By introducing virtual water (VW) flow correlation coefficients and risk indicators, this study examines the VW transmission relationship between urban agglomerations and cities in the Yellow River Basin (YRB) and its impact on regional water resources pressure. The results show that: except for the Shandong Peninsula Urban Agglomeration (SPUA) and Central Plains Urban Agglomeration (CPUA), the other urban agglomerations primarily act as VW exporting regions, while virtual water-importing cities are concentrated in the eastern regions. Notably, the Ningxia Urban Agglomeration (NUA) demonstrates significantly higher values in VW impact and sensitivity coefficients than the remaining six urban agglomerations. First-tier cities generally display lower virtual water impact and sensitivity coefficients, whereas emerging cities exhibit the opposite trend. Additionally, we observe uneven risk variations between VW importing and exporting regions. Taking NUA as an example, the risk increase resulting from VW exports significantly exceeds the risk reduction associated with VW imports in the corresponding regions. It's important to highlight that first-tier cities consistently decrease water resource risk through VW imports in both study years. However, among second and third-tier cities, only 38.9% experience reduced water resource risk through VW imports. Therefore, we recommend a focused examination of VW imports and exports in western region urban agglomerations, cities, and second and third-tier cities within the watershed. Leveraging virtual water's asymmetric and high-value transfer can alleviate water resource pressure and scarcity risks in high-pressure regions by shifting them to lower-pressure regions, thus mitigating water resource stress across regions.

On-time mapping dynamics of crop area, yield, and production is important for global food security. Such information, however, is often not available. Here, we used satellite information, a spectral-phenology integration approach for mapping crop area, and a machine learning model for predicting yield in the war-stricken Ukraine. We found that in Ukraine crop area and production declined in 2022 relative to 2017–2021 and 2021 for winter-triticeae crops, which was invaded before the cropping season in February of that year. At the same time, crop area and production for rapeseed increased in Ukraine, with yields consistently lower by 6.5% relative to 2021. The low precipitation and the Russian-Ukrainian conflict-related factors contributed to such yield variations by -1.3% and -0.9% for winter-triticeae crops and -4.2% and -0.5% for rapeseed in 2022. We demonstrate a robust framework for monitoring country-wide crop production dynamics in near real-time, serving as an early-food-security-warning system.

The amount of impervious surface is increasing rapidly worldwide. Although urban expansion has been studied extensively, the alteration of impervious land cover in rural regions remains under-examined. In particular, insights into the utilization of these sealed surfaces are crucially needed to unravel the underlying dynamics of land use changes beyond urban areas. This study focuses on rural regions from a Swiss case study and presents an analysis of the use of sealed surfaces in such regions, rather than solely quantifying the extent of sealed surfaces. Utilizing a synergistic approach that merges detailed cadastral plans with very-high-resolution remote sensing imagery and sophisticated deep learning algorithms, we characterized the uses of sealed surfaces, including buildings and their surroundings. Our findings reveal that 2.1% of the study area's rural regions comprises sealed surfaces - an area comparable to the sealed surfaces in the urban regions. Within these rural regions, transport infrastructure represents 68% of this impervious surface. Buildings account for 12%, and their surroundings, constituting 13%, are utilized primarily for agricultural purposes, including farming and livestock activities. The deep learning approach achieved a classification accuracy of 72% for a shallow model and 79% for a deeper model, indicating that mapping building types is possible with reasonable accuracy. The outcomes of this study underscore the critical need to factor in the presence and utilization of impervious land cover within rural regions for the sustainable management of land resources.

