As mid-southern U.S. rice producers continue to adopt furrow-irrigated rice (FIR) production practices, supplementary management efforts will be vital in combating Palmer amaranth due to the extended germination period provided by the lack of a continual flood. Previous research has revealed the ability of cover crops to suppress Palmer amaranth emergence in corn, cotton, and soybean production systems; however, research on cover crop weed control efficacy in rice production is scarce. Therefore, trials were initiated in Arkansas in 2022 and 2023 to evaluate the effect of cover crops across five site years on rice emergence, groundcover, grain yield, and total Palmer amaranth emergence. The cover crops evaluated were cereal rye, winter wheat, Austrian winterpea, and hairy vetch. Cover crop biomass accumulation varied by site year, ranging from 430 to 3,440 kg ha-1, with cereal rye generally being the most consistent producer of high-quantity biomass across site years. Rice growth and development were generally unaffected by cover crop establishment; however, all cover crops reduced rice emergence by up to 30% in one site year. Rice groundcover was reduced by 13% from cereal rye in one site year two weeks before heading but cover crops did not impact rough rice grain yield in any of the site years. Palmer amaranth emergence was reduced by 19 and 35% with cereal rye relative to the absence of a cover crop when rice was planted in April in Marianna and May in Fayetteville, respectively. In most trials, Palmer amaranth emergence was not reduced by a cereal cover crop. In most instances, legume cover crops resulted in less Palmer amaranth emergence than without a cover crop. Based on these results, legume cover crops appear to provide some suppression of Palmer amaranth emergence in FIR while having a minimal effect on rice establishment and yield.

Passive microwave measurements of Arctic sea ice have been conducted over the last 50 years from space and during airborne, ship- and ground-based measurement campaigns. The different radiometric signatures of distinct surface types have led to satellite retrievals of, e.g., sea-ice concentration. In contrast, ground-based upward-viewing radiometers measure radiation emitted from the atmosphere and are used to retrieve atmospheric variables. Here, we present results from a ship-based radiometer setup with a mirror construction, which allows us to switch between atmospheric and surface measurements flexibly. This way, in summer 2022, surface observations in the Arctic marginal sea-ice zone could be performed from the research vessel Polarstern by two radiometers covering the frequency range from 22 to 243 GHz. At low frequencies, the brightness temperatures show clear signatures of different surface conditions. We estimate emissivities at 53∘ zenith angle from infrared-based skin temperatures. Predominantly vertically polarized 22–31 GHz emissivities are between 0.51 and 0.55 for open ocean and around 0.95 for sea ice. Predominantly horizontally polarized 243 GHz ocean emissivities are around 0.78 and ice surfaces exhibit a large variability from 0.67 to 0.82. Our results can improve the characterization of surface emissions in satellite retrieval algorithms.

Extreme weather events caused by climate change, such as drought and heavy rainfall, will further increase in Central Europe in the near future. Resilient crop production requires in-depth knowledge of soil moisture (SM), its spatial and temporal variability and the dynamics of agriculturally used land. In the current study, different SM estimation methods, including measurement and simulation-based methods, were evaluated over a 17-ha experimental arable crop field with respect to their abilities to capture the spatial and temporal SM dynamics of within-field areas and their related uncertainty and spatial representativeness. The high-spatial resolution in-situ topsoil moisture measurements (50 m grid) were compared with the estimated SM from satellite-based remote sensing (S1ASCAT) and the simulated SM from three different crop water balance models (Agricultural Risk Information System [ARIS], AquaCrop and DSSAT). The evaluation revealed that the spatial variability in the experimental field obtained from the reference could not be captured by the alternative methods investigated because of the limitations of the grid size-related soil map information. Nevertheless, the analysis revealed a very good temporal correlation of SM dynamics with the field area average across all approaches, with AquaCrop and ARIS at a soil depth of 0–10 cm and S1ASCAT soil–water index 05 achieving a R2 and a Kling–Gupta efficiency >0.80. These results indicate the added value of complementary methods for estimating SM to reduce spatial and temporal uncertainties in the estimated topsoil water content.

