Global Spatial Distribution Research Articles

The air we breathe: An In-depth analysis of PM2.5 pollution in 1312 cities from 2000 to 2020.

In recent decades, the phenomenon of rapid urbanization in various parts of the world has led to a significant increase in PM2.5 concentration, which has emerged as a growing social concern. In order to achieve the objective of sustainable development, the United Nations Global Sustainable Development Goals (SDGs) have established the goal of creating inclusive, safe, resilient, and sustainable cities and human habitats (SDG 11). Goal 11.6 aims to decrease the negative environmental impact per capita in cities, with an emphasis on urban air quality and waste management. However, the global distribution of PM2.5 pollution varies due to disparities in urbanization development in different regions. The purpose of this paper is to explore the global spatial distribution and temporal variation of PM2.5 in cities with populations greater than 300,000 from 2000 to 2020, to gain insight into the issue. The findings indicate that PM2.5 concentrations are expected to continue increasing as urbanization progresses, but the rate of evolution of PM2.5 concentration varies depending on the continent, country, and city. From 2000 to 2020, PM2.5 concentration increased significantly in Asia and Africa, with the majority of the increased concentrations located in Asian countries and some African countries. On the other hand, most European and American countries had lower PM2.5 concentrations. The results of this study have the potential to inform urbanization policy formulation by providing knowledge about the spatial distribution of PM2.5 pollution during global urbanization. Addressing the issue of PM2.5 pollution is critical in achieving SDG 11.6 and promoting sustainable and coordinated development in cities worldwide.

Development of a compact space-borne Lidar for atmospheric aerosol and cloud detection

Space-borne lidar is preferred for observing different characteristic aerosols that should be distributed vertically in every corner of the world. Therefore, a compact space-borne lidar (CSBL) with a high repetition rate laser and photon counting mode is developed to meet the detection requirements in this paper. The detection principle is described in detail, and the design parameters of the CSBL are simulated. The CSBL is assembled and calibrated in its entirety after the development of a long-life, high-beam quality laser and a highly sensitive, quantum-efficient single-photon counting core component. Moreover, after ground and flight tests, the CSBL completed the integrated inversion of atmospheric aerosols and clouds, and it can remotely detect global atmospheric aerosols and cloud spatial distribution structures.

Global spatial distribution of Chromolaena odorata habitat under climate change: random forest modeling of one of the 100 worst invasive alien species

Anthropogenic activities and global climate change increase the risk of Chromolaena odorata invasion and habitat expansion. To predict its global distribution and habitat suitability under climate change, a random forest (RF) model was employed. The RF model, utilizing default parameters, analyzed species presence data and background information. The model revealed that the current spatial distribution of C. odorata covers 7,892,447 km2. Predictions for 2061– 2080 indicate expansion of suitable habitat (42.59 and 46.30%), reduction of suitable habit (12.92 and 12.20%), and preservation of suitable habitat (87.08 and 87.80%) under the SSP (Shared Socio-economic Pathway) 2-4.5 and SSP5-8.5 scenarios, respectively, in comparison to the present distribution. Currently, C. odorata is predominantly found in South America, with limited presence in other continents. However, the data suggest that climate change will elevate the global invasion risk of C. odorata worldwide, particularly in Oceania, Africa, and Australia. Countries such as Gambia, Guinea-Bissau, and Lesotho, which currently have unsuitable habitats, are predicted to have highly suitable habitats with climate change, supporting the idea that global habitat expansion for C. odorata will occur due to climate change. This study indicates that proper management of C. odorata is crucial during the early invasion phase.

Surface Properties of Global Land Surface Microwave Emissivity Derived from FY-3D/MWRI Measurements

