Predicting particulate matter, nitrogen dioxide, and ozone across Great Britain with high spatiotemporal resolution based on random forest models

In Great Britain, limited studies have employed machine learning methods to predict air pollution especially ozone (O3) with high spatiotemporal resolution. This study aimed to address this gap by developing random forest models for four key pollutants (fine and inhalable particulate matter [PM2.5 and PM10], nitrogen dioxide [NO2] and O3) by integrating multiple-source predictors at a daily level and 1-km resolution. The out-of-bag R2 (root mean squared error, RMSE) between predictions from models and measurements from monitoring stations in 2006-2013 was 0.85 (3.63 μg/m3) for PM2.5, 0.77 (6.00 μg/m3) for PM10, 0.85 (9.71 μg/m3) for NO2, and 0.85 (9.39 μg/m3) for maximum daily 8-h average (MDA8) O3 at daily level, and the predicting accuracy was higher at monthly and annual level. The high-resolution predictions captured characterized spatiotemporal patterns of the four pollutants. Higher concentrations of PM2.5, PM10, and NO2 were distributed in densely populated southern regions of Great Britain while O3 showed an inverse spatial pattern in general, which could not be fully depicted by monitoring stations. Therefore, predictions produced in this study could improve exposure assessment with less exposure misclassification and flexible exposure windows for future epidemiological studies to investigate the impact of air pollution across Great Britain.

Trends and drivers of Arctic-boreal fire intensity between 2003 and 2022

Climate change has disproportional effects on Arctic-boreal ecosystems, as the increase of air temperatures in these northern regions is several times higher than the global average. Ongoing warming and drying have resulted in recent record-breaking fire years in Arctic-boreal ecosystems, resulting in substantial carbon emissions that might accelerate climate change. While recent trends in Arctic-boreal burned area have been well documented, it is still unclear how fire intensity has changed. Fire intensity relates to the energy release from combustion and to a large extent drives the impact of a fire on the vegetation and soils, the emission of various gasses and the combustion completeness of different fuels. Here, we used the active fire product from the Moderate Resolution Imaging Spectroradiometer (MODIS) to examine trends in fire radiative power (FRP) over the entire Arctic-boreal region. We found a significant increase in annual median fire intensity between 2003 and 2022 in Eurasian boreal forests, for both daytime (increase of 0.392 MW/km2 per year, R2 = 0.56, p < 0.001) and nighttime fires (increase of 0.175 MW/km2 per year, R2 = 0.47, p < 0.001), while no general trend in FRP was observed in boreal North America. This increase in FRP in Eurasian boreal forests was strongly associated with simultaneous increases in air temperature, vapour pressure deficit, fire weather and fuel availability. We estimated that for Eurasia with each degree increase in air temperature, annual median daytime FRP increases with 1.58 MW/km2 in the tundra and 0.94 MW/km2 in the taiga. Climate change has thus resulted in a widespread and clear increase in fire intensity in central and eastern Eurasia while we could not discern clear trends in Arctic-boreal North America. Arctic-boreal fire intensity may further increase with climate change, with potentially major consequences for fire regimes, carbon emissions and society.

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %-25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %-27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %-7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91-0.93, RMSE = 314.2-376.6 g C m-2 yr-1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Short-term high-temperature pretreated compost increases its application value by altering key bacteria phenotypes

Short-term high-temperature pretreatment can effectively shorten the maturity period of organic waste composting and improve the fertilizer efficiency and humification degree of products. To investigate the effect and mechanism of the end products on the saline-alkali soil improvement and plant growth, the short-term high-temperature pretreatment composting (SHC) and traditional composting (STC) were separately blended with saline-alkali soil in a ratio of 0-40 % to establish a soil-fertilizer blended matrix for cultivating Lolium perenne L. The pot experiments combined with principal component analysis showed Lolium perenne L. planted in 20 % SHC-blended saline-alkali soil had the best growth effect, and its biomass, chlorophyll content, and plant height were 109-113 % higher than STC. The soil physicochemical property analysis showed that SHC and STC increased the soil nutrient content, humification degree, and enzyme activity at any blending ratio. The microbial analysis showed that 20 % SHC in the saline-alkali soil stimulated the growth of functional microorganisms and the addition of SHC promoted the sulfur cycle, nitrogen fixation, and carbon metabolism in the soil-plant system. The correlation analysis showed that pH; nutrient contents; and urease, catalase, sucrase, and phosphatase activities in the saline-alkali soil were significantly correlated with plant growth indexes (p < 0.05). Georgenia and norank_f__Fodinicurvataceae had a stronger correlation with four types of enzyme activities (p < 0.01). SHC improved the saline-alkali soil and promoted plant growth by adjusting soil pH, increasing soil nutrients, and influencing soil enzyme activity and dominant flora. This study provides a theoretical basis for applying SHC products in soil improvement.

Remediation of soda-saline-alkali soil through soil amendments: Microbially mediated carbon and nitrogen cycles and remediation mechanisms

Due to the high salt content and pH value, the structure of saline-sodic soil was deteriorated, resulting in decreased soil fertility and inhibited soil element cycling. This, in turn, caused significant negative impacts on crop growth, posing a major challenge to global agriculture and food security. Despite numerous studies aimed at reducing the loss of plant productivity in saline-sodic soils, the knowledge regarding shifts in soil microbial communities and carbon/nitrogen cycling during saline-sodic soil improvement remains incomplete. Consequently, we developed a composite soil amendment to explore its potential to alleviate salt stress and enhance soil quality. Our findings demonstrated that the application of this composite soil amendment effectively enhanced microbial salinity resistance, promotes soil carbon fixation and nitrogen cycling, thereby reducing HCO3- concentration and greenhouse gas emissions while improving physicochemical properties and enzyme activity in the soil. Additionally, the presence of CaSO4 contributed to a decrease in water-soluble Na+ content, resulting in reduced soil ESP and pH by 14.64 % and 7.42, respectively. Our research presents an innovative approach to rehabilitate saline-sodic soil and promote ecological restoration through the perspective of elements cycles.

Deciphering the regulatory networks involved in mild and severe salt stress responses in the roots of wild grapevine Vitis vinifera spp. sylvestris

Transcriptional regulatory networks are pivotal components of plant's response to salt stress. However, plant adaptation strategies varied as a function of stress intensity, which is mainly modulated by climate change. Here, we determined the gene regulatory networks based on transcription factor (TF) TF_gene co-expression, using two transcriptomic data sets generated from the salt-tolerant "Tebaba" roots either treated with 50 mM NaCl (mild stress) or 150 mM NaCl (severe stress). The analysis of these regulatory networks identified specific TFs as key regulatory hubs as evidenced by their multiple interactions with different target genes related to stress response. Indeed, under mild stress, NAC and bHLH TFs were identified as central hubs regulating nitrogen storage process. Moreover, HSF TFs were revealed as a regulatory hub regulating various aspects of cellular metabolism including flavonoid biosynthesis, protein processing, phenylpropanoid metabolism, galactose metabolism, and heat shock proteins. These processes are essentially linked to short-term acclimatization under mild salt stress. This was further consolidated by the protein-protein interaction (PPI) network analysis showing structural and plant growth adjustment. Conversely, under severe salt stress, dramatic metabolic changes were observed leading to novel TF members including MYB family as regulatory hubs controlling isoflavonoid biosynthesis, oxidative stress response, abscisic acid signaling pathway, and proteolysis. The PPI network analysis also revealed deeper stress defense changes aiming to restore plant metabolic homeostasis when facing severe salt stress. Overall, both the gene co-expression and PPI network provided valuable insights on key transcription factor hubs that can be employed as candidates for future genetic crop engineering programs.

