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Ocean plastic pollution is one of the global environmental problems of our time. "Rubbish islands" formed in the ocean are increasing every year, damaging the marine ecosystem. In order to effectively address this type of pollution, it is necessary to accurately and quickly identify the sources of plastic entering the ocean, identify where it is accumulating, and track the dynamics of waste movement. To this end, remote sensing methods using satellite imagery and aerial photographs from unmanned aerial vehicles are a reliable source of data. Modern machine learning technologies make it possible to automate the detection of floating plastics. This review presents the main projects and research aimed at solving the "plastic" problem. The main data acquisition techniques and the most effective deep learning algorithms are described, various limitations of working with space images are analyzed, and ways to eliminate such shortcomings are proposed.

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As the mainstream and trend of urban development in China, deeply exploring the spatiotemporal patterns and influencing mechanisms of ecosystem service value in the Yangtze River Delta urban agglomeration is of great significance for achieving sustainable development goals in urban agglomerations. This paper uses the normalized difference vegetation index and net primary productivity as dynamic adjustment factors to measure the ecosystem service value of the Yangtze River Delta urban agglomeration and analyze its spatiotemporal evolution characteristics. Furthermore, a panel quantile regression model is constructed to explore the response differences of ecosystem service value at different levels to various influencing factors. The results show that: (1) From 2006 to 2020, the ecosystem service value of the Yangtze River Delta urban agglomeration decreased by 37.086 billion yuan, with high-value areas mainly concentrated in the southern part of the urban agglomeration. (2) The value structure of various land type ecosystems and primary ecosystem sub-services in the Yangtze River Delta urban agglomeration is stable. (3) The number of grid units with reduced ecosystem service value is continuously increasing, mainly distributed in the eastern coastal areas. (4) The degree of interference of various types of land on ecosystem service value varies, and the response of ecosystem service value at different levels to the same influencing factor also shows heterogeneity. In summary, exploring the spatiotemporal patterns of ecosystem service value in the Yangtze River Delta urban agglomeration and analyzing its influencing mechanisms is conducive to adjusting the intensity of human utilization and protection methods of ecosystems, which is of great significance for enhancing the value of ecosystem products in urban agglomerations.

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As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.

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The cropland ecosystem stability (CES) has received increasing attention, especially in ecologically fragile areas, because of its impact on cropland quality, agricultural production and its ability to resist external disturbances. In this study, we first introduced the concepts of resilience and resistance, proposed the ecosystem disturbance-resistance-response process, and established a framework for evaluating the spatial and temporal dynamics of the CES based on RS data, and innovatively combined the RS assessment results of CES with soil field samples data to further classify cropland ecological types (CET) in a key agricultural areas of the Qinghai-Tibetan Plateau, which can effectively identify those croplands in need of priority ecological protection. Results indicate that the combined interactions of disturbance, resistance and response systems affect CES, forming a complex process with significant fluctuations and spatial variations. We also conclude that the disturbance system is positively influenced by topography and precipitation, while slope negatively affects resistance system. Hydrothermal conditions positively influence resistance system, while the response system is influenced by environmental factors at a lower intensity in six periods. It was interesting to note that soil α-biodiversity indicators are significantly and positively correlated with CES at the end of the study period. Therefore, based on the CES assessment results, we further combined the soil α-biodiversity indicators to classify the type of spatial pattern of CET and found that the eastern and northern areas have better quality, which implied an increase in the CES and a higher level of soil biodiversity, which was ideal for cropland expansion. On the contrary, we concluded that the ecosystem maintenance of the Huangshui headwaters and the northern mountainous areas needs to be strengthened in order to reverse the ecological fragility here and safeguard the cropland productive capacity.

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Accurately estimating large-area crop yields, especially for soybeans, is essential for addressing global food security challenges. This study introduces a deep learning framework that focuses on precise county-level soybean yield estimation in the United States. It utilizes a wide range of multi-variable remote sensing data. The model used in this study is a state-of-the-art CNN-BiGRU model, which is enhanced by the GOA and a novel attention mechanism (GCBA). This model excels in handling intricate time series and diverse remote sensing datasets. Compared to five leading machine learning and deep learning models, our GCBA model demonstrates superior performance, particularly in the 2019 and 2020 evaluations, achieving remarkable R2, RMSE, MAE and MAPE values. This sets a new benchmark in yield estimation accuracy. Importantly, the study highlights the significance of integrating multi-source remote sensing data. It reveals that synthesizing information from various sensors and incorporating photosynthesis-related parameters significantly enhances yield estimation precision. These advancements not only provide transformative insights for precision agricultural management but also establish a solid scientific foundation for informed decision-making in global agricultural production and food security.

