Particles from deserts and semi-vegetated areas (mineral dust) are important for Earth's climate and human health, notably depending on their size. In this paper we collect and make a synthesis of a body of these observations since 1972 in order to provide researchers modeling Earth's climate and developing satellite observations from space with a simple way of confronting their results and understanding their validity.

In Northeast China, the permafrost is more sensitive to climate warming and fire disturbances than the boreal and Arctic permafrost. Since 2016, a continuous ground hydrothermal regime and soil nutrient content observation system has been gradually established in Northeast China. The integrated dataset includes soil moisture content, soil organic carbon, total nitrogen, total phosphorus, total potassium, ground temperatures at depths of 0–20 m, and active layer thickness from 2016 to 2022.

Cloncurry METAL shifts the big-data paradigm in mineral exploration by developing a quantitative, fully integrated, multi-modal, scale-consistent methodology for mineral system characterisation. The data comprise collocated petrophysical–mineralogical–geochemical–structural–metasomatic characterisation of 23 deposits from a highly complex mineral system. This approach enables translation of the mineral system processes into physics, providing a framework for smarter geophysics-based exploration.

Radiative transfer models play a key role in monitoring vegetation using remote sensing data such as satellite or airborne images. The development of these models has been hindered by a lack of comprehensive ground reference data on structural and spectral characteristics of forests. Here, we reported datasets on the structural and spectral properties of temperate, hemiboreal, and boreal European forest stands. We anticipate that these data will have wide use in remote sensing applications.

Long-term (2015–2021) quasi-continuous measurements have been obtained at 13 French urban sites using online mass spectrometry, to acquire the comprehensive chemical composition of submicron particulate matter. The results show their spatial and temporal differences and confirm the predominance of organics in France (40–60 %). These measurements can be used for many future studies, such as trend and epidemiological analyses, or comparisons with chemical transport models.

This study provides a 10 m global oil palm extent layer for 2021 and a 30 m oil palm planting-year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.

Internal wave (IW) is an important ocean process and is frequently observed in the South China Sea (SCS). This study presents a detailed IW dataset for the northern SCS spanning from 2000 to 2022, with a spatial resolution of 250 m, comprising 3085 IW MODIS images. This dataset can enhance understanding of IW dynamics and serve as a valuable resource for studying ocean dynamics, validating numerical models, and advancing AI-driven model building, fostering further exploration into IW phenomena.

Knowledge about past natural disasters can help adaptation to their future occurrences. Here, we present a dataset of 2521 riverine, pluvial, coastal, and compound floods that have occurred in 42 European countries between 1870 and 2020. The dataset contains available information on the inundated area, fatalities, persons affected, or economic loss and was obtained by extensive data collection from more than 800 sources ranging from news reports through government databases to scientific papers.

The study provided an annual 100 m resolution glimpse into the grazing activities across the Qinghai–Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.

Oceanic dissolved oxygen (DO) is fundamental for ocean biogeochemical cycles and marine life. To ease the computation of the ocean oxygen budget from in situ DO data, mapping of data on a regular 3D grid is useful. Here, we present a new DO gridded product from the Argo database. We compare it with existing DO mapping from a historical dataset. We suggest that the ocean has generally been losing oxygen since the 1980s, but large interannual and regional variabilities should be considered.

This study presented new annual maps of irrigated cropland in China from 2000 to 2020 (CIrrMap250). These maps were developed by integrating remote sensing data, irrigation statistics and surveys, and an irrigation suitability map. CIrrMap250 achieved high accuracy and outperformed currently available products. The new irrigation maps revealed a clear expansion of China’s irrigation area, with the majority (61%) occurring in the water-unsustainable regions facing severe to extreme water stress.

Climate-related studies need information about the distribution of stratospheric aerosols, which influence the energy balance of the Earth’s atmosphere. In this work, we present a merged dataset of vertically resolved stratospheric aerosol extinction coefficients, which is derived from data of six limb and occultation satellite instruments. The created aerosol climate record covers the period from October 1984 to December 2023. It can be used in various climate-related studies.

The energy reaching Earth's surface from the Sun is a quantity of great importance for the climate system and for many applications. SARAH-3 is a satellite-based climate data record of surface solar radiation parameters. It is generated and distributed by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). SARAH-3 covers more than 4 decades and provides a high spatial and temporal resolution, and its validation shows good accuracy and stability.

The national-scale continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.

Satellites have brought new opportunities for monitoring atmospheric NO₂, although the results are limited by clouds and other factors, resulting in missing data. This work proposes a new process to obtain reliable data products with high coverage by reconstructing the raw data from multiple satellites. The results are validated in terms of traditional methods as well as variance maximization and demonstrate a good ability to reproduce known polluted and clean areas around the world.

Continuous monitoring of shoreline dynamics is critical to understanding the drivers of shoreline change and evolution. This study uses long-term sequences of Landsat Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) images to analyze the spatio-temporal evolution characteristics of the coastlines of Hainan, mainland China, Taiwan, and other countries from 1990 to 2019.

As part of the ATL2MED demonstration experiment, two autonomous surface vehicles undertook a 9-month mission from the northeastern Atlantic to the Adriatic Sea. Biofouling affected the measurement of variables such as conductivity and dissolved oxygen. COVID-19 limited the availability of discrete samples for validation. We present correction methods for salinity and dissolved oxygen. We use model products to correct salinity and apply the Argo floats in-air correction method for oxygen

Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.

This paper uses machine learning and linear unmixing to produce rangeland health indicators: Landsat time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground in arid and semi-arid Kenya, Ethiopia, and Somalia. This represents the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangeland health in the arid and semi-arid rangelands of this portion of eastern Africa.

Surface topography across the marginal zone of the Greenland Ice Sheet is constantly evolving. Here we present an annual series (2019–2022) of summer digital elevation models (PRODEMs) for the Greenland Ice Sheet margin, covering all outlet glaciers from the ice sheet. The PRODEMs are based on fusion of CryoSat-2 radar altimetry and ICESat-2 laser altimetry. With their high spatial and temporal resolution, the PRODEMs will enable detailed studies of the changes in marginal ice sheet elevations.

Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.

The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global 30 m 23-year (2000–2022) daily seamless data cube (SDC) of surface reflectance based on Landsat 5, 7, 8, and 9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.