Regional-scale Studies Research Articles

Dynamic patterns of particulate matter concentration and size distribution in urban street canyons: insights into diurnal and short-term seasonal variations.

Time-varying characteristics of particulate matter (PM) pollution play a crucial role in shaping atmospheric dynamics, which impact the health and welfare of urban commuters. Previously published studies on the diurnal patterns of PMs are not consistent, especially in the context of field experiments in central China, and most field studies have only focused on particles with a single particle size. This study conducted regional-scale studies across 72 street canyon sets in Wuhan, China, investigated diurnal and seasonal PM concentration variations while also evaluating various PM size and the key driving factors. During summer (July, August, and September), evergreen tree-lined street canyons maintained a stable linear trend for smaller dp particulates (i.e., PM1, PM2.5, and PM4), while deciduous street canyons exhibited a bimodal distribution. In winter (January and February), fine particulates (i.e., PM1 and PM2.5) remained a linear trend in evergreen street canyons, while deciduous street canyons show a slightly wavy fluctuating pattern. Meanwhile, it exhibited quadrimodal-peak and triple-trough patterns in both PM7, PM10, and TSP concentrations. The lowest PM concentrations were observed between 14:00 and 16:00 for all particle sizes, with decreased summer pollution (7.81% lower in PM2.5, 53.47% lower in PM10, and 50.3% lower in TSP) noted in our seasonal analysis. Among the various meteorological factors, relative humidity (RH) was identified as the dominant influencing PM factor in both summer and winter. Results from this study will help us better understand field-based air pollutant dispersion processes within pedestrian spaces while laying the groundwork for future research into street PM experiments.

Modeling Pure qP-wave in Attenuative TI Media Using Complex-Valued Fractional Viscoacoustic Wave Equation

Seismic anisotropy, characterized by direction-dependent seismic velocity and atten- uation, is a fundamental property of the Earth, widely observed in both field and lab measurements. This anisotropy significantly influences seismic wavefield attributes, such as amplitude, phase, and traveltime. Accurate quantification of anisotropy-related seismic wave propagation is essential for global and regional scale studies in seismology, including imaging and inversion. Since the elastic assumption of the Earth requires contending with shear waves, which are often weak in our seismic recording, thus we derive a new general complex-valued spatial fractional Laplacian (SFL) viscoacoustic anisotropic pure quasi-P (qP) wave equation (WE) in attenuative TI (A-TI) media under the acoustic assumption, and employ the fixed-order SFL operator to quantify seismic wave propagation in heteroge- neous media. The proposed WE accurately captures anisotropic effects in seismic velocity and attenuation, even in strongly attenuative and anisotropic environments. It also de- scribes the decoupled energy attenuation and velocity dispersion behaviors of the frequency independent quality factor Q, beneficial for seismic imaging and inversion. Compared with existing viscoacoustic anisotropic pure qP WEs in A-TI media, our WE simplifies computa- tional complexity and is easy to implement, as it decomposes into a complex-valued scalar operator and two differential operators. The evaluated dispersion curves further confirm the accuracy of our proposed approach. Numerical examples in complex geological settings demonstrate the new equation’s precision, stability and efficiency in modeling seismic wave propagation.

Semantic segmentation of coastal aerial/satellite images using deep learning techniques: An application to coastline detection

A new CNN based approach supported by semantic segmentation, was proposed. This approach is frequently used to carry out regional-scale studies. The core of our method revolves around a CNN model, based on the famous U-Net architecture. Its purpose is to identify different classes of pixels on satellite images and later to automatically detect the coastline. The recently launched Coast Train dataset was used to train the CNN model. Traditional coastline detection was improved (“water/land” segmentation) by means of two new aspects the use of the Sobel-edge loss function and the segmentation of the satellite images into several categories like built-up areas, vegetation and land besides beach/sand and water classes. The approach used ensures a more precise coastline extraction, distinguishing water pixels from all other categories. Our model adeptly identifies features, such as cliff vegetation or coastal roads, that some models might overlook. In this way, coastline localization and its drawing for regional scale study, have minor uncertainties. The performance of the CNN-based method, achieving 85% accuracy and 80% IoU (Intersection over Union) in the segmentation process. The ability of the model to extract the coastline was validated on a Sicilian case study, notably the San Leone beach (Agrigento). The model's results align closely with the ground truth, moreover, its reliability was further confirmed when it was tested on other Sicilian coastal regions.Beyond robustness, the model offers a promising avenue for enhanced coastal analysis potentially applicable to coastal planning and management.