Spectral ecophysiology: hyperspectral pressure-volume curves to estimate leaf turgor loss

Turgor loss point (TLP) is an important proxy for plant drought tolerance, species habitat suitability, and drought-induced plant mortality risk. Thus, TLP serves as a critical tool for evaluating climate change impacts on plants, making it imperative to develop high-throughput and in situ methods to measure TLP. We developed hyperspectral pressure-volume curves (PV curves) to estimate TLP using leaf spectral reflectance. We used partial least square regression models to estimate water potential (Ψ) and relative water content (RWC) for two species, Frangula caroliniana and Magnolia grandiflora. RWC and Ψ's model for each species had R2 ≥ 0.7 and %RMSE = 7-10. We constructed PV curves with model estimates and compared the accuracy of directly measured and spectra-predicted TLP. Our findings indicate that leaf spectral measurements are an alternative method for estimating TLP. F. caroliniana TLP's values were -1.62 ± 0.15 (means ± SD) and -1.62 ± 0.34 MPa for observed and reflectance predicted, respectively (P > 0.05), while M. grandiflora were -1.78 ± 0.34 and -1.66 ± 0.41 MPa (P > 0.05). The estimation of TLP through leaf reflectance-based PV curves opens a broad range of possibilities for future research aimed at understanding and monitoring plant water relations on a large scale with spectral ecophysiology.

Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time

Monitoring and modelling soil organic carbon (SOC) in space and time can help us to better understand soil carbon dynamics and is of key importance to support climate change research and policy. Although machine learning (ML) has attracted a lot of attention in the digital soil mapping (DSM) community for its powerful ability to learn from data and predict soil properties, such as SOC, it is better at capturing soil spatial variation than soil temporal dynamics. By contrast, process-oriented (PO) models benefit from mechanistic knowledge to express physiochemical and biological processes that govern SOC temporal changes. Therefore, integrating PO and ML models seems a promising means to represent physically plausible SOC dynamics while retaining the spatial prediction accuracy of ML models. In this study, a hybrid modelling framework was developed and tested for predicting topsoil SOC stock in space and time for a regional cropland area located in eastern China. In essence, the hybrid model uses predictions of the PO model in unsampled years as additional training data of the ML model, with a weighting parameter assigned to balance the importance of SOC values from the PO model and real measurements. The results indicated that temporal trends of SOC stock modelled by PO and ML models were largely different, while they were notably similar between the PO and hybrid models. Cross-validation showed that the hybrid model had the best performance (RMSE = 0.29 kg m-2), with a 19 % improvement compared with the ML model. We conclude that the proposed hybrid framework not only enhances space-time soil carbon mapping in terms of prediction accuracy and physical plausibility, it also provides insights for soil management and policy decisions in the face of future climate change and intensified human activities.

Amundsen Sea Ice Loss Contributes to Australian Wildfires

Wildfires in Australia have attracted extensive attention in recent years, especially for the devastating 2019-2020 fire season. Remote forcing, such as those from tropical oceans, plays an important role in driving the abnormal weather conditions associated with wildfires. However, whether high latitude climate change can impact Australian fires is largely unclear. In this study, we reveal a robust relationship between Antarctic sea ice concentration (SIC), primarily over the Amundsen Sea region, with Australian springtime fire activity, by using reanalysis data sets, AMIP simulation results, and a state-of-the-art climate model simulation. Specifically, a diminished Amundsen SIC leads to the formation of a high-pressure system above Australia as a result of the eastward propagation of Rossby waves. Meanwhile, two strengthened meridional cells originating from the tropic and polar regions also enhance subsiding airflow in Australia, resulting in prolonged arid and high-temperature conditions. This mechanism explains about 28% of the variability of Australian fire weather and contributed more than 40% to the 2019 extreme burning event, especially in the eastern hotspots. These findings contribute to our understanding of polar-low latitude climate teleconnection and have important implications for projecting Australian fires as well as the global environment.

Synoptic weather patterns during fire spread events in Siberia

Several recent studies have indicated a strong relationship between extensive wildfires in Siberia and synoptic-scale weather processes. In this study, we used the concept of fire spread events to investigate the relationships between synoptic and surface-level weather conditions and extensive wildfires in Siberia during 2001-2022 using the MODIS and ERA5 reanalysis products. We analyzed the spatio-temporal features and seasonality of fire spread events in the region and found that most of them occurred in the central part of Eastern Siberia (ES) during the summer months, following the wildfire season in the region. A significant positive trend in the annual count of fire spread events was found in ES, coinciding with non-significant negative trends in cloud cover and precipitation and non-significant positive trends in air temperature and the fire weather index. Results show that in the ES region, which accounts for 46 % of the total number of considered events, the main driver of fire spread events is the formation of a positive geopotential height anomaly, which, based on the pattern of the meridional wind component, indicates the presence of an anticyclone above the area of fire spread events. The presence of a high-pressure zone causes a decrease in cloud cover over regions with fires, leading to increases in the amount of incoming solar radiation and surface air temperature and a decrease in precipitation. These conditions contribute to the drying of fuel and an increase in the overall fire hazard level, which in turn leads to an intensification of the combustion process, as evidenced by an increase in the radiative power of fires.

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = -0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

A review of organic and inorganic amendments to treat saline-sodic soils: Emphasis on waste valorization for a circular economy approach

Soil salinization poses a significant challenge to the sustainable advancement of agriculture on a global scale. This environmental issue not only hampers plant growth and soil fertility but also hinders the advancement of the national economy due to restrictions on plant development. The utilization of organic and/or inorganic amendments has demonstrated the ability to mitigate the detrimental impacts of salt stress on plant life. At the outset, this review, in addition to summarizing current knowledge about soil amendments for saline-sodic soils, also aims to identify knowledge gaps requiring further research. The organic or inorganic amendments modify soil conditions and impact plant development. For instance, organic amendments have the potential to improve the structure of the soil, augment its capacity to retain water, and stimulate microbial activity. As this occurs, salts gradually leach through the porous structure of the soil. Conversely, inorganic amendments, such as gypsum and phosphogypsum, displace sodium from soil-negative sorption sites reducing the salinity, they also increase base saturation, altogether positively impacting plant growth conditions. This review emphasizes that, under adequate rates, the combination of organic and inorganic amendment has a high potential to enhance the poor physicochemical properties of saline-sodic soils, thereby reducing their salinity. Consequently, an in-depth examination of the mineral composition, texture, and chemical composition of the soil is required to choose the most effective amendment to implement. Future research necessitates a thorough investigation of techno-economic and life cycle assessment, with active involvement from stakeholders, to enhance the decision-making process of the amendments in specific localities.