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As an inland dryland lake basin, the rivers and lakes within the Lake Bosten basin provide scarce but valuable water resources for a fragile environment and play a vital role in the development and sustainability of the local societies. Based on the Google Earth Engine (GEE) platform, combined with the geographic information system (GIS) and remote sensing (RS) technology, we used the index WI2019 to extract and analyze the water body area changes of the Bosten Lake basin from 2000 to 2021 when the threshold value is -0.25 and the slope mask is 8°. The driving factors of water body area changes were also analyzed using the partial least squares-structural equation model (PLS-SEM). The result shows that in the last 20 years, the area of water bodies in the Bosten Lake basin generally fluctuated during the dry, wet, and permanent seasons, with a decreasing trend from 2000 to 2015 and an increasing trend between 2015 and 2019 followed by a steadily decreasing trend afterward. The main driver of the change in wet season water bodies in the Bosten Lake basin is the climatic factors, with anthropogenic factors having a greater influence on the water body area of dry season and permanent season than that of wet season. Our study achieved an accurate and convenient extraction of water body area and drivers, providing up-to-date information to fully understand the spatial and temporal variation of surface water body area and its drivers in the basin, which can be used to effectively manage water resources.

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The feasibility of risk assessment of a Siberian silk moth (Dendrolimus sibiricus Tschetv.) outbreak was analyzed by means of landscape and weather characteristics and tree condition parameters. Difficulties in detecting forest pest outbreaks (especially in Siberian conditions) are associated with the inability to conduct regular ground surveillance in taiga territories, which generally occupy more than 2 million km2. Our analysis of characteristics of Siberian silk moth outbreak zones under mountainous taiga conditions showed that it is possible to distinguish an altitudinal belt between 400 and 800 m above sea level where an outbreak develops and trees are damaged. It was found that to assess the resistance of forest stands to pest attacks, researchers can employ new parameters: namely, characteristics of a response of remote sensing variables to changes in land surface temperature. Using these parameters, it is possible to identify in advance (2-3 years before an outbreak) forest stands that are not resistant to the pest. Thus, field studies in difficult-to-access taiga forests are not needed to determine these parameters, and hence the task of monitoring outbreaks of forest insects is simplified substantially.

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Geobotanical subdivision of landcover is a baseline for many studies. The High-Low Arctic boundary is considered to be of fundamental natural importance. The wide application of different delimitation schemes in various ecological studies and climatic scenarios raises the following questions: (i) What are the common criteria to define the High and Low Arctic? (ii) Could human impact significantly change the distribution of the delimitation criteria? (iii) Is the widely accepted temperature criterion still relevant given ongoing climate change? and (iv) Could we locate the High-Low Arctic boundary by mapping these criteria derived from modern open remote sensing and climatic data? Researchers rely on common criteria for geobotanical delimitation of the Arctic. Unified circumpolar criteria are based on the structure of vegetation cover and climate, while regional specifics are reflected in the floral composition. However, the published delimitation schemes vary greatly. The disagreement in the location of geobotanical boundaries across the studies manifests in poorly comparable results. While maintaining the common principles of geobotanical subdivision, we derived the boundary between the High and Low Arctic using the most up-to-date field data and modern techniques: species distribution modeling, radar, thermal and optical satellite imagery processing, and climatic data analysis. The position of the High-Low Arctic boundary in Western Siberia was clarified and mapped. The new boundary is located 50-100 km further north compared to all the previously presented ones. Long-term anthropogenic press contributes to a change in the vegetation structure but does not noticeably affect key species ranges. A previously specified climatic criterion for the High-Low Arctic boundary accepted in scientific literature has not coincided with the boundary in Western Siberia for over 70 years. The High-Low Arctic boundary is distinctly reflected in biodiversity distribution. The presented approach is appropriate for accurate mapping of the High-Low Arctic boundary in the circumpolar extent.