Linking watershed formation with the phylogenetic distribution of a soil microscopic fungus in Yunnan Province, China

BackgroundPhylogeographic studies have gained prominence in linking past geological events to the distribution patterns of biodiversity, primarily in mountainous regions. However, such studies often focus on plant taxa, neglecting the intricate biogeographical patterns of microbes, particularly soil microbial communities. This article explores the spatial distribution of the nematode-trapping fungus Arthrobotrys oligospora, a widespread microorganism, in a tectonically active region at the southeastern edge of the Qinghai-Tibetan Plateau. By analysing the genetic variation of this fungus alongside the historical structure of major river watersheds, we sought to uncover potential connections between the two. Our study involved sampling 149 strains from 116 sites across six major watersheds in the region.ResultsThe resulting haplotype network revealed five distinct clusters, each corresponding closely to a specific watershed. These clusters exhibited high haplotype diversity and low nucleotide diversity, supporting the notion of watershed-based segregation. Further analysis of haplotypes shared across watersheds provided evidence for three proposed past river connections. In particular, we found numerous shared haplotypes between the Yangtze and Mekong basins, as well as between the Yangtze and the Red basins. Evidence for a Irrawaddy-Salween-Red and a Yangtze-Pearl-Red river connections were also portrayed in our mapping exercise.ConclusionsThese findings emphasize the crucial role of historical geomorphological events in shaping the biogeography of microbial biodiversity, alongside contemporary biotic and abiotic factors. Watershed perimeters emerged as effective predictors of such patterns, suggesting their suitability as analytical units for regional-scale studies. Our study also demonstrates the potential of microorganisms and phylogeographic approaches to complement traditional geological analyses, providing a more comprehensive understanding of past landscape structure and its evolution.

A novel Slepian approach for determining mass-term sea level from GRACE over the South China Sea

Treatment of land-to-ocean leakage is crucial in modeling the mass-term sea level (MSL) using the Gravity Recovery and Climate Experiment (GRACE) satellite gravity measurements. In this study, we utilized the Spherical Slepian functions (SSFs) to determine MSL variations in the South China Sea. A sensitivity simulation in terms of trend, annual amplitude, and phase revealed that the SSF solution with a 1° coastal buffer zone provides a better land-to-ocean leakage correction than traditionally used Spherical Harmonics (SH). It was also found that an additional smoothing procedure for SSF with low concentrating energy could significantly reduce the high-frequency noise in GRACE (e.g., the north–south strips). The spatiotemporal characteristics of the true MSL were further compared among GRACE SH and Mascon solutions, model-predicted ocean bottom pressure, and steric-corrected altimetric data (i.e., satellite-altimetric sea level minus steric effect). Results revealed that despite the SSF-inverted regional MSL solution being generally similar to other results, this technique notably recovered the realistic magnitude of detailed features. The apparent MSL signal was, for instance, precisely recovered in the central part of the Gulf of Thailand and the Sunda Shelf by the SSF, while the SH significantly underestimated it due to smoothing. In addition to the seasonality, the interannual signal decomposed from the SSF-inverted residual MSL (by removing the trend and periodic signals) was the strongest and well cross-correlated with the Southern Oscillation Index. With more detailed spatial patterns revealed by MSL from GRACE SSF, our findings demonstrate that SSF is more suitable for regional-scale studies.