Homogenized gridded dataset for drought and hydrometeorological modeling for the continental United States

We present a novel data set for drought in the continental US (CONUS) built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from its native geo-referenced polygon format to a 0.5 degree regular grid. We merged known environmental drivers of drought, including those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data. The resulting data set permits statistical and probabilistic modeling of drought with explicit spatial and/or temporal dependence. Such models could be used to forecast drought at short-range, seasonal to sub-seasonal, and inter-annual timescales with uncertainty, extending the reach and value of the current US Drought Outlook from the National Weather Service Climate Prediction Center. This novel data product provides the first common gridded dataset that includes critical variables used to inform hydrological and meteorological drought.

Coarse-resolution burned area datasets severely underestimate fire-related forest loss

Global coarse-resolution (≥250 m) burned area (BA) products have been used to estimate fire related forest loss, but we hypothesised that a significant part of fire impacts might be undetected because of the underestimation of small fires (<100 ha), especially in the tropics. In this paper, we analysed fire-related forest cover loss in sub-Saharan Africa (SSA) for 2016 and 2019 based on a BA product generated from Sentinel-2 data (20 m), which was observed to have significantly lower omission errors than the coarse-resolution BA products. Using these higher resolution BA datasets, we found that fires contribute to >46 % of total forest losses over SSA, more than twice the estimates from coarse-resolution BA products. In addition, burned forest areas showed more than twofold likelihood of subsequent loss compared to unburned ones. In moist tropical forests, the most fire-vulnerable biome, burning had even six times more chance to precede forest loss than unburned areas. We also found that fire-related characteristics, such as fire size and season, and forest fragmentation play a major role in the determination of tree cover fate. Our results reveal that medium-resolution BA detects more fires in late fire season, which tend to have higher impact on forests than early season ones. On the other hand, small fires represented the major driver of forest loss after fires and the vast majority of these losses occur in fragmented landscapes near forest edge (<260 m). Therefore medium-resolution BA products are required to obtain a more accurate evaluation of fire impacts in tropical ecosystems.

CARM30: China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 using multi-source data

Rapeseed is a critical cash crop globally, and understanding its distribution can assist in refined agricultural management, ensuring a sustainable vegetable oil supply, and informing government decisions. China is the leading consumer and third-largest producer of rapeseed. However, there is a lack of widely available, long-term, and large-scale remotely sensed maps on rapeseed cultivation in China. Here this study utilizes multi-source data such as satellite images, GLDAS environmental variables, land cover maps, and terrain data to create the China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 (CARM30). Our product was validated using independent samples and showed average F1 scores of 0.869 and 0.971 for winter and spring rapeseed. The CARM30 has high spatial consistency with existing 10 m and 20 m rapeseed maps. Additionally, the CARM30-derived rapeseed planted area was significantly correlated with agricultural statistics (R2 = 0.65-0.86; p < 0.001). The obtained rapeseed distribution information can serve as a reference for stakeholders such as farmers, scientific communities, and decision-makers.

Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012-2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha-1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (-25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

Eutrophication evolution of lakes in China: Four decades of observations from space

The lake eutrophication is highly variable in both time and location, and greatly restricts the sustainable development of water resources. The lack of national eutrophication evaluation for multi-scale lakes limits the pertinent governance and sustainable management of water quality. In this study, a remote sensing approach was developed to capture 40-year dynamics of trophic state index (TSI) for nationwide lakes in China. 32% of lakes (N = 1925) in China were eutrophic and 26% were oligotrophic, and a longitudinal pattern was discovered, with the 40-year average TSI of 62.26 in the eastern plain compared to 23.72 in the Tibetan Plateau. A decreasing trend was further observed in the past four decades with a correlation of -0.16, which was mainly discovered in the Tibetan Plateau lakes (r > -0.90, p < 0.01). The contribution of climate change and human activities was quantified and varied between lake zones, with anthropogenic factors playing a dominant role in the east plain lakes (88%, N = 473) and large lakes are subject to a more complex driving mechanism (≥ 3 driving factors). The study expands the spatiotemporal scale for eutrophication monitoring and provides an important base for strengthening lake management and ecological services.

Dataset of building locations in Poland in the 1970s and 1980s

The aim of this study was to create a dataset of building locations in Poland from the 1970s-1980s. The source information was the historical 1:10 000 Polish topographic map. Building footprints were detected and extracted from approximately 8,500 scanned map sheets using the Mask R-CNN model implemented in Esri ArcGIS Pro software, and converted to point building locations. The dataset of building locations covers the entire country and contains approximately 11 million points representing buildings. The accuracy of the dataset was assessed manually on randomly selected map sheets. The overall accuracy is 95% (F1 = 0.98). The dataset may be used in conjunction with various contemporary land use, land cover and cadastral datasets in a broad range of applications related to long-term changes in rural and urban areas, including urban sprawl and its environmental and social consequences. It can also serve as a highly reliable reference dataset for regional or global settlement products derived, e.g., from early Landsat data.

Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era

Geospatial science is the science of location or place that harnesses geospatial tools, such as geographic information systems (GIS), to understand the features of the environment according to their locations. Geospatial science has been transformative for cancer epidemiologic studies through enabling large-scale environmental exposure assessments. As the research paradigm for the exposome, or the totality of environmental exposures across the life course, continues to evolve, geospatial science will serve a critical role in determining optimal practices for how to measure the environment as part of the external exposome. The objectives of this article are to provide a summary of key concepts, present a conceptual framework that illustrates how geospatial science is applied to environmental epidemiology in practice and through the lens of the exposome, and discuss the following opportunities for advancing geospatial science in cancer epidemiologic research: enhancing spatial and temporal resolutions and extents for geospatial data; geospatial methodologies to measure climate change factors; approaches facilitating the use of patient addresses in epidemiologic studies; combining internal exposome data and geospatial exposure models of the external exposome to provide insights into biological pathways for environment-disease relationships; and incorporation of geospatial data into personalized cancer screening policies and clinical decision making.

Harnessing urban analytics and machine learning for sustainable urban development: A multidimensional framework for modeling environmental impacts of urbanization in Saudi Arabia

Sustainable urban development is crucial for managing natural resources and mitigating environmental impacts induced by rapid urbanization. This study demonstrates an integrated framework using machine learning-based urban analytics techniques to evaluate spatiotemporal urban expansion in Saudi Arabia (1987-2022) and quantify impacts on leading land, water, and air-related environmental parameters (EPs). Remote sensing and statistical techniques were applied to estimate vegetation health, built-up area, impervious surface, water bodies, soil characteristics, thermal comfort, air pollutants (PM2.5, CH4, CO, NO2, SO2), and nighttime light EPs. Regression assessment and Principal Component Analysis (PCA) were applied to assess the relationships between urban expansion and EPs. The findings highlight the substantial growth of urban areas (0.067%-0.14%), a decline in soil moisture (16%-14%), water bodies (60%-22%), a nationwide increase of PM2.5 (44 μg/m3 to 73 μg/m3) and night light intensity (0.166-9.670) concentrations resulting in significant impacts on land, water, and air quality parameters. PCA showed vegetation cover, soil moisture, thermal comfort, PM2.5, and NO2 are highly impacted by urban expansion compared to other EPs. The results highlight the need for effective and sustainable interventions to mitigate environmental impacts using green innovations and urban development by applying mixed-use development, green space preservation, green building technologies, and implementing renewable energy approaches. The framework recommended for environmental management in this study provides a robust foundation for evidence-based policies and adaptive management practices that balance economic progress and environmental sustainability. It will also help policymakers and urban planners in making informed decisions and promoting resilient urban growth.