Land surface microwave emissivity is crucial to the accurate retrieval of surface and atmospheric parameters and the assimilation of microwave data into numerical models over land. The microwave radiation imager (MWRI) sensors aboard on Chinese FengYun-3 (FY-3) series satellites provide valuable measurements for the derivation of global microwave physical parameters. In this study, an approximated microwave radiation transfer equation was used to estimate land surface emissivity from MWRI by using brightness temperature observations along with corresponding land and atmospheric properties obtained from ERA-Interim reanalysis data. Surface microwave emissivity at the 10.65, 18.7, 23.8, 36.5, and 89 GHz vertical and horizontal polarizations was derived. Then, the global spatial distribution and spectrum characteristics of emissivity over different land cover types were investigated. The seasonal variations of emissivity for different surface properties were presented. Furthermore, the error source was also discussed in our emissivity derivation. The results showed that the estimated emissivity was able to capture the major large-scale features and contains a wealth of information regarding soil moisture and vegetation density. The emissivity increased with the increase in frequency. The smaller surface roughness and increased scattering effect may result in low emissivity. Desert regions showed high emissivity microwave polarization difference index (MPDI) values, which suggested the high contrast between vertical and horizontal microwave signals in this region. The emissivity of the deciduous needleleaf forest in summer was almost the greatest among different land cover types. There was a sharp decrease in the emissivity at 89 GHz in the winter, possibly due to the influence of deciduous leaves and snowfall. The land surface temperature, the radio-frequency interference, and the high-frequency channel under cloudy conditions may be the main error sources in this retrieval. This work showed the potential capabilities of providing continuous and comprehensive global surface microwave emissivity from FY-3 series satellites for a better understanding of its spatiotemporal variability and underlying processes.

Constraining the Capacity of Global Croplands to CO2 Drawdown via Mineral Weathering.

Terrestrial enhanced weathering of alkaline silicate minerals is a promising climate change mitigation strategy with the potential to limit the global temperature rise. The formation and accumulation of pedogenic carbonate and bicarbonate in soils/subsoils and groundwater offers a large sink for C storage; the amount of soil inorganic carbon (SIC) presently held within soils has been estimated to be 720-950 Gt of C. These values can be augmented by the addition of a variety of calcium and magnesium silicates via enhanced weathering. While the concept of the application of finely milled silicate rocks for faster weathering rates is well established, there has been limited discussion on the role of local climate, natural SIC content (i.e., the SIC innately present in the soil), and soil pH (among other important agronomic factors) on silicate weathering when applied to croplands, especially in view that the aim is to establish terrestrial enhanced weathering as a carbon dioxide removal (CDR) strategy on a global scale. In this work, we emphasized the importance of soil pH and soil temperature on silicate weathering and looked to estimate an upper limit of (i.e., constrain) the global capacity until the year 2100 for enhanced rock weathering (ERW) to draw down CO2 in the form of accumulated pedogenic carbonate or soluble bicarbonate. We assessed the global spatial distribution of cropland soil pH, which serves as a proxy for local innate SIC; annual rate of pluvial (rainfall) precipitation; and soil temperature, and found that the potential CO2 drawdown difference between faster and slower weathering silicates is narrower in Asia, Africa, and South America, while the gap is larger for Europe, North America, and Oceania.

The Global Spatial and Temporal Distribution of Ice Cloud Optical Thickness Based on MODIS Satellite Data during 2000–2021

Ice cloud optical thickness (IOT) is an important parameter to characterize ice cloud properties and in the determination of cloud–radiation parameterization schemes, and the variation trend of ice clouds is more concerned with the study of weather and climate. In this paper, we analyzed the spatial and temporal distributions of IOT over the region between ±60° latitude. Cloud product data from March 2000 to February 2021 acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Aqua satellite were used in this study. Theil–Sen median trend analysis and EOF analysis methods were used to study the variation trend of IOT. The research results indicate that the monthly average IOT shows a “W” distribution from January to December, with a maximum reached in July (12.15) and a double bottom reached in March (10.7) and October (10.99), respectively. The average global IOT reaches the maximum in June–August, it tends to decrease with time, and its slope is −0.01 year−1. The statistical analysis results show that the area with an increase accounted for 49.4% of the total ice cloud coverage area; the area with a trend of significant increased and decreased is both 2.2%. The probability distribution of IOT reaches the maximum, around 3.25%, when the IOT is larger than 1.5 and less than or equal to 2.