Currently, methods for mapping agricultural crops have been predominantly developed for a number of the most important and popular crops. These methods are often based on remote sensing data, scarce information about the location and boundaries of fields of a particular crop, and involve analyzing phenological changes throughout the growing season by utilizing vegetation indices, e.g., the normalized difference vegetation index. However, this approach encounters challenges when attempting to distinguish fields with different crops growing in the same area or crops that share similar phenology. This complicates the reliable identification of the target crops based solely on vegetation index patterns. This research paper aims to investigate the potential of advanced techniques for crop mapping using satellite data and qualitative information. These advanced approaches involve interpreting features in satellite images in conjunction with cartographic, statistical, and climate data. The study focuses on data collection and mapping of three specific crops: lavender, almond, and barley, and relies on various sources of information for crop detection, including satellite image characteristics, regional statistical data detailing crop areas, and phenological information, such as flowering dates and the end of the growing season in specific regions. As an example, the study attempts to visually identify lavender fields in Bulgaria and almond orchards in the USA. We test several state-of-the-art methods for semantic segmentation (U-Net, UNet++, ResUnet). The best result was achieved by a ResUnet model (96.4%). Furthermore, the paper explores how vegetation indices can be leveraged to enhance the precision of crop identification, showcasing their advanced capabilities for this task.

Comprehensive housing stock information is crucial for informing the development of climate resilience strategies aiming to reduce the adverse impacts of extreme climate hazards in high-risk regions like the Caribbean. In this study, we propose an end-to-end workflow for rapidly generating critical baseline exposure data using very high-resolution drone imagery and deep learning techniques. Specifically, our work leverages the segment anything model (SAM) and convolutional neural networks (CNNs) to automate the generation of building footprints and roof classification maps. We evaluate the cross-country generalizability of the CNN models to determine how well models trained in one geographical context can be adapted to another. Finally, we discuss our initiatives for training and upskilling government staff, community mappers, and disaster responders in the use of geospatial technologies. Our work emphasizes the importance of local capacity building in the adoption of AI and Earth Observation for climate resilience in the Caribbean.

Glaciers play a crucial role in the Asian Water Tower, underscoring the necessity of accurately assessing their mass balance and ice volume to evaluate their significance as sustainable freshwater resources. In this study, we analyzed ground-penetrating radar (GPR) measurements from a 2020 survey of the Xiao Dongkemadi Glacier (XDG) to determine ice thickness, and we extended the glacier’s volume-change record to 2020 by employing multi-source remote-sensing data. Our findings show that the GPR-derived mean ice thickness of XDG in 2020 was 54.78 ± 3.69 m, corresponding to an ice volume of 0.0811 ± 0.0056 km3. From 1969 to 2020, the geodetic mass balance was −0.19 ± 0.02 m w.e. a−1, and the glacier experienced area and ice volume losses of 16.38 ± 4.66% and 31.01 ± 4.59%, respectively. The long-term mass-balance reconstruction reveals weak fluctuations occurred from 1967 to 1993 and that overall mass losses have occurred since 1994. This ongoing shrinkage and ice loss are mainly associated with the temperature increases in the warm season since the 1960s. If the climate trend across the central Tibetan Plateau follows to the SSP585 scenario, then XDG is at risk of disappearing by the end of the century.

Tropical cyclones can significantly impact mangrove forests, with some recovering rapidly, whilst others may change permanently. Inconsistent approaches to quantifying these impacts limit the capacity to identify patterns of damage and recovery across landscapes and cyclone categories. Understanding these patterns is critical as the changing frequency and intensity of cyclones and compounding effects of climate change, particularly sea-level rise, threaten mangroves and their ecosystem services. Improvements in Earth observation data, particularly satellite-based sensors and datacube environments, have enhanced capacity to classify time-series data and advanced landscape monitoring. Using the Landsat archive within Digital Earth Australia to monitor annual changes in canopy cover and extent, this study aims to quantify and classify immediate and long-term impacts of category 3–5 cyclones for mangroves in Australia. Closed canopy mangrove forests experienced the greatest immediate impact (loss of canopy cover). Most immediate impacts were minor, implying limited immediate mortality. Impacts varied spatially, reflecting proximity to exposed coastlines, cyclone track and forest structure (height, density, condition and species). Recovery was evident across all cyclones, although some areas exhibited permanent damage. Understanding the impacts and characteristics of vulnerable and resilient forests is crucial for managers tasked with protecting mangroves and their services as the climate changes.