The China Active Faults Database (CAFD) and its web system

Abstract. Active faults serve as potential sources of destructive earthquakes. Studies and investigations of active faults are necessary for earthquake disaster prevention. This study presents a nation-scale database of active faults in China and its adjacent regions, in tandem with an associated web-based query system. This database is an updated version of the active faults data included in the Seismotectonic Map of China and its Adjacent Regions (1:4 000 000), which is one of the four essential maps of the mandatory Chinese standard GB 18306-2015 Seismic Ground Motion Parameter Zonation Maps of China. The data update and integration stem from regional-scale studies and surveys conducted over the past 2 decades (at reference scales from 1:250 000 to 1:50 000). The information amassed from these regional-scale studies and surveys encompasses geophysical probing, drill logging, measurement of offset landforms, sample dating, as well as geometric and kinematic parameters of exposed and blind faults, paleo-earthquake sequences, and recurrence intervals. These data have been acquired and analyzed utilizing a uniform technical standard framework and reviewed by expert panels in both field and laboratory settings. Our system hosts this nation-scale database accessible through a Web Geographic Information System (GIS) application, enabling browsing, querying, and downloading functionalities via a web browser. The system we built also publishes the Open Geospatial Consortium (OGC) Web Feature Service and the OGC Web Map Service of active faults data. Users can incorporate map layers and obtain fault data in OGC-compliant GIS software for further analysis through these services. The Chinese government, research institutions, and companies have widely used the active faults data from the previous versions of the database. The database is available at https://doi.org/10.12031/activefault.china.400.2023.db (Xu, 2023) and via the web system (https://data.activetectonics.cn/arcportal/apps/webappviewer/index.html?id=684737e8849c4170bbca14447608c451, CEFIS, 2023; http://data.activetectonics.cn/arcserver/services/Hosted/CAFD400_2022_WFS/MapServer/WFSServer, CAFD WFS, 2024).

A dataset of gridded phenology of woody plants in Europe from 1951 to 2021

Plant phenology records the timing of plant cyclic growth events, and stand as one of the most important indicators of climate change. The monitoring and research of plant phenology is of great significance for understanding the response of ecosystems to global changes and simulating the material and energy balance of terrestrial ecosystems. Based on the ground observation phenological data of six representative woody plants compiled by the Pan European Phenology Project (PEP725) in the past 70 years, this paper employed three phenology models (Unichill, Unified and TSC) to predict and upscale the phenology data on the continental scale, and prepared a dataset of gridded phenology of woody plants in Europe. This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1 ° and a temporal resolution of one day. The quality assessment of the grid phenology data shows that the average error for FLD and FFD is 7.9 and 7.6 days respectively, which are close to simulation errors of spring phenology observed in other regional-scale studies, revealing the high accuracy of the data. This dataset can better characterize the temporal and spatial patterns of plant phenology at a continental scale in Europe, and provide an effective verification approach for plant phenology products from other sources. Furthermore, it can offer valuable data support for research on global change and terrestrial ecosystem simulation.

Scaling of soil organic carbon in space and time in the Southern Coastal Plain, USA

Soil organic carbon (SOC) is a dynamic soil property (DSP) that represents the largest portion of terrestrial carbon. Its relevance to carbon sequestration and the potential effects of land use on SOC storage, make it imperative to map across both space and time. Most regional-scale studies mapping SOC give static estimates and train different models for different periods with varying accuracies. We developed a flexible modeling approach called DSP-Scale to map SOC in both space and time. DSP-Scale uses ecological concepts and empirical data to predict DSP dynamics using inherent soil properties (static factors) and land cover changes (dynamic factors). We compiled SOC data for the 0–20 cm depth (SOC20) from 1441 points spanning a 25 million ha study area in the southeastern U.S. Coastal Plain, incorporating data from the Rapid Carbon Assessment, National Cooperative Soil Survey Soil Characterization database, and other regional studies. We developed a random forest model using climate, topography, soil survey, and land cover changes to predict SOC20 dynamics for five-year periods between 2001 and 2019. Our model explained 66 % and 59 % of the variation for the training and test data, respectively. Top predictors included mean annual precipitation, slope, and soil erosion class. Land cover 10 years before measurements of SOC20 was more important than current land cover for estimating SOC20. We estimated total SOC stocks of 207.1 and 208.3 Tg for 2001 and 2019, respectively. Highest gains of total SOC stock (0.9 Tg from 2001 to 2019) were associated with land cover change from mixed to evergreen forest. The greatest loss of total SOC stock (0.2 Tg) in the same period was associated with land cover change from pasture/hay to evergreen forest. We concluded that the DSP-Scale approach provides a flexible way to use dynamic and static factors affecting SOC stocks to predict changes in space and time at regional scales.

