrated local and global analyses provide key insights, but often fail to capture ‘meso-scale’ phenomena that operate at scales between the local and the global, leading to erroneous predictions and a constrained scope of analysis. Meso-scale phenomena are difficult to model because of their complexity and computational challenges, where adding additional scales can increase model run-time exponentially. These additions, however, are necessary to make models that include sufficient detail for policy-makers to assess tradeoffs. Here, we synthesize research that explicitly includes meso-scale phenomena and assess where further efforts might be fruitful in improving our predictions and expanding the scope of questions that sustainability science can answer. We emphasize five categories of models relevant to sustainability science, including biophysical models, integrated assessment models, land-use change models, earth-economy models and spatial downscaling models. We outline the technical and methodological challenges present in these areas of research and discuss seven directions for future research that will improve coverage of meso-scale effects. Additionally, we provide a specific worked example that shows the challenges present, and possible solutions, for modeling meso-scale phenomena in integrated earth-economy models.

IntroductionThe lack of a holistic view of agriculture’s social and economic aspects emerges as a limiting factor in policy implementations of sustainable intensification. The aim of this study was to (i) synthesize data from the five domains of sustainable agriculture intensification framework focusing on Senegal as a case study, (ii) harmonize and scale data used to minimize spatial–temporal differences, and (iii) build a digital tool aiming researchers and policymakers to facilitate the data visualization and connection.MethodsWe propose and prototype an interactive digital decision-support tool as a potential solution to integrate environmental, social, economic, human and production domains in agriculture projects. To demonstrate the use of the tool, a case study using data from Senegal was developed to show the benefits of open science and connectivity among domains.Results and discussionThe digital tool presented here allows users to explore an open repository from Senegal serving as a foundational approach to integrate diverse agricultural domains when developing sustainable intensification projects.

Agricultural scientists are pursuing sustainable intensification strategies to increase global food availability, but integration from research to impact at the local-level requires knowledge of demographic and human-environment to enhance the adaptive capacity of farmers cultivating <10 ha. Enhancing close collaboration among transdisciplinary teams and these smallholders is critical to co-elaborate policy solutions to ongoing food security crises that are likely to be attuned with local conditions. Human and socio-cultural aspects need to be considered to facilitate both adoption and dissemination of adapted management practices. Despite this well-known need to co-produce knowledge in human systems, we demonstrate the inequality of current agricultural research in smallholder farming systems with heavy focus on a few domains of the sustainable intensification agricultural framework (SIAF), ultimately reducing the overall impact of interventions due to the lack compatibility with prevailing social contexts. Here we propose to integrate agriculture and agronomic models with social and demographic modeling approaches to increase agricultural productivity and food system resilience, while addressing persistent issues in food security. Researchers should consider the scale of interventions, ensure attention is paid to equality and political processes, explore local change interactions, and improve connection of agriculture with nutrition and health outcomes, via nutrition-sensitive agricultural investments.

Wet carbon (WC) ecosystems are a critical part of the carbon cycle, yet they are underrepresented in many policy and science communities due to the relative under-investment in stakeholder and boundary organizations. WC systems include the hydrosphere and carbon cycling systems that operate in wetlands, oceans, rivers, streams, lakes, ponds, and permafrost. In this article, we provide evidence from a desk review of WC stakeholders, that includes individuals, groups or organizations that are affected by climate change, and utilize carbon data. These stakeholders are involved in decision-making processes in WC ecosystems, and can be private companies, non-governmental organizations, government agencies ranging in scope from local to federal, parastatals, international organizations, and more. In this paper, we identify and describe the links and interests of WC stakeholders and analyze the gaps between scientific understanding and information needs. A continued focus on WC systems could lead to increased stakeholder engagement and methodological and scientific progress. Our study revealed that stakeholder interest in WC systems was not primarily determined by its role in the carbon cycle, but rather by its significance for local policy, economics, or ecology. To bridge the gap between stakeholders and available WC data, we need improved communication of data availability and uncertainty, capacity building, engagement between stakeholder groups, and data continuity. Enhanced stakeholder engagement across various systems will facilitate greater utilization of carbon monitoring data derived from remote sensing; thereby creating more informed stakeholders as well as more effective decision-making processes.