Guests or pests? Eirenid hydroids living on the soft tissue of bivalves

AbstractBoth wild and reared bivalves can host hydroids from the Eirenidae family which settle on the soft body parts of their host. Thousands of hydroids have been observed on a single bivalve, and often have severe detrimental effects on the host. However, this relationship has received little attention in aquaculture research, which is probably due to a lack of baseline data. An extensive review was conducted (i) to show the current level of knowledge on the association between eirenids and bivalves, providing a benchmark for future research; (ii) to detect a critical combination of biotic/abiotic factors that could switch the relationship from commensalism to infestation and (iii) to identify research priorities for future studies. Seventy scientific papers were screened to provide ecological information useful in understanding the hydroid‐host‐environment interactions and to show the global spatial and bathymetric distribution of the relationship. Although the main parameters influencing the hydroid abundance were identified, it was challenging to collate information from such heterogeneous data sources. A standardized method for data collection is proposed to obtain more robust and comparable data on the association. The most relevant and unstudied issue is the potential physiological and qualitative changes that could occur in infested bivalves. Monitoring the association could provide data needed to prevent or to control hyper‐proliferation of the symbionts and to detect eventual synergistic effects with climate change. This could be fundamental for species living in areas particularly prone to climate regime shifts, such as semi‐enclosed basins and estuarine habitats.

Developing global annual land surface phenology datasets (1982–2018) from the AVHRR data using multiple phenology retrieval methods

Land surface phenology that reflects periodical changes in terrestrial vegetation plays an important role in influencing the functioning and services of ecosystems. Satellite-derived metrics of land surface phenology have been widely used to assess the patterns in land cover dynamics. There are still limited satellite-based datasets of global land surface phenology available to date due to uneven qualities of satellite data. Here, we developed a global annual land surface phenology dataset based on Advanced Very High Resolution Radiometer (AVHRR) data from 1982 to 2018. The number of global vegetation growing seasons for each individual pixel was counted for each year from 1982 to 2018. We implemented four phenology retrieval methods, including the amplitude threshold method, the second-order derivative method, the third-order derivative method, and the curvature change rate method, to retrieve four key phenological metrics for each vegetation growth cycle, including the start of the growing season (SOS), maturity, senescence, and the end of the growing season (EOS). The results show that the phenological metrics retrieved using different phenology retrieval methods have similar patterns of global spatial distribution. From 1982 to 2018, the timing of both maturity and senescence within a year advanced in the Northern Hemisphere and the timing of SOS delayed in the Southern Hemisphere. The phenological metrics extracted from in-situ data such as the USA National Phenology Network (USA-NPN), the Pan European Phenology Database (PEP725), and flux tower measurements were positively correlated with satellite-derived phenological metrics. Despite the effects of mismatching spatial scales, the retrieved phenological metrics based on 5 km AVHRR data were consistent with those based on 500 m Moderate-resolution Imaging Spectroradiometer (MODIS) data and 30 m fused satellite data at both site-scale and regional-scale. The developed dataset released for usages and applications on global land surface dynamics offers potential end users options for choosing desirable phenological metrics derived from different methods.

Global methyl halide emissions from biomass burning during 2003-2021.

Methyl halides (CH3Cl, CH3Br, and CH3I) are ozone-depleting substances. Biomass burning (BB) is an important source of methyl halides. The temporal variations and global spatial distribution of BB methyl halide emissions are unclear. Thus, global methyl halide emissions from BB during 2003-2021 were estimated based on satellite data. A significant decreasing trend (p<0.01) in global methyl halide emissions from BB was found between 2003 and 2021, with CH3Cl emissions decreasing from 302 to 220Ggyr-1, CH3Br emissions decreasing from 16.5 to 11.7Ggyr-1, and CH3I emissions decreasing from 8.9 to 6.1Ggyr-1. From a latitudinal perspective, the northern high-latitude region (60-90° N) was the only latitude zone with significant increases in BB methyl halide emissions (p<0.01). Based on an analysis of the drivers of BB methyl halide emissions, emissions from cropland, grassland, and shrubland fires were more correlated with the burned area, while BB emissions from forest fires were more correlated with the emissions per unit burned area. The non-BB emissions of CH3Cl increased from 4749Ggyr-1 in 2003 to 4882Ggyr-1 in 2020, while those of CH3Br decreased from 136Ggyr-1 in 2003 to 118Ggyr-1 in 2020 (global total CH3I emissions are not available). The finding indicates that global CH3Cl and CH3Br emissions from sources besides BB increased and decreased during 2003-2020. Based on our findings, not only searching for unknown sources is important, but also re-evaluating known sources is necessary for addressing methyl halide emissions.