Compilation of Apatite Fission-Track Data from the Northeast Atlantic Realm: A Jigsaw Puzzle with Missing Pieces

Abstract The northeast (NE) Atlantic is one of the best-studied geological regions in the world, incorporating a wide array of geological phenomena including extensional tectonism, passive margin development, orogenesis, and breakup-related volcanism. Apatite fission-track (AFT) thermochronology has been an important tool in studying the onshore evolution of the NE Atlantic for several decades. Unfortunately, large regional-scale studies are rare, making it difficult to study geological processes across the whole region. In this work, a compilation of published AFT data is presented from across Fennoscandia, the British Isles, East Greenland, and Svalbard, with the goal of providing an accessible overview of the data and how this vast body of work has improved our understanding of the region’s evolution. Alongside a review of previous literature, interpolated maps of fission track age and mean track length (MTL) highlight regional trends in the data that may result from major first-order processes and areas of low sample density that should be targeted for future study. Additionally, in the absence of metadata required for thermal history modeling, apparent exhumation rate estimates are calculated from available elevation profiles and the timing of major exhumation events inferred from “boomerang plots” of fission track ages against MTL values. Across Fennoscandia, data suggests that the opening of the NE Atlantic and exhumation of the margin have clearly played a major role in the thermal history of the upper crust. The remaining areas of Britain, Ireland, East Greenland, and Svalbard all present more complex trends consistent with a combination of the NE Atlantic’s opening and the interplay between specific bedrock geology of sampling sites and localized geological processes. Areas of low sample density include southern Britain, NE Britain, southeast Greenland, southern Svalbard, and Eastern Fennoscandia, each of which provides the natural laboratory required to answer many unresolved questions.

Compilation of Apatite Fission-Track Data from the Northeast Atlantic Realm: A Jigsaw Puzzle with Missing Pieces

Abstract The northeast (NE) Atlantic is one of the best-studied geological regions in the world, incorporating a wide array of geological phenomena including extensional tectonism, passive margin development, orogenesis, and breakup-related volcanism. Apatite fission-track (AFT) thermochronology has been an important tool in studying the onshore evolution of the NE Atlantic for several decades. Unfortunately, large regional-scale studies are rare, making it difficult to study geological processes across the whole region. In this work, a compilation of published AFT data is presented from across Fennoscandia, the British Isles, East Greenland, and Svalbard, with the goal of providing an accessible overview of the data and how this vast body of work has improved our understanding of the region’s evolution. Alongside a review of previous literature, interpolated maps of fission track age and mean track length (MTL) highlight regional trends in the data that may result from major first-order processes and areas of low sample density that should be targeted for future study. Additionally, in the absence of metadata required for thermal history modeling, apparent exhumation rate estimates are calculated from available elevation profiles and the timing of major exhumation events inferred from “boomerang plots” of fission track ages against MTL values. Across Fennoscandia, data suggests that the opening of the NE Atlantic and exhumation of the margin have clearly played a major role in the thermal history of the upper crust. The remaining areas of Britain, Ireland, East Greenland, and Svalbard all present more complex trends consistent with a combination of the NE Atlantic’s opening and the interplay between specific bedrock geology of sampling sites and localized geological processes. Areas of low sample density include southern Britain, NE Britain, southeast Greenland, southern Svalbard, and Eastern Fennoscandia, each of which provides the natural laboratory required to answer many unresolved questions.

Investigation of the use of topographic data derived from Pléiades imagery for high-resolution hillslope-scale morphometry

Obtaining accurate representations of the Earth surface topography is paramount in many geomorphological investigations. While regional scale studies use Digital Elevation Models (DEM) with resolution of the order of 10–100 m, in many applications resolution <5 m are mandatory to appropriately represent landforms and processes of interest. In particular, hillslopes, which cover the most of continental surfaces, have typical length of the order of 100 m. For that reason, high resolution topographic data are mandatory to correctly represent their morphology. The massive use of higher resolution topographic data has been enabled by a vast array of methodological developments, among which terrestrial and airborne Light Detection And Ranging (LiDAR) or uncrewed aerial vehicle-based photogrammetric approaches are the most commonly used. However, such techniques might be difficult to deploy and not cost-effective, to cover large areas in remote locations.In this study, we present an exploration of the potential of topographic data derived from Pléiades imagery for the characterizing hillslope morphology. We focus on the Gabilan Mesa area in California, where a LiDAR DEM is available and is used as a benchmark. We systematically compare Pléiades-derived topographic data with the DEM constructed from LiDAR data, starting from simple elevation analysis and then using topographic derivatives, such as slope or curvature, which are commonly used in morphometric studies. Finally, we take advantage of the high resolution of the datasets to extract and compare hillslope-scale morphometric parameters, such as length, relief, or hilltop curvature. While some particular situations, such as planar and horizontal surfaces, requires specific attention in terms of the relative differences between the two type of data, the absolute values for the metrics are in good agreement over their relevant range of utilization for hillslope-scale geomorphology.