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Forest canopy closure (FCC) is an important parameter to evaluate forest resources and biodiversity. Using multi-source remote sensing collaborative means to achieve regional forest canopy closure inversion with low cost and high-precision is a research hotspot. Taking ICESat-2/ATLAS data as the main information source and combined with data of 54 measured plots, we estimated FCC value by the Bayesian optimization (BO) algorithm improved random forest (RF), K-nearest neighbor (KNN), and gradient boosting regression tree (GBRT) model at footprint-scale. Combined with multi-source remote sensing image Sentinel-1/2 and terrain factors, we estimated the regional-scale FCC value of Shangri-La in the northwest Yunnan based on deep neural network (DNN) optimized by BO algorithm. The results showed that six characteristic parameters (percentage of tree canopy, standard deviation of relative height of photons at the top of the canopy, minimum canopy height, difference between 98% canopy height and median canopy height in the segment, number of top canopy photons, apparent surface reflectance) out of the 50 parameters that were extracted from ATLAS lidar footprint had higher contribution rate after RF characteristic variable optimization, which could be used as model variable for footprint-scale remote sensing estimation. Among BO-RF, BO-KNN, and BO-GBRT models, the FCC results estimated by the BO-GBRT model were the best at footprint-scale. The coefficient of determination (R2) was 0.65, the root mean square error (RMSE) was 0.10, the mean absolute residual (RS) was 0.079, and the prediction accuracy (P) was 0.792 for leave-one-out cross validation. It could be used as the FCC estimation model of 74808 ATLAS footprints for forest in the study area. We used the ATLAS footprint-scale FCC value of forest as the large sample data of the regional-scale BO-DNN model and combined with multi-source remote sensing factors to estimate FCC in the study area, the accuracy of the 10-fold cross-validation BO-DNN model was R2=0.47, RMSE=0.22, P=0.558. The mean values of FCC in the study area estimated by BO-DNN model and ordinary Kriging (OK) interpolation were 0.46 and 0.52, respectively, and the values mainly distributed in 0.3-0.6, accounting for 77.8% and 81.4%, respectively. The FCC efficiency obtained directly by the OK interpolation method was higher (R2=0.26), but the prediction accuracy was significantly lower than the BO-DNN model (R2=0.49). The FCC high value was distributed from northwest to southeast in the study area, and the northern and southeastern regions were the main distribution areas of high and low FCC values, respectively. It had certain advantages to estimate mountain area FCC based on ICESat-2/ATLAS high-density footprint, and the estimation results of small sample data at footprint-scale could be used as large sample data of deep learning model at region-scale, which would provide a reference for the low-cost and high-precision to FCC estimation on the footprint-scale up to the extrapolated regional-scale.

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This study aimed to shed new light on the land finance and eco-product value nexus from the perspective of fiscal decentralization, using data collected from 276 Chinese prefectures between 2005 and 2020. We employed a two-way fixed effects model to explore land finance, fiscal decentralization, and the eco-product value nexus. Our findings revealed that land finance has a noticeable disincentive influence on eco-product value. The impact of land finance on the ecological value of wetlands is much higher than on that of other land types. Additionally, fiscal expenditure decentralization plays a negative regulatory role between land finance and eco-product value. This effect is further strengthened with an increase in the fiscal decentralization level. Our findings suggest that standardizing local government land-granting behavior and making land finance more ecologically friendly through policy implementation will effectively contribute to the sustainable development of China.

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As the urbanization rate in the world has increased rapidly, the housing vacancy problem has become serious and attracting more attention. Calculating and analyzing vacant housing can help reduce the wasteful use of resources. This paper measures the housing vacancy rate and housing vacancy stock in the Shandong Peninsula urban agglomeration using night-time lighting and land use data. The results show that the average housing vacancy rate in the Shandong Peninsula urban agglomeration rose rapidly from 14.68% in 2000 to 29.71% in 2015 before declining slowly to 29.49% in 2020. Since urban population growth is lower than the housing construction rate, the average annual growth of housing vacancy stock between 2000 and 2020 exceeds 3 million square meters in megacities and is around 1-2 million square meters in large and medium-sized cities. The vacant housing has caused considerable waste of housing resources. The driving factors of the housing vacancy were further analyzed using the LMDI decomposition method. Results indicate that the economic development level is the most significant driving factor of the vacant housing stock. In addition, the value effect of unit floor areas is the major driving factor inhibiting the growth of vacant housing stock, while the decline of unit floor area value is conducive to the reduction of this stock.

