Radiative trigger thresholds of foliar photoprotective pigment regulation for global vegetation.

Adjustments in foliar photoprotective pigments are crucial for plant adaptation to harsh environments, serving as indicators of environmental stress. However, understanding when and where these adjustments occur across diverse biomes remains unclear due to challenges in large-scale observation. Here, we propose a novel approach to assess dynamics in photoprotective pigments at the canopy level using a new index derived from space-borne optical sensors. This approach generates a global map depicting the daily mean shortwave radiation threshold at which adjustments typically occur under prevailing climatic conditions. The global average of this threshold is 262 ± 50 W m⁻2, with lower values at high latitudes and peaks near 40° in both hemispheres. Temperature exerts a stronger influence on this latitudinal pattern than humidity. Future projections suggest a decrease in this threshold over northern high latitudes, implying exacerbated vulnerability under identical radiation levels due to negative warming responses. Based on this threshold, a high-stress zone around 60°N is identified and is predicted to shift southward in the future. These findings bridge critical gaps in photoprotection research and offer a new perspective on understanding the biogeochemical cycles of global ecosystems. This framework can also enhance our ability to predict the fate of diverse ecosystems under future climate.

Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests.

The role of tropical forests in the global carbon budget remains controversial, as carbon emissions from deforestation are highly uncertain. This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation. New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar. We found that lost forests are special cases, and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography. Thus, using biomass mapping, we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions. Here, using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests, we found that deforestation in the 2000s in Latin America, one of the severely deforested regions, mainly occurred in forests with a significantly lower carbon stock density than typical mature forests. Deforestation areas with carbon stock densities lower than 20.0, 50.0, and 100.0 Mg C/ha accounted for 42.1%, 62.0%, and 83.3% of the entire deforested area, respectively. The average carbon stock density of lost forests was only 49.13 Mg C/ha, which challenges the current knowledge on the carbon stock density of lost forests (with a default value 100 Mg C/ha according to the Intergovernmental Panel on Climate Change Tier 1 estimates, or approximately 112 Mg C/ha used in other studies). This is demonstrated over both the entire region and the footprints of the spaceborne lidar. Consequently, our estimate of carbon loss from deforestation in Latin America in the 2000s was 253.0 ± 21.5 Tg C/year, which was considerably less than existing remote-sensing-based estimates, namely 400-600 Tg C/year. This indicates that forests in Latin America were most likely not a net carbon source in the 2000s compared to established carbon sinks. In previous studies, considerable effort has been devoted to rectify the underestimation of carbon sinks; thus, the overestimation of carbon emissions should be given sufficient consideration in global carbon budgets. Our results also provide solid evidence for the necessity of renewing knowledge on the role of tropical forests in the global carbon budget in the future using observations from new space missions.

A future for digital public goods for monitoring SDG indicators.

Digital public goods (DPGs), if implemented with effective policies, can facilitate the realization of the United Nations Sustainable Development Goals (SDGs). However, there are ongoing deliberations on how to define DPGs and assure that society can extract the maximum benefit from the growing number of digital resources. The International Research Center of Big Data for Sustainable Development Goals (CBAS) sees DPGs as an important mechanism to facilitate information-driven policy and decision-making processes for the SDGs. This article presents the results of a CBAS survey of 51 respondents from around the world spanning multiple scientific fields, who shared their expert opinions on DPGs and their thoughts about challenges related to their practical implementation in supporting the SDGs. Based on the survey results, the paper presents core principles in a proposed strategy, including establishment of international standards, adherence to open science and open data principles, and scalability in monitoring SDG indicators. A community-driven strategy to develop DPGs is proposed to accelerate DPG production in service of the SDGs while adhering to the core principles identified in the survey.

Progress toward the sustainable development of world cultural heritage sites facing land-cover changes.

The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals (SDGs) that form part of the United Nations 2030 Sustainable Development Agenda. In terms of anthropogenic factors threatening the conservation of heritage properties, such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability. Therefore, in this study, 589 cultural World Heritage List (WHL) properties from 115 countries were analyzed, encompassing globally distributed and statistically significant samples of "monuments and groups of buildings" (73.2%), "sites" (19.3%), and "cultural landscapes" (7.5%). Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification. Sustainability indexes (SIs) were estimated for the protection zones of each property, and the results were employed, for the first time, to assess the progress of each country toward SDG Target 11.4. Despite the apparent advances in SIs (10.4%), most countries either exhibited steady (20.0%) or declining (69.6%) SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances. This study confirms that land-cover changes are among serious threats for heritage conservation, with heritage in some countries wherein the need to address this threat is most crucial, and the proposed spatiotemporal monitoring approach is recommended.

Dynamic landscapes and the influence of human activities in the Yellow River Delta wetland region.

