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Abstract Wetlands play a key role in maintaining ecological balance and climate regulation. However, due to the complex and variable spectral characteristics of wetlands, there are no publicly available global 30-meter time-series wetland dynamic datasets at present. In this study, we present novel global 30 m annual wetland maps (GWL_FCS30D) using time-series Landsat imagery on the Google Earth Engine platform, covering the period of 2000–2022 and containing eight wetland subcategories. Specifically, we make full use of our prior globally distributed wetland training sample pool, and adopt the local adaptive classification and spatiotemporal consistency checking algorithm to generate annual wetland maps. The GWL_FCS30D maps were found to achieve an overall accuracy and Kappa coefficient of 86.95 ± 0.44% and 0.822, respectively, in 2020, and show great temporal variability in the United States and the European Union. We expect the dataset would provide vital support for wetland ecosystems protection and sustainable development.

For many applications, raw satellite observations need to be converted to high-level products of various essential environmental variables. While numerous products are available at kilometer spatial resolutions, there are few global products at high spatial resolutions (10–30 m), which are also referred to fine or medium resolutions in the literature. To facilitate the development of more high spatial resolution products, this paper systematically reviews the state-of-the-art progress on inversion algorithms and publicly available regional and global products. We begin with an inventory of available high-resolution satellite data, and then present different algorithms for determining cloud masks, estimating aerosol optical depth, and performing atmospheric correction and topographic correction for land surface reflectance retrieval. The majority of this paper reviews the inversion algorithms and existing regional to global products of 18 variables in four major categories: 1) Land surface radiation, including broadband albedo, land surface temperature, and all-wave net radiation; 2) Terrestrial ecosystem variables, including leaf area index, fraction of absorbed photosynthetically active radiation, fractional vegetation cover, fractional forest cover, tree height, forest above-ground biomass gross primary production, net primary production, and agricultural crop yield; 3) Water cycle and cryosphere, including soil moisture, evapotranspiration, and snow cover; and 4) Land surface types, such as global land cover, impervious surface, inland water, crop type, and fire. Since the existing products over large regions are usually spatially discontinuous due to cloud contamination, different data fusion and data assimilation algorithms and some products for producing spatially seamless and temporally continuous products are presented. In the end, we discuss a variety of challenges in generating global high spatial resolution satellite products.

The tensile properties and microstructures of ZL114A alloy component with a complex shape are investigated at room temperature and 200°C, using the tensile tests, scanning electron microscopy and electron backscattering diffraction. Both thin wall and thick structure exhibit excellent properties, of which max ultimate tensile strength and elongation at break reach 314 MPa and 2.5% at room temperature, respectively. The ultimate tensile strengths of thin wall are 40 MPa and 25 MPa greater than those of thick structure at room temperature and 200°C, respectively. Moreover, the eutectic Si phases of thin wall exhibit a predominantly spherical morphology while of the morphology of thick structure are rod-like, resulting in the different mechanical properties between thin wall and thick structure. The fracture morphologies of thin wall and thick structure are studied to explain the difference in performance between thin wall and thick structure.

BackgroundGeriatric diseases (e.g., chronic diseases and geriatric syndromes) may result in impaired physical performance and a decline in the quality of life. The results of previous studies reported the positive effects of comprehensive community-based rehabilitation (CBR) services on physical and social functioning and psychosocial wellbeing. However, to provide adequate and personalised rehabilitation services, it is essential to understand the needs of the older adults population. There have been no studies on the need for CBR in older adults populations that consider their heterogeneity. Therefore, high-quality studies are required to recognise the heterogeneity and latent classes of CBR needs in older adults population groups. This study aims to identify the heterogeneity of the rehabilitation needs of older adults in the community and explore whether older adults with similar characteristics have similar needs through a cross-sectional survey and latent class analysis (LCA) to provide support for personalised rehabilitation services.MethodsThe study is structured into four phases. The first phase will focus on constructing a comprehensive questionnaire to assess rehabilitation needs. In the second phase, a pilot study will be conducted to evaluate the reliability and validity of the completed questionnaire. This step ensures the robustness of the instrument for data collection. The third phase will involve cross-sectional surveys using the finalised questionnaires to collect the necessary data from the targeted population. The fourth phase will focus on conducting LCA to determine the CBR needs of the older adult population.DiscussionThe results of this study will provide novel and critical information for a better understanding of the rehabilitation needs, potential categories, and influencing factors of older adults in the community. The study will be conducted in Guizhou Province in western China, where economic and social development is relatively low, and the results will inform and benefit other regions and developing countries facing similar challenges. However, because of the complete social security and rehabilitation service systems in developed areas, our research results may not fully reflect the situation in these areas. Future studies may need to be conducted in places with different levels of social development.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=191398, ChiCTR2300071478.