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As the arid and semi-arid grassland with the most extensive distribution area in northern China, the carbon stored in Inner Mongolia (IM) grassland is highly susceptible to environmental changes. With the global warming and drastic climate changes, exploring the relationship between carbon pool changes and environmental changes and their spatiotemporal heterogeneity is necessary. This study estimates the carbon pool distribution of IM grassland during 2003-2020 by combining the measured below ground biomass (BGB) dataset, measured soil organic carbon (SOC) dataset, multi-source satellite remote sensing data products, and random forest regression modeling method. It also discusses the variation trend of BGB/SOC and its correlation with critical environmental factors, vegetation condition factors and drought index. The results show that the BGB/SOC in IM grassland was stable during 2003-2020, with a weak upward trend. The correlation analysis reveals that high temperature and drought environment were unfavorable for developing vegetation roots and would lead to a decrease in BGB. Furthermore, temperature rise, soil moisture decrease, and drought adversely effected grassland biomass and SOC in areas with low altitude, high SOC density, suitable temperature and humidity. However, in areas with relatively poor natural environments and relatively low SOC content, SOC was not significantly affected by environmental deterioration and even showed an accumulation trend. These conclusions provide directions for SOC treatment and protection. In areas where SOC is abundant, it is important to reduce carbon loss caused by environmental changes. However, in areas with poor SOC, due to the high carbon storage potential of grasslands, carbon storage can be improved through scientifically managing grazing and protecting vulnerable grasslands.

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Implementing emission reduction policies at county level is important to realize high-quality development in the Yellow River Basin and achieve national "carbon peaking" and "carbon neutrality" goals. Based on remote-sensing data of night light, net primary productivity, and land use, the present study utilized the light­carbon conversion and carbon footprint measurement models to quantify the carbon footprint of energy consumption. An exploratory spatiotemporal data analysis method was implemented to analyze the spatiotemporal evolution path. Panel quantile regression and spatiotemporal transition-nested models were used to reveal the influence mechanism of the spatiotemporal evolution of the carbon footprint. The following results were obtained. (1) The carbon footprint of counties increased from 2001 to 2020. Counties with high­carbon footprint diffused around the "one center and two axes". Carbon-deficit counties exhibited a diffused trend towards the west. In 2020, 506 counties exhibited carbon deficits, and the carbon balance of the ecosystem was severely unbalanced. (2) The carbon footprint showed evident path dependence and Matthew effect. The high­carbon footprint lock-in area comprising 177 counties is a challenging zone for governance. The 86 counties that exhibit carbon footprint changes are the key zones to drive the carbon footprint changes in the Basin. The change direction of the county's carbon footprint type, with evident spatial correlation characteristics, is in accordance with adjacent counties. (3) The carbon footprint spatiotemporal transition types and influence mechanisms in counties exhibited significant differences, with the coexistence of low-carbon footprint driving, low-carbon footprint restriction, high-carbon footprint driving and high-carbon footprint restriction modes. As the influence mechanisms of different modes and the paths to achieve "dual carbon" goals are different, the governance of different modes should focus on optimizing and strengthening restriction factors or controlling and improving of driving factors.

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The COVID-19 pandemic altered the human mobility and economic activities immensely, as authorities enforced unprecedented lock down regulations. In order to reduce the spread of COVID-19, a complete lockdown was observed between 24 March - 31 May 2020 in Pakistan. This paper aims at investigating the PM2.5, AOD and column amounts of six trace gases (NO2, SO2, CH4, HCHO, C2H2O2, and O3) by comparing periods of reduced emissions during lockdown periods with reference periods without emission reductions over Lahore, Pakistan. HYSPLIT cluster trajectory analyses were performed, which confirmed similar meteorological flow conditions during lockdown and reference periods. This provides confidence that any change in air quality conditions would be due to changes in human activities and associated emissions. The results show about 38% reduction in ambient surface PM2.5 levels during the lockdown period. This change also positively correlated with MODISDB and AERONETAOD data with a decrease of AOD by 42% and 35%, respectively. Reductions for tropospheric columns of NO2 and SO2 were about 20% and 50%, respectively during a semi lockdown period, while no reduction in the CH4, C2H2O2, HCHO and O3 levels occurred. During the lockdown period NO2, O3 and CH4 were about 50%, 45% and 25% lower, respectively, but no reduction in SO2, C2H2O2 and HCHO levels were noticed compared to the reference lockdown period for Lahore. HYSPLIT cluster trajectory analysis revealed the greatest impact on Lahore air quality through local emissions and regional transport from the east (agricultural burning and industry).