Comparison between key crop production and farmers' perspectives in Eastern Hungary: A regional study

&lt;abstract&gt; &lt;p&gt;Agricultural production and technology are crucial in supplying agricultural commodities and ensuring food security. Farmers are pivotal in this process. However, there is a noticeable gap in research: While numerous researchers focus on the national perspective, there is a relative scarcity of research conducted at the regional level. This lack of regional-scale studies highlights the need for more localized research to understand and address the unique agricultural challenges and opportunities in specific areas. We aimed to analyze wheat and maize production in a county in Eastern Hungary. We also aimed to delve into farmers' perspectives regarding the agricultural sector and research. Key findings included the average maize, with a yield at 5,896.2 ± 1,624.2kg/hectare (2000–2020), which appeared to be superior to wheat, which had a mean yield of 4,135.7 kg/hectare with a standard deviation of 788.4. The Tukey test confirmed significant differences between wheat and maize in terms of harvested area, production, and yield, highlighting distinct performance variations of these crops in the region. Moreover, we identified a weak but significant correlation between the value of the golden crown and aspects such as cost-effective crop protection, reduced tillage costs, and increased production value and income. A similar weak significant relationship was found between farmers' age and research topics related to increasing sales revenue. We aimed to analyze wheat and maize production in Szabolcs-Szatmár-Bereg County in Eastern Hungary. We found that 58% of farmers did not maintain accurate records of production-related costs and income, which could have significant implications for revenue calculation and decision-making. The outcomes of this research are instrumental for decision-makers, providing insights that could guide the development and implementation of agricultural policies and practices at a regional scale.&lt;/p&gt; &lt;/abstract&gt;

A new assessment framework to forecast land use and carbon storage under different SSP-RCP scenarios in China

The vision of achieving “carbon neutrality” has created new requirements for the projection of land use and land cover (LULC), as well as the carbon storage (CS) of terrestrial ecosystem. Global-scale LULC scenario assessments with coarser resolution introduces uncertainties to national and regional-scale studies, which in turn has a negative impact on CS analysis based on land use perspective. Therefore, we proposed a new framework for scenario-based assessment that integrates the global-scale Land Use Harmonization (LUH2) dataset, Patch-generating Land Use Simulation (PLUS) model, and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which we called LUH2-PLUS-InVEST (LPI) model. Our aim is to investigate the potential impacts of the combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) on China's future LULC and CS. By calibrating the demands, we generated structural predictions that were consistent with the actual land use. Furthermore, we explored the spatial heterogeneity of potential land use changes using 500 m × 500 m downscale simulations. Additionally, we developed a quantitative evaluation of CS from a spatiotemporal perspective and made recommendations on potential ecological threats. Our findings indicate that the basic characteristics of LULC and CS are determined by the natural context and that the prospects of land use distribution and carbon sequestration capacity are influenced by global emission pressure, regional competition, and China's unique development pattern. The results demonstrate that the LUH2-PLUS-INVEST model can provide an effective method for modeling the feedbacks of LULC and CS to the climate-society system.

Maximizing soil organic carbon stocks under cover cropping: insights from long-term agricultural experiments in North America

Cover crops are widely advocated for increasing soil organic carbon (SOC) levels, thereby benefiting soil health improvement and climate change mitigation. Few regional-scale studies have robustly explored SOC stocks under cover cropping, due to limited long-term experiments. We used the unique experimental data from the North American Project to Evaluate Soil Health Measurements conducted in 2019 to address this issue. This study included 19 agricultural research sites with 36 pairs of cover cropping established between 1896 and 2014. Explanatory variables related to site-specific environmental conditions and management practices were collected to identify and prioritize contributing factors that affect SOC stocks with cover crops, by coupling the Boruta algorithm and structural equation modeling. Overall, cover crops significantly (P < 0.05) improved several indicators of soil health, including greater SOC (concentration: +8%; stock: +7%), total nitrogen (+8%), water-stable aggregates (+15%), and potential carbon mineralization (+34%), on average, compared to no cover crop control. Likewise, on average, cover crops sequestered SOC 3.55 Mg C ha-1 (0–15 cm depth), with a sequestration rate of 0.24 Mg C ha-1 yr-1. In addition, we found climate (Hargreaves climatic moisture deficit) was important in explaining the variation of SOC stocks with cover crops, followed by soil properties (e.g., soil clay content). In terms of management practices, cover crop type had a significant positive (0.36) effect on SOC stocks, with non-legumes showing a greater impact, compared to legumes and mixtures. Crop rotational diversity also had a positive (0.28) effect on SOC accumulation. Our findings suggested that integrating non-legume cover crops into diverse crop rotation is likely to be a promising strategy to maximize SOC stocks with cover crops across North America.