The alpine wetlands are vital in regulating climate, maintaining biodiversity, stabilizing ecosystem balance, maintaining water resources and ecological security. However, influenced by extreme climate and human activities, the ecological degradation of alpine wetlands seriously threatens the sustainable development of the region. Nowadays, it is urgent to develop an ecological monitoring index suitable for high-altitude alpine wetlands, and timely carry out long-term remote sensing monitoring and evaluation on the ecological degradation and restoration of alpine wetlands. Based on Landsat images of Google Earth Engine (GEE) year by year, we constructed the Alpine Wetland Degradation Index (AWDI) and studied the ecological change process of alpine wetland in the first meander of the Yellow River during 1990–2022 with the help of the Unary linear regression, the Theil-Sen median and the Mann-Kendall test methods. In addition, we also discussed the main driving factors of ecological degradation of alpine wetlands with the help of the Pearson correlation analysis and Geodetector. Our research found that the AWDI based on long-time series MNDWI, NDVI, FVC and WET has significant accuracy in evaluating the ecological degradation of alpine wetlands (POA = 95.69 %, Kappa = 0.9460). In the past 33 years, the ecological situation of the alpine wetland in the first meander of the Yellow River has experienced a process of degradation first and then improvement. Precipitation is the main factor that affects the ecological change of the alpine wetland, and the role of human activities can not be ignored. Our AWDI can quickly and accurately reveal the ecological degradation process of alpine wetlands, and then provide a scientific basis and case reference for the evaluation, restoration, management and protection of alpine wetland degradation.

Wetlands are among the most productive ecosystems on Earth, providing various key ecosystem services to humans and other organisms. However, an insufficient understanding of the economic value and importance of wetland resources has seriously weakened the ecosystem service value of wetlands. Thus, a more comprehensive understanding of wetland ecosystem services must be revealed in order to propose appropriate conservation policies. Therefore, to achieve the abovementioned goals, we used the emergy value accounting method to account for the ecosystem service value of the Maqu alpine wetlands, and the Logarithmic Mean Divisia Index was used to analyze the driving forces affecting changes in ecosystem services. The results indicate that the ecosystem service value of the Maqu alpine wetlands showed a trend of decline and then rebound during 1990–2020; its unit area emergy value decreased from 3.85 × 1010 sej/m2/y to 3.04 × 1010 sej/m2/y during 1990–2000, and after 2010, it began to gradually rebound. Moreover, the monetary ecosystem service value of the Maqu alpine wetlands continued to rise during the study period, reaching 41.45 USD/m2/y in 2020, thus reflecting the great potential value of wetland ecosystems. Based on our analysis, the key drivers affecting the changes in ecosystem services include economic scale and emergy benefit. Rapid and intensive human activities have boosted local economic development, but these developments have come at the cost of environmental degradation. Finally, some suggestions are proposed. This study suggests that the alpine wetlands in Maqu occupy an important ecological position within the whole Yellow River basin. Therefore, it is worthwhile to invest a significant amount of funding in their restoration and protection.

With the development of remote sensing technology, a number of fine-resolution (30-m) global/national land cover (LC) products have been developed. However, accuracy assessments for the developed LC products are commonly conducted at global and national scales. Due to the limited availability of representative validation observations and reference data, knowledge relating to the accuracy and applicability of existing LC products on a regional scale is limited. Since Xinjiang, China, exhibits diverse surface cover and fragmented urban landscapes, existing LC products generally have high classification uncertainty in this region. This makes Xinjiang suitable for assessing the accuracy and consistency of exiting fine-resolution land cover products. In order to improve knowledge of the accuracy of existing fine-resolution LC products at the regional scale, Xinjiang province was selected as the case area. First, we employed an equal-area stratified random sampling approach with climate, population density, and landscape heterogeneity information as constraints, along with the hexagonal discrete global grid system (HDGGS) as basic sampling grids to develop a high-density land cover validation dataset for Xinjiang (HDLV-XJ) in 2020. This is the first publicly available regionally high-density validation dataset that can support analysis at a regional scale, comprising a total of 20,932 validation samples. Then, based on the generated HDLV-XJ dataset, the accuracies and consistency among three widely used 30-m LC products, GLC_FCS30, GlobeLand30, and CLCD, were quantitatively evaluated. The results indicated that the CLC_FCS30 exhibited the highest overall accuracy (88.10%) in Xinjiang, followed by GlobeLand30 (with an overall accuracy of 83.58%) and CLCD (81.57%). Moreover, through a comprehensive analysis of the relationship between different environmental conditions and land cover product performance, we found that GlobeLand30 performed best in regions with high landscape fragmentation, while GLC_FCS30 stood out as the most outstanding product in areas with uneven proportions of land cover types. Our study provides a novel insight into the suitability of these three widely-used LC products under various environmental conditions. The findings and dataset can provide valuable insights for the application of existing LC products in different environment conditions, offering insights into their accuracies and limitations.