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Understanding the factors that controlling the agricultural soil heavy metals/metalloids distribution is vital for cropland soil remediation and management. For this objective, 227 agricultural soils were sampled in the Guanzhong Plain, China, to measure the concentration of five heavy metals (Pb, Cd, Ni, Zn, and Cu) and one metalloid (As) by X-ray fluorescence spectrometer, meanwhile, 24 possible influencing factors to agricultural soil heavy metals/metalloid distribution were collected and grouped into three categories. A sequential multivariate statistical analysis was carried out to provide insight into the controlling factors of soil heavy metals/metalloid distribution, then stepwise multiple linear regression (SMLR) and partial least squares regression (PLS) were used to predict heavy metals/metalloid concentrations in agricultural soil based on the result of soil heavy metals/metalloid controlling factors identification. The results demonstrated the types of soil and land use did not have a substantial effect on soil heavy metals/metalloid distribution, except Zn and Cu. The soil properties category played a major role in influencing the soil heavy metals/metalloid concentration. The concentrations of Mn and Fe, which are the main constitute elements of soil inorganic colloid, were the most significant factors, followed by the concentrations of P, K and Ca. Soil pH and soil organic matter (SOM) content, which are often considered as important factors for soil heavy metals/metalloid distribution, were not important in the present study. The SMLR was more effective than the PLS for predicting soil heavy metals/metalloid content. The results of this study enlighten that future soil heavy metals/metalloid contamination treatment in regions with high pH and low SOM content should concentrate on inorganic colloid particles, which have strong adsorption capacity for soil heavy metals/metalloid and are environmentally friendly. Moreover, the combination of successive multivariate statistical analysis and SMLR provide an effective tool to predict and monitor agricultural soil heavy metals/metalloid distribution, and facilitate the improvement of environmental and territorial management.

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Towards the improvement of predicting and analyzing the infection transmission, a novel CNN (Convolution Neural Network) based Covid Infection Transmission Analysis (CNN-CITA) is presented in this article. The method works based on both GIS data set and the Covid data set. The method reads all the data from the data sets. From the remote sensing data, the method extracts different climate conditions like temperature, humidity, and rainfall. Similarly from Global Information System data set, the locations of the peoples are fetched and merged. The merged data has been split into number of time frame, at each condition, the data sets are merged. Such merged data has been trained with deep learning networks which support the search of person location and mobility. Based on the result and the data set maintained by the governments, the infection transmission rate has been measured on region basis. In each region of movement performed by any person, the method computes the infection Transmission Rate (ITR) in two time window as before and after. According to the infection rate and ITR value of different region, a subset of sources are selected as vulnerable sources. The method produces higher performance in predicting the vulnerable sources and supports the reduction of infection rate.

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As part of the modern transport infrastructure, high-speed railways (HSRs) have been considered an important factor affecting eco-efficiency (EE). This study used multisource remote sensing and statistical data from 185 counties representing urban agglomerations in the middle reaches of the Yangtze River (UAMRYR) in China from 2009 to 2018. The study integrated ArcGIS analysis, the Super-SBM (super slack-based measure) model, and the DSPDM (dynamic spatial panel Durbin model) to explore the spatial effects of HSRs on EE. The results showed that the coordinates of the interannual centers of gravity for EE and HSRs both fell in the same county, possessing similar parameter values for the standard deviation elliptical, a negative spatial mismatch index, and obvious spatial mismatch characteristics. In different spatially dislocated areas, the spatial effects of HSRs on EE are variable. Overall, the short-term effects are more intense than the long-term effects, and both the long-term and short-term effects are dominated by the effects of spatial spillover. A new perspective is proposed to explore the green development effects of HSRs, with a view to providing policy implications for the enhancement of EE and the planning of HSRs.