Spatiotemporal light exposure modeling for environmental circadian misalignment and solar jetlag

Background

Light exposure is the most powerful resetting signal for circadian rhythms. The objective of this study was to develop and validate a high-resolution geospatial light exposure model that measures environmental circadian misalignment (or solar jetlag) as the mismatch between the social clock and sun clock, which occurs from geographic variation in light exposure leading to delayed circadian phase from relatively less morning light exposure and greater evening light exposure with increasing westward position within a time zone.

Methods

The light exposure model (30 m2 spatial resolution) incorporated geospatial data across the United States on time zones, elevation (using Google Earth Engine), sunrise time, and sunset time to estimate solar jetlag scores (higher values indicate higher environmental circadian misalignment). The validation study compared the light exposure model in 2022, which was linked with geocoded residential addresses of n = 20 participants in Boston, MA (eastern time zone position) and Seattle, WA (western time zone position) using a geographic information system, with illuminance values captured from wearable LYS light sensors and with sun times from the Solar Calculator.

Results

Western versus eastern positions within a time zone were associated with higher solar jetlag scores from the light exposure model (P < 0.01) and relatively larger differences in sunset time measured using light sensors (social clock) and the Solar Calculator (sun clock) (P = 0.04).

Conclusion

We developed and validated a geospatial light exposure model, enabling high spatiotemporal resolution and comprehensive characterization of geographic variation in light exposure potentially impacting circadian phase in epidemiologic studies.

Knowledge-guided mixture density network for chlorophyll-a retrieval and associated pixel-by-pixel uncertainty assessment in optically variable inland waters

Machine learning has been increasingly used to retrieve chlorophyll-a (Chl-a) in optically variable waters. However, without the guidance of physical principles or expert knowledge, machine learning may produce biased mapping relationships, or waste considerable time searching for physically infeasible hyperparameter domains. In addition, most Chl-a retrieval models cannot evaluate retrieval uncertainty when ground observations are not available, and the retrieval uncertainty is crucial for understanding the model limitations and evaluating the reliability of retrieval results. In this study, we developed a novel knowledge-guided mixture density network to retrieve Chl-a in optically variable inland waters based on Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery. The proposed method embedded prior knowledge derived from spectral shape classification into the mixture density network. Compared to another deterministic model, the knowledge-guided mixture density network outputted the conditional distribution of Chl-a given an input spectrum, enabling us to estimate the optimal retrieval and the associated uncertainty. The proposed method showed favorable correspondence with the field Chl-a, with root mean square error (RMSE) of 6.56 μg/L, and mean absolute percentage error (MAPE) of 43.64 %. Calibrated against Sentinel-3 OLCI spectrum, the proposed method also performed well when applied to field spectrum (RMSE = 4.58 μg/L, MAPE = 72.70 %), suggesting its effectiveness and good generalization. The proposed method provided the standard deviation of each estimated Chl-a, which enabled us to inspect the reliability of the estimated results and understand the model limitations. Overall, the proposed method improved the Chl-a retrieval in terms of model accuracy and uncertainty evaluation, providing a more comprehensive Chl-a observation of inland waters.

Satellite-based estimation of total suspended solids and chlorophyll-a concentrations for the Gold Coast Broadwater, Australia

Satellite retrieval of total suspended solids (TSS) and chlorophyll-a (chl-a) was performed for the Gold Coast Broadwater, a micro-tidal estuarine lagoon draining a highly developed urban catchment area with complex and competing land uses. Due to the different water quality properties of the rivers and creeks draining into the Broadwater, sampling sites were grouped in clusters, with cluster-specific empirical/semi-empirical prediction models developed and validated with a leave-one-out cross validation approach for robustness. For unsampled locations, a weighted-average approach, based on their proximity to sampled sites, was developed. Confidence intervals were also generated, with a bootstrapping approach and visualised through maps. Models yielded varying accuracies (R2 = 0.40-0.75). Results show that, for the most significant poor water quality event in the dataset, caused by summer rainfall events, elevated TSS concentrations originated in the northern rivers, slowly spreading southward. Conversely, high chl-a concentrations were first recorded in the southernmost regions of the Broadwater.

UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe

Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data.

Ability of Different Growth Indicators to Detect Salt Tolerance of Advanced Spring Wheat Lines Grown in Real Field Conditions

Plant growth indicators (GIs) are important for evaluating how different genotypes respond to normal and stress conditions separately. They consider both the morphological and physiological components of plants between two successive growth stages. Despite their significance, GIs are not commonly used as screening criteria for detecting salt tolerance of genotypes. In this study, 36 recombinant inbred lines (RILs) along with four genotypes differing in their salt tolerance were grown under normal and 150 mM NaCl in a two-year field trial. The performance and salt tolerance of these germplasms were assessed through various GIs. The analysis of variance showed highly significant variation between salinity levels, genotypes, and their interaction for all GIs and other traits in each year and combined data for two years, with a few exceptions. All traits and GIs were significantly reduced by salinity stress, except for relative growth rate (RGR), net assimilation rate (NAR), and specific leaf weight (SLW), which increased under salinity conditions. Traits and GIs were more correlated with each other under salinity than under normal conditions. Principal component analysis organized traits and GIs into three main groups under both conditions, with RGR, NAR, and specific leaf area (SLA) closely associated with grain yield (GY) and harvest index, while leaf area duration (LAD) was closely associated with green leaf area (GLA), plant dry weight (PDW), and leaf area index (LAI). A hierarchical clustering heatmap based on GIs and traits organized germplasms into three and four groups under normal and salinity conditions, respectively. Based on the values of traits and GIs for each group, the germplasms varied from high- to low-performing groups under normal conditions and from salt-tolerant to salt-sensitive groups under salinity conditions. RGR, NAR, and LAD were important factors determining genotypic variation in GY of high- and low-performing groups, while all GIs, except leaf area duration (LAR), were major factors describing genotypic variation in GY of salt-tolerant and salt-sensitive groups. In conclusion, different GIs that reveal the relationship between the morphological and physiological components of genotypes could serve as valuable selection criteria for evaluating the performance of genotypes under normal conditions and their salt tolerance under salinity stress conditions.

Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening

Vegetation greening has been suggested to be a dominant trend over recent decades, but severe pulses of tree mortality in forests after droughts and heatwaves have also been extensively reported. These observations raise the question of to what extent the observed severe pulses of tree mortality induced by climate could affect overall vegetation greenness across spatial grains and temporal extents. To address this issue, here we analyse three satellite-based datasets of detrended growing-season normalized difference vegetation index (NDVIGS) with spatial resolutions ranging from 30 m to 8 km for 1,303 field-documented sites experiencing severe drought- or heat-induced tree-mortality events around the globe. We find that severe tree-mortality events have distinctive but localized imprints on vegetation greenness over annual timescales, which are obscured by broad-scale and long-term greening. Specifically, although anomalies in NDVIGS (ΔNDVI) are negative during tree-mortality years, this reduction diminishes at coarser spatial resolutions (that is, 250 m and 8 km). Notably, tree-mortality-induced reductions in NDVIGS (|ΔNDVI|) at 30-m resolution are negatively related to native plant species richness and forest height, whereas topographic heterogeneity is the major factor affecting ΔNDVI differences across various spatial grain sizes. Over time periods of a decade or longer, greening consistently dominates all spatial resolutions. The findings underscore the fundamental importance of spatio-temporal scales for cohesively understanding the effects of climate change on forest productivity and tree mortality under both gradual and abrupt changes.

Carbon cycle responses to climate change across China's terrestrial ecosystem: Sensitivity and driving process

Investigations into the carbon cycle and how it responds to climate change at the national scale are important for a comprehensive understanding of terrestrial carbon cycle and global change issues. Contributions of carbon fluxes to the terrestrial sink and the effects on climate change are still not fully understood. In this study, we aimed to explore the relationship between ecosystem production (GPP/SIF/NDVI) and net ecosystem carbon exchange (NEE) and to investigate the sensitivity of carbon fluxes to climate change at different spatio-temporal scales. Furthermore, we sought to delve into the carbon cycle processes driven by climate stress in China since the beginning of the 21st century. To achieve these objectives, we employed correlation and sensitivity analysis techniques, utilizing a wide range of data sources including ground-based observations, remote sensing observations, atmospheric inversions, machine learning, and model simulations. Our findings indicate that NEE in most arid regions of China is primarily driven by ecosystem production. Climate variations have a greater influence on ecosystem production than respiration. Warming has negatively impacted ecosystem production in Northeast China, as well as in subtropical and tropical regions. Conversely, increased precipitation has strengthened the terrestrial carbon sink, particularly in the northern cool and dry areas. We also found that ecosystem respiration exhibits heightened sensitivity to warming in southern China. Moreover, our analysis revealed that the control of terrestrial carbon cycle by ecosystem production gradually weakens from cold/arid areas to warm/humid areas. We identified distinct temperature thresholds (ranging from 10.5 to 13.7 °C) and precipitation thresholds (approximately 1400 mm yr-1) for the transition from production-dominated to respiration-dominated processes. Our study provides valuable insights into the complex relationship between climate change and carbon cycle in China.

Remote sensing and spectroscopy of lichens

Lichens are combinations of two symbiotic organisms, a green alga or cyanobacterium and a fungus. They grow in nearly all terrestrial ecosystems and survive in habitats, which are very dry or cold, or too poor in nutrients to maintain vegetation growth. Because lichens grow on visible surfaces and exhibit spectral properties, which are clearly different from, for example, vegetation, it is possible to distinguish them in remote sensing data. In this first systematic review article on remote sensing of lichens, we analyze and summarize which lichen species or genera, and in which habitats and geographical regions, have been remotely sensed, and which remote sensing or spectroscopic technologies have been used. We found that laboratory or in situ measured spectra of over 70 lichen species have been reported to date. We show that studies on remote sensing of lichens fall under seven broad themes: (1) collection of lichen spectra for quantification of lichen species or characteristics, (2) pollution monitoring with lichens as ecological indicators, (3) geological and lithological mapping, (4) desert and dryland monitoring, (5) animal habitat monitoring, (6) land cover or vegetation mapping, and (7) surface energy budget modeling.

Diverse projected climate change scenarios affect the physiology of broccoli plants to different extents

Climate change caused by global warming involves crucial plant growth factors such as atmospheric CO2 concentration, ambient temperature or water availability. These stressors usually co-occur, causing intricate alterations in plant physiology and development. This work focuses on how elevated atmospheric CO2 levels, together with the concomitant high temperature, would affect the physiology of a relevant crop, such as broccoli. Particular attention has been paid to those defence mechanisms that contribute to plant fitness under abiotic stress. Results show that both photosynthesis and leaf transpiration were reduced in plants grown under climate change environments compared to those grown under current climate conditions. Furthermore, an induction of carbohydrate catabolism pointed to a redistribution from primary to secondary metabolism. This result could be related to a reinforcement of cell walls, as well as to an increase in the pool of antioxidants in the leaves. Broccoli plants, a C3 crop, grown under an intermediate condition showed activation of those adaptive mechanisms, which would contribute to coping with abiotic stress, as confirmed by reduced levels of lipid peroxidation relative to current climate conditions. On the contrary, the most severe climate change scenario exceeded the adaptive capacity of broccoli plants, as shown by the inhibition of growth and reduced vigour of plants. In conclusion, only a moderate increase in atmospheric CO2 concentration and temperature would not have a negative impact on broccoli crop yields.

Madagascar's burned area from Sentinel-2 imagery (2016-2022): Four times higher than from lower resolution sensors

Madagascar is one of the most burned regions in the world, to the point that it has been called the 'Isle of fire' or the 'Burning Island'. An accurate characterization of the burned area (BA) is crucial for understanding the true situation and impacts of fires on this island, where there is an active scientific debate on how fire affects multiple environmental and socioeconomic aspects, and how fire regimes should be in a complex context with differing interests. Despite this, recent advances have revealed that BA in Madagascar is poorly characterised by the currently available global BA products. In this work, we present, validate, and explore a BA database at 20 m spatial resolution for Madagascar covering the period 2016-2022. The database was built based on 75,010 Sentinel-2 images using a two-phase BA detection algorithm. The validation with independent long-term reference units showed Dice coefficients ≥79 %, omission errors ≤24 %, commission errors ≤18 %, and a relative bias ≥ - 8 %. An intercomparison with other available global BA products (GABAM, FireCCI51, C3SBA11, or MCD64) demonstrated that our product (i) exhibits temporal consistency, (ii) represents a significant accuracy improvement, as it reduces BA underestimations by about eightfold, (iii) yields BA estimates four times higher, and (iv) shows enhanced capability in detecting fires of all sizes. The observed BA spatial patterns were heterogeneous across the island, with 32 % of the grasslands burning annually, in contrast to other land cover types such as the dense tropical forest where <2 % burned every year. We conclude that the BA characterization in Madagascar must be addressed using imagery at spatial resolution higher than MODIS or Sentinel-3 (≥250 m), and temporal resolution higher than Landsat (16 days) to deal with cloudiness, the rapid attenuation of burn scars signals, and small fire patches.

Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia

Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.