Tidal flats are an important part of coastal ecosystems and play an important role in shoreline protection and biodiversity maintenance. Although many efforts have been made in tidal flat mapping, an accurate global tidal flat product covering all coasts globally is still lacking and urgently needed. In this study, a novel method is proposed for the automated mapping of global tidal flats at 30 m (GTF30) in 2020 based on the Google Earth Engine, which is also the first global tidal flat dataset covering the high latitudes (>60°N). Specifically, we first propose a new spectral index named the LTideI index through a sensitivity analysis, which is robust and can accurately capture low-tide information. Second, globally distributed training samples are automatically generated by combining multisource datasets and the spatiotemporal refinement method. Third, the global coasts are divided into 588 5°×5° geographical tiles, and the local adaptive classification strategy is used to map tidal flats in each 5°×5° region by using multisourced training features and the derived globally distributed training samples. The statistical results show that the total global area of tidal flats is about 140,922.5 km2, with more than 75% distributed on 3 continents in the Northern Hemisphere, especially in Asia (approximately 43.1% of the total). Finally, the GTF30 tidal flat dataset is quantitatively assessed using 13,994 samples, yielding a good overall accuracy of 90.34%. Meanwhile, the intercomparisons with several existing tidal flat datasets indicate that the GTF30 products can greatly improve the mapping accuracy of tidal flats. Therefore, the novel method can support the automated mapping of tidal flats, and the GTF30 dataset can provide scientific guidance and data support for protecting coastal ecosystems and supporting coastal economic and social development. The GTF30 tidal flat dataset in 2020 is freely accessible via https://doi.org/10.5281/zenodo.7936721.

Measurement(s) leaf area index Technology Type(s) destructive sampling method Sample Characteristic - Environment area of cropland Sample Characteristic - Location China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15124524

Healthy and sustainable urban agglomerations development relies heavily on land use optimization. However, there is insufficient scientific basis and reliable quantitative analysis for land use pattern identification and optimal prediction in embryonic urban agglomeration. Therefore, taking the Lanzhou–Xining (LX) region, a typical primary developing urban agglomeration, as the study area, we first assessed the land ecological suitability (LES). Then, we embedded the LES evaluation results in the land optimization process and constructed the MCR-MOP- Dyna-CLUE model framework, simulating and optimizing land use patterns for the year 2035 under ecological optimization and business as usual scenarios, which aimed to explore a sustainable land use pattern for embryonic urban agglomerations. The results indicated that the ecological optimization scenario based on LES had a more rational land use pattern. It appropriately controlled the expansion rate of construction land and effectively alleviated the problems of construction land encroaching on farming land and ecological land. Meanwhile, the ecosystem services increased in value based on adequately addressing the need for food security and economic development. Compared to the business as usual scenario, the construction land under the ecological optimization scenario was 19,622.69 ha less, and the cultivated land was 32,103.29 ha more. Moreover, the ecological benefit and the economic benefit increased by 187,490.4595 million yuan and 151,808,605.1 million yuan from 2020 to 2035, respectively, under the ecological optimization scenario. Our research is of great value for making decisions on sustainable land use and land resource management in initial developing agglomerations.

Desertification, a current serious global environmental problem, has caused ecosystems and the environment to degrade. The total area of desertified land is about 1.72 million km2 in China, which is extensively affected by desertification. Estimating land desertification risks is the top priority for the sustainable development of arid and semi-arid lands in China. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model was used to assess the sensitivity of land desertification in China. Based on multi-source remote sensing data, this study integrated natural and human factors, calculated the land desertification sensitivity index by overlaying four indicators (soil quality, vegetation quality, climate quality, and management quality), and explored the driving forces of desertification using a principal component and correlation analysis. It was found that the spatial distribution of desertification sensitivity areas in China shows a distribution pattern of gradually decreasing from northwest to southeast, and the areas with very high and high desertification sensitivities were about 620,629 km2 and 2,384,410 km2, respectively, which accounts for about 31.84% of the total area of the country. The very high and high desertification sensitivity areas were mainly concentrated in the desert region of northwest China. The principal component and correlation analysis of the sub-indicators in the MEDALUS model indicated that erosion protection, drought resistance, and land use were the main drivers of desertification in China. Furthermore, the aridity index, soil pH, plant coverage, soil texture, precipitation, soil depth, and evapotranspiration were the secondary drivers of desertification in China. Moreover, the desertification sensitivity caused by drought resistance, erosion protection, and land use was higher in the North China Plain region and Guanzhong Basin. The results of the quantitative analysis of the driving forces of desertification based on mathematical statistical methods in this study provide a reference for a comprehensive strategy to combat desertification in China and offer new ideas for the assessment of desertification sensitivity at macroscopic scales.