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The adverse effects of global climate change on human production and life are becoming increasingly prominent. Responding to climate change has become a severe challenge faced by human society, and the reduction in greenhouse gas emissions has gradually become a common action by all countries. Therefore, analyzing carbon emissions through scientific methods has become an important foundation for responding to the national "dual carbon" strategy. This study used provincial-level carbon emission statistics, combined with nighttime light data and population data, and assigned carbon emissions to the grid scale. It also analyzed the temporal and spatial characteristics and evolution characteristics of carbon emissions in China in 2000, 2005, 2010, 2015, and 2018, as well as the correlation between carbon emissions and the economy. The results showed that:① from 2000 to 2018, the total CO2 emissions in China continued to grow, but the growth rate slowed over time. The average annual growth rate of carbon emissions dropped from 9.9% in 2000-2010 to 7.4% in 2010-2018. From the perspective of spatial distribution, carbon-free areas were mainly distributed in the northwest uninhabited area and northeast forest and mountainous areas, low-carbon emissions were mainly distributed in the vast small and medium-sized cities and towns, and high-carbon emissions were concentrated in northern, central, eastern coastal, and western provincial capitals and urban agglomerations. ② Carbon emissions had high-value or low-value agglomerations at prefecture-level cities; this agglomeration tended to stabilize as a whole and had strengthened after 2005. Low-low agglomeration areas were mainly distributed in the western contiguous areas and Hainan Island. With economic and social development, low-low agglomeration areas began to fragment and reduce in size; high-high agglomeration areas were mainly distributed in the Beijing-Tianjin-Hebei urban agglomeration, Taiyuan urban agglomeration, Yangtze River Delta urban agglomerations, and Pearl River Delta urban agglomerations, and the scale was gradually strengthened and consolidated; high-low and low-high agglomeration areas mainly appeared in neighboring cities with large differences in economic development levels. ③ Carbon emissions in most parts of China were relatively stable. The areas where carbon emissions had changed were mainly distributed in the peripheral areas of provincial capitals and key cities, and there was a circle structure with no changes in the central urban area and changes in carbon emissions in the peripheral areas. ④ The overall process of urban development in China from 2000 to 2018 followed a shift from "low emission-low income" to "high emission-low income" to "high emission-high income" and finally to "low emission-high income." The growth rate of carbon emissions in China is slowing down. Under the background of the "dual carbon" strategy, different regions face different carbon emission reduction tasks and pressures due to different carbon emission situations. Therefore, the differentiated carbon emissions policy should be implemented by regions and industries.

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Species vary in their responses to urban habitat; most species avoid these environments, whereas others tolerate or even thrive in them. To better characterize the extent to which species vary in their responses to urban habitat (from this point forwards "urban tolerance"), we used several methods to quantify these responses at a continental scale across all birds. Using open access community science-derived data from the eBird Status and Trends Products and two different types of high-resolution geospatial data that quantify urbanization of landscapes, we calculated urban tolerance for 432 species with breeding ranges that overlap large cities in Canada or the USA. We developed six different calculations to characterize species-level urban tolerance, allowing us to assess how each species' relative abundance across their breeding range varied with estimates of urban habitat use and intensity. We assessed correlations among these six indices, then compressed the two best-performing indices into a single principal component (multivariate urban tolerance index) that captured variation in urban tolerance among species. We assessed the accuracy of our single and multivariate urban tolerance indices using 24 test species that have been well characterized for their tolerance or avoidance of the urban habitat, as well as with previously published, independent urban tolerance estimates. Here, we provide this new dataset of species-level urban tolerance estimates that improves upon previous metrics by incorporating continental-scale, continuous estimates that better differentiate species' tolerance of urban habitat compared with existing, categorical methods. These refined metrics can be used to test hypotheses that link ecological, life history, and behavioral traits to avian urban tolerance. The dataset is licensed as CC-By Attribution 4.0 International. Users must appropriately cite the data paper and dataset if used in publications and scientific presentations.

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Soil moisture in the root zone is the most important factor in eco-hydrological processes. Even though soil moisture can be obtained by remote sensing, limited to the top few centimeters (<5 cm). Researchers have attempted to estimate root-zone soil moisture using multiple regression, data assimilation, and data-driven methods. However, correlations between root-zone soil moisture and its related variables, including surface soil moisture, always appear nonlinear, which is difficult to extract and express using typical statistical methods. The artificial intelligence (AI) method, which is advantageous for nonlinear relationship analysis and extraction is applied for root-zone soil moisture estimation, but by only considering its separate temporal or spatial correlations. The convolutional long short-term memory (ConvLSTM) model, known to capture spatiotemporal patterns of large-scale sequential datasets with the advantage of dealing with spatiotemporal sequence-forecasting problem, was used in this study to estimate root-zone soil moisture based on remote sensing-based variables. Owing to limitation of regional soil moisture observation data, the physical model Hydrus-1D was used to generate large and spatiotemporal vertical soil moisture dataset for the ConvLSTM model training and verification. Then, normalized difference vegetation index (NDVI) etc. remote sensing-based factors were selected as predictive variables. Results of the ConvLSTM model showed that the fitting coefficients (R2) of the root-zone soil moisture estimation significantly increased compared to those achieved by Global Land Data Assimilation System products, especially for deep layers. For example, R2 increased from 0.02 to 0.60 at depth of 40 cm. This study suggests that a combination of the physical model and AI is a flexible tool capable of predicting spatiotemporally continuous root-zone soil moisture with good accuracy on a large scale.

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