The Yellow River Delta (YRD) wetland is one of the largest and youngest wetland ecosystems in the world. It plays an important role in regulating climate and maintaining ecological balance in the region. This study analyzes the spatiotemporal changes in land use, wetland migration, and landscape pattern from 2013 to 2022 using Landsat-8 and Sentinel-1 data in YRD. Then wetland landscape changes and the impact of human activities are determined by analyzing correlation between landscape and socio-economic indicators including nighttime light centroid, total light intensity, cultivated land area and centroid, building area and centroid, economic and population. The results show that the total wetland area increased 1426 km2 during this decade. However, the wetland landscape pattern tended to be fragmented from 2013 to 2022, with wetlands of different types interlacing and connectivity decreasing, and distribution becoming more concentrated. Different types of human activities had influences on different aspects of wetland landscape, with the expansion of cultivated land mainly compressing the core area of wetlands from the edge, the expansion of buildings mainly disrupting wetland connectivity, and socio-economic indicators such as total light intensity and the centroid mainly causing wetland fragmentation. The results show the changes of the YRD wetland and provide an explanation of how human activities effect the change of its landscape, which provides available data to achieve sustainable development goals 6.6 and may give an access to measure the change of wetland using human-activity data, which could help to adject behaviors to protect wetlands.

Delayed Antarctic melt season reduces albedo feedback.

Antarctica's response to climate change varies greatly both spatially and temporally. Surface melting impacts mass balance and also lowers surface albedo. We use a 43-year record (from 1978 to 2020) of Antarctic snow melt seasons from space-borne microwave radiometers with a machine-learning algorithm to show that both the onset and the end of the melt season are being delayed. Granger-causality analysis shows that melt end is delayed due to increased heat flux from the ocean to the atmosphere at minimum sea-ice extent from warming oceans. Melt onset is Granger-caused primarily by the turbulent heat flux from ocean to atmosphere that is in turn driven by sea-ice variability. Delayed snowmelt season leads to a net decrease in the absorption of solar irradiance, as a delayed summer means that higher albedo occurs after the period of maximum solar radiation, which changes Antarctica's radiation balance more than sea-ice cover.

iEarth: an interdisciplinary framework in the era of big data and AI for sustainable development.

The Intelligent Earth (iEarth) framework, composed of four major themes: iEarth data, science, analytics, and decision, is proposed to define and build an interdisciplinary and synergistic framework for research, practice, and education that simultaneously safeguards the sustainable development of our living planet.

Challenges and opportunities in remote sensing-based crop monitoring: a review.

Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making. Satellite-driven crop monitoring has become a main method to derive crop information at local, regional, and global scales by revealing the spatial and temporal dimensions of crop growth status and production. However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences. In this paper, we review recent progress in crop monitoring and identify the challenges and opportunities in future efforts. We find that satellite-derived metrics do not fully capture determinants of crop production and do not quantitatively interpret crop growth status; the latter can be advanced by integrating effective satellite-derived metrics and new onboard sensors. We have identified that ground data accessibility and the negative effects of knowledge-based analyses are two essential issues in crop monitoring that reduce the applicability of crop monitoring for decisions on food security. Crowdsourcing is one solution to overcome the restrictions of ground-truth data accessibility. We argue that user participation in the complete process of crop monitoring could improve the reliability of crop information. Encouraging users to obtain crop information from multiple sources could prevent unconscious biases. Finally, there is a need to avoid conflicts of interest in publishing publicly available crop information.

Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios.

Sustainable development goals (SDGs) in the United Nations 2030 Agenda call for action by all nations to promote economic prosperity while protecting the planet. Projection of future land-use change under SDG scenarios is a new attempt to scientifically achieve the SDGs. Herein, we proposed four scenario assumptions based on the SDGs, including the sustainable economy (ECO), sustainable grain (GRA), sustainable environment (ENV), and reference (REF) scenarios. We forecasted land-use change along the Silk Road (resolution: 300 m) and compared the impacts of urban expansion and forest conversion on terrestrial carbon pools. There were significant differences in future land use change and carbon stocks, under the four SDG scenarios, by 2030. In the ENV scenario, the trend of decreasing forest land was mitigated, and forest carbon stocks in China increased by approximately 0.60% compared to 2020. In the GRA scenario, the decreasing rate of cultivated land area has slowed down. Cultivated land area in South and Southeast Asia only shows an increasing trend in the GRA scenario, while it shows a decreasing trend in other SDG scenarios. The ECO scenario showed highest carbon losses associated with increased urban expansion. The study enhances our understanding of how SDGs can contribute to mitigate future environmental degradation via accurate simulations that can be applied on a global scale.

Urban-rural disparities of carbon storage dynamics in China's human settlements driven by population and economic growth.