The canopy chlorophyll content (CCC) and leaf area index (LAI) are both essential indicators for crop growth monitoring and yield estimation. The PROSAIL model, which couples the properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAIL) radiative transfer models, is commonly used for the quantitative retrieval of crop parameters; however, its homogeneous canopy assumption limits its accuracy, especially in the case of multiple crop categories. The adjusted average leaf angle (ALAadj), which can be parameterized for a specific crop type, increases the applicability of the PROSAIL model for specific crop types with a non-uniform canopy and has the potential to enhance the performance of PROSAIL-coupled hybrid methods. In this study, the PROSAIL-D model was used to generate the ALAadj values of wheat, soybean, and maize crops based on ground-measured spectra, the LAI, and the leaf chlorophyll content (LCC). The results revealed ALAadj values of 62 degrees for wheat, 45 degrees for soybean, and 60 degrees for maize. Support vector regression (SVR), random forest regression (RFR), extremely randomized trees regression (ETR), the gradient boosting regression tree (GBRT), and stacking learning (STL) were applied to simulated data of the ALAadj in 50-band data to retrieve the CCC and LAI of the crops. The results demonstrated that the estimation accuracy of singular crop parameters, particularly the crop LAI, was greatly enhanced by the five machine learning methods on the basis of data simulated with the ALAadj. Regarding the estimation results of mixed crops, the machine learning algorithms using ALAadj datasets resulted in estimations of CCC (RMSE: RFR = 51.1 μg cm−2, ETR = 54.7 μg cm−2, GBRT = 54.9 μg cm−2, STL = 48.3 μg cm−2) and LAI (RMSE: SVR = 0.91, RFR = 1.03, ETR = 1.05, GBRT = 1.05, STL = 0.97), that outperformed the estimations without using the ALAadj (namely CCC RMSE: RFR = 93.0 μg cm−2, ETR = 60.1 μg cm−2, GBRT = 60.0 μg cm−2, STL = 68.5 μg cm−2 and LAI RMSE: SVR = 2.10, RFR = 2.28, ETR = 1.67, GBRT = 1.66, STL = 1.51). Similar findings were obtained using the suggested method in conjunction with 19-band data, demonstrating the promising potential of this method to estimate the CCC and LAI of crops at the satellite scale.

The leaf maximum carboxylation rate (Vcmax) is a key parameter in modeling plant photosynthesis. The rapid and accurate acquisition of Vcmax at large scales can improve understanding of global vegetation productivity and the terrestrial carbon cycle. In this article, we assessed the retrieval of Vcmax from satellite data by validating these data using flux observations made at eight crop and grass sites. Firstly, an empirical model applicable to C3 species that was based on the semimechanistic linkage between leaf chlorophyll and Vcmax was used to derive Vcmax from satellite data. Then, using vegetation, soil, and meteorological variables as inputs, the SCOPE model was used to estimate Vcmax from half-hourly or hourly flux observations at each site. The estimates of Vcmax were assessed by comparing the simulated gross primary production (GPP) against the observed GPP: that is, the Vcmax value corresponding to its simulated diurnal GPP data with minimum root-mean-square error (RMSE) was selected as the inverted Vcmax value. Finally, the Vcmax values retrieved from MERIS and Sentinel-3 OLCI satellite data were validated using the in situ flux site observations. The results showed that the estimates of Vcmax based on satellite data successfully captured the seasonal variations in Vcmax retrieved from the tower-based GPP data, giving a mean RMSE value of 15.30 μmol m−2 s−1. Our results support the retrieval of Vcmax from satellite data based on the link with leaf chlorophyll content and show that there was good agreement between Vcmax derived from remote sensing and flux data.