Global Spatial Distribution of Hack's Law Exponent on Mars Consistent With Early Arid Climate

AbstractWidespread valley networks (VNs) on Mars and other evidence point to an early warm and wet climate. However, ongoing debates still exist about VN's formation processes and associated climatic conditions. The power law relationship between basin length and area (Hack's Law) can be diagnostic of different fluvial processes related to climatic conditions. Past studies of Hack's Law on Mars at local sites have produced inconclusive results. Here we used a parameter‐free method to delineate watersheds globally on Mars based on mapped VNs and extracted their Hack's Law exponent (h). The majority of h values on Mars are similar to those in arid areas on Earth, suggesting similar runoff processes and arid conditions for VN formation on early Mars. Statistical analyses show that the spatial distribution of h on Mars is not random, but with a few clustered high and low values, likely controlled by local conditions (e.g., regional topographic slope).

Local indicators of climate change impacts described by indigenous peoples and local communities: Study protocol.

In the quest to improve the understanding of climate change impacts on elements of the atmospheric, physical, and life systems, scientists are challenged by the scarcity and uneven distribution of grounded data. Through their long history of interaction with the environment, Indigenous Peoples and local communities have developed complex knowledge systems that allow them to detect impacts of climate change in the local environment. The study protocol presented here is designed 1) to inventory climate change impacts on the atmospheric, physical, and life systems based on local knowledge and 2) to test hypotheses on the global spatial, socioeconomic, and demographic distribution of reported impacts. The protocol has been developed within the framework of a project aiming to bring insights from Indigenous and local knowledge systems to climate research (https://licci.eu). Data collection uses a mixed-method approach and relies on the collaboration of a team of 50 trained partners working in sites where people's livelihood directly depend on nature. The data collection protocol consists of two steps. Step 1 includes the collection of secondary data (e.g., spatial and meteorological data) and site contextual information (e.g., village infrastructure, services). Step 1 also includes the use of 1) semi-structured interviews (n = 20-30/site) to document observations of environmental change and their drivers and 2) focus group discussions to identify consensus in the information gathered. Step 2 consist in the application of a household (n from 75 to 125) and individual survey (n from 125 to 175) using a standardized but locally adapted instrument. The survey includes information on 1) individual and household socio-demographic characteristics, 2) direct dependence on nature, 3) household's vulnerability, and 4) individual perceptions of climate change impacts. Survey data are entered in a specifically designed database. This protocol allows the systematic documentation and analysis of the patterned distribution of local indicators of climate change impacts across climate types and livelihood activities. Data collected with this protocol helps fill important gaps on local climate change impacts research and can provide tangible outcomes for local people who will be able to better reflect on how climate change impacts them.

Modeling and Analysis of Discrete Facial Expressions with Dense Optical Flow-derived Features

Facial expressions (FEs) are one of the most preeminent means of conveying one’s emotions and are pivotal to nonverbal communication. Potential applications in a wide range of areas in computer vision have lent a strong impetus to research in the domain of automatic facial expression recognition. This work discusses the effectiveness of two optical flow-based features for modeling the FEs associated with prototypic emotions based on the pattern of nonrigid deformable motion of facial components occurring during their portrayal. The discernible motion patterns are categorized into distinct discrete classes with the descriptive features indicating the global spatial distribution of deformation derived from the dense optical flow field associated with emotional and neutral face images. Results obtained with evaluation on images and video clips taken from Extended Cohn-Kanade, Japanese Female Facial Expressions, and Dynamic Karolinska Directed Emotional Faces datasets with multi-class support vector machine and [Formula: see text]-nearest neighbor classifiers are competent with the state-of-the-art techniques and concordant with empirical psychological studies in emotion science.

Global mapping of the landside clustering of aquaculture ponds from dense time-series 10 m Sentinel-2 images on Google Earth Engine

• Spatial and dense temporal features for extracting global land-side aquaculture. • Global aquaculture pond distribution information was obtained for the first time. • Asia accounted for 89.12% of global land-side aquaculture. • A 30-km buffer of coastline inland side accounted for 75.57% • The result is highly consistent with FAO aquaculture statistics. Spatial distribution information offers a valuable resource for the growing research on evaluating and managing aquaculture and the mechanism of its interaction with the environment, especially concerning landside clustering aquaculture ponds (LCAP). However, studies aimed at obtaining data on global LCAP distribution remains limited. This study combined edge detection and the morphology implemented on Google Earth Engine (GEE) platform to produce the first global spatial distribution data of LCAP using 4,015,054 tiles of the 10-m Sentinel-2 time-series images collected throughout 2020. The study findings showed a total area of global LCAP of 55,337.03 km 2 . Asia, led by China, had the highest distribution area of LCAP, accounting for 89.12 % of global LCAP. The main LCAP distribution area was the landside 30-km buffer region of coastline, accounting for 75.57 % of global LCAP. Meanwhile, the greatest proportion of LCAP (69.63 %) was located in China. Accuracy verification revealed that the precision and recall error of the results were 83.91 % and 92.49 % respectively, and F 1 score was 0.88. A comparison of China, Vietnam, and India, based on data drawn from most of the existing local studies that used remote sensing methods, showed that these countries’ aquaculture area differs by less than 10 %. Despite some variation from FAO statistics or official national statistics, the results of our remote sensing-based method show promise in such aspects as global coverage and temporal coherency when compared with the classical statistical method.