Toward a ground-based and long-term meteorological forcing dataset for South Korea

The Modern-Era Retrospective Analysis for Research and Application version 2 (MERRA-2) is a well-established reanalysis dataset and is widely used for driving global-scale hydrological models. However, owing to its relatively coarse spatial resolution (0.5°), the capability of MERRA-2 is repeatedly challenged in regional-scale studies, especially for smaller areas of interest. In addition, the availability of in situ observation data is a pressing issue for generating meteorological forcing. We developed a grid-based high spatial (0.125°) and temporal (hourly) resolution meteorological forcing dataset, which can evaluate hydrological processes in South Korea using state-of-the-art meteorological observations from 1980 to 2020. The forcing dataset was created by combining Automated Synoptic Observing System (ASOS) in situ measurement data from the Korean Meteorological Administration and MERRA-2 reanalysis datasets. Five meteorological variables were provided in the ASOS-MERRA2 (precipitation, air temperature, surface pressure, specific humidity, and wind speed). The study demonstrates that the region-based and high spatial resolution of ASOS-MERRA2 is superior to the existing MERRA-2 with improvements of all five weather variables, for example, from 5.6 to 2.8 mm root mean square error of precipitation. The ASOS-MERRA2 was more capable of reducing the biases and root mean squared error by improving the coefficient of determination compared with MERRA-2 for all five variables. The newly developed ASOS-MERRA2 provides an opportunity to drive land surface models to evaluate the hydroclimatic conditions in South Korea.

Accelerated glacier mass loss in the southeastern Tibetan Plateau since the 1970s

The southeastern Tibetan Plateau (SETP) is a region in High Mountain Asia with the most serious glacier mass loss. However, long-term and large regional-scale studies that estimate glacier mass balance in this area remain limited. In this study, we generated a KH-9 Digital Elevation Model (DEM) (covering 87.6% of the glacier area) for the 1970s from KH-9 Hexagon imagery and quantified geodetic glacier mass over the SETP from the 1970s to 2020 using KH-9 DEM, NASADEM and ICESat-2 ATL06 data. The results show that the SETP was in a state of serious mass loss (at a rate of −0.35 ± 0.03 m w.e. per year) from the 1970s to 2020, and the rate of mass loss accelerated from −0.15 ± 0.05 to −0.56 ± 0.10 m w.e. per year between the 1970s–2000 and 2000–2020. Within the SETP, the glacier mass balance revealed a remarkable spatial heterogeneity. The maximum mean glacier mass loss rate was observed in the Hengduan Shan. Nyainqentanglha exhibited the highest acceleration in the mass loss rate since the 1970s. The warming of air temperature and decreasing snowfall can partly explain the accelerated glacier mass loss in the SETP. The study provides a new long-term glacier mass balance estimation covering almost the entire SETP that suggests the acceleration in glacier mass loss observed in the SETP since the 1970s is a regional tendency, which is crucial for understanding the relationship between glaciers and climate changes.

A regional-scale study of the contribution of local, management and climate factors to the infestation of processing tomato fields with Amaranthus species

Determining the various factors affecting weed infestation in arable fields is complicated by the considerable spatiotemporal variability of agricultural systems. A possible solution lies in regional-scale studies of weeds in commercial fields to provide insights into the influence of field properties, climate, and weed management practices on the efficacy of weed management. The current study focused on Amaranthus species – noxious weeds infesting many crops worldwide – in processing tomato fields in Northern Israel, a geographical region characterized by a significant climate gradient. This gradient provides an ideal opportunity to study the effect of climate on weed infestation and management. The objective of this study was to investigate the associations between Amaranthus infestations and local (field properties), climate, and management factors in processing tomato fields. A survey of 103 commercial tomato fields was carried out in 2018 and 2019 throughout the four major tomato-growing areas in Northern Israel. The spatiotemporal change in infestation for fields surveyed both pre- and post-weed control was evaluated relative to management and climate variables. A bootstrapped model selection for beta regression models with varying dispersion was performed to determine factors associated with weed infestation before and after weed management. The analysis showed that the application of sulfosulfuron pre-planting, could potentially reduce Amaranthus infestation depending on environmental and local conditions of the field; management comprising of more intense control measures reduced infestation; and precipitation led to an increase in mean infestation when occurring before planting, but a decrease when occurring after weed management. In addition, the onset timing of weed control was found to be negatively correlated with precipitation occurring post-treatment. Our findings show that management actions are key factors in controlling Amaranthus infestation but that these actions are affected by the local climate conditions. Therefore, this work indicates the need for a regional-scale perspective when making weed management decisions.