Use of trophic ecology of omnivorous fish and abiotic factors as supporting tools for assessing environmental impacts in a neotropical river

The study of diet is one of the mechanisms by which competition for resources between species that cohabit in the same ecosystem can be inferred. Therefore, the relationships of the indices that measure specialization in the diet of fish species are necessary to characterize the nutritional quality of these populations and the ecosystem's environmental health. Three species of catfish were selected: one invasive (Clarias gariepinus) and two natives (Trachelyopterus striatulus and Rhamdia quelen), with similar distribution along the Guapi-Macacu River, in the Guapimirim Protection Area (Rio de Janeiro). Fifty-nine catfish of the three species were collected in total, along 32 collection points in the Guapi-Macacu River in two periods (dry and rainy) in 2018. Non-parametric statistics showed the partition of resources between species and the influence of abiotic factors (temperature, pH, transparency, and dissolved oxygen) contributing to the selection of available resources in the environment. Diet-related indices-repletion index (RI), condition factor (K), niche width, and trophic position (TP) of the specimens collected-contributed to measuring the nutritional status of each of these catfish species, showing that R. quelen has a relationship between RI and K, tending to absorb and metabolize nutrients faster than other species. In addition, the invasive species occupies a wide range of TPs compared to native species, confirming its feeding plasticity. On the contrary, T. striatulus needs large amounts of terrestrial insects to maintain its poor condition factor. Also, the RI showed direct influences of abiotic variables, with the temperature being the most prominent. Our results suggest that the invasive species can benefit from this environment that shows signs of environmental degradation.

Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 μg/m3 (interquartile range: 29.8-46.8) in 2008 and 30.4 μg/m3 (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 μg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3-16.5 μg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.

A study on siting of emergency shelters for dam failure floods considering population distribution and weather effects

In recent years, dam failures have occurred frequently because of extreme weather, posing a significant threat to downstream residents. The establishment of emergency shelters is crucial for reducing casualties. The selection of suitable shelters depends on key information such as the number and distribution of affected people, and the effective capacity and accessibility of the shelters. However, previous studies on siting shelters did not fully consider population distribution differences at a finer scale. This limitation hinders the accuracy of estimating the number of affected people. In addition, most studies ignored the impact of extreme rainfall on the effective capacity and accessibility of shelters, leading to a low applicability of the shelter selection results. Therefore, in this study, land-use and land-cover change (LUCC) and nighttime lighting data were used to simulate population distribution and determine the number and distribution of affected people. Qualified candidate shelters were obtained based on screening criteria, and their effective capacity and accessibility information under different weather conditions were quantified. Considering factors such as population transfer efficiency, construction cost and shelter capacity constraints, a multi-objective siting model was established and solved using the non-dominated sorting genetic algorithm II (NSGA- II) to obtain the final siting scheme. The method was applied to the Dafangying Reservoir, and the results showed the following: (1) The overall mean relative error (MRE) of the population in the 35 downstream streets was 11.16 %, with good fitting accuracy. The simulation results truly reflect the population distribution. (2) Normal weather screening generated 352 qualified candidate shelters, whereas extreme rainfall weather screening generated 266 candidate shelters. (3) Based on the population distribution and weather factors, four scenarios were set up, with 63, 106, 73, and 131 shelters selected. These two factors have a significant impact on the selection of shelters and the allocation of evacuees, and should be considered in the event of a dam-failure floods.

Drought triggers and sustains overnight fires in North America

Overnight fires are emerging in North America with previously unknown drivers and implications. This notable phenomenon challenges the traditional understanding of the 'active day, quiet night' model of the diurnal fire cycle1-3 and current fire management practices4,5. Here we demonstrate that drought conditions promote overnight burning, which is a key mechanism fostering large active fires. We examined the hourly diurnal cycle of 23,557 fires and identified 1,095 overnight burning events (OBEs, each defined as a night when a fire burned through the night) in North America during 2017-2020 using geostationary satellite data and terrestrial fire records. A total of 99% of OBEs were associated with large fires (>1,000 ha) and at least one OBE was identified in 20% of these large fires. OBEs were early onset after ignition and OBE frequency was positively correlated with fire size. Although warming is weakening the climatological barrier to night-time fires6, we found that the main driver of recent OBEs in large fires was the accumulated fuel dryness and availability (that is, drought conditions), which tended to lead to consecutive OBEs in a single wildfire for several days and even weeks. Critically, we show that daytime drought indicators can predict whether an OBE will occur the following night, which could facilitate early detection and management of night-time fires. We also observed increases in fire weather conditions conducive to OBEs over recent decades, suggesting an accelerated disruption of the diurnal fire cycle.

Associations between fine particulate air pollution with small-airway inflammation: A nationwide analysis in 122 Chinese cities

Alveolar nitric oxide is a non-invasive indicator of small-airway inflammation, a key pathophysiologic mechanism underlying lower respiratory diseases. However, no epidemiological studies have investigated the impact of fine particulate matter (PM2.5) exposure on the concentration of alveolar nitric oxide (CANO). To explore the associations between PM2.5 exposure in multiple periods and CANO, we conducted a nationwide cross-sectional study in 122 Chinese cities between 2019 and 2021. Utilizing a satellite-based model with a spatial resolution of 1 × 1 km, we matched long-term, mid-term, and short-term PM2.5 exposure for 28,399 individuals based on their home addresses. Multivariable linear regression models were applied to estimate the associations between PM2.5 at multiple exposure windows and CANO. Stratified analyses were also performed to identify potentially vulnerable subgroups. We found that per interquartile range (IQR) unit higher in 1-year average, 1-month average, and 7-day average PM2.5 concentration was significantly associated with increments of 17.78% [95% confidence interval (95%CI): 12.54%, 23.26%], 8.76% (95%CI: 7.35%, 10.19%), and 4.00% (95%CI: 2.81%, 5.20%) increment in CANO, respectively. The exposure-response relationship curves consistently increased with the slope becoming statistically significant beyond 20 μg/m3. Males, children, smokers, individuals with respiratory symptoms or using inhaled corticosteroids, and those living in Southern China were more vulnerable to PM2.5 exposure. In conclusion, our study provided novel evidence that PM2.5 exposure in long-term, mid-term, and short-term periods could significantly elevate small-airway inflammation represented by CANO. Our results highlight the significance of CANO measurement as a non-invasive tool for early screening in the management of PM2.5-related inflammatory respiratory diseases.

Can lettuce plants grow in saline soils supplemented with biochar?