An Investigation of Green Roof Spatial Distribution and Incentive Policies Using Green Buildings as a Benchmark

The global spread of green roofs is becoming increasingly important in the context of increasing urbanization and climate change as they provide multiple benefits, including urban heat island effect (UHI) mitigation, stormwater retention and urban flood risk reduction. However, current research provides little insight into how green roofs are used and promoted globally. Therefore, it is essential to look at the global spatial distribution of green roofs and the related policies by using green buildings which have been successfully promoted as a benchmark. This study collected data from multiple sources, such as a dataset of spatial distribution of green roofs from eight countries, and 124 green roof incentive policy texts from 88 cities. Spatial analysis was conducted using ArcGIS Pro, and different types of incentive policies were explored using word frequency analysis. The results show that (1) the unbalanced distribution of green roofs in cities is prominent, with significant regional differences and clustering characteristics along water systems; (2) in the vast majority of countries, the types of incentive policies for green roofs lack diversity; (3) green roofs lack a well-developed rating system compared to how green buildings are promoted. Combining green buildings with green roofs and exploring the gaps between them, this study will help further explore the application of green roofs worldwide and guide governments or non-governmental organizations to develop and implement more efficient policies and provide recommendations to promote the popularity of green roofs worldwide.

Global spatial distribution of vegetation and 20-years dynamic changes

Global spatial distribution of vegetation is determined jointly by climate, monsoon, ocean current and human activities. In recent decades, surface vegetation changes caused by global warming, extreme climate events and intensified human activities need a further understood. In this paper, we adopted available MODIS land cover dataset (MCD12Q1) to investigate the spatial distribution feature of global vegetation sand dynamics during the past 20 years. Results show that: (1) the spatial distribution pattern of global vegetation formed by the long-term ecosystem evolution exhibits stable with less variation in both latitude and vertical dimensions. Generally, the percent of forest, shrubland, grass and cropland respectively are 15.27%, 9.39%, 39.46% and 8.91% of surface covers, with a distinct land cover transition trend from rainforest at equator region to moss and lichen at the north and south pole. (2) In the past 20 years, the global forest land decreased by 3.0×105 km2, with an average annual decrease of 1.255%, open shrubs experienced slight fluctuations in 2001-2017 and increased sharply by 1.6×105 km2 in 2017-2018. And the grassland, croplands and cropland/natural vegetation mosaics had different degrees of growth rate, with an average annual dynamic increase of 0.021%, 0.019% and 0.502%, respectively. (3) There are about 4.2×106 km2 of forest changed into grassland in West Siberia and the Amazon, and 2.5×106 km2 of grassland into shrubs, as well as 1.9×106 km2 of grassland vegetation into farmland, and 9.4×104 km2 of forest into shrubs.

Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of formaldehyde and nitrogen dioxide at three sites in Asia and comparison with the global chemistry transport model CHASER