The accuracy assessment of the global leaf area index (LAI) products is an indispensable step before applications. Four popular LAI products, namely MCD15A2H, GLASS, GEOV2, and GLOBMAP, were assessed over croplands in China using field measurements. All of these four products were validated with upscaled reference LAI maps. The validation results revealed uncertainties in these products for crops with RMSE ranging from 0.49 to 1.37 over the validation sites in Beijing, in Zhoukou and Jiaozuo counties in Henan province, Youyi farm in Heilongjiang province, and in Longkang farm in Anhui province. GEOV2 gave the highest accuracy (R2 = 0.85, RMSE = 0.49, relative bias = −7.2%) compared to MCD15A2H (R2 = 0.54, RMSE = 0.91, and relative bias = −24.4%), GLASS (R2 = 0.80, RMSE = 0.73, and relative bias = −23.3%), and GLOBMAP (R2 = 0.25, RMSE = 1.37, and relative bias = −55.4%). The LAI products overestimated over the croplands with dark soil-backgrounds in Youyi farm while they underestimated over other validation sites. The seasonal variation of these products was assessed with continuous in situ measurements at Daman Station, Gansu province. All of these four products showed good temporal consistency at Daman Station, while GEOV2 showed the highest accuracy (R2 = 0.92, RMSE = 0.41, relative bias = −10.3%). The influence of scaling effect on the products’ accuracy was also investigated, and the scaling differences of these LAI products contributes overestimation at coarse-resolution for croplands with dark soil-backgrounds, while underestimation at coarse-resolution in other validation sties.

Water color is an important parameter in water quality assessment. However, the existing water color investigations have mostly focused on the lakes with areas greater 1 km2. In order to improve the understanding of the color of water bodies in China, a cloud-free composite image of China for the summer of 2015 was generated using time-series of Landsat-8 imagery and the best-available-pixel (BAP) compositing algorithm. Then, the first Forel–Ule index (FUI) water color product with a resolution of 30 m was produced for China using the generated BAP composite and the Google Earth Engine computing platform. Finally, the first national-scale assessment of the FUI of natural lakes with an area >0.01 km2 (N = 60026) was conducted based on the generated FUI product. The generated FUI product was shown to have a high degree of consistency with in situ water surface reflectance-derived FUI (R2 = 0.90, P in situ Secchi depth (R2 = 0.90, P 2 = 0.62, P 2) found to be yellow was much larger than for large lakes (area ≥ 1 km2) (50% against 28%). Our results will provide important information that can be used for preserving and restoring inland water resources.

Fine-scale mapping of inland water is important for regional or global ecosystem monitoring; however, due to the spectral heterogeneity of inland water, large-scale dynamic mapping of these water bodies is usually challenging. In this article, we propose a water change tracking (WCT) algorithm, that uses only optical data, for accurate inland water mapping. Simultaneously, time-series of Landsat imagery are used as an example for illustrating the effectiveness of the method. First, time-series of Minimum Normalized Water Score (MNWS) images and corresponding `bad-pixel' (cloud, ice and snow) masks were developed using time-series of Landsat imagery. Secondly, based on the MNWS time-series stack and `bad-pixel' masks, time-series trajectories were formed to track changes in water bodies. A 30-m Dynamic Inland Water Body Map (DIWBM) of China was then produced to test the robustness of the WCT algorithm. Eight test sites were selected, and the water detection algorithms used in the Global 1 arc-second Water Body Map (G1WBM) and the function of mask (FMASK) were applied to our data to provide a comparison with the DIWBM results. The results indicated that the DIWBM gave a better performance of 96.20% against 85.91% for the G1WBM results and against 76.36% for the FMASK results. The DIWBM achieved user's accuracies of 98.36% and 92.53%, and producer's accuracies of 97.45% and 95.39% for permanent and temporary water, respectively. The proposed method was also better at identifying small water bodies. It is concluded, therefore, that the WCT algorithm is a promising method for large-scale dynamic inland water mapping.

The excessive exploitation of mineral resources will lead to environmental pollution, resource depletion, environmental disaster, and other problems. The contradiction between the environment and development, and the management of the ecological environment in mining areas are urgent p-problems to be solved. An ecological environment assessment is an important part of the ecological environment in a mining area. The accurate evaluation of the ecological environment is the premise behind environmental governance in a mining area. However, current ecological assessment indicators were not developed specifically for mine environment monitoring and, thus, cannot provide an effective and comprehensive assessment of the mineral environment. To this end, in order to improve the environmental monitoring performance in mining areas, a novel Mine-Specific Eco-Environment Index (MSEEI) was proposed, integrating factors from five main aspects associated with minerals, including temperature, vegetation, soil moisture, atmospheric environment, and mining scale. Meanwhile, a widely concerned mine—Luanchuan mine—was used as the case area to test the performance of our MSEEI. The results showed a significant correlation between RSEI and MSEEI (p < 0.01). The mean correlation achieved between RSEI and MSEEI was 0.91, which was much higher than the correlations between RSEI and enhanced vegetation index (EVI), soil moisture monitoring index (SMMI), normalized difference built-up and soil index (NDBSI), PM2.5 concentration (DI), and heat (LST). In addition, based on our long-term MSEEI results of Luanchuan mine from 1997 to 2021, the ecological status of Luanchuan mine showed a trend of first declining and then rising. Specifically, the MSEEI first declined from 0.85 to 0.77 between 1997 and 2012, and then rebounded to about 0.8 in recent years. The MSEEI exhibited a good applicability in the ecological assessment of mining areas. Our MSEEI can provide useful guidance for mine environment monitoring. MSEEI can directly reflect the ecological damage after mining, provide scientific guidance for the exploitation and utilization of mineral resources, and promote the protection and sustainable development of Earth’s resources and mine ecological environments.