In the warming world, climate extremes tend to be more frequent and intensive. The exceptional response of ecosystems triggered by extreme events under a warmer and wetter climate in northwest China (NWC) has aroused growing concern. However, understanding the responses of vegetation to climate extremes from a compound perspective remains challenging. In this study, we identify the climate dynamics in NWC during 1971–2020 based on daily meteorological observations, focusing on the changes in compound hot-dry events (CHDEs) during the warmer and wetter period. We further explore the effects of CHDEs on vegetation by examining vegetation anomalies and recovery time using daily gross primary productivity (GPP) data. The results show a clearly warmer and wetter period in NWC during 2000–2020. No signs of a stalled increase in CHDEs are observed during this period, and even the duration of CHDEs in western NWC keeps showing an increasing tendency. Vegetation in eastern NWC, with a lower probability of GPP anomalies, exhibits stronger resistance of ecosystems to CHDEs than in western NWC. In NWC, vegetation typically returns to its normal state in 5.50 days on average, but exhibits greater resilience in the western region, where it takes less recovery time (4.82 days). Vegetation in central region shows the lowest probability of GPP anomalies and relatively long recovery time, likely due to its higher altitudes. Our research underscores the imperative to address the considerable impacts of CHDEs on vegetation growth even as the regional climate tends to be warmer and wetter.

Understanding vegetation water availability can be important for managing vegetation and combating climate change. Changes in vegetation water availability throughout China remains poorly understood, especially at a high spatial resolution. Standardized Precipitation Evapotranspiration Index (SPEI) is an ideal water availability index for assessing the spatiotemporal characteristics of drought and investigating the vegetation-water availability relationship. However, no high-resolution and long-term SPEI datasets over China are available. To fill this gap, we developed a new model based on machine learning to obtain high-resolution (1 km) SPEI data by combining climate variables with topographical and geographical features. Here, we analyzed the long-term drought over the past century (1901–2020) and vegetation-water availability relationship in the past two decades (2000–2020). The century-long drought trend analyses indicated an overall drying trend across China with increasing drought frequency, duration, and severity during the past century. We found that drought events in 1901–1961 showed a larger increase than that in 1961–2020, with the Tibetan Plateau showing a significant drying trend during 1901–1960 but a wetting trend during 1961–2020. There were 13.90% and 28.21% of vegetation in China showing water deficit and water surplus respectively during 2000–2020. The water deficit area significantly shrank from 2000 to 2020 across China, which is dominated by the significant decrease in water deficit areas in South China. Among temperature, precipitation, and vegetation abundance (LAI), temperature is the most important factor for the vegetation-water availability dynamics in China over the past two decades, with high temperature contributing to water deficit. Our findings are important for water and vegetation management under a warming climate.

The relationship between ecosystem services (ES) and human well-being (HWB) is fundamental to the science and practice of sustainability. But studies have shown conflicting results, which has been attributed to the influences of indicators, contexts, and scales. Yet, another potential factor, which has been overlooked, may be the mixed use of spatial and temporal approaches. Using twelve ES and seven well-being indicators and multiple statistical methods, we quantified and compared the spatial and temporal ES-HWB relationships for Inner Mongolia, China. The spatial and temporal relationships differed in both correlation direction and strength. Most relationships of economic and employment-related indicators with food provisioning and supporting services were temporally positive but spatially nonsignificant or negative, while some relationships of economic and employment-related indicators with Water Retention, Sandstorm Prevention, and Wind Erosion were temporally negative but spatially complex. However, the spatial and temporal ES-HWB relationships could also be similar in some cases. We conclude that although both the spatial and temporal approaches have merits, space generally cannot substitute for time in the study of ES-HWB relationship. Our study helps reconcile the seemingly conflicting findings in the literature, and suggests that future studies should explicitly distinguish between the spatial and temporal relationships between ES and HWB.

The paper introduces a Special Issue based on presentations to the Agricultural Geography and Land Engineering (AGLE) Commission sessions of the International Geographical Union (IGU) at the IGU's Congress in Paris in 2022. The sessions contrasted different approaches towards attaining greater sustainability in agricultural production to satisfy the need to feed the ever-increasing human population, currently expected to reach close to ten billion by 2050. After considering the multi-faceted problem of defining sustainable agriculture, this introduction systematically outlines broad strategies to attain the varied outcomes desired by agricultural systems. Presenting a contrast between ecocentric and technocentric approaches provides opportunities to synthesize recent literature addressing the pros and cons of these two broad alternatives. Recognition of the ecological and socio-cultural benefits accruing from the ecocentric has long been championed by proponents of a wide range of environmentally friendly farming systems, including organic farming, climate-smart agriculture, agroforestry, and permaculture. The technocentric lies at the heart of so-called Agriculture 4.0, in which innovations such as precision farming, digital technology, and genetic modification are applied to increase production per unit area. The potential for technology to ‘solve’ the world's food crisis is supported by those who argue that ecocentric approaches alone cannot meet the rising demand for food. Yet, questions remain about the sustainability of new technology-based methods, so a strong and ongoing debate continues regarding how to attain greater sustainability alongside increasing agricultural output. This debate is exemplified in the contributions to the Special Issue outlined herewith.