IntroductionThe scarcity of resources have affected food production, which has challenged the ability of Iran to provide adequate food for the population. Iterative and mounting sanctions on Iran by the international community have seriously eroded Iran's access to agricultural technology and resources to support a growing population. Limited moisture availability also affects Iran's agricultural production. The aim of this study was to analyze the influence of inflation, international sanctions, weather disturbances, and domestic crop production on the price of rice, wheat and lentils from 2010 to 2021 in Iran. MethodData were obtained from the statistical yearbooks of the Ministry of Agriculture in Iran, Statistical Center of Iran, and the Central Bank of Iran. We analyzed econometric measures of food prices, including CPI, food inflation, subsidy reform plan and sanctions to estimate economic relationships. After deflating the food prices through CPI and detrending the time series to resolve the non-linear issue, we used monthly Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data to analyze the influence of weather disturbances on food prices. Results and discussionThe price of goods not only provides an important indicator of the balance between agricultural production and market demand, but also has strong impacts on food affordability and food security. This novel study used a combination of economic and climate factors to analyze the food prices in Iran. Our statistical modeling framework found that the monthly precipitation on domestic food prices, and ultimately food access, in the country is much less important than the international sanctions, lowering Iran's productive capability and negatively impacting its food security.

Carbon cycle science is at the heart of research on global climate change and its long-term impacts, as it examines the exchange of carbon between the atmosphere, oceans, land, and the impact of fossil fuel emissions on this cycling. Given the urgency of the climate challenge, NASA’s Carbon Monitoring System (CMS) requires all funded investigators to identify and work with stakeholder organizations at project inception to accelerate the transfer of the products developed by funded research into decision making systems. In this study, we contribute to the literature through the implementation of a quantitative analysis of 908 unique survey responses from funded investigators to explore the maturity of the scientist-stakeholder engagement. The paper employs multiple correspondence analysis to provide evidence to support policy options to increase stakeholder integration into research programs. Despite limitations of the dataset used, we demonstrated that multiple funding rounds, long-standing relationships between the stakeholder and scientist, and the scientific productivity of the Principal Investigator, including the ability to produce datasets and research papers on these datasets, all contribute to carbon products moving from research to operational use. The maturity of relationships between scientists and stakeholders was shown to result improved stakeholder engagement. The use of carbon products should be identified in every stage of the program, and that capacity building is needed to support both existing and newly identified stakeholders better understand and use CMS products. As federal, state, and local policy on climate adaptation and mitigation matures, the need for information on carbon will expand. Building of stakeholder-scientist relationships in CMS results in an effective generation and use of datasets to support this need and prototype ways that improved information needed for decision making can be created.

The objectives of this systematic review were to synthesize available information on social support and social networks among individuals experiencing chronic homelessness, examine the measurement of social support, the impact of social support on outcomes, and the impact of interventions on social support. The authors searched Academic Search Complete, PsycArticles, Pubmed, PsycINFO, ScienceDirect, Cinahl, and ProQuest for English-language empirical articles published between 1998 and 2019. Studies were included if they sampled single and unaccompanied adults experiencing chronic homelessness and examined the impact of social support and/or social networks. The search yielded 29 studies. Results were synthesized according to research questions and patterns of findings that emerged from included studies. Wide variability in the measurement of social support was found across studies. Nearly all studies found changes in social support associated with housing interventions. A substantial evidence base indicated intervention effects on social support are largely positive. Review findings suggest individuals with substance use disorders may require additional social functioning support once housed. Indigenous people may require tailored interventions to center their cultural values and facilitate existing community strengths. Overall, the results from this review suggest social support plays a key role in the housing experiences and outcomes of individuals experiencing chronic homelessness. Future studies should include qualitative and mixed methods work as these methods revealed important insights about social support. More evidence is needed to inform the development of specific interventions targeting social support to facilitate community integration of formerly homeless individuals. (PsycInfo Database Record (c) 2022 APA, all rights reserved).