China has experienced a rapid expansion of human settlement in both urban and rural areas over the past three decades. Regarding the impacts on carbon storage, previous studies that only focus on certain ecosystems cannot reflect urban-rural disparities, resulting in the carbon storage changes in human settlement remaining unknown. In this study, we aimed to explore China's urban-rural disparities in human settlement expansion and direct impacts on carbon storage by using the big Earth data technology. The results showed that from 1990 to 2018, the total amount of China's human settlement expansion reached 175,703.80 km2, and the inner-city, peri-urban, and rural components accounted for 21.00 %, 20.18 %, and 58.82 %, respectively. Along with the general tendency of impervious surface area (ISA) growth, there was more soil organic carbon (SOC) (1254.33 TgC) being sealed beneath ISA (0-100 cm depth), compared to a huge reduction in vegetation biomass carbon (VBC) (91.44 TgC) during the study period. The results further indicated that the change density of either VBC or SOC presented a slightly rising trend along the urban-rural gradient, due to the increasingly common encroachment on vegetation and soil types with higher carbon content. We also found that socioeconomic drivers had a greater influence in urban areas than rural areas, and the related correlation exhibited a descending trajectory in both urban and rural areas. There is thus an urgent need to preserve lands with abundant carbon storage and contain the waste of land resources in rural areas. All stakeholders should pay more attention to concerted and targeted regulation policies for well-planned and eco-friendly human settlement expansion such as enhancing rural land use efficiency and promoting large-scale afforestation and continuous urban greening, which will be critical not only for guiding sustainable urbanization all over China but also for mitigating climate change for the entire world.

Measuring and evaluating SDG indicators with Big Earth Data.

The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic, social, and environmental action. A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals (SDGs) is unfortunately limited in many countries due to lack of data. The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs. Big Earth Data, a special class of big data with spatial attributes, holds tremendous potential to facilitate science, technology, and innovation toward implementing SDGs around the world. Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities, and have successfully carried out case studies to demonstrate their utility in sustainability science. This paper presents implementations of Big Earth Data in evaluating SDG indicators, including the development of new algorithms, indicator expansion (for SDG 11.4.1) and indicator extension (for SDG 11.3.1), introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1, and several new high-quality data products, such as global net ecosystem productivity, high-resolution global mountain green cover index, and endangered species richness. These innovations are used to present a comprehensive analysis of SDGs 2, 6, 11, 13, 14, and 15 from 2010 to 2020 in China utilizing Big Earth Data, concluding that all six SDGs are on schedule to be achieved by 2030.

Glacial Lake Area Changes in High Mountain Asia during 1990-2020 Using Satellite Remote Sensing.

Changes in a large-scale glacial lake area directly reflect the regional glacier status and climate changes. However, long time series of glacial lake dataset and comprehensive investigation of the spatiotemporal changes in the glacial lake area in the whole High Mountain Asia (HMA) region remained elusive. Satellite remote sensing provides an indispensable way for dynamic monitoring of glacial lakes over large regions. But glacial lakes are quite small and discretely distributed, and the extraction of glacial lakes is usually influenced by clouds, snow/ice cover, and terrain shadows; thus, there is a lack of an automatic method to continuously monitor the dynamic changes of glacial lakes in a large scale. In this paper, we developed a per-pixel composited method named the "multitemporal mean NDWI composite" to automatically extract the glacial lake area in HMA from 1990 to 2020 using time-series Landsat data. There were 19,294 glacial lakes covering a total area of 1471.85 ± 366.42 km2 in 1990, and 22,646 glacial lakes with an area of 1729.08 ± 461.31 km2 in 2020. It is noted that the glacial lake area in the whole HMA region expanded by 0.58 ± 0.21%/a over the past three decades, with high spatiotemporal heterogeneity. The glacial lake area increased at a consistent speed over time. The fastest expansion was in East Kun Lun at an average rate of 2.01 ± 0.54%/a, while in the Pamir and Hengduan Shan, they show slow increases with rates of 0.33 ± 0.08%/a and 0.39 ± 0.01%/a, respectively, during 1990-2020. The greatest increase in lake area occurred at 5000-5200 m a.s.l., which increased by about 45 km2 (~25%). We conclude that the temperature rise and glacier thinning are the leading factors of glacial lake expansion in HMA, and precipitation is the main source of lake water increase in West Kun Lun. Using the proposed method, a large amount of Landsat images from successive years of melting seasons can be fully utilized to obtain a pixel-level composited cloud-free and solid snow/ice-free glacial lake map. The uncertainties from supraglacial ponds and glacial meltwater were also estimated to improve the reliability and comparability of glacial lake area changes among different regions. This study provides important technical and data support for regional climate changes, glacier hydrology, and disaster analysis.