Leaf chlorophyll content (LCC) is a prominent plant physiological trait and a proxy for leaf photosynthetic capacity. The acquisition of LCC data over large spatial and temporal scales facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. In this study, a global 500 m LCC weekly dataset (GLCC) was produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physical radiative transfer modeling approach that considers the influence of canopy structure and soil background. Firstly, five look-up-tables (LUTs) were generated using PROSPECT-D+4-Scale and PROSAIL-D models for woody and non-woody plants. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. The one LUT applicable to non-woody vegetation type includes 25 sub-LUTs corresponding to five kinds of canopy structures and five kinds of soil backgrounds. The final retrieval was considered the aggregation of the LCC inversion results of all sub-LUTs for each plant function type (PFT). Then, the GLCC dataset was generated and validated using field measurements, yielding an overall accuracy of R2 = 0.41 and RMSE = 8.94 μg cm−2. Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset. OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity to analyze variations in vegetation dynamics.

Hepatocellular carcinoma is a highly malignant and lethal tumor. In addition to surgery, immunotherapy is currently a more effective treatment for hepatocellular carcinoma. The tumor immune microenvironment (TIME) largely determines the efficacy of cancer immunotherapy. Based on the universal targeting of TIME modulators in clinical treatment, TIME modulators are promising targets for tumor immunotherapy. We investigated the effect of a double gene expression vector (recombinant galactose-terminal glycol-poly-L-lysine coupled MIP-3α-FL) on dendritic cells (DCs) regulation within the TIME of mice with liver cancer. H22 cells were transfected with a recombinant MIP-3α-FL plasmid to induce DCs differentiation and chemotaxis. The effects of transfection were investigated by flow cytometry following the modified Boyden’s method. Cytokine-induced killer (CIK) cells co-culture revealed changes in the antigen presentation ability of DCs. Further, tumor-bearing mice were injected with the recombinant double gene vector via the tail vein. We compared the survival time, tumor volume, weight of the mice, as well as the number and phenotype of tumor-infiltrating DCs (TIDCs) between groups. The supernatant of transfected H22 cells promoted the phenotypic maturation of DCs, enhancing their chemotaxis. Further, treated DCs promoted the cytokine secretion and killing ability of CIK cells. The survival time of mice injected with the double gene vector was significantly prolonged, while their tumor weight and volume were relatively reduced. Flow cytometry revealed that the number of TIDCs (as well as CD80 and CD86 expression) in the MouseMIP-3α-FL group, were significantly higher than in the control group. The combination of MIP-3α and FL can significantly promote DCs aggregation, maturation, and enhance their antigen presentation ability. The coupling of the double gene vector with glycosylated polylysine can improve the precise targeting of the liver and inhibit tumor growth in vivo, providing a novel approach for immune therapy in liver cancer.

Land-cover mapping is one of the foundations of Earth science. As a result of the combined efforts of many scientists, numerous global land-cover (GLC) products with a resolution of 30 m have so far been generated. However, the increasing number of fine-resolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications. To provide guidelines for users, in this study, the recent developments in currently available 30 m GLC products (including three GLC products and thematic products for four different land-cover types, i.e., impervious surface, forest, cropland, and inland water) were first reviewed. Despite the great efforts toward improving mapping accuracy that there have been in recent decades, the current 30 m GLC products still suffer from having relatively low accuracies of between 46.0% and 88.9% for GlobeLand30-2010, 57.71% and 80.36% for FROM_GLC-2015, and 65.59% and 84.33% for GLC_FCS30-2015. The reported accuracies for the global 30 m thematic maps vary from 67.86% to 95.1% for the eight impervious surface products that were reviewed, 56.72% to 97.36% for the seven forest products, 32.73% to 98.3% for the six cropland products, and 15.67% to 99.7% for the six inland water products. The consistency between the current GLC products was then examined. The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes (such as shrub, tree, and grassland) in specific areas such as transition zones. Finally, the prospects for fine-resolution GLC mapping were also considered. With the rapid development of cloud computing platforms and big data, the Google Earth Engine (GEE) greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability. The synergy between the spectral, spatial, and temporal features derived from multisource satellite datasets and stored in cloud computing platforms will definitely improve the classification accuracy and spatiotemporal resolution of fine-resolution GLC products. In general, up to now, most land-cover maps have not been able to achieve the maximum (per class or overall) error of 5%–15% required by many applications. Therefore, more efforts are needed toward improving the accuracy of these GLC products, especially for classes for which the accuracy has so far been low (such as shrub, wetland, tundra, and grassland) and in terms of the overall quality of the maps.

Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.

Desert locust plagues can easily cause a regional food crisis and thus affect social stability. Preventive control of the disaster highlights the early detection of hopper gregarization before they form devastating swarms. However, the response of hopper band emergence to environmental fluctuation exhibits a time lag. To realize the dynamic forecast of band occurrence with optimal temporal predictors, we proposed an SVM-based model with a temporal sliding window technique by coupling multisource time-series imagery with historical locust ground survey observations from between 2000–2020. The sliding window method was based on a lagging variable importance ranking used to analyze the temporal organization of environmental indicators in band-forming sequences and eventually facilitate the early prediction of band emergence. Statistical results show that hopper bands are more likely to occur within 41–64 days after increased rainfall; soil moisture dynamics increasing by approximately 0.05 m³/m³ then decreasing may enhance the chance of observing bands after 73–80 days. While sparse vegetation areas with NDVI increasing from 0.18 to 0.25 tend to witness bands after 17–40 days. The forecast model combining the optimal time lags of these dynamic indicators with other static indicators allows for a 16-day extended outlook of band presence in Somalia, Ethiopia, and Kenya. Monthly predictions from February to December 2020 display an overall accuracy of 77.46%, with an average ROC-AUC of 0.767 and a mean F-score close to 0.772. The multivariate forecast framework based on the lagging effect can realize the early warning of band presence in different spatiotemporal scenarios, supporting early decisions and response strategies for desert locust preventive management.

Land-cover plays an important role in the Earth’s energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 ± 0.68%; this was followed by GLC_FCS30-2015 (84.33 ± 0.80%) and FROM_GLC2015 (65.31 ± 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions.

Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery.

The fraction of absorbed photosynthetically active radiation (FAPAR) is generally divided into the fraction of radiation absorbed by the photosynthetic components ( F A P A R g r e e n ) and the fraction of radiation absorbed by the non-photosynthetic components ( F A P A R w o o d y ) of the vegetation. However, most global FAPAR datasets do not take account of the woody components when considering the canopy radiation transfer. The objective of this study was to develop a generic algorithm for partitioning F A P A R c a n o p y into F A P A R g r e e n and F A P A R w o o d y based on a triple-source leaf-wood-soil layer (TriLay) approach. The LargE-Scale remote sensing data and image simulation framework (LESS) model was used to validate the TriLay approach. The results showed that the TriLay F A P A R g r e e n had higher retrieval accuracy, as well as a significantly lower bias (R2 = 0.937, Root Mean Square Error (RMSE) = 0.064, and bias = −6.02% for black-sky conditions; R2 = 0.997, RMSE = 0.025 and bias = −4.04% for white-sky conditions) compared to the traditional linear method (R2 = 0.979, RMSE = 0.114, and bias = −18.04% for black-sky conditions; R2 = 0.996, RMSE = 0.106 and bias = −16.93% for white-sky conditions). For FAPAR that did not take account of woody components ( F A P A R n o W A I ), the corresponding results were R2 = 0.920, RMSE = 0.071, and bias = −7.14% for black-sky conditions, and R2 = 0.999, RMSE = 0.043, and bias = −6.41% for white-sky conditions. Finally, the dynamic F A P A R g r e e n , F A P A R w o o d y , F A P A R c a n o p y and F A P A R n o W A I products for a North America region were generated at a resolution of 500 m for every eight days in 2017. A comparison of the results for F A P A R g r e e n against those for F A P A R n o W A I and F A P A R c a n o p y showed that the discrepancy between F A P A R g r e e n and other FAPAR products for forest vegetation types could not be ignored. For deciduous needleleaf forest, in particular, the black-sky F A P A R g r e e n was found to contribute only about 23.86% and 35.75% of F A P A R c a n o p y at the beginning and end of the year (from January to March and October to December, JFM and OND), and 75.02% at the peak growth stage (from July to September, JAS); the black-sky F A P A R n o W A I was found to be overestimated by 38.30% and 28.46% during the early (JFM) and late (OND) part of the year, respectively. Therefore, the TriLay approach performed well in separating F A P A R g r e e n from F A P A R c a n o p y , which is of great importance for a better understanding of the energy exchange within the canopy.