Water recreation is valuable to people, and its value can be affected by changes in water quality. This paper presents the results of a revealed preference survey to elicit coastal New England, USA, residents’ values for water recreation and water quality. We combined the survey responses with a comprehensive data set of coastal attributes, including in-water and remotely sensed water quality metrics. Using a travel cost model framework, we found water clarity and the bacterial conditions of coastal waters to be practical water quality inputs to economic analysis, available at appropriate scales, and meaningful to people and their behavior. Changes in clarity and bacterial conditions affected trip values, with a $4.5 change for a meter in clarity in Secchi depth and $0.08 for a one-unit bacteria change in colony-forming units per 100 ml. We demonstrate the large potential value of improving water quality through welfare analysis scenarios for Narragansett Bay, Rhode Island, and Cape Cod, Massachusetts, USA. The paper discusses lessons for improving the policy relevance and applicability of water quality valuation studies through improved water quality data collection, combined with the application of scalable analysis tools for valuation.

The behaviour of mountain glaciers on decadal time scales is a useful indicator for assessing climate change. Although less monitored and studied than the ice sheet, local glaciers and ice caps along the coast of Greenland are substantial contributors to meltwater runoff and sea level rise. This study analyses the cumulative area, ice mass and Equilibrium Line Altitude (ELA) change that occurred on 4100 glaciers and ice caps in West Greenland from 1985 to approximately 2020, using remotely sensed data and including glaciers smaller than 1 km2 in the calculations. The glaciers involved in the study decreased in area by 1774 ± 229 km2 which corresponds to almost −15%. Their surface elevation decreased on average by 20.6 ± 3.9 m, corresponding to a rate of −0.5 ± 0.1 m w.e. a−1. The ELA shows a median regional rise of 150 m with marked local variability and higher median rise in the northern part of the study area. Strong regional gradients in ELA of individual glaciers are found, both towards the ice sheet and in areas where local orography affects precipitation. The observed high spatial variability of changes suggests that more monitoring on sub-regional level is needed.

Information related to the climate, sowing time, harvest, and crop development is essential for defining appropriate strategies for agricultural activities, which helps both producers and responsible bodies. Paraná, the second largest soybean producer in Brazil, has high climatic variability, which greatly influences planting, harvesting, and crop productivity periods. Therefore, the objective of this study was to regionalize the state of Paraná, considering decennial metrics associated with climate variables and the enhanced vegetation index (EVI) during the soybean cycle. Individual and global analyses of these metrics were conducted performed using multivariate techniques. These analyses were carried out in agricultural scenarios with low, medium, and high precipitation, corresponding to harvest years 2011/2012, 2013/2014, and 2015/2016, respectively. The results obtained from the scores of the retained factors and the cluster analysis were the profile of the groups, with Group 1 presenting more favourable climatic and agronomic conditions for the development of soybean crops for the three harvest years. The opposite occurred for Groups 2 (2011/2012 and 2013/2014) and Group 3 (2015/2016). During the soybean reproductive phases (R2 – R5), precipitation values were inadequate, especially for Group 2 (2011/2012 and 2013/2014) with high water deficit, resulting in a drop in soybean productivity. The climatic and agronomic regionalization of Paraná made it possible to identify the regions most suitable for growing soybeans, the effect of climatic conditions on phenological stages, and the variability of soybean productivity in the three harvest years.

Icebergs are part of the glacial mass balance and they interact with the ocean and with sea ice. Optical satellite remote sensing is often used to retrieve the above-waterline area of icebergs. However, varying solar angles introduce an error to the iceberg area retrieval that had not been quantified. Herein, we approximate the iceberg area error for top-of-atmosphere Sentinel-2 near-infrared data at a range of solar zenith angles. First, we calibrate an iceberg threshold at a $56^\circ$ solar zenith angle with reference to higher resolution airborne imagery at Storfjorden, Svalbard. A reflectance threshold of 0.12 yields the lowest relative error of 0.19% ± 15.74% and the lowest interquartile spread. Second, we apply the 0.12 reflectance threshold to Sentinel-2 data at 14 solar zenith angles between $45^\circ$ and $81^\circ$ in the Kangerlussuaq Fjord, south-east Greenland. Here we quantify the error variation with the solar zenith angle for a consistent set of large icebergs. The error variation is then standardized to the error obtained in Svalbard. Up to a solar zenith angle of $65^\circ$, the mean standardized iceberg area error remains between 5.9% and −5.67%. Above $65^\circ$, iceberg areas are underestimated and inconsistent, caused by a segregation into shadows and sun-facing slopes.