Abstract. Formaldehyde (HCHO) and nitrogen dioxide (NO2) concentrations and profiles were retrieved from ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations during January 2017–December 2018 at three sites in Asia: (1) Phimai (15.18∘ N, 102.5∘ E), Thailand; (2) Pantnagar (29∘ N, 78.90∘ E) in the Indo-Gangetic Plain (IGP), India; and (3) Chiba (35.62∘ N, 140.10∘ E), Japan. Retrievals were performed using the Japanese MAX-DOAS profile retrieval algorithm ver. 2 (JM2). The observations were used to evaluate the NO2 and HCHO partial columns and profiles (0–4 km) simulated using the global chemistry transport model (CTM) CHASER (Chemical Atmospheric General Circulation Model for Study of Atmospheric Environment and Radiative Forcing). The NO2 and HCHO concentrations at all three sites showed consistent seasonal variation throughout the investigated period. Biomass burning affected the HCHO and NO2 variations at Phimai during the dry season and at Pantnagar during spring (March–May) and post-monsoon (September–November). Results found for the HCHO-to-NO2 ratio (RFN), an indicator of high ozone sensitivity, indicate that the transition region (i.e., 1 &lt; RFN &lt; 2) changes regionally, echoing the recent finding for RFN effectiveness. Moreover, reasonable estimates of transition regions can be derived, accounting for the NO2–HCHO chemical feedback. The model was evaluated against global NO2 and HCHO columns data retrieved from Ozone Monitoring Instrument (OMI) observations before comparison with ground-based datasets. Despite underestimation, the model well simulated the satellite-observed global spatial distribution of NO2 and HCHO, with respective spatial correlations (r) of 0.73 and 0.74. CHASER demonstrated good performance, reproducing the MAX-DOAS-retrieved HCHO and NO2 abundances at Phimai, mainly above 500 m from the surface. Model results agree with the measured variations within the 1-sigma (1σ) standard deviation of the observations. Simulations at higher resolution improved the modeled NO2 estimates for Chiba, reducing the mean bias error (MBE) for the 0–2 km height by 35 %, but resolution-based improvements were limited to surface layers. Sensitivity studies show that at Phimai, pyrogenic emissions contribute up to 50 % and 35 % to HCHO and NO2 concentrations, respectively.

Study on the Impact of the Doppler Shift for CO2 Lidar Remote Sensing

Atmospheric carbon dioxide (CO2) is recognized as the most important component of the greenhouse gases, the concentration of which has increased rapidly since the pre-industrial era due to anthropogenic emissions of greenhouse gases (GHG). The accurate monitoring of carbon dioxide is essential to study the global carbon cycle and radiation budget on Earth. The Aerosol and Carbon Detection Lidar (ACDL) instrument onboard the Atmospheric Environmental Monitoring Satellite (AEMS) was successfully launched in April 2022, which allows a new perspective to quantify the global spatial distribution of atmospheric CO2 with high accuracy. In this work, the impact of the Doppler shift on CO2 measurements for an integrated-path differential absorption (IPDA) light detection and ranging (lidar) system was evaluated to meet the weighted column-averaged mixing ratio of carbon dioxide (XCO2) measurement requirements of less than one part per million (ppm). The measurement uncertainties due to the Doppler shift were first evaluated in airborne IPDA observations. The result shows that most of the Doppler shift is in the range of 6–8 MHz, resulting in 0.26-0.39 ppm deviations in the XCO2 results. The deviations between the XCO2 retrievals and in situ measurements decreased to 0.16 ppm after the correction of the Doppler shift from 11:28:29 to 11:28:49 in the flight campaign. In addition, the online Doppler shift accounts for 98% of the deviations between XCO2 retrievals and in situ measurements. Furthermore, the impact of the Doppler shift on ACDL measurements is also assessed. The differences between the XCO2 retrievals with and without Doppler shift are used to quantify measurement uncertainties due to the Doppler effect. The simulations reveal that a pointing misalignment of 0.067 mrad can lead to a mean bias of about 0.30 ppm (0.072%) in the CO2 column. In addition, CO2 measurements are more sensitive to the Doppler shift at high altitudes for IPDA lidar, so the largest differences in the CO2 columns are found on the Qinghai–Tibet Plateau in China.