Salt stress is presently a major environmental concern, given the huge number of soils affected by the presence of dissolved salts. Therefore, it is necessary to find solutions, preferably nature-based ones, to deal with this problem. In this study, biochar, a product made from plant biomass residues through the process of pyrolysis, was tested to alleviate salt stress on lettuce (Lactuca sativa L.) plants. Six different concentrations of NaCl were tested: 0, 50, 100, 200, 300 and 400 mM with and without the addition of 5% (w/w) biochar. Biochar ability to mitigate salinity damage was assessed by means of both biometric (fresh weight), physiological (chlorophyll content), and biochemical (i.e., electrolyte leakage, total antioxidant power, total soluble proteins, free amino acids, and mineral content) parameters. The experiment lasted four weeks. The results showed that NaCl has a negative effect from the concentration of 100-200 mM and that biochar was to some extent effective in mitigating the negative effects of salt on plant physiology; nevertheless, biochar failed to counteract Na accumulation. Similarly, biochar did not influence the content of free amino acids in lettuce leaves, but enhanced the expression of several parameters, such as total antioxidant power, fresh weight, chlorophyll content, total soluble protein, K content, although only clearly evident in some cases. Overall, the present study showed that biochar is a viable solution to counteract the damage caused by high salt concentrations on plant growth.

Improving on mapping long-term surface water with a novel framework based on the Landsat imagery series

Surface water plays a crucial role in the ecological environment and societal development. Remote sensing detection serves as a significant approach to understand the temporal and spatial change in surface water series (SWS) and to directly construct long-term SWS. Limited by various factors such as cloud, cloud shadow, and problematic satellite sensor monitoring, the existent surface water mapping datasets might be short and incomplete due to losing raw information on certain dates. Improved algorithms are desired to increase the completeness and quality of SWS datasets. The present study proposes an automated framework to detect SWS, based on the Google Earth Engine and Landsat satellite imagery. This framework incorporates implementing a raw image filtering algorithm to increase available images, thereby expanding the completeness. It improves OTSU thresholding by replacing anomaly thresholds with the median value, thus enhancing the accuracy of SWS datasets. Gaps caused by Landsat7 ETM + SLC-off are respired with the random forest algorithm and morphological operations. The results show that this novel framework effectively expands the long-term series of SWS for three surface water bodies with distinct geomorphological patterns. The evaluation of confusion matrices suggests the good performance of extracting surface water, with the overall accuracy ranging from 0.96 to 0.97, and user's accuracy between 0.96 and 0.98, producer's accuracy ranging from 0.83 to 0.89, and Matthews correlation coefficient ranging from 0.87 to 0.9 for several spectral water indices (NDWI, MNDWI, ANNDWI, and AWEI). Compared with the Global Reservoirs Surface Area Dynamics (GRSAD) dataset, our constructed datasets promote greater completeness of SWS datasets by 27.01%-91.89% for the selected water bodies. The proposed framework for detecting SWS shows good potential in enlarging and completing long-term global-scale SWS datasets, capable of supporting assessments of surface-water-related environmental management and disaster prevention.

Chemically Mediated Plant-Plant Interactions: Allelopathy and Allelobiosis

Plant-plant interactions are a central driver for plant coexistence and community assembly. Chemically mediated plant-plant interactions are represented by allelopathy and allelobiosis. Both allelopathy and allelobiosis are achieved through specialized metabolites (allelochemicals or signaling chemicals) produced and released from neighboring plants. Allelopathy exerts mostly negative effects on the establishment and growth of neighboring plants by allelochemicals, while allelobiosis provides plant neighbor detection and identity recognition mediated by signaling chemicals. Therefore, plants can chemically affect the performance of neighboring plants through the allelopathy and allelobiosis that frequently occur in plant-plant intra-specific and inter-specific interactions. Allelopathy and allelobiosis are two probably inseparable processes that occur together in plant-plant chemical interactions. Here, we comprehensively review allelopathy and allelobiosis in plant-plant interactions, including allelopathy and allelochemicals and their application for sustainable agriculture and forestry, allelobiosis and plant identity recognition, chemically mediated root-soil interactions and plant-soil feedback, and biosynthesis and the molecular mechanisms of allelochemicals and signaling chemicals. Altogether, these efforts provide the recent advancements in the wide field of allelopathy and allelobiosis, and new insights into the chemically mediated plant-plant interactions.

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems

Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

Spatiotemporal disturbances and attribution analysis of mangrove in southern China from 1986 to 2020 based on time-series Landsat imagery

As one of the most productive ecosystems in the world, mangrove has a critical role to play in both the natural ecosystem and the human economic and social society. However, two thirds of the world's mangrove have been irreversibly damaged over the past 100 years, as a result of ongoing human activities and climate change. In this paper, adopting Landsat for the past 36 years as the data source, the detection of spatiotemporal changes of mangrove in southern China was carried out based on the Google Earth Engine (GEE) cloud platform using the LandTrendr algorithm. In addition, the attribution of mangrove disturbances was analyzed by a random forest algorithm. The results indicated the area of mangrove recovery (5174.64 hm2) was much larger than the area of mangrove disturbances (1625.40 hm2) over the 35-year period in the study area. The disturbances of mangrove in southern China were dominated by low and low-to-medium-level disturbances, with an area of 1009.89 hm2, accounting for 57.50 % of the total disturbances. The mangrove recovery was also dominated by low and low-to-medium-level recovery, with an area of 3239.19 hm2, accounting for 62.61 % of the total recovery area. Both human and natural factors interacted and influenced each other, together causing spatiotemporal disturbances of mangrove in southern China during 1986-2020. The mangrove disturbances in the Phase I (1986-2000) and Phase III (2011-2020) were characterized by human-induced (50.74 % and 58.86 %), such as construction of roads and aquaculture ponds. The mangrove disturbances in the Phase II (2001-2010) were dominated by natural factors (55.73 %), such as tides, flooding, and species invasions. It was also observed that the area of mangrove recovery in southern China increased dramatically from 1986 to 2020 due to the promulgation and implementation of the Chinese government's policy on mangrove protection, as well as increased human awareness of mangrove wetland protection.

Producing context specific land cover and land use maps of human-modified tropical forest landscapes for infectious disease applications

Satellite-based land cover mapping plays an important role in understanding changes in ecosystems and biodiversity. There are global land cover products available, however for region specific studies of drivers of infectious disease patterns, these can lack the spatial and thematic detail or accuracy required to capture key ecological processes. To overcome this, we produced our own Landsat derived 30 m maps for three districts in India's Western Ghats (Wayanad, Shivamogga and Sindhudurg). The maps locate natural vegetation types, plantation types, agricultural areas, water bodies and settlements in the landscape, all relevant to functional resource use of species involved in infectious disease dynamics. The maps represent the mode of 50 classification iterations and include a spatial measure of class stability derived from these iterations. Overall accuracies for Wayanad, Shivamogga and Sindhudurg are 94.7 % (SE 1.2 %), 88.9 % (SE 1.2 %) and 88.8 % (SE 2 %) respectively. Class classification stability was high across all three districts and the individual classes that matter for defining key interfaces between human habitation, forests, crop, and plantation cultivation, were generally well separated. A comparison with the 300 m global ESA CCI land cover map highlights lower ESA CCI class accuracies and the importance of increased spatial resolution when dealing with complex landscape mosaics. A comparison with the 30 m Global Forest Change product reveals an accurate mapping of forest loss and different dynamics between districts (i.e., Forests lost to Built-up versus Forests lost to Plantations), demonstrating an interesting complementarity between our maps and the % tree cover Global Forest Change product. When studying infectious disease responses to land use change in tropical forest ecosystems, we recommend using bespoke land cover/use classifications reflecting functional resource use by relevant vectors, reservoirs, and people. Alternatively, global products should be carefully validated with ground reference points representing locally relevant habitats.