The Battle of al-Qadisiyyah (c. AD 637/8) was a crucial victory by the Arab Muslims over the forces of the Sasanian Empire during the early Islamic conquests. Analysis of satellite imagery of south-west Iraq has now revealed the likely location of this important historic battle.

This project documents the current archaeological record of the Qaraçay River Basin in western Azerbaijan. Integrating intensive pedestrian survey, satellite imagery analysis and topographic mapping, the study identified 85 kurgans, six necropolises and nine sites from the Chalcolithic or medieval periods. The authors believe this demonstrates the potential for further archaeological studies in the region.

Precipitation is one of the most relevant weather and climate processes. Its formation rate is sensitive to perturbations such as by the interactions between aerosols, clouds, and precipitation. These interactions constitute one of the biggest uncertainties in determining the radiative forcing of climate change. High-resolution simulations such as the ICOsahedral non-hydrostatic large-eddy model (ICON-LEM) offer valuable insights into these interactions. However, due to exceptionally high computation costs, it can only be employed for a limited period and area. We address this challenge by developing new models powered by emerging machine learning approaches capable of forecasting autoconversion rates—the rate at which small droplets collide and coalesce becoming larger droplets—from satellite observations providing long-term global spatial coverage for more than two decades. In particular, our approach involves two phases: (1) we develop machine learning models which are capable of predicting autoconversion rates by leveraging high-resolution climate model data, (2) we repurpose our best machine learning model to predict autoconversion rates directly from satellite observations. We compare the performance of our machine learning models against simulation data under several different conditions, showing from both visual and statistical inspections that our approaches are able to identify key features of the reference simulation data to a high degree. Additionally, the autoconversion rates obtained from the simulation output and satellite data (predicted) demonstrate statistical concordance. By efficiently predicting this, we advance our comprehension of one of the key processes in precipitation formation, crucial for understanding cloud responses to anthropogenic aerosols and, ultimately, climate change.

The site of Guiengola is an example of one of the settlements built by the Zapotecs during their fourteenth- to fifteenth-century migration to the Southern Isthmus of Tehuantepec. Although Guiengola is well known in the ethnohistorical record as being the place where the Mexica armies were defeated by Zapotec forces during the late fifteenth century, the full extension of the site was previously unknown. Despite evidence of a dense population at the site, it has been mistakenly characterized as a fortress for housing soldiers and troops from the nearby town of Tehuantepec. Here, I present the research of the Guiengola Archeological Project, which conducted a lidar scan and archaeological surveys between 2018 and 2023. In this article, I share a comprehensive map of Guiengola, a Postclassic Mesoamerican city. My analysis identifies a large settlement that covered 360 ha and included a walled system of fortifications, an internal road network, and a hierarchically organized city. The findings of this project expand our understanding of the variations and social divisions in the city's internal urban organization, which in turn, allow us to deepen our comprehension of the transition to the Early Colonial barrio organization of Tehuantepec.

In this study, the U-net with ResNet-34, i.e. a residual neural network with 34 layers, backbone semantic segmentation network is applied to C-band sea-ice SAR imagery over the Baltic Sea. Sentinel-1 Extra Wide Swath mode HH/HV-polarized SAR data acquired during the winter season 2018–2019, and corresponding segments derived from the daily Baltic Sea ice charts were used for training the segmentation algorithm. C-band SAR image mosaics of the winter season 2020–2021 were then used to evaluate the segmentation. The major objective was to study the suitability of semantic segmentation of SAR imagery for automated SAR segmentation and also to find a potential replacement for the outdated iterated conditional modes (ICM) algorithm currently in operational use. The results compared to the daily Baltic Sea ice charts and the operational ICM segmentation and visual interpretation were encouraging from the operational point of view. Open water areas were located very well and the oversegmentation produced by ICM was significantly reduced. The correspondence between the ice chart polygons and the segmentation results was also reasonably good. Based on the results, the studied method is a potential candidate to replace the operational ICM SAR segmentation used in the Copernicus Marine Service automated sea-ice products at Finnish Meteorological Institute.