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m.

Land cover change has been identified as an important cause or driving force of global climate change and is a significant research topic. Over the past few decades, global land-cover mapping has progressed, however, long time-series global land-cover change monitoring data are still sparse, especially at 30-m resolution. In this study, GLC_FCS30D is described as the first global 30-m land-cover dynamic monitoring dataset, containing 35 land-cover subcategories and covering the period of 1985-2022 with 26 time-steps (maps updated every five years before 2000 and annually after 2000). GLC_FCS30D has been developed using continuous change detection and all available Landsat imagery based on the Google Earth Engine platform. In specific, we first take advantage of the continuous change-detection model and full time-series Landsat observations to capture the time-points of changed pixels and identify the temporally stable areas. Then, we apply a spatiotemporal refinement method to derive the globally distributed and high-confidence training samples from these temporally stable areas. Next, locally adaptive classification models are used to update the land-cover information for the changed pixels, and a temporal-consistency optimization algorithm is adopted to improve their temporal stability and suppress some false changes. Further, the GLC_FCS30D product is validated using 84,526 globally distributed validation samples in 2020 and achieves an overall accuracy of 80.88 % (±0.27 %) for the basic classification system (10 major land-cover types) and 73.24 % (±0.30 %) for the LCCS level-1 validation system (17 LCCS land-cover types). Meanwhile, two third-party time-series validation datasets in the United States and Europe Union are also collected for analyzing accuracy variations, and the results show that the GLC_FCS30D offers significant stability for time-series accuracy variation and achieves the mean accuracies of 79.50 % (±0.50 %) and 81.91 % (±0.09 %) over the two regions. Last, we conclude the global land-cover change information from GLC_FCS30D dataset, namely, the forest and cropland variations dominate global land cover change over past 37 years, and net loss of forests reaches about 2.5 million km2 and net gain in cropland area is approximately 1.3 million km2. Therefore, the novel GLC_FCS30D is an accurate time-series land-cover dynamic monitoring product benefiting from its diverse classification system, high spatial resolution and the long time span of 1985-2022, thus, it will effectively support global climate change research and promote sustainable development analysis. The GLC_FCS30D datasets are available via https://doi.org/10.5281/zenodo.8239305 (Liu et al, 2023). [ABSTRACT FROM AUTHOR]

Wetlands, often called the "kidneys of the earth", play an important role in maintaining ecological balance, conserving water resources, replenishing groundwater, and controlling soil erosion. Wetland mapping is very challenging because of its complicated temporal dynamics and large spatial and spectral heterogeneity. An accurate global 30-m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping by combining an automatic sample extraction method, multisource existing products, time-series satellite images, and a stratified classification strategy. This approach allowed for the generation of the first global 30-m wetland map with a fine classification system (GWL_FCS30), including four inland wetland sub-categories (swamp, marsh, flooded flat, and saline) and three coastal wetland sub-categories (mangrove, salt marsh, and tidal flats), which was developed using Google Earth Engine platform. We first combined existing multi-sourced global wetland products, expert knowledge, training sample refinement rules, and visual interpretation to generate a large and geographically distributed wetland training samples. Second, we integrated the time-series Landsat reflectance products and Sentinel-1 SAR imagery to generate water-level and phenological information to capture the complicated temporal dynamics and spectral heterogeneity of wetlands. Third, we applied a stratified classification strategy and the local adaptive random forest classification models to produce the wetland dataset with a fine classification system at each 5°×5° geographical tile in 2020. Lastly, the GWL_FCS30, mosaicked by 961 5°×5° regional wetland maps, was validated using 18,701 validation samples, which achieved an overall accuracy of 87.7% and a kappa coefficient of 0.810. The cross-comparisons with other global wetland products demonstrated that the GWL_FCS30 dataset performed better in capturing the spatial patterns of wetlands and had significant advantages over the diversity of wetland subcategories. The statistical analysis showed that the global wetland area reached 3.57 million km2, including 3.10 million km2 of inland wetlands and 0.47 million km2 of coastal wetlands, approximately 62.3% of which were distributed poleward of 40°N. Therefore, we can conclude that the proposed method is suitable for large-area wetland mapping and that the GWL_FCS30 dataset is an accurate wetland mapping product that has the potential to provide vital support for wetland management. The GWL_FCS30 dataset in 2020 is freely available at https://doi.org/10.5281/zenodo.6575731 (Liu et al. 2022). [ABSTRACT FROM AUTHOR]