Polyethylene Glycol Priming Enhances the Seed Germination and Seedling Growth of Scutellaria baicalensis Georgi under Salt Stress

Seed priming has become a practical pre-sowing strategy to deal with abiotic stresses. This study aims to explore the effects of polyethylene glycol (PEG) priming on seed germination and seedling growth of Scutellaria baicalensis Georgi under salt stress. Regardless of seed priming, salt stress significantly inhibited the seed germination and seedling growth of S. baicalensis. PEG priming significantly alleviates the inhibitory effects of salt stress on seed germination and seedling growth when compared to non-priming and water priming. Among all treatments, PEG priming exhibited the highest germination rate, germination potential, seed vigor index, fresh weight, dry weight, and plant length; the highest contents of proline, soluble sugar, and soluble protein; the highest K+/Na+ ratio and relative water content; the highest antioxidant activities and contents; but the lowest H2O2, malondialdehyde (MDA) content, and relative electrical conductivity in response to salt stress. In addition, PEG priming had the highest transcript levels of antioxidant-related genes among all treatments under NaCl stress. Taken together, the results demonstrated that seed priming with PEG could be recommended as an effective practice to enhance the germination and early seedling growth of S. baicalensis under saline conditions.

Columnar optical-radiative properties and components of aerosols in the Arctic summer from long-term AERONET measurements

Aerosols as an external factor have an important role in the amplification of Arctic warming, yet the geography of this harsh region has led to a paucity of observations, which has limited our understanding of the Arctic climate. We synthesized the latest decade (2010-2021) of data on the microphysical-optical-radiative properties of aerosols and their multi-component evolution during the Arctic summer, taking into consideration the important role of wildfire burning. Our results are based on continuous observations from eight AERONET sites across the Arctic region, together with a meteorological reanalysis dataset and satellite observations of fires, and utilize a back-trajectory model to track the source of the aerosols. The summer climatological characteristics within the Arctic Circle showed that the aerosols are mainly fine-mode aerosols (fraction >0.95) with a radius of 0.15-0.20 μm, a slight extinction ability (aerosol optical depth ∼ 0.11) with strong scattering (single scattering albedo ∼0.95) and dominant forward scattering (asymmetry factor ∼ 0.68). These optical properties result in significant cooling at the Earth's surface (∼-13 W m-2) and a weak cooling effect at the top of the atmosphere (∼-5 W m-2). Further, we found that Arctic region is severely impacted by wildfire burning events in July and August, which primarily occur in central and eastern Siberia and followed in subpolar North America. The plumes from wildfire transport aerosols to the Arctic atmosphere with the westerly circulation, leading to an increase in fine-mode aerosols containing large amounts of organic carbon, with fraction as high as 97-98 %. Absorptive carbonaceous aerosols also increase synergistically, which could convert the instantaneous direct aerosol radiative effect into a heating effect on the Earth-atmosphere system. This study provides insights into the complex sources of aerosol loading in the Arctic atmosphere in summer and emphasizes the important impacts of the increasingly frequent occurrence of wildfire burning events in recent years.

Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine

Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

Temperate grassland vegetation restoration influenced by grazing exclusion and climate change

Grasslands are one of the most important terrestrial biomes, supporting a wide range of ecological functions and services. Grassland degradation due to overgrazing is a severe issue worldwide, especially in developing regions. However, observations from multiple sources have shown that temperate grasslands in China have significantly increased during the past two decades. It remains controversial what factors have driven the vegetation restoration in this region. In this study, we combined remote-sensing images and field survey datasets to quantify the contributions of different factors to vegetation restoration in six temperate grasslands in northern China. Across the six grasslands, the Normalized Difference Vegetation Index (NDVI) increased by 0.003-0.0319 year-1. The average contributions of grazing exclusion and climate change to the NDVI increase were 49.23 % and 50.77 %, respectively. Precipitation change was the primary climate factor driving vegetation restoration, contributing 50.76 % to the NDVI variance. By contrast, climate warming tended to slow vegetation restoration, and atmospheric CO2 concentration change contributed little to the NDVI increase in the temperate grasslands. These results emphasize the significant contributions of both climate change and human management to grassland vegetation restoration.

A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China

Crop residue cover plays a key role in the protection of black soil by covering the soil in the non-growing season against wind erosion and chopping for returning to the soil to increase organic matter in the future. Although there are some studies that have mapped the crop residue coverage by remote sensing technique, the results are mainly on a small scale, limiting the generalizability of the results. In this study, we present a novel corn residue coverage (CRC) dataset for Northeast China spanning the years 2013-2021. The aim of our dataset is to provide a basis to describe and monitor CRC for black soil protection. The accuracy of our estimation results was validated against previous studies and measured data, demonstrating high accuracy with a coefficient of determination (R2) of 0.7304 and root mean square error (RMSE) of 0.1247 between estimated and measured CRC in field campaigns. In addition, it is the first of its kind to offer the longest time series, enhancing its significance in long-term monitoring and analysis.

High-resolution Annual Dynamic dataset of Curve Number from 2008 to 2021 over Conterminous United States

The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales.

Humification levels of dissolved organic matter in the eastern plain lakes of China based on long-term satellite observations

Under the influence of intensive human activities and global climate change, the sources and compositions of dissolved organic matter (DOM) in the eastern plain lake (EPL) region in China have fluctuated sharply. It has been successfully proven that the humification index (HIX), which can be derived from three-dimensional excitation-emission matrix fluorescence spectroscopy, can be an effective proxy for the sources and compositions of DOM. Therefore, combined with remote sensing technology, the sources and compositions of DOM can be tracked on a large scale by associating the HIX with optically active components. Here, we proposed a novel HIX remote sensing retrieval (IRHIX) model suitable for Landsat series sensors based on the comprehensive analysis of the covariation mechanism between HIX and optically active components in different water types. The validation results showed that the model runs well on the independent validation dataset and the satellite-ground synchronous sampling dataset, with an uncertainty ranging from 30.85 % to 36.92 % (average ± standard deviation = 33.6 % ± 3.07 %). The image-derived HIX revealed substantial spatiotemporal variations in the sources and compositions of DOM in 474 lakes in the EPL during 1986-2021. Subsequently, we obtained three long-term change modes of the HIX trend, namely, significant decline, gentle change, and significant rise, accounting for 74.68 %, 17.09 %, and 8.23 % of the lake number, respectively. The driving factor analysis showed that human activities had the most extensive influence on the DOM humification level. In addition, we also found that the HIX increased slightly with increasing lake area (R2 = 0.07, P < 0.05) or significantly with decreasing trophic state (R2 = 0.83, P < 0.05). Our results provide a new exploration for the effective acquisition of long-term dynamic information about the sources and compositions of DOM in inland lakes and provide important support for lake water quality management and restoration.