Diffuse optical spectroscopy (DOS) techniques characterize scattering media by examining their optical response to laser illumination. Time-domain DOS methods involve illuminating the medium with a laser pulse and using a fast photodetector to measure the time-dependent intensity of light that exits the medium after multiple scattering events. While DOS research traditionally focused on characterizing biological tissues, we demonstrate that time-domain diffuse optical measurements can also be used to characterize snow. We introduce a model that predicts the time-dependent reflectance of a dry snowpack as a function of its density, grain size, and black carbon content. We develop an algorithm that retrieves these properties from measurements at two wavelengths. To validate our approach, we assembled a two-wavelength lidar system to measure the time-dependent reflectance of snow samples with varying properties. Rather than measuring direct surface returns, our system captures photons that enter and exit the snow at different points, separated by a small distance (4–10 cm). We observe clear, linear correlations between our retrievals of density and black carbon concentration, and ground truth. For black carbon concentration the correlation is nearly one-to-one. We also find that our method is capable of distinguishing between small and large grain sizes.

The ongoing deceleration of Whillans Ice Stream, West Antarctica, provides an opportunity to investigate the co-evolution of ice-shelf pinning points and ice-stream flux variability. Here, we construct and analyze a 20-year multi-mission satellite altimetry record of dynamic ice surface-elevation change (dh/dt) in the grounded region encompassing lower Whillans Ice Stream and Crary Ice Rise, a major pinning point of Ross Ice Shelf. We developed a new method for generating multi-mission time series that reduces spatial bias and implemented this method with altimetry data from the Ice, Cloud, and land Elevation Satellite (ICESat; 2003–09), CryoSat-2 (2010–present), and ICESat-2 (2018–present) altimetry missions. We then used the dh/dt time series to identify persistent patterns of surface-elevation change and evaluate regional mass balance. Our results suggest a persistent anomalous reduction in ice thickness and effective backstress in the peninsula connecting Whillans Ice Plain to Crary Ice Rise. The multi-decadal observational record of pinning-point mass redistribution and grounding zone retreat presented in this study highlights the on-going reorganization of the southern Ross Ice Shelf embayment buttressing regime in response to ice-stream deceleration.

Surface meltwater can influence subglacial hydrology and ice dynamics if it reaches ice sheet's base. Firn aquifers store meltwater and drain into wide crevasses marking the aquifer's downstream boundary, indicating water from firn aquifers can drive hydrofracture to establish surface-to-bed hydraulic connections at inland locations. Yet, sparse observations limit our understanding of the physical processes controlling firn aquifer drainage. We assess the potential for future inland firn aquifer drainage migration with field observations and linear elastic fracture mechanics (LEFMs) modeling to determine the conditions needed to initiate and sustain hydrofracture on Helheim Glacier, Greenland. We find that local stress conditions alone can drive crevasse tips into the firn aquifer, allowing hydrofracture initiation year-round. We infer inland expansion of crevasses over the firn aquifer from crevasse-nucleated whaleback dune formation and Global Navigation Satellite System-station detected crevasse opening extending 14 and 4 km, respectively, inland from the current, farthest-upstream drainage point. Using our LEFM model, we identify three vulnerable regions with coincidence between dry crevasse depth and water table variability, indicating potential future inland firn aquifer drainage sites. These results suggest the downstream boundary of firn aquifers can migrate inland under future warming scenarios and may already be underway.

During the second half of the first millennium BC, hundreds of hillforts dotted the central Italian Apennines. Often interpreted as ‘proto-towns’, the authors present results of investigations at Monte Santa Croce-Cognolo that challenge this idea. Previous studies identified a small area (<1ha) of occupation and suggested that habitation extended across the whole 18ha site. Combining geophysical and pedestrian survey with remotely sensed data, and local ethnographic accounts, the authors detect little evidence for permanent habitation and instead argue for activities connected with animal husbandry. The results challenge urban-centric interpretations by demonstrating the coexistence of monumental but uninhabited hillforts and urban sites—usually seen across the Mediterranean and Europe.