Trees provide multiple ecosystem services such as carbon sequestration, hydrological regulation and habitat for arboreal animals. However, they are often removed to support agricultural enterprises. Despite the importance of tree remnants, we know relatively little about how soils differ across sites of varying condition. Here, we describe a study where examined the relative effects of trees, compared with unvegetated interspaces, on soil functions in remnant patches at sites in good and poor condition in two eucalypt communities in eastern Australia. We found that, in general, carbon and nutrient cycling were relatively greater beneath trees, and in surface soils, but there were no clear trends in relation to site condition. The values of most soil attributes (e.g., soluble and exchangeable cations, nitrogen, phosphorus) were greater beneath trees, indicating strong fertile island effects in both communities. Overall, our study confirms the importance of trees in remnant patches in agricultural landscapes, particularly those in poor condition sites. It also suggests that soil processes may still be relatively intact, even in sites in poor condition. Our study reinforces the need to protect trees in remnant woodland reserves to maintain critical ecosystem functions related to nutrient retention. These remnants are important for achieving sustainable management of agricultural systems.

Assessment of SDG11.3.1 indicator of the United Nations Sustainable Development Goals (SDGs) is a valuable tool for policymakers in urban planning. This study aims to enhance the accuracy of the SDG11.3.1 evaluation and explore the impact of varying precision levels in urban built-up area on the indicator's assessment outcomes. We developed an algorithm to generate accurate urban built-up area data products based on China's Geographical Condition Monitoring data with a 2 m resolution. The study evaluates urban land-use efficiency in China from 2015 to 2020 across different geographical units using both the research product and data derived from other studies utilizing medium and low-resolution imagery. The results indicate: (1) A significant improvement in the accuracy of our urban built-up area data, with the SDG11.3.1 evaluation results demonstrating a more precise reflection of spatiotemporal characteristics. The indicator shows a positive correlation with the accuracy level of the built-up area data; (2) From 2015 to 2020, Chinese prefecture-level cities have undergone faster urbanization in terms of land expansion relative to population growth, leading to less optimal land resource utilization. Only in extra-large cities does urban population growth show a relatively balanced pattern. However, urban population growth in other regions and cities of various sizes lags behind land urbanization. Notably, Northeast China and small to medium cities encounter significant challenges in urban population growth. Our comprehensive framework for evaluating SDG11.3.1 with high-precision urban built-up area data can be adapted to different national regions, yielding more accurate SDG11.3.1 outcomes. Our urban area and built-up area data products provide crucial inputs for calculating at least four indicators related to SDG11.

The gap between the projected urban areas in the current trend (UAC) and those in the sustainable scenario (UAS) is a critical factor in understanding whether cities can fulfill the requirements of sustainable development. However, there is a paucity of knowledge on this cutting-edge topic. Given the extensive and rapid urbanization in the United States (U.S.) over the past two centuries, accurately measuring this gap between UAS and UAC is of critical importance for advancing future sustainable urban development, as well as having significant global implications. This study finds that although the 740 U.S. cities have a large UAC in 2100, these cities will encompass a significant gap from UAC to UAS (approximately 165000 km²), accounting for 30% UAC at that time. The study also reveals the spatio-temporal heterogeneity of the gap. The gap initially increases before reaching a inflection point in 2090, and it disparates greatly from -100% to 240% at city level. While cities in the Northwestern U.S. maintain UAC that exceeds UAS from 2020 to 2100, cities in other regions shift from UAC that exceeds UAS to UAC that falls short of UAS. Filling the gap without additional urban growth planning could lead to a reduction of crop production ranging from 0.3% to 3% and a 0.68% loss of biomass. Hence, dynamic and forward-looking urban planning is essential for addressing the challenges of sustainable development posed by urbanization, both within the U.S. and globally.