A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data

Passive microwave remote sensing of soil moisture is an underdetermined problem, as observed microwave emission from the landscape is affected by a variety of unknown surface parameters. Increasing observation information is an effective means to make retrievals more robust. In this study, a multi-temporal and multi-angular (MTMA) approach is proposed using SMOS (Soil Moisture and Ocean Salinity) satellite L-band data for retrieving vegetation optical depth (VODp, p indicates the polarization with H for horizontal and V for vertical)), effective scattering albedo (ωpeff), soil surface roughness (Zps), and soil moisture (SMp). The advantage of the MTMA approach is that it does not need auxiliary data as inputs or constraints. SMOS polarization-dependent VOD are produced and compared at a global scale for the first time, and it is found that the polarization difference of vegetation effects should not be ignored in the SM retrieval algorithm. The MTMA VOD retrievals are found to have a reasonable global spatial distribution, which is generally consistent with the VOD retrievals obtained from the SMOS Level 3 (SMOS-L3) and SMOS-IC Version 2 (V2) (referred to as SMOS-IC), except for showing relatively lower values over densely vegetated areas compared with the other two SMOS products. The spatial distribution of retrieved ωpeff generally shows a dependence on both VOD and land cover types. In addition, the values of MTMA-ωVeff are higher than that of MTMA-ωHeff, indicating stronger microwave scattering of V-pol in the vegetation layer than that of H-pol. The retrieved surface roughness parameter (Zps) ranges from 0.04 to 0.22 cm, and its spatial distribution is partially different from the existing roughness products/auxiliary data from SMOS and SMAP. The retrieved MTMA SM shows generally high correlations with in-situ measurements (11 dense observation networks) with overall correlation coefficients of > 0.75. The overall ubRMSE of MTMA-SMH and MTMA-SMV are < 0.055 m3/m3 and lower than that of SMOS-IC and SMOS-L3 products. SMOS-IC generally presents higher correlation coefficients compared to MTMA in most sites outside China; in China, RFI filtering is crucial and makes it very difficult when comparing algorithms based on different brightness temperature products. The number of effective retrievals of MTMA-SMH and MTMA-SMV ranges from 1409 to 1640 and 1104 to 1603 respectively, which is more than that from SMOS-IC (from 236 to 1358) over the selected 11 networks. Therefore, it is concluded that by incorporating multi-temporal SMOS data, the proposed method of MTMA can be used to systematically retrieve SM, VOD and additional surface parameters (effective scattering albedo and surface roughness) with comparable or better performance of SM than that of SMOS-IC and SMOS-L3. Moreover, this paper for the first time produced a polarization-dependent SMOS VOD product at a global scale.

Research on the spatiotemporal distribution and evolution of remote sensing: A data-driven analysis

The development of remote sensing technology largely reflects the scientific research level of a country or region. Given that the quantity and quality of research works are important indicators for scientific prowess evaluation, exploratory spatial data analysis and scientometric analysis of remote sensing work published from 2012 to 2021 were performed in this study, utilizing the Web of Sciences database. This study probed the spatial distribution and spatiotemporal evolution at the country/regional level to reveal the spatiotemporal characteristics of knowledge spillover in remote sensing. According to the results, the global spatial distribution of research output in remote sensing presented a significant dispersion; the United States and China were the most active countries. During the study period, Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery was one of the most influential studies, both in the field of remote sensing and in the whole scientific community. With respect to the spatial evolution of research output in remote sensing, the gap between continents and the regional imbalance showed a downward trend, while Asia ranked first in the intracontinental disparity and Europe ranked last. For relevant countries/regions and institutions trying to optimize the spatial allocation of scientific and technological resources to narrow regional disparities, this study provides fundamental data and decision-making references.

Study on the Spatial and Temporal Evolution Patterns of Green Innovation Efficiency and Driving Factors in Three Major Urban Agglomerations in China—Based on the Perspective of Economic Geography

Sustainable development has become a global consensus, and green innovation is the key starting point, and it has become a ballast stone and stabilizer for regional ecological environmental protection and high-quality economic development. Based on GIS tools and multi-method models, this paper studies the spatio-temporal characteristics and influence mechanism of green innovation in three major urban agglomerations in China from 2010 to 2019 from the perspective of economic geography. The study found that: (1) the green innovation efficiency of the three major urban agglomerations in China is in a fluctuating upward trend as a whole, with obvious spatial disequilibrium; (2) from the spatial point of view, the characteristics of global spatial agglomeration distribution have positive spatial correlation, and the law of local spatial autocorrelation is obvious, and the spatio-temporal pattern transitions from “low efficiency, big difference” to “high efficiency, small difference” during the study period; (3) there are obvious spatial spillover and diffusion effects on the green innovation efficiency of the three major urban agglomerations in China as a whole. However, the spatial dependence of green innovation efficiency is inconsistent in China’s three major urban agglomerations; the YRD Urban region and the PRD Urban region show a positive impact, while the JJJ Urban region shows a negative impact; (4) the level of economic development, the operating environment of science and technology, and the guiding factors of government system function with significant differences and regional spatial heterogeneity on the efficiency of green innovation in the three major urban agglomerations in China.