Influence of soil geography on the occurrence and intensity of gully erosion in the Hengduan Mountain region

How to assess gully erosion at large spatial scales is a major challenge, and few previous regional-scale studies have identified the vegetation and topography as the main factors of gully erosion, while little attention has been paid to the effects of soil properties on the regional gully occurrence and intensity. The Hengduan Mountain region has high pedodiversity owing to its significant horizontal and vertical zonation, and gully erosion is an important soil loss process in this region. This study aimed to identify the occurrence, intensity, and key influencing factors of gully erosion in the different soil zones of the Hengduan Mountain region. A total of 2,300 investigation quadrats were randomly set around the region with the size of 1 km × 1 km, and the gully erosion within each quadrat was investigated using Google Earth images. Approximately 25.5 % of the quadrats contained gullies (Qeg), 96.1 % of which was concentrated in 13 major soil groups. The annual temperature, vegetation, and slope were the key factors that influenced the occurrence of gullies in the alpine (>3,700 m a.s.l.), middle mountain (2,000–3,700 m a.s.l.), and low mountain (<2,000 m a.s.l.) soil zones, respectively. The average gully density (GD) and number of gullies (GN) were 2.22 km km−2 and 20.4 in the region, respectively, and both the average GD and GN of each main soil group exhibited significant exponentially decreasing relationships with the soil properties, including the soil organic matter (SOC) and silt content (0.43 < R2 < 0.76, P < 0.001). Within a specific soil zone, the vegetation (NDVI) and topography (elevation and slope) became the key influencing factors on GD and GN. These results show that, at the regional scale, gully erosion is initially controlled by the geographic pattern of the soil. Within the same soil zone, the vegetation and topography (slope and elevation) predominantly influence the occurrence and intensity of gully erosion.

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5 m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R2 = 0.22, relative RMSE = 32.09%). We also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

Comparative evaluation of five global gravity models over a part of the Bay of Bengal

Several global gravity models (GGMs) are freely available in the public domain, which can be utilised to study the earth's gravity field in almost every part of the globe. The present study compared the free-air gravity anomalies calculated from the five GGMs EGM2008, EIGEN6C4, GECO, XGM2019e_2159, and SGG-UGM-2 archived by the International Centre for Global Earth Models (ICGEM) with respect to shipborne gravity in the Bay of Bengal. The average correlation coefficient and covariance are ∼ 96 % and ∼ 450mGal2. The mean difference between the shipborne and the modelled gravity is − 5 mGal. Relatively higher amplitude gravity differences observed at the continental-oceanic transition, the 85°E and Ninetyeast ridges, and the western basin are possibly due to high gradient, dominant density contrasts, and rugged topography. The average standard deviation and root-mean-square-error (RMSE) of the differences are ∼ 6.5 mGal and ∼ 7.5 mGal. A significantly lower standard deviation and RMSE found for the models generated at higher degree/order compared to lower degree/order is due to diminishing omission error of the GGMs with increasing degrees of truncation. The spectral analysis and coherence estimation of the modelled gravity demonstrate excellent correspondence for anomalies wider than ∼ 25 km. The agreement between anomaly amplitudes and shapes and calculated statistics indicates that the accuracy and resolution of the modelled gravity data are certainly good enough for regional-scale studies aiming to map Moho topography and mantle structures. However, the delineation of shorter wavelength features originating from the shallow-level basement/sedimentary might be uncertain and requires further validations. The present study confirms that all five models show promising results in terms of their accuracy and resolution. Moreover, the SGG-UGM-2 and XGM2019e_2159 models compare favourably with respect to the GECO, EIGEN6C4 and EGM2008 models in the Bay of Bengal.