Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times between image processing and dataset release. We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) land use land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery. We utilize a highly scalable cloud-based system to apply this approach and provide an open, continuous feed of LULC predictions in parallel with Sentinel-2 acquisitions. This first-of-its-kind NRT product, which we collectively refer to as Dynamic World, accommodates a variety of user needs ranging from extremely up-to-date LULC data to custom global composites representing user-specified date ranges. Furthermore, the continuous nature of the product’s outputs enables refinement, extension, and even redefinition of the LULC classification. In combination, these unique attributes enable unprecedented flexibility for a diverse community of users across a variety of disciplines.

An issue of concern in landscape and urban planning, articulated in the United Nation’s (UN’s) Sustainable Development Goals (SDGs), is the increase in urban land consumption over time. Indicator 11.3.1 of the SDGs is dedicated to measuring it, underlining the importance of decreasing urban land consumption per person, a strategy that is understood to contribute positively to climate mitigation and to a host of other social, economic, and environmental objectives. This article aims to explore the practical implications of the official methods for measuring Indicator 11.3.1, as well as two alternatives, and to calculate and compare the global and regional trends of these indicators for the 2000–2020 period for a universe of 3470 cities and metropolitan areas that had 100,000 people or more in the year 2020. Built-up area and population data for this universe were obtained from the Global Human Settlements Layer (GHS-BUILT-S and GHS-POP) published by the European Commission. We applied methods adapted from New York University’s Atlas of Urban Expansion to map the urban extents of all cities in 2000 and 2020, and then we used these urban extents, the built-up areas, and population estimates within them to calculate values for Indicator 11.3.1 and for two alternative indicators for the 2000–2020 period. We found that the current definition of Indicator 11.3.1 of the SDGs—“Ratio of land consumption rate to population growth rate”—has significant limitations in conveying meaningful information and interpretability for practical applications. We suggest two alternative indicators that address these shortcomings: the rate of change of land consumption per person and the rate of density change. Our analysis found that, for the world at large, urban densities declined at an annual rate of 0.5–0.7% between 2000 and 2020, with significant variation in the direction and magnitude of density trends by world region. Additionally, we found density declines to be faster in smaller cities than in larger ones and faster in cities with slower population growth or population declines compared to those with more rapid population growth.

Sustaining multiple ecosystem services alongside food production is a central challenge for agriculture, yet how soil health practices shape services on working horticultural farms remains poorly resolved. We assessed six soil-based ecosystem services across 28 organic lettuce fields on California’s Central Coast, USA, spanning diverse soils and management regimes, combining soil measurements with farmer surveys and satellite-derived estimates of continuous living cover (CLC). Multifunctionality was evaluated under four weighting schemes reflecting production, environmental and biodiversity priorities. CLC was the strongest and most consistent management correlate of multifunctionality under every weighting and was associated with greater nutrient cycling, soil fertility and microbial diversity, and lower nitrate leaching risk, without any detected yield penalty. Reduced disturbance, crop diversification and organic amendments were each associated with lower residual nitrate at harvest; organic amendments were also associated with higher yield, whereas crop diversification carried a yield penalty. Management explained more variation than inherent soil properties for four of six services, suggesting that management benefits are not strongly constrained by soil type. Interactions among practices did not improve model fit for any service, providing no evidence that combining practices was necessary to obtain benefits. Trade-offs were context dependent: a yield–carbon trade-off emerged only under below-average CLC and above-average disturbance. Continuous living cover is a practical management target for improving soil multifunctionality in organic horticulture, with potential to deliver multiple environmental benefits without compromising crop production.

Climate change not only threatens agricultural producers but also strains related public agencies and financial institutions. These important food system actors include government entities tasked with insuring grower livelihoods and supporting response to continued global warming. We examine future risk within the U.S. Corn Belt geographic region for one such crucial institution: the U.S. Federal Crop Insurance Program. Specifically, we predict the impacts of climate-driven crop loss at a policy-salient "risk unit" scale. Built through our presented neural network Monte Carlo method, simulations anticipate both more frequent and more severe losses that would result in a costly doubling in the annual probability of maize Yield Protection insurance claims at mid-century. We also provide a configurable open source pipeline and interactive visualization tools to further explore these results. Altogether, we fill an important gap in current understanding for climate adaptation by bridging existing historic yield estimation and climate projection to predict crop loss metrics at policy